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prPred-DRLF
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      ## prPred-DRLF
      prPred-DRLF is a tool to identify the plant resistance proteins (R proteins) based on deep representation learning features
      prPred-DRLF is an open-source Python-based toolkit, which operates depending on the Python environment (##Python Version 3.7).
      ### **If your computer has GPU,it will be faster. **
      ### **Download**
      git clone git@github.com:Wangys-prog/prPred-DRLF.git
      ### **Install dependencies**
      conda create -n prPred-DRLF python=3.7
      conda activate prPred-DRLF
      pip3 install -r requirements.txt
      or
      pip3 install joblib==1.0.1
      pip3 install tape_proteins==0.5
      pip3 install numpy==1.19.2
      pip3 install pandas==1.2.0
      pip3 install Bio==0.4.1
      pip3 install lightgbm-2.3.0
      #### For python3.7
      #### If you have GPU # CUDA 9.2
      pip install torch==1.2.0 torchvision==0.4.0
      #### If you have GPU CUDA 10.0
      pip install torch==1.2.0+cu92 torchvision==0.4.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
      **# CUDA 10.0**
      conda install pytorch==1.3.1 torchvision==0.4.2 cudatoolkit=10.0 -c pytorch
      #### CPU Only python3.7
      pip install torch==1.2.0+cpu torchvision==0.4.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
      **# CPU Only**
      conda install pytorch==1.3.1 torchvision==0.4.2 cpuonly -c pytorch
      ### Input parameters
      python3 prPred-DRLF.py -h
      $ usage: Script for predicting plant R protein using deep representation learning features
      $ [-h] [-i I] [-o O]
      $ optional arguments:
      $ -h, --help show this help message and exit
      $ -i I input sequences in Fasta format
      $ -o O path to saved CSV file
      python3 prPred-DRLF.py -i ./dataset/test_data.fasta -o ./dataset/predict_result
      ### Webserver
      http://lab.malab.cn/soft/prPred-DRLF
      ### Other tools
      http://lab.malab.cn/~wys/
      ### Please cite
      (1) Yansu Wang, Lei Xu, Quan Zou, Chen Lin. prPred-DRLF: plant R protein predictor using deep representation learning features. Proteomics. 2021. DOI: 10.1002/pmic.202100161
      (2) Yansu Wang, Murong Zhou, Quan Zou, Lei Xu. Machine learning for phytopathology: from the molecular scale towards the network scale. Briefings in Bioinformatics. 2021, Doi:10.1093/bib/bbab037
      (3) Wang Y, Wang P, Guo Y, et al. prPred: A Predictor to Identify Plant Resistance Proteins by Incorporating k-Spaced Amino Acid (Group) Pairs. Frontiers in bioengineering and biotechnology, 2021, 8: 1593.
      dataset/bilstm_feature.csv 0 → 100644
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      ,Sequence_ID,R protein possibility
      0,negative212|0,0.002
      1,positive29|1,1.0
      dataset/selection_feature.csv 0 → 100644
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      ,UniRep_F232,BiLSTM_F3253,BiLSTM_F231,UniRep_F1029,UniRep_F1338,UniRep_F747,BiLSTM_F750,BiLSTM_F2662,UniRep_F774,UniRep_F1757,UniRep_F650,UniRep_F1574,BiLSTM_F1420,BiLSTM_F77,UniRep_F662,BiLSTM_F1270,BiLSTM_F1404,BiLSTM_F1775,BiLSTM_F213,UniRep_F57,BiLSTM_F101,UniRep_F1800,UniRep_F1827,UniRep_F682,BiLSTM_F1758,UniRep_F204,UniRep_F861,BiLSTM_F2557,UniRep_F1793,BiLSTM_F655,BiLSTM_F1786,BiLSTM_F853,BiLSTM_F1448,UniRep_F4,BiLSTM_F671,UniRep_F510,BiLSTM_F1116,UniRep_F1754,BiLSTM_F482,UniRep_F212,BiLSTM_F1526,UniRep_F945,UniRep_F1573,BiLSTM_F1912,UniRep_F1241,BiLSTM_F1677,BiLSTM_F74,BiLSTM_F1696,BiLSTM_F524,UniRep_F814,BiLSTM_F1124,BiLSTM_F528,UniRep_F1299,UniRep_F408,UniRep_F1736,BiLSTM_F415,UniRep_F868,BiLSTM_F596,UniRep_F1818,UniRep_F1369,UniRep_F529,UniRep_F472,UniRep_F165,UniRep_F860,UniRep_F1376,BiLSTM_F3362,UniRep_F1214,BiLSTM_F22,UniRep_F1187,UniRep_F1182,UniRep_F1576,UniRep_F156,UniRep_F1095,UniRep_F1082,BiLSTM_F2843,UniRep_F1780,UniRep_F322,UniRep_F1791,BiLSTM_F154,BiLSTM_F252,UniRep_F51,BiLSTM_F3119,UniRep_F1843,UniRep_F660,UniRep_F1459,BiLSTM_F3485,BiLSTM_F2284,BiLSTM_F974,UniRep_F1336,BiLSTM_F1002,BiLSTM_F3578,BiLSTM_F865,BiLSTM_F969,BiLSTM_F3463,BiLSTM_F1066,UniRep_F657,BiLSTM_F1233,BiLSTM_F2256,UniRep_F538,UniRep_F1389,BiLSTM_F1481,BiLSTM_F1997,UniRep_F72,UniRep_F314,UniRep_F288,UniRep_F1625,UniRep_F97,BiLSTM_F1631,UniRep_F237,BiLSTM_F1705,BiLSTM_F1707,BiLSTM_F1740,BiLSTM_F1755,UniRep_F201,UniRep_F1423,UniRep_F183,BiLSTM_F2003,UniRep_F337,BiLSTM_F1424,UniRep_F49,BiLSTM_F1096,BiLSTM_F2179,BiLSTM_F1186,UniRep_F1711,UniRep_F1709,UniRep_F25,UniRep_F32,UniRep_F1581,UniRep_F1615,UniRep_F409,BiLSTM_F2134,BiLSTM_F2117,BiLSTM_F1339,UniRep_F362,BiLSTM_F2097,BiLSTM_F1414,UniRep_F1335,UniRep_F1229,UniRep_F1024,BiLSTM_F75,UniRep_F980,UniRep_F1877,BiLSTM_F2869,UniRep_F1840,BiLSTM_F87,BiLSTM_F525,BiLSTM_F2725,BiLSTM_F336,UniRep_F1065,BiLSTM_F2711,UniRep_F879,UniRep_F1817,UniRep_F889,BiLSTM_F3019,UniRep_F1090,BiLSTM_F3181,UniRep_F902,UniRep_F904,BiLSTM_F370,UniRep_F1832,UniRep_F1093,UniRep_F925,BiLSTM_F110,BiLSTM_F2747,BiLSTM_F2877,BiLSTM_F273,BiLSTM_F2879,BiLSTM_F166,BiLSTM_F794,UniRep_F703,UniRep_F704,BiLSTM_F2,BiLSTM_F3382,UniRep_F749,UniRep_F770,UniRep_F1215,BiLSTM_F2837,BiLSTM_F38,UniRep_F919,UniRep_F790,UniRep_F1539,BiLSTM_F3312,BiLSTM_F696,BiLSTM_F2839,BiLSTM_F677,UniRep_F1478,UniRep_F1149,BiLSTM_F53,BiLSTM_F3114,BiLSTM_F3051,BiLSTM_F3053,UniRep_F62,UniRep_F73,BiLSTM_F3423,UniRep_F92,UniRep_F129,UniRep_F155,UniRep_F152,BiLSTM_F2987,BiLSTM_F2991
      negative212|0,-0.034811634570360184,0.004029435571283102,0.20001398026943207,-0.2960071265697479,-0.0024540922604501247,0.006126032676547766,0.0010587710421532393,4.685978638008237e-06,-0.05614127963781357,0.1427001953125,-0.041672732681035995,-0.05256567895412445,0.0012017363915219903,0.01676754467189312,0.008841787464916706,-7.350556552410126e-05,0.011933702044188976,-0.015835225582122803,0.05146706476807594,-0.04539719223976135,0.07343664765357971,-0.006802583113312721,0.17321082949638367,0.15729841589927673,-0.008490246720612049,-0.07197565585374832,0.11062388867139816,0.020558515563607216,0.0015170032856985927,0.0009310527821071446,-0.016358856111764908,-0.00043481437023729086,0.0002815321204252541,-0.008164050057530403,0.0014553946675732732,-0.32523977756500244,-0.001188446767628193,-0.029281049966812134,0.1990051567554474,0.0030880800914019346,0.0009616818861104548,-0.025534940883517265,0.0034448658116161823,-0.0011311048874631524,0.02875017188489437,0.03008027747273445,0.18625584244728088,-0.0007277324330061674,0.0065430221147835255,0.00959217268973589,-0.06674424558877945,0.0062814378179609776,-0.17130865156650543,0.044943418353796005,0.22742120921611786,0.010491505265235901,0.015428616665303707,-0.05097590386867523,0.16133497655391693,-0.08295519649982452,-0.081154964864254,-0.0017777030589058995,-0.007788260001689196,0.0965556874871254,-0.013366347178816795,0.08124931901693344,0.03668227419257164,0.013384564779698849,-0.0794229805469513,-0.0679733008146286,0.0654965341091156,0.0057541728019714355,-0.024419866502285004,-0.36622166633605957,-0.00881290528923273,0.043333500623703,-0.1656431257724762,-0.021231194958090782,0.7599426507949829,0.05204673483967781,0.014051737263798714,-0.0025678942911326885,-0.038811005651950836,0.028863953426480293,0.010074766352772713,0.018057193607091904,0.009611126966774464,-9.985068572859745e-06,-0.29714322090148926,-0.0022403011098504066,0.0007539559155702591,-0.0007802600739523768,0.0012872798833996058,-0.020394984632730484,0.00018372003978583962,-0.01142818108201027,-0.12469439953565598,0.0030504970345646143,0.005137742962688208,0.04964349791407585,-0.063686802983284,-0.008631475269794464,-0.05681881681084633,-0.03665829077363014,-0.024518385529518127,-0.013852118514478207,0.01943495310842991,0.045649390667676926,-0.03180487081408501,-0.00045372816384769976,0.06544964760541916,0.007043574005365372,0.0034767272882163525,0.12061934918165207,0.002497767796739936,0.00977579690515995,0.00952797383069992,-0.3852512240409851,0.0011000894010066986,0.03510650619864464,-0.0007191549520939589,-0.046695832163095474,-0.08960983902215958,-0.04537704586982727,-0.024351483210921288,-0.07213440537452698,-0.02844931185245514,0.010713263414800167,0.14630551636219025,0.0008363188244402409,0.0043046604841947556,6.591193960048258e-05,0.013596250675618649,0.03074769861996174,-0.0036781695671379566,0.052703484892845154,-0.1296735256910324,-0.05928082391619682,-0.11912056058645248,0.2954358160495758,0.02697659283876419,0.16921155154705048,-0.008907645009458065,-0.10611321777105331,0.03804710879921913,0.015096589922904968,-0.009671171195805073,0.008266295306384563,0.032908570021390915,0.0051622698083519936,-0.02861451357603073,0.2160898745059967,-0.06824861466884613,-0.05382305756211281,-0.21336129307746887,0.0024776076897978783,0.025232091546058655,0.017331881448626518,0.039423130452632904,-0.15889021754264832,-0.16561467945575714,-0.02627953328192234,0.01685287244617939,0.04109898954629898,-0.00025445071514695883,0.11988529562950134,0.3718585968017578,0.08955643326044083,0.03052324242889881,-0.10895266383886337,-0.6846140623092651,0.04898446798324585,0.010754983872175217,-0.004667660221457481,0.00487252464517951,-0.005179384723305702,-0.014620037749409676,0.03433913365006447,-0.0711006224155426,0.0013567559653893113,0.006823370233178139,-0.015461510978639126,-0.0019957523327320814,0.0009553214185871184,0.06192902475595474,-0.05836094543337822,0.04120407626032829,0.09122627973556519,0.0026462250389158726,-0.027500014752149582,0.003278030315414071,-0.3406601548194885,0.064644455909729,-0.043833933770656586,-0.2154310941696167,0.035033516585826874,-0.004995195660740137,0.003603357821702957,0.06984248757362366,-0.008638323284685612
      positive29|1,0.047021638602018356,0.014518939889967442,0.30660468339920044,-0.0997648760676384,-0.021379224956035614,0.21550607681274414,0.0017186225159093738,0.011030955240130424,-0.03562738373875618,0.12322363257408142,-0.511938750743866,0.0559275783598423,0.0007715504034422338,0.1915474236011505,0.02599368616938591,0.0009804734727367759,7.705090683884919e-05,0.23463262617588043,0.05079019069671631,-0.013588722795248032,0.10005445033311844,-0.12152118980884552,0.21101221442222595,0.11690635234117508,0.0007002240163274109,-0.08805426210165024,0.1521615982055664,0.026477152481675148,0.008704038336873055,0.006376922130584717,-0.01420477032661438,-0.001107917632907629,0.004063126165419817,-0.009208302944898605,9.926391794579104e-05,-0.1387002170085907,-0.0005364833050407469,-0.05399543419480324,0.38372454047203064,0.08831854164600372,0.0015583375934511423,0.0480845645070076,-0.21832117438316345,-0.0013783295871689916,-0.019745778292417526,0.018389929085969925,0.372477263212204,-0.000593259755987674,0.017678584903478622,0.08803900331258774,-0.015332366339862347,0.015322189778089523,-0.029843388125300407,0.04684191197156906,0.06406532227993011,0.154341921210289,-0.033147308975458145,-0.0544540137052536,-0.005841437727212906,-0.026871688663959503,-0.11748846620321274,0.04943282529711723,-0.11073572933673859,0.013253855518996716,0.0037885296624153852,0.03634146228432655,0.0020501711405813694,0.0045037781819701195,-0.06989450752735138,-0.05759497731924057,0.06161860376596451,0.03754503279924393,-0.08671807497739792,-0.3750254511833191,-0.001583936857059598,-0.008361173793673515,-0.25496914982795715,-0.08594770729541779,0.9308525919914246,0.012229636311531067,0.07179277390241623,-0.002392892260104418,-0.17682287096977234,-0.042692698538303375,0.029441799968481064,0.024413809180259705,0.034294478595256805,-0.00017307627422269434,-0.11905552446842194,-0.012487738393247128,0.012764235958456993,-0.0004319600120652467,0.0007568246219307184,0.004885090980678797,-0.0023544190917164087,0.035879358649253845,-0.08125337213277817,0.009058305062353611,0.12687283754348755,0.25194916129112244,-0.1339229792356491,-0.0035269600339233875,-0.13834422826766968,0.058383695781230927,-0.014322595670819283,0.013162012211978436,0.001530149718746543,0.02281617373228073,-0.014277858659625053,0.0003172357683070004,0.0232255682349205,-0.00930514745414257,0.005449910182505846,0.21019788086414337,0.07679086923599243,0.0696309506893158,0.008368267677724361,-0.09474487602710724,0.0053260428830981255,0.18975666165351868,-0.000974863360170275,-0.07034582644701004,-0.028055427595973015,-0.2005874514579773,-0.00982706993818283,-0.2449072301387787,0.019937530159950256,-0.017223266884684563,0.09532631933689117,0.027423355728387833,0.0014937054365873337,-0.015802418813109398,0.01190674863755703,0.08190274238586426,-0.007283797487616539,0.03169621154665947,-0.13005363941192627,-0.10680436342954636,-0.21628670394420624,0.23169612884521484,0.1669270396232605,-0.12550489604473114,-0.011671730317175388,-0.08160116523504257,0.023034395650029182,0.02697242982685566,-0.0029739653691649437,0.014881371520459652,0.017498156055808067,-0.0025019815657287836,-0.040142972022295,-0.10432545095682144,-0.13928277790546417,-0.004976244177669287,-0.041907068341970444,-0.0027595101855695248,0.0168609656393528,0.023636601865291595,0.022314006462693214,-0.011900339275598526,-0.1638420820236206,-0.027477165684103966,0.16686472296714783,0.0824504867196083,-0.004815066233277321,0.1017688736319542,-0.0021219602786004543,0.08912322670221329,0.030671877786517143,-0.14924025535583496,-0.420635461807251,0.06387920677661896,0.013131505809724331,0.01728537678718567,0.1849440187215805,0.037930022925138474,-0.08270659297704697,0.022981420159339905,0.03790099918842316,-0.09256094694137573,-0.01590036414563656,-0.012757503427565098,-0.008787378668785095,0.00020024998229928315,0.0389237254858017,0.00031973194563761353,0.013933354057371616,0.05314409360289574,0.0014059138484299183,-0.009592892602086067,-0.0022570237051695585,-0.11398699134588242,0.047493431717157364,-0.123441681265831,-0.1520315706729889,0.13869796693325043,-0.016330208629369736,0.04511628672480583,-0.9886535406112671,0.02411423623561859
      dataset/test.fasta 0 → 100644
      View file @ 80dfc643
      >negative1|0
      MFTTRKVGFVIENDRFARSICVNSDMTFYELEAAVAGEFHVDANAWITNLSYWLPKQLSIFSTSKKPPVALNSTVAPKGFLLVKDTKAHLNFCHSIEPSVGGDIQKFDGRELGESFGTAKYIQRELESVRVESQVVQFVTAPTIETNGDVDDGAGKYFSSEVEGVHVELAVVKSVSAPTTETNGEAVDADAGTPSLSTDIDGRCEEDFFANYSEDEVDQRELEALERAEAEKARVKGISLRMKKALKLVIEYAVEWCDGDTRLPGRVMRSSSGLAMG
      >negative10|0
      MRCLRPGGARRRCQLLDGDTAAFCASLVDGLAQLESTLLREEDDGDGGGGGGGAVSMRWCADAMRLVKRMQRELLVMFKKADVPVGSAVSYGGGGGGGDGGGCWFEHYMQETAALLDFCNAFKSAVSRLHRYCMVVDFAAQVGCAGAGAAAENGGGAGGWWLEEEPGGDDAGAIRHRLSDVRAAVSEAERLGRKIMSSSSGGGGAGDDDAGGMVVVMLVAKITMAVVSMFVLQALTSPIVPLAADVDDGHCTLGRAAAVPVPELQPWRESLSVITDRFPRRPGVAEHERVAMVVKSMMINTKMEGEEETKNGKQEQEDDHVELLRTRSGELREGVEMFDCVLDEVFDEVIKGRNEMLGIFRDKALTLG
      >negative11|0
      MAMAYVAAAVLAAVTLTASFAGGEACNFVASMTWTAACQQTGRWENLCQQTLQTAPATAEVTVYALVATRLAKLRYENTLGEVDTMLWPRNAPADARAALDNCKYKYSVALGRMDAVSDEIFACDFSHARQEYVDAEVAVRSCQDGLLRLPNQYHSWPLFAKVSADYDLTMVAYLLGAIFLGR
      >negative21|0
      MLISNGFCSKREKGGKLATWTIFIFLFFFLXTIISXPFSTNMKSKTVCFGIRGRNNKSKENKVEDLEKLSHVKPNHKGKTKRCASTTPVGDDDGGINVHGANIVASNDAGVAAAVITAAHMSLMCVNDGQDGSGHGHGGESGVDGDGG
      >negative32|0
      MASLVNPLKTDVALRRPSTLDDAIMLAQAYEQRLHLGATDPIPERAVRALRMVLPAPSTASSTPSTDAASTTASGKQPLVAALPRKHLSPVEMAQRRTEGLCYNCHEKYVSGQHCEKSFIIEVISFPDDTEGEDDPPPAANTILADAGKLLISLHALAGIRALMFNTIKVCACVGTVDYLALLDSGSMHNFLSETVAHVRISSSSRAPASVSPSRMATASPRWDAARHSTSSSARPLSTLISTPFCWEGMTSSWAHIGSARSVRRCGTLLTNRFRSAQVTTK
      >negative34|0
      MDSSREANRKHDAEEKREDEKTSLMEVNLMVVLLSQLSTKKEDENGLEKERRDHVAHEARESRARANKRFMKDTKLMEKRTHVLQPMAKEFRHYRKM
      >negative37|0
      MVGGKSPIDAFKCQLVCKLFRDSATLDTIYQTMDKKRLRFRPFSADMYEVLRRCRKLNNPYILFNYGMLGEDDAGKQLLQYATDKGQLDAIFVLGMLLMAEGSERKQEALIMLNNAYINTRRCWNLRQTCYKVRSHLDACLVEFAKMFDISLE
      >negative42|0
      MEAKVVATVLIVLFLTLGGEAAAKICHDHSQTFKGMCFHTSNCIACCTNEGYTGGYCKPFTYRCMCTKDCGGDSPPDDPPPAMPTSPAATTTVA
      >negative51|0
      MSERRRRDLGGASDGAPFARQRWFPDEGAVTMTPEARAPAVAQGAASGQRLVRRCRPKVCAMVEVPRVRTAATTPSRGRARRDCLQRRRPHEARVAPAVSVVAQGACCKGLWSARRFRRPSITNSSDLVVSVASSDDDLPQSFVSYMRLLGDHILGFFSLIMMYDVSIYATFYNDLYLARY
      >negative52|0
      MEGLNVGLEEVVRHRIIRGAVIGDGDLNLNLNKSYFYRIRLPFHCVEEMVGITGYDPACIPFSYLGLPFGANMNRGVNWNPVIQSETKREIPWVKRNLVLNFKEKGGLRIGSLKTFNLALRIKWRLCFYKEVDVLWAKVIQNIDGMHDGLGLSSPKLRGGVWSNLVKAFDNWDLL
      >negative53|0
      MEKGPARHDWFKFGLARHESRAGPCSKFEPVVLPGPTRCCGPCSCWPGTETSRSSVGRHLIRFSRFPRRPPVSDSLLDSPGVGRRQPIHSSSPQARRPAPQARRPDSSGPAATRHAAISPAWRPDSSGPAATRHAAISLGVTPSGQRPRAHAQRPSSGPAATMPCMPAARQAPSGQSISPSAISGSPEAPAQRPSPDLPTATEIQT
      >negative54|0
      MSLLTNRMAFSLLLMIIFGHPLALLAHSTPPISSRITVVGAVYCDTCLSDGFSKHSYFIPGADVHIQCRFNANVPKTSEMISFSVNRTTDIYGVYKLEIPSVEGVDCVDGPPIQSFCQASLLESSSSACNVPSLKATTTEVSVKSKQENLCVYSFTPLSFRPSKRNDTICRKRGHKSSQASSRSISQAANLQPLSFPFSSPPPFLPFPFPFSSPPPSLPFPFPFSSPPPSLPFPFPFSSPPPSLPFPFPFSSQPPSLPFPFPHLPPFPSIPSFLHSPPPPPPSFDLGDPRTWIPHIPLISPPPPPPPPPPPVFDLRDPKTWTPLIPPLSPPNLQKQNP
      >negative55|0
      MSFGTRLLLFLILTLPLVTSSSPNTLHVSGIVKTGTTSRFLMMTIEDYDDPSANTRHDPSVPTNAKADTTP
      >negative67|0
      MAGFHIPGDPYFPNQGNARWIDDEPEQQIEEDPEEDPEENPEEEPKEEDKQEEEEEDEHEGEEEDEEEEEEEDEEEVEEDDDIVMVGNEMEEQPEVFNPPYITRVPAHRFGYNRPEPPWVTTIERWSRQQRQRSPYGNQRGYYDLIHGGPTDRALPVTIHRIASMDDRGRTTTDQVKELSAIVQSTTDRTRDLERDSHHRDQLIQDLLAARAETREYRERYMALEERIVAAERRLAELQGESTSSQAPSKKGRHD
      >negative72|0
      MASSSTSSSLAAAAMVAVVLLLLGATATSTQAARLLDEELPTAAIPAIPGVPGVPAVGPGIPVIPGVPGVPVVGPGIPFVPVIPGVPVIVPIIPGVPMIAGMTTLPVPPFVPPIDPGAGAGFPGVPPASSTTVQEDPQPPMPSVVPPVP
      >negative73|0
      MSDHRLQPIPARHVIAAAAAPNPEPPPPPRPAWAQDRPARAHRSPSSSPPRASLPASPSAAASLPASQLPLAAFVPATAGERQVAADRASAPRLLGEPRSPDPRARAPFSLLAPLDASLSPPDMSDHRLQPTPARHVIAATAAPNPEPPQPPRPAWARDGPARAHRSPSRRPSSSPPHAPLPASPSAAPSPSASQPPLAAFVPGHHRRAAGRRRPRLHHAAPGRATLARARSPPPPPIVASRRRPSPCCPTPPSPTPPTSPAPPRAVVAGRPRRRVELAAAPREAPRASAAPLLPRRRRPPPLARTSPALLRRTPTIPI
      >negative80|0
      MIYFCFLGFGIKSRVMENMNEEIDLGLSLGATNQQRVESCSDSGAGVNADLGSRIDTTNTNTKPFVRPHPLTELVWSTQNGLTIKYTGCSPCFAHTKDDERVIDASFFRSTLPLAHTGRQNRRTADLLPLLSNEPEKLEIKTENSDRFTMDVGLPLKMTQEHTNVKRGEELKEEGSSSAPFLEKMEETAENDVIVKDLENSEKDQKGGENREEGCKEDDDESHKSMESCNSANLSSKKNKGWRFEEQLIIGSKRIKKQSQEYSPVVKQDSSFMNWISTMVKSVKPYQEETPRPFDDDQNKRLGFQTVFQSLYSQDPKRLETKTDIDDKSVDASKEIILFDKTASDHNPYDNLNQKAFGNLWITRLFPKIPSNVNTLVAKDSSTIDASAPCGMNQFEKPVEKSKCFFCGKTGHELRDCLQINENGNVFFKKKVSNINEIASTSGNNKLFNEEKIHRIPKGMFDTIRSLRLSRTDILKWMNSHLPLTNLDGYFLRLRLAKWEEGVGEAGYYVACITDENPSKGSKKPIRVNIGGVECLVESRYISNCDFLEDELIAWWQRTLTSGQVPMEERLTFKLEEREKLGF
      >negative88|0
      MEGFTNFLQVMETKVDLLARKVDLLTSKMDLCATNVDLLTSKMNLCATNSDLKAMLTSMKGKNVPAKKTLAMAASDSEDSSDSSSSDDIELCFRFLIKIVIQLRPSYFIIIFAYIDLKEGANKALELNGSDMAGKELVVKTALLMRDSYLGYSCNGGIGGRFGGRFGSVGGIFGGGRCGGLGQYYAQV
      >negative96|0
      MSMFTIHIERGNALRRLLLPIANLSNTAQINCSIPCLSFVVSPRNNFIVVLRIFPAFFTSYQFNPNYAGNYTSFHAKLYLRLLHTNISNMFTDELSVTIYLNRFESTIPVRFYEPRSGYLQFSALLLARARRIPMYPVDMGVFFAIRSDEFLKILTDLRVLSGEYFVIYVSTTKALFKSLRREIAYNTRDDENMMVMGGVPTGGARQFFLICPSPLHFFYYTASISKMVWFFIAAENHTRHFLSCCVNIHASYLVCF
      >negative108|0
      MAMEICAEAFPLSRFPTLEKSYCADSRDTERSHNTNWITEVHFSNRGSNKLWISEVAKIKKRIRSLNLRKSLGQARFSSSTGCIHEQLLKFYSLNYIHAGLSAESLPFPCLLKKLSGLCFAILLYTRFAGYIRKGTCQLSLCKGFRWSSA
      >negative110|0
      MQFVIGSVSPFACTSLGGGVIRLKTTTVPVLAFFRSGEKIQDIVWPQTIEIKGRVKWGVFKKFLKDLRHSQNRSVMVISLLLNVESSKNGLEGMKQVANMLKVNKRIGLATTEDGSYIYVCPGHKDIITVLAKYGFWKGASTIDRNQDSLIGFVVRDRKPPVNTSEGEVSIEKIQEIGGTHVEAQEVIYDKQSYPVHPLCAPVDHGLYFRRPPLVLAEQGSYYPYPPPGLLGHDFHARHPPIGLSGQGLHPHAPECQGVFHQLPPCARGQGFITHCAPNFALEYFKPLTDHGNNNGSQHMRPPSGSAPGHLWHRMSCARSGNNLPGFERSEGFSGSSSTYYSRFENGSGSMPPN
      >negative111|0
      MESYRVFTTICLLLCLFLSANFFTHYVVDARKSVGFEREPKKVMMIKALKHTSLLQKMMTQLNLAQPLDYSSSSNTQPYGVSTTLTLPPYVSLPPLSVPGNAPPFCINPPNTPPSSSYPGLSPPPGPITLPNPPDSSSNPNSNPNPPESSSNPNPPDSSSNPNSNPNPPVTVPNPPESSSNPNPPDSSSNPNSNPNPPESSSNPNPPVTVPNPPESSSNPNPPESSSNPNPPITIPYPPESSSPNPPEIVPSPPESGYTPGPVLGPPYSEPGPSTPTGSIPSPSSGFLPPIVYPPPMAPPSPSVTPTSAYWCVAKPSVPDPIIQEAMNFACGSGADCHSIQPNGPCFKPNTLWAHASFAYNSYWQRTKSTGGSCTFGGTGMLVTVDPSFNGCHFDFF
      >negative112|0
      MENILNYLQMSLEFQIAHFIEVVKAKAIQVGIQHDGLLTVLTLCFVFYLIVVSTGHLWKNKKKKKRGKIQRVLTRSMSIGVLHGGELALERLVDYHQAKANVQLLDVTETELDSLLKEGLPDFKKLQRCIAKLEMSGKESKAVKILESAIKEARLEGKPHEAYEFEMLLVESLIYQGEFIKALSYECLNDEFITDARRPLYKAIIYLSLGYTEEEAKKFWWEFKRIRGHMKRSRNTQDVQLFEITTDFDKFMCIVKSLEEDIKQVKAKANKNK
      >negative118|0
      MVVFPVVKLATLALRTACKPIANRLKKEAGYHPKFRNFIISIAQANHRLTTRVQRRIYGHATDVAIHPLNEEKAVQAAADLLGELFVFSTNVADAILIAVADILKTLMLQPKTRTHATKFSSVPSIKISAIIDFVLFRLQELPSSLRCKEVQDQKQEKKNYVDRRYRLSSCLCLSWIICFEGKLWVEAKLVFL
      >negative126|0
      MAERAWIFSLPGVRQLGRSFLSAARRFPFPKLHGCCTSPAERLFPTALPPCFSFPLASSPRRRAPAPAIGRAPASMDAAPAACSPCFSSPSSFPSPSHGQLRAHPALSLFLSRPSLFFHGCSRFPVPRPRPAVPPLSSSSGSVQFALALAMVAVVPMPTGARSAAVSSRLPATARHVPVFEFVDLHSSPHGRTTSSMLLFGGRLLA
      >negative127|0
      MKNLKASKFQFLCKPTQHGSKLTQIRKRKRNNYQYQQMKSGVLERCNQRRFSWKAAIKDVGGGNLNGYYKSDILLEGFEIQNRETHHNLQHDLTEYIWERQFQGQNDGEDDDDDDGDDEEEDDGGDEADDTGDDNDDD
      >negative137|0
      MGRFCDLAILCPDMVPTDSKKIERYIWGLSPQIQLSVLASRPDTFDSAKELAQSLIDHGNYWNSVVGVQDQQKENNFGKQGGNKRKDESSQESSRKQHQGHTIRYCKNPARPINQLPNAGVSQACYGCGKVGHYKRDCPGTANTGTDGRMLTITAAGEATPNPR
      >negative138|0
      MRDVHLLFGQSTPFLHMQMHEGLLDQNGLRKTTQSKPILHMQMHTPTNSAFCDDKNVCESPVFVPELKKNSFGKFLTEEIPEFIPSRTGVTDSEKKAAEDHSKEDSSLTASEKQKETSIPDEEQKSETSSEDQTTEGECFEDSSDETIEDQDSESSSEDQSCSDSSYEASEELNSSEYQTSESSTDSDNDEECSEDESRLDYLKKSVKNLVNSTAYF
      >negative139|0
      MPRYKRFKFGLRESEQSLRLSERSLEKTYKRLVEKEQQLCDRLNQRSKKVKQRLKETELSLDEISAKLEERHRRLFDRLEKSVQKLELSETNLEECLKSYDYAITKLRKCG
      >negative141|0
      MAARPALARLDVSTVAAGAGKQQLVHARRRRRPAHAHRLPVPGAGSAIRLACSSPFLGGSNGSRSLKHNAAAGEKSAADHAAGALEDELIQKENSGGDAAAGASPPSSCDNHGAPQQIEVTADTNDGDKEKTNGPARDVHIKAKLLGYNLEPGSGPHYNHLGPV
      >negative148|0
      MCPANTHEFMTVFPKDISHEPVATCPITVAGAVSTIHVQQSKHGKGVGSRGPRCFLGGGYVAGTAWCRVQVGTPEGDKSRFPPDKDGLFVGGFMQTSSTIFVQANNWGLSVFGIKELSAVFGPKTLLSCCCFVLKTMSQYQAWCLGGVFGSEILVSLGSEKDELMMRAHRHILPHHTTVSRKRDRQPFFSRHDPRKRRRPLPETTEEERPTKRPAPPRTVVVMGLPQDCSVLDLKSRFEIYGAISRIRIDRDAVGYITYRTKDSADASIAADLDPSFGITVNSKKVRVLWATDPLAMWREGVGNSRDKSSMSKLVRAEVPLSKHGRGNRLSSAIGNTKRSEDSSGSSVLEVPFKGREIVAYDDIL
      >negative150|0
      MAIEELVVRTADIIDQQAMETLLIENHSSEAKDMEKMRSSMEILSRVDLDLAYSDEKLTNLDNLLMRVLACENEVEAISFENDEISPDSIEKVLTLDHLSAILNSEIRQLDNFIGTLQDLITEARQKMSTCREFSELHMIMENKFHDTEELLRQSKERVLEMKMQLAKLQMTSLAFDKNEWRHSMSLDLSDINNASTREFKPHMQTVEQRRILRMLEKSLERELDLEKKLTLLKQNEDDLKLKLRLTEQVALCMEEGAEVIWGRFLESENTAEVLMGISREMVSRLQVVNFNLNGSLQREKDFTSKIDNCIGQINAKDITIQNLNSCNKQLAAENAEVSSLRDRLKLLEDKLKESESKLLKANELNEASEERLKELECIVESQKEDIDIAEHKAESAEEKVAHLTETNLELHEELDFLKSSNESNAKKVSILEKKVRELELQLQNAKTSSEAGQEQQNMLYSAIWDMETLIDELKQKVLKAESKTEHAEEQCLILSEANLELTKEVEFLRSRVEGLETSLNQATVEKLASAKDINIKTSFIMDLVMQLAIERERVQKQMFCLAKENKSLVRTLESKKDQLSINVLVNGVDDIGLSSSRVASTIAAAKDSSCELLTESSLKSSQVINELTPDKTTDQSSKSSISTNDEPSMVVKSEETQVQLQTTQHQHKYIVMAIFVLLLSTLGLYIFDKRNNILDWSMVRL
      >negative160|0
      MGRSDSRSPARGRGSPRRRSPSRRERSPAHKRSSHAASSAVAEKPSRRARSRSPVPLSPARERPSSRNRSPKRRKSFSPASHSPIREKPTSCVRSPKRANSRSPDLKLLQGEKFSGRARSPRRAKLQSPEPRSPSSRTKRLRRAERDAEEKSREREPEKNHGRSSDRATHREKDSDRILPEPRSPSPLTKRLKRAERDAEEKLREREPEKNHGRSSDRAKHREKDSDRMLPESRSPSPRTKRLRRAEREAEEKSREREPGKNHGRSSDRATLRENDSDRVMQSERRERKSGKDSIDNGSYKSRNGRSASPSERQHRSRHRSRSPAAADSRAHPEATNSRRGEHRNGEDDSLSKMKEAEEALEAKNKDKPSFELSGKLAAETNRVRGITLLFNEPPDARKPDIRWRLYVFKGGEVLNEPLYVHRQSCYLFGRERRVADIPTDHPSCSKQHAVLQYRQVEKDNPDGTSSKQVRPYVMDLGSTNGTFINDNRIEPERYYELFEKDTLKFGNSSREYVLLRSMEVSFALIGLMLDAFTRRRSGFCYAKFTSASSRLEIWALLS
      >negative163|0
      MSIVPISIAVASDLDTGHSEADQMFDKNPQGELPLSQTDLSSDAKELLNECIHHGEDGEFKVSATSILEESFFNTRNDPKADDAYSNAHFIAPPIVVNLVADKSAGLPFPVTQMLGGNTKSIEIDFLDTLQPKESADKDGEWLRNIADIVFDKSLELKATKLQCQLANVAPLETAKYVSTEILSAHFDEPTWNEGMTSLFLHTSVAHEDDKIVEFLPYIFKQPSLTLVLDTFRDLAMNLKYVLHTCNCFDTGQHASNEVVSTLLTTKRRKDCHLYSAHRKGTRDIGLPICGYVDSCFATGQVLWACFNMIELYVSSLLRQVQLIGYPHVFQVLDIAVVSSRCKPSEWRADPVNSLQIQHFRVKCNGCKGFRPSRLLLLLLASDEMVDSTPAHSERHDFIDPGANFFIVTSRANADLYVWDLGINSASLALTGCIENVAALIFMGCTGKFCFITFSNSKTGVWDPGQPWFVNYYNSQSNFALSVSSLPKLTHFNIIDWTCMRSQDPSDIALNGSYSSDDTNNSFLPCSLKIPLFLRVANFSGELLLAKGDTTLLRNIIYQSTQVAILGDILELGPTEFTFNELMLQFCCDARFNVTAPIERTENINCAEEIKLLCFNDAHCPESKLPRRGVCSS
      >negative180|0
      MAAEMALVKPITKFNTINTTTARLSSRRLPFTVRMSAATTTPPTSKPSKKPQKQGIKESLLTPRFYTTDFDEMETLFNTEINKNLNEAEFEALLQEFKTDYNQTHFVRNKEFKEAADKMQGPLRQIFVEFLERSCTAEFSGFLLYKELGRRLKKTNPVVAEIFSLMSRDEARHAGFLNKGLSDFNLALDLGFLTKARKYTFFKPKFIFYATYLSEKIGYWRYITIYRHLKTNPEFICYPIFKYFENWCQDENRHGDFFSALMKAQPQFLNDWKAKLWSRFFCLSVINHLHLKYPFIMISQLEKALYIAFYIVLIIILLVLTKMQVYVTMYLNDCQRTDFYEGIGLNTKEFDMHVIIETNRTTARIFPAVLDVENPEFKRKLDRMVEINTKLIAVSESDEIPLVKNFKKIPLIAALASELLAAYLMKPIESGSVDFAEFEPQLTY
      >negative191|0
      MPFRRFCGRTILRAIRTLLRPDRIGNLQPHNSEVQRLYINKLPTFWSKPFTKKQPFLIHILQTHQSALEKLRQQLQMENQMATNLVRSTSLPTITHPLIASAEEQLQRLKSSEGTSSSSHSTICQKLDGLRKLYECVDDVLHLPLSQQVLSYEKHVKLMEQVSNSSLKVLDTCSITKDAFSQMKERVRLLESSLRRRRGGEFSLSNEVGVYIASNKKLNKVICKGFRNLHKKENDQTIVVENSELTSLVSLIKGVEDVTLMVLESTSSFISHTKMISKQNSWSLVSKLLKPKRVSCEGIDIEIEKINMELLLLLRNEKIDHSQILNAAKKLQVFESSIQELEDGLGAIFRLLLKNRVSLLNILSH
      >negative193|0
      MSVVPLMPSMSVVPAMLVVPVVPSTSVVPVFEWWPEVVPVIVWWLEVVPVLGVKEEPSITTKIMKLFGVIISEEEDAEEPPSKRPSLSSSSSSSNRRHRYECPYCIRKFKNSQGLGGHQNAHKRERLHLKRPELQPNTNPAVASFAPLLHKEATESENQAVFQSTATERLLSVASSVASGVRQPAGSLSCTTDGVGMSSVVGPQSNNHVNDGVTSFDHIDLHLSL
      >negative195|0
      MFLFYYLHHHSSSLVSMLISITISYNSTKWADEERYQTSMFQWLHGLLITSSSLSWIKLKERRKTGVKHLCMTHYNML
      >negative203|0
      MRRLLSGKKLQLVLDLDHTLIQTRQIKKTPHLKREKDIYRFYPIYLLSMISLQQLILPNLTKSNIMEQNFMWYDNESCNSITTSSLQCRHSSFMDNKCARCTQSIADCGNPVTIPFGYIHPHLSHTLEEIDRLRDENLQCLLERKKLHLVLDLDHTLVHATKIMQPMLGRESTESMNDFHEILDSRYMIKIRPGVHEFLEQVSSMFDLSIYTRSVREYAHKVVEVLNSGDGLSKFSWVITREDCLKNKRKGLDVVLSHERVVLIVDDKERVWEESGRENLIKIRPYFYIQEEEDMDEEDMDDELDRVLNVLKEVHNGFYDDNIEFGKLDYGKRDVREVLKRVLLSSKG
      >negative209|0
      MADSGKKQVGESSISQKGSKRKGQGQRYSVEATQKLEAFFKKCRFPDEDQRNQLAIEVGLDPDQIKGWFQNKRTQTKTKNERSDNQNFQNENEKFRREIIEMKEAMENNMRSKCDGPLIGEEERAGNIEKLKINMQRLREERKRIISNIISSYHEKSFVMDSNLAPPNSTLGSLTDSSDECLLRQTICGSPIGYNSSFHPENNNDNNNVRAHSINIKNIPIISQLEQENYGFHHDNNGEKSVIFEIVVPAINEMLGLVYVNEPLWVKSSVDEGWLIHRESYDRTFSNSNRPYKSTARIESSRSFGVVPMTAIDLIKNFRDPIKWMNMFPTIVTKARIVDVLDSGNTEMYEKLHILSPLVEARDFVFIRCCKQLDQTTWIMVDVSYDLFKEIKTCAPSYAWKFPSGCVIQDTGDGKSMVAWIEHVQIDEKCQVNHIFRDLLCGRQTYGAERWIVTLQRMSERYNFAMPVTCATTDDSQGGSVLYLTLYVFMIFQN
      >negative213|0
      MVFVVLLVAVGGGVGIGGLGGGISGFDISIGGIDGGVGCLGGLGASIGGISGGISGFCGSLGALGGGIGSLRGIVGGFGGSGGRLGGGGGGGLGGPSISTGLVDTIAEPTVKLIKKKLDGAIAIRRAIRQGLPNFEALHDQYTVTNSNASSGGITGGVVDIGGSHLDADTDASRDDEHGRQLANPNAYAVADKIVDLNFYNNFKDRYNDLKRQDETPATVGVVPQLIEIFLSLFITADMQFVAAYSLSVANDR
      >negative214|0
      MALTPRELAYASVNPLLSTTWLKFSVSPSTSLGHENLLTKYAKGSQTLPVTPLINLASKQLADEISFMVLTKITKFVESCCGSVVKNTKMLVPTYFHETQCQATTDTNINGLNITSIINKPTAAAIAYGLTNSPITVCKPLHDFLEKLAGGERVFLVGLNFGGAYISLAMELYPSKVSKAVFVAAENIQGHILFFSHRELIRFLLVFVANFSSSRSREEARDRESLEEAEGIFVLRYCGKRSVIAVNLGASEVALRDRVEAFAIAIKVASG
      >negative216|0
      MPSSSIQNKVPLFILFPQSHLYSIPPRVFGSTCFVHNLAPGKDKLALRALKYVFLGYSRVQKGYRCYSPDLRRYFMSIDVIFFESRPYYISSDHPDVSEILPVSPVLPAPSLVLPVSPDLPKPTFAKSTFTSTSLTAVPPLLTYHRRPRPLFVPDDSCHVSDAAPTADLPSPSQSIALQKGSVSGNGSEHSAENVEKSVEKLEPCTSHQVKAKAGRHKQNHSRSLGLLAAKLFDDEVPLRKKLKLFNRLATVQDDGTVQFEVPGDIKPEKLDFGTGVVYNGALGEAANDVDNVADIPELPPLQIVMLIVGTRGDVQPFVAIGKKFQENGHRVRLATHANFKEFVLGAGLEFYPLGGDPKVLAAYMVKNKGFLPSGPSEILIQRNQIKDIVFSLLPACIDPDPESNVPFKVNAIIANPPAYGHMHVAEALKVPLHIFFTMPWTFLLSYVGRPTSEFPHPLSRVKQSVGYRLSYQVVDGLIWLGIRDVINDFRKKKLKLRPVTYLSNSNSYHPDVPYGYIWSPHLVPKPKDWGPKIDVVGFCFLDLASNYEPRESLVKWLEDGEKPIYIGFGSLPVQEPEKMTKIIVQALEMTGQRGIINKGWGGLGNLKEPKDFVYLLDNCPHDWLFLQCAAVVHHGGAGTTAAGLKTACPTTVVPFFGDQTFWGERVHARGVGPAPIPVDEFSLEKLVAAIRFMLDPKVKERAVELAKAMEHEDGVTGAVKAFYKHFPRESLEPKPEVSPRPHHFFSLRRCFGRS
      >negative217|0
      MGSCLSSEAPATGDGPAAWRKRHHGSQEGAAGGGGKKLPGGAGEMTEDELAWVLERLCGKGASAVASLHTQQDRKGTNQDAMVVWEGGGTAEGADAGRGGDGLGQRSCGGDRRREDGADAACR
      >negative222|0
      MRSGEAAGAVRSSSDQGNSRKKPRFDAGEEEEEELARMPLADAFVGAGSSGDGDGAAAGAGGCAAAPSVELLDIVQHPLPGYGAPVALSFSPDDRRVAFLYSPDGTLHRNVYAFDPAQRRQELLFGPPDGGGLEEGNLSAEERLRRERARERGLGVTRYEWRARLPGTPASRAGIVVPLPSGVYFQDLSGAEPVLKLQSSATSPIIDPHLSPDGSMIAYVRDDELHTVGFSDGQTTQLTYGASESGKIHGLAEYIAQEEMERKMGFWWSPDSKHLAFTEVDSSEIPLYRIMHQGKSSVGPDAQEDHAYPFAGAANVKVRLGVVSSHGGEITWMDLLCGEPNSIHGDEEYLARVNWMHNSAIAVQVLNRTHSKLKLLKFDIASGKREVILEEEHDTWITLHDCFTPLDKGVNSKHPGGFIWASEKTGFRHLYLHDKNGVCLGPLTQGDWMIDQIAGVNESSGVIYFTGTLDGPLETNLYSTNLFPDWSLPLQVPKRLTHGTGRHSVILDHQLLRFIDVYDSIKSPPVILLCSLLDGSVIMPLYEQPLTVQPLKKFQQLSPEIVQIEGKDGTALYGTLYLPDEKKYGPPPYKTLVNVYGGPSVQLVSDSWISTVDMRAQFLRSKGILVWKMDNRGTARRGLQFEGQLKYNIGRVDAEDQLAGAEWLIKKGLAKPGHIGLYGWSYGGFLSAMCLARFPDTFSCAVSGAPVTAWDGYDTFYTEKYMGLPSEQRDAYRYGSIMHHVKNLRGRLLLIHGMIDENVHFRHTARLINSLMAEGKPYDILLFPDERHMPRRLGDRIYMEERIWDFVERNL
      >negative231|0
      MASEIGRGKRQASEIGRGMREASKIKSGGARKLMHTDVQLHVYGVPFSLNRGLLAARSSKLAALLKENDEDDISHLLGDIPTDSETFELVARFCHGFDIILSPDNIIKVLCLAHYLGMSEIHSTNNLTKKAGLYFQNNVLSSWNKTIKALKSAEIILQQAADLSLVDACAEFIIAKVLHNPSLLGEPMRNITTADDDSENDENVYKSNVKRRLFVHDWKSEDLTLLSIALYEPIIRAMVHREVPLEYVASSLFQYLSKWVFLDTKREDDDPSTYTRNSQREIIEAVERLLPQKRGLIPSSLLSKMLQSAIILDAHTECKNGLETRIGKQLDQATVKDLLIPAQGYAKEEQYDTVSVKRILKNFYSNYESTEKSGLVVVAELVDDFLAEVSSDIDLKLNTFLSLAELSQAATAGTNRNSDGIYRAIDIYLDRHRYLTDWEREEVCRVLDCSKMSPEACEHAATNEKLPVRVTVQILFSVQLKLKDNVTKRIKRGPDNRLLKLEEDEEDAKGTSNSEEEMMKAEMEMMGNKVLELEKECHMMRREIQRGSSQHKIQKEKTSMWKEMKRKIGCMTSSHETNCHVKKKKVHPR
      >negative235|0
      MTPYSLNPPGPSIQAGQNQLFNISPNNQDCRTFFNIFDPRQTSIEIGGLRENYRQDDKMILHDGSSSNCNSSFNISPETVVMVDPLSSACDRRNLPSEEESKNNDHGSGNKWMSSKMRLMKKMMRPSISPTTDKAINSSPRFQNHQGLESRRYSQRSPRNNNGSSTPRVCSDCNTSTTPLWRTGPKGPKSLCNACGIRQRKARRAMAEAANGLVTPIACEKTRLHNKEKKSRMNHFAQFKNKYKSTTTTTTTTVGSSEGVRKLEYFNNFAISLRSNNSDFEQMFPRDEVAEAALLLMDLSCGFVHL
      >negative245|0
      MVETEVAALIAPWLTPTSLFVLLNILLATIFLANNNNNNNHNNNSKNNLETHHHHQYHQSPDDGASLVPPPPQLVRAPSLFSRVASFNFSNYYSESHASDPATVTFTRAPSFLQRVTSFKFGRGNGTENEEEARDSGVTHHVRRENKSDSDSEDRKSEEKVVKVKKKVNKKVKKEVTAVKKEKEVVVPEVKETTSLFRKKDEVAEVKETTSLKREDEEGVDSKADDFIHKFKQQLKLQRLESLLRYRNN
      >negative249|0
      MCSPKNLCSIVRQKKQNMSGKSDFAQKLLYDLKLRKERVAAAQNTGYSYSTPRDGRANPGQTYRGSRQTKTLEYVNLKVGSTSNRSFRVEESSGQIVTYGTGRVRNSERVGDLSMALAFAIENGGKFTKMDSGSSRNPVLKFLQQFGQRSIDISKAERRTYHLPNGQFPSISHVHINEVSKGIQKLNQILRACSNGLNFNRNSIEVGQELLKGAMDLEESLRMLVNLQEASDHMIKPQNKNRIKLLDEDEDEDEDDDVKIVDQKQLDLPRFSFDKPSRNSYITKGTTKNNIKQQLMALTYPDQTPKLHEKQPLSTNKPVSHKRSASCAPDFKNLDQKNQSSGPKSGSEKGRISNVIAKLMGLDEIPQKEDKKASRKGSDPKKKQEPVLKRRSDPTGTRDAENMPSLSVDKNMISNMLSVQDAKHVRRADNARASPRRNSDMVSSGRPQQQEERSIAVNKDTGPVSSGLQTINNVMDKQHSKDIQLNQVPGHQVNFQQKQTEKNQTSVKGKITKTIEIKETISQLKKPQTSRVPLINIVLQEDTIQKEKKEIDKFPLSNEQKALHKDEVRQVQKLKKPEGQDEKHQAGKKEQLPSNKTMQVRTHKANQVETITSPKARNGAASSKTKQSSLKQSILGTKNSTKSKNGAPSKDSLDKIKQVALVKHRHSSTSAAIMQSSENKNANQNISPGAENLNSIDQSSKREKPINLPSTDRKGHHTKIHITETFPKIEKLPRKREDILQEESAPPKHLSPTLQDMKLQKDDKSCSQLTKNQAREAKADNIGSNDSEMSTERLNLQTELHCKIENSTSCNTTMEKEREVLIGSETMISNENHQEKTLQEVDISMDQKLGEDRPKISQAMDQFNGIHQEASLNSKLFNDEQNQCFPAKFTGKGGTKLANVVRHDQETTLPLVAQEPLTEPEKHFKESVIKNHLFLNTAEALFKLNIPISILHASDQNNQGEDVKLMIDCGYEIMKRKAIRQELALHPYATISIGYTKTRSLDGLIKQLCKDFDTLKSYGGNGNVSAECDEADYLHKMIGKDMQNGNPDVNSMWDFGWSTEQLMFGFLEKDDIVKDVEKHLLNGLIDEITMDLLRIAISV
      >negative256|0
      MLRTRLDGANPNDNTTSCWTVNYNQHCVSLLLSRSFCIFPLTQTSHVCPFLLPQEIDCYIWRGFEPHKRHRLLPLDIDAQKTDSCRLHLSSSTFVNGICFTPPNSREQELLSLKLHRNPVAAVILDYRVFCTPRVISGFWIGPDVEDGSGFVEAIVYQMFDA
      >negative263|0
      MCLILSPLLIYLGSFCFGKSNRFSAPEKIRGDFTTLKVETCEQMHHSPSLKIFFDLNPNKQIYEKVYVDHRLNIIREGKGLERRMRAFWCFSFADFSFYSADYITINQEKKICLEKKDLSL
      >negative270|0
      MTQILIFSPSPILSPFNEREQRWRKKVICCSRQHVWDESLLQSNGGFRKFRASNSTAGSYGGSAEISKGQPLPWISTWPNKQMLMPFSKVNSKFCISHVLVN
      >negative272|0
      MYLSSRTGSRLLFLVKASFNTFEKVATISSTLIYFVSHKLVSSRSCSHNFRNICGYIRHNCRKCIRFCFCTFSIKISLPLVIARRIEY
      >negative274|0
      MEDTGFSGVNGSCDYLVQSPDHQPLQLVPYHVGCSSHSVGCSSSGINLDRRRVGRKSSSKPKASKKKNGYHQPQGEKKQRLIWTPELHQCFVDIFNRTPSTKLFPRTILEQMSGKYPFVTRENIASHLQKHKKNLNKKDEEENSAKASSKSVPLKIPKESNSQSINPKTSNSNSTFPNSQLILQSYPNSTNPISSQVLLHSHPNSAIPSFDDKTWVQNSQGCGFSREQFEQFALEQIALEQLYGCLKRSINHQVDYQQQQHSFNNNVCESQENPNWALQIGQSSTAPISDLAVSTNLCPPPISANQISGNDGNGIGFHPIMDFNSFGGWNNTNNDPFLDTDLPNFAFGNCYDNPEPLDFRYPLNLNTINGFQTDSEQMYYPSINPMENDFTFQQCFDGYNHPNVVPQTFGLGYNEVHAKNNVELRNLDALADNDLQIFNGLNNDQISQGNECFPGPKGSVT
      >negative285|0
      MTSPASTSSWFSGIVRTASGNPPSSAMPPAGGVTSAPVSLPDTPAAVSGKGGVVAPVATTGVGARRKQLQGTLFKYGPKSAQGCWIQFRSSAACFAIPLAWEFFGSDVLDASRMVAFRTGDFTRQVIFIGGLADGLLATDYLEPLSLALEVEKWSLVQPLLSSSYTGYGISSLEQDALELDQLIGYLINKDNSEGVILLGHSTGCQDIIHYMQTNFACSKAVSGVILQAPVSDREYRATLPETGEMIDLAAKMISAGRGMDLMPREANSDAPITAYRFHSLCAYMGDDDMFSSDLSEDQLKQRLGHMSTTQCLVIFSMADEYVPEYVDKKALVDRLCRALGDSEKVEIKWGNHALSNRVQEAVEVIVDFVKREGPKGWDDPWS
      >negative286|0
      MVAVMAMVMVEMIHHHFLKWFIKNDFESSAPLPPEAKITSGSRASYLN
      >negative289|0
      MGIPLVAIRFWEKSPDHVSLEFLLALVHHPQGSLGSLLFLVYGIDEVSLFSHASFLDLAPKSASDSTIALDRVVDPVAMTISFRFNHRLRSCSRSSCYDQTWYCSRFAIIITGVDAAELGSAMGQAGEELVGALLCFFVDA
      >negative290|0
      MEFDVQLKKLQIGLSAKNTVSISPALSPSDPFLRHFTLLNAATFLLRSTLHLLLLLLLYTFQSTRSDFFYLESDETVDFSGCRNQRFEEKMLGFSNGSGGGGVGAEDLAQLQSTMRAIELACSYIQINSNPVAAEATILSLHQSPQPYKACRYILENSQVANARFQAAAAIRESAIREWSFLATDDKGGLISFCLGYVMQHANSSEGYVLSKVSSVAAQLMKRGWLEFTPAQKEVFFYQINQAILGSHGLDVQFIGVNFLESLVSEFSPSTSSAMGLPREFHENCRKSLEQNFLKSFYQWAQDAALSVTSKIIESHSSVPEVKVCNATLRLMHQILNWEFPYSKGGTRASINVFSDGIRPDNALSRKTECVIVQPGASWCDVLLSSSHVGWLINFYSSVRQKFDLEGYWLDCPVAVSARKLIVQLCSLAGEIFPSNNVQMRDQHLLLLLTGVLPWIDPPDVISKEIEEGRSGSEMIDGCRALLSIGTVTTPVVFDQLLRSLRPFGTLTLLSMLMGEVVKVLMANSTDEETWSYEARDILLDTWTTLLTSMDGSGGNAWLPPEGIHAAASLFSLIVESELKVASASATTEDDADCLASVSAMDERLGSYALIARAAVDATIPFLAKLFSDHVARLHQGRGTVDPTETLEEVYSLLLIIGHVLADEGEGETALVPDALQSHFVDVVEANNHPVVVLSSSIIKFAEQCLDAEMRSSIFSPRLMEAVIWFLARWSFTYLLLVEECNLGSNKLQSLPSRACLFTYFNEHNQGKFVLDIIVRISLTSLTSYPGEKDLQELTCFQLLHALVRRRNICFHLLSLDSWRNLANAFANDKTLFLLNSVSQRSLAQTLVLSAYGMRSSDASNQYVKDLMAHMTSSLVDLSNSSDLKNLAQQPDIIMLVSCVLERLRGAASATEPRTQRAIYEMGLSVMNPVLRLLEVYKHESAVIYLLLKFVVDWVDGQLSYLEAHETAVVINFCMSLLQIYSSHNIGKISLSLSSTLLNEAKTEKYKDLRALLQLLSHLCSKDMVDFSSDSIETQSTNISQVVYFGLHIITPLITLELLKYPKLCFDYFSLISHMLEVYPETLAQLNNDAFSHVLTTVDFGLHQQDVDIVTMCLRALKALASYHYKEKNAGNSGLGSHAAGHTDPNGVFHEGILSRFLRTLLHFLLFEDYSTDLVSTAADALFPLILCEPNLYQGLGNELIEKQANPNFKTRLANALQVLTTSNQLSSSLDRLNYQRFRKNLNNFLVEVRGFLKTR
      >negative294|0
      MAHANTGPSRGPNLRVQGGGEKQQSPTSSASSRPHPASPPQTLSPPASPLLHGGGGHHRTMRSSPAASGTTPSNMDSGSESDSAPEELTAVQGVEKHDEISKVEKDSAIRVSQQEKERRRRWAQRRTSSKPDKKEPLEVEDKDIKQKAENEEDEESEETHTMPGMLPTNVIEMLAAREKQTFSSDSEEEITNQKVQKRKKRLKSSGPETILLKDVRSTQHVKNALAFLEQRKNQVPRSNAVLKNANKALRLLSSKGNFLS
      >negative299|0
      MMILDDRNNKRSPFPFTASQWKELEQQLLTFKYLISGVPVPPQLFSNVITNPFASSSSTLFPYQQSLPFGWGCFQVGYGRKIDPEPGRCRRTDGKKWRCSKEAFPDSKYCEKHMHRGKNKTKKHIQVFSTTPPKTTTTNTIIPTTPSTTNKPYLSSPVISSSVSPSYINQLHSYDHTTSFYPFLSPQSSSFSTHFTESTQNVTAPHWLLDTQLYDDQPKKDMRYLKEMKGAVVNEKRSSQFQDTRYNKINKEERGERFGGVDRVDETQMLFHHFLGD
      >negative300|0
      MQRKPPMATRPSLRSSDGARPRSKSGAGRPPSSSRSPGPSRPSSAAAADKPVPSFLRPTVSSSMHSSSSSSSLPLPCASSSSAAGSKGAAAATARRSADKAPARPVGAPRPITPKDRASAQPVGAPRPITPKDRASAQPVGAPRPITLKDKAPAQPVGVPRPAKAKAPASASLWGAVSPRQLMQRASNAFKASSRSRSKKGKEEAAASASSGGKGGAGASARAKGQTSRAQHQQQQPETPAEPSPAVTPLELEAEEPVLLESDAAGQNGQDVATSQEAASTDITTVAVRCQEEQVGAGQPKEKAEAEMSDLQEERPQSSEVVETETGAHERTDDGSPAVVGEAAATPEGEDELATSAAEEKIVEEIQRAEATENSKANANPEEPDQETGVISEEPKEGTSVVSEEPKPKEAADPTTAQKREEASDEPKTAAGSNASAPTTPLTEAANYDGNDDVEATPKQVSASEPVTPVAEGISKGKEVMETQQQSASAPTTPASKQAAIPEDAASALAFRGRRVRTAMERRSEEEQPKRKEVARSNDVIEEAKSKLMEKRKSKVKALVGAFETVMDSPRAS
      >positive1|1
      MKNLDHIAASVDWEKESLPEYQDLIFLLFFALFFPVLRFILDRFVFEALAKRMIFGKKTVVNINGREERKKINKFKESAWKFVYFLSAELLALSVTCNEPWFTDSRYFWAGPGDVVWPNLKMKLKLKLLYMYAGGFYFYSIFATLYWETRRYDFAAQIIHHVTTVSLIVLSYVYGFARIGSVVLALHDGSDVFMEIAKMSKYSGFDLIADIFFSLFALVFTSLRIICYPFWIIRSTCYELLYVLDIQKERTTGIILYFVFNALLICLLVLHLFWFKIILRMVKNQILSRGHITDDVREDSESDDDHKD
      >positive2|1
      MAHASVASLMRTIESLLTFNSPMQSLSCDHREELCALREKVSSLEVFVKNFEKNNVFGEMTDFEVEVREVASAAEYTIQLRLTGTVLGENKSQKKKARRRFRQSLQQVAEDMDHIWKESTKIQDKGKQVSKESLVHDFSSSTNDILKVKNNMVGRDDQRKQLLEDLTRSYSGEPKVIPIVGMGGIGKTTLAKEVYNDESILCRFDVHAWATISQQHNKKEILLGLLHSTIKMDDRVKMIGEAELADMLQKSLKRKRYLIVLDDIWSCEVWDGVRRCFPTEDNAGSRILLTTRNDEVACYAGVENFSLRMSFMDQDESWSLFKSAAFSSEALPYEFETVGKQIADECHGLPLTIVVVAGLLKSKRTIEDWKTVAKDVKSFVTNDPDERCSRVLGLSYDHLTSDLKTCLLHFGIFPEDSDIPVKNLMRSWMAEGFLKLENDLEGEVEKCLQELVDRCLVLVSKRSRDGTKIRSCKVHDLIYDLCVREVQRENIFIMNDIVLDVSYPECSYLCMYKMQPFKRVTGDEINYCPYGLYRALLTPVNRQLRDHDNNNLLKRTHSVFSFHLEPLYYVLKSEVVHFKLLKVLELRHRQIDGFPREILSLIWLRYLSLFSYGNFDVPPEICRLWNLQTFIVQRFRSDIIIFAEEIWELMQLRHLKLPRFYLPDCPSGSVDKGRHLDFSNLQTISYLSPRCCTKEVIMGIQNVKKLGISGNKDDYKSFRDSGLPNNLVYLQQLEILSLISVDYSLLPVIISSAKAFPATLKKLKLERTYLSWSYLDIIAELPNLEVLKLMDDACCGEEWHPIVMGFNRLKLLLIKYSFLKFWKATNDNFPVLERLMIRSCKNLKEIPIEFADIHTLQLIELRECPPKLGESAARIQKEQEDLGNNPVDVRISNPLKESDSDSEEH
      >positive18|1
      MNFNNELSDLKNRFLFRTLRAQKCSDVARDRIDFFIWELKFLNCFLHLQSFAFASECGMLDISQKMIEICKRFNTPPPHNSFAYWKEVICKRLCAISIQPDASSDDGFACWKKVIWKTKQEFRAKYSFPKTLLADNKVYDDDDTNPKFVMEFIDAVVGNLNVLVKINDPSSLLFVPGPKEQIEQVLKELKLLRFFVCFVSNKCIEPQYQHTTFYTHALIEASHIAMVVWLNLPIYGNRNQDLASSEVSCLLSDFMEMKIKSIQPDISRNNIYIDVLRALKSTIPQAQDKHAAESGIVETPTHNLMVGLSDQMANLQEMLCLLRDNLIHLPILDLEFHLQDMDSVIVDAGLLIYSLYDIKGQKEDTTLEDINQALGFDLPRNIEPIKAMINLVMQKAFQCNLPRIHGLGYVDFLLKNLKDFQGRYSDSLDFLKNQLQVIQTEFESLQPFLKVVVEEPHNKLKTLNEDCATQIIRKAYEVEYVVDACINKEVPQWCIERWLLDIIEEITCIKAKIQEKNTVEDTMKTVIARTSSKLARTPRMNEEIVGFEDVIENLRKKLLNGTKGQDVISIHGMPGLGKTTLANSLYSDRSVFSQFDICAQCCVSQVYSYKDLILALLRDAIGEGSVRRELHANELADMLRKTLLPRRYLILVDDVWENSVWDDLRGCFPDVNNRSRIILTTRHHEVAKYASVHSDPLHLRMFDEVESWKLLEKKVFGEESCSPLLKNVGLRIAKMCGQLPLSIVLVAGILSEMEKEVECWEQVANNLGSYIHNDSRAIVDKSYHVLPCHLKSCFLYFGAFLEDRVIDISRLIRLWISEAFIKSSEGRRLEDIAEGYLENLIGRNLVMVTQRSISDGKAKECRLHDVLLDFCKERAAEENFLLWINRDQITKPSSCVYSHKQHAHLAFTEMHNLVEWSASCSFVGSVVLSNKYDSYFSTRDISSLHDFSISRILPNFKFLKVLDLEHRVFIDFIPTELVYLKYFSAHIEQNSIPSSISNLWNLETLILKSPIYALRCTLLLPSTVWDMVKLRHLYIPDFSTRIEAALLENSAKLYNLETLSTLYFSRVEDAELMLRKTPNLRKLICEVECLEYPPQYHVLNFPIRLEILKLYRSKFKTIPFCISAPNLKYLKLCGFSLDSQYLSETADHLKHLEVLILYKVEFGDHREWKVSNGKFPQLKILKLEYLSLVKWIVADDAFPNLEQLVLRGCQDLMEIPSCFMDILSLKYIGVEYCNESVVKSALNIQETQVEDYQNTNFKLVLIEFSLQKKAWKLNLTDAEDMHNAVKNILAEIR
      >positive38|1
      MAPAVSASQGVIMRSLTSKLDSLLLQPPEPPPPAQPSSLRKGERKKILLLRGDLRHLLDDYYLLVEPPSDTAPPPDSTAACWAKEVRELSYDVDDFLDELTTQLLHHRGGGDGSSTAGAKKMISSMIARLRGELNRRRWIADEVTLFSARVKEAIRRQESYHLGRRTSSSRPREEVDDDDREDSAGNERRRFLSLTFGMDDAAVHGQLVGRDISMQKLVRWLADGEPKLKVASIVGSGGVGKTTLATEFYRLHGRRLDAPFDCRAFVRTPRKPDMTKILTDMLSQLRPQHQHQSSDVWEVDRLLETIRTHLQDKRYFIIIEDLWASSMWDIVSRGLPDNNSCSRILITTEIEPVALACCGYNSEHIIKIDPLGDDVSSQLFFSGVVGQGNEFPGHLTEVSHDMIKKCGGLPLAITITARHFKSQLLDGMQQWNHIQKSLTTSNLKKNPTLQGMRQVLNLIYNNLPHCLKACLLYLSIYKEDYIIRKANLVRQWMAEGFINSIENKVMEEVAGNYFDELVGRGLVQPVDVNCKNEVLSCVVHHMVLNFIRCKSIEENFSITLDHSQTTVRHADKVRRLSLHFSNAHDTTPLAGLRLSQVRSMAFFGQVKCMPSIADYRLLRVLILCFWADQEKTSYDLTSIFELLQLRYLKITGNITVKLPEKIQGLQHLQTLEADARATAVLLDIVHTQCLLHLRLVLLDLLPHCHRYIFTSIPKWTGKLNNLRILNIAVMQISQDDLDTLKGLGSLTALSLLVRTAPAQRIVAANEGFGSLKYFMFVCTAPCMTFVEGAMPSVQRLNLRFNANEFKQYDSKETGLEHLVALAEISARIGGTDDDESNKTEVESALRTAIRKHPTPSTLMVDIQWVDWIFGAEGRDLDEDLAQQDDHGYGFFILFPGYNLQGLLSFFLSLPWLLSLPSMHLQPDLMIV
      >positive40|1
      MKRRRLFFSVLLSILTLFINGPLITTAQSPPSSSTSCNRICGGIEIPFPFGIGRRDCFLNDWYEVVCNSTTSGKSLAPFLYKINRELVSITLRSSIDSSYGVVHIKSPVTSSGCSQRPVKPLPLNLTGKGSPFFITDSNRLVSVGCDNRALITDIESQITGCESSCDGDKSRLDKICGGYTCCQAKIPADRPQVIGVDLESSGGNTTQGGNCKVAFLTNETYSPANVTEPEQFYTNGFTVIELGWYFDTSDSRLTNPVGCVNLTETGIYTSAPSCVCEYGNFSGFGYSNCYCNQIGYRGNPYLPGGCIDIDECEEGKGLSSCGELTCVNVPGSWRCELNGVGKIKPLFPGLVLGFPLLFLVLGIWGLIKFVKKRRKIIRKRMFFKRNGGLLLKQQLTTRGGNVQSSKIFSSKELEKATDNFNMNRVLGQGGQGTVYKGMLVDGRIVAVKRSKVLDEDKVEEFINEVGVLSQINHRNIVKLMGCCLETEVPILVYEHIPNGDLFKRLHHDSDDYTMTWDVRLRISVEIAGALAYLHSAASTPVYHRDVKTTNILLDEKYRAKVSDFGTSRSINVDQTHLTTLVAGTFGYLDPEYFQTSQFTDKSDVYSFGVVLVELITGEKPFSVMRPEENRGLVSHFNEAMKQNRVLDIVDSRIKEGCTLEQVLAVAKLARRCLSLKGKKRPNMREVSVELERIRSSPEDLELHIEEEDEEECAMEINMDDSWSVDMTAPASLFDLSPKLDVEPLVPQRTW
      >positive47|1
      MGGCFSVSLPCDQVVSQFSQLLCVRGSYIHNLSKNLASLQKAMRMLKARQYDVIRRLETEEFTGRQQRLSQVQVWLTSVLIIQNQFNDLLRSNEVELQRLCLCGFCSKDLKLSYRYGKRVIMMLKEVESLSSQGFFDVVSEATPFADVDEIPFQPTIVGQEIMLEKAWNRLMEDGSGILGLYGMGGVGKTTLLTKINNKFSKIDDRFDVVIWVVVSRSSTVRKIQRDIAEKVGLGGMEWSEKNDNQIAVDIHNVLRRRKFVLLLDDIWEKVNLKAVGVPYPSKDNGCKVAFTTRSRDVCGRMGVDDPMEVSCLQPEESWDLFQMKVGKNTLGSHPDIPGLARKVARKCRGLPLALNVIGEAMACKRTVHEWCHAIDVLTSSAIDFSGMEDEILHVLKYSYDNLNGELMKSCFLYCSLFPEDYLIDKEGLVDYWISEGFINEKEGRERNINQGYEIIGTLVRACLLLEEERNKSNVKMHDVVREMALWISSDLGKQKEKCIVRAGVGLREVPKVKDWNTVRKISLMNNEIEEIFDSHECAALTTLFLQKNDVVKISAEFFRCMPHLVVLDLSENQSLNELPEEISELASLRYFNLSYTCIHQLPVGLWTLKKLIHLNLEHMSSLGSILGISNLWNLRTLGLRDSRLLLDMSLVKELQLLEHLEVITLDISSSLVAEPLLCSQRLVECIKEVDFKYLKEESVRVLTLPTMGNLRKLGIKRCGMREIKIERTTSSSSRNKSPTTPCFSNLSRVFIAKCHGLKDLTWLLFAPNLTFLEVGFSKEVEDIISEEKAEEHSATIVPFRKLETLHLFELRGLKRIYAKALHFPCLKVIHVEKCEKLRKLPLDSKSGIAGEELVIYYGEREWIERVEWEDQATQLRFLPSSRWRWRET
      >positive48|1
      MISLPLLLFVLLFSALLLCPSSSDDDGDAAGDELALLSFKSSLLYQGGQSLASWNTSGHGQHCTWVGVVCGRRRRRHPHRVVKLLLRSSNLSGIISPSLGNLSFLRELDLGDNYLSGEIPPELSRLSRLQLLELSDNSIQGSIPAAIGACTKLTSLDLSHNQLRGMIPREIGASLKHLSNLYLYKNGLSGEIPSALGNLTSLQEFDLSFNRLSGAIPSSLGQLSSLLTMNLGQNNLSGMIPNSIWNLSSLRAFSVRENKLGGMIPTNAFKTLHLLEVIDMGTNRFHGKIPASVANASHLTVIQIYGNLFSGIITSGFGRLRNLTELYLWRNLFQTREQDDWGFISDLTNCSKLQTLNLGENNLGGVLPNSFSNLSTSLSFLALELNKITGSIPKDIGNLIGLQHLYLCNNNFRGSLPSSLGRLKNLGILLAYENNLSGSIPLAIGNLTELNILLLGTNKFSGWIPYTLSNLTNLLSLGLSTNNLSGPIPSELFNIQTLSIMINVSKNNLEGSIPQEIGHLKNLVEFHAESNRLSGKIPNTLGDCQLLRYLYLQNNLLSGSIPSALGQLKGLETLDLSSNNLSGQIPTSLADITMLHSLNLSFNSFVGEVPTIGAFAAASGISIQGNAKLCGGIPDLHLPRCCPLLENRKHFPVLPISVSLAAALAILSSLYLLITWHKRTKKGAPSRTSMKGHPLVSYSQLVKATDGFAPTNLLGSGSFGSVYKGKLNIQDHVAVKVLKLENPKALKSFTAECEALRNMRHRNLVKIVTICSSIDNRGNDFKAIVYDFMPNGSLEDWIHPETNDQADQRHLNLHRRVTILLDVACALDYLHRHGPEPVVHCDIKSSNVLLDSDMVAHVGDFGLARILVDGTSLIQQSTSSMGFIGTIGYAAPEYGVGLIASTHGDIYSYGILVLEIVTGKRPTDSTFRPDLGLRQYVELGLHGRVTDVVDTKLILDSENWLNSTNNSPCRRITECIVWLLRLGLSCSQELPSSRTPTGDIIDELNAIKQNLSGLFPVCEGGSLEF
      >positive52|1
      MDKWKYARLAQFLFTLSLLFLETSFGLGGNKTLCLDKERDALLEFKRGLTDSFDHLSTWGDEEDKQECCKWKGIECDRRTGHVTVIDLHNKFTCSAGASACFAPRLTGKLSPSLLELEYLNYLDLSVNEFERSEIPRFIGSLKRLEYLNLSASFFSGVIPIQFQNLTSLRTLDLGENNLIVKDLRWLSHLSSLEFLSLSSSNFQVNNWFQEITKVPSLKELDLSGCGLSKLAPSQADLANSSFISLSVLHLCCNEFSSSSEYSWVFNLTTSLTSIDLLYNQLSGQIDDRFGTLMYLEHLDLANNLKIEGGVPSSFGNLTRLRHLDMSNTQTVQWLPELFLRLSGSRKSLEVLGLNENSLFGSIVNATRFSSLKKLYLQKNMLNGSFMESAGQVSTLEYLDLSENQMRGALPDLALFPSLRELHLGSNQFRGRIPQGIGKLSQLRILDVSSNRLEGLPESMGQLSNLESFDASYNVLKGTITESHLSNLSSLVDLDLSFNSLALKTSFNWLPPFQLQVISLPSCNLGPSFPKWLQNQNNYTVLDISLASISDTLPSWFSSFPPDLKILNLSNNQISGRVSDLIENTYGYRVIDLSYNNFSGALPLVPTNVQIFYLHKNQFFGSISSICRSRTSPTSLDLSHNQFSGELPDCWMNMTSLAVLNLAYNNFSGEIPHSLGSLTNLKALYIRQNSLSGMLPSFSQCQGLQILDLGGNKLTGSIPGWIGTDLLNLRILSLRFNRLHGSIPSIICQLQFLQILDLSANGLSGKIPHCFNNFTLLYQDNNSGEPMEFIVQGFYGKFPRRYLYIGDLLVQWKNQESEYKNPLLYLKTIDLSSNELIGGVPKEIADMRGLKSLNLSRNELNGTVIEGIGQMRMLESLDMSRNQLSGVIPQDLANLTFLSVLDLSNNQLSGRIPSSTQLQSFDRSSYSDNAQLCGPPLQECPGYAPPSPLIDHGSNNNPQEHDEEEEFPSLEFYISMVLSFFVAFWGILGCLIVNSSWRNAYFKFLTDTTSWLDMISRVWFARLKKKLRRAR
      >positive55|1
      MMNQNCFNSCSPLTVDALEPKKSSCAAKCIQVNGPLIVGAGPSGLATAAVLKQYSVPYVIIERADCIASLWQHKTYDRLRLNVPRQYCELPGLPFPPDFPEYPTKNQFISYLVSYAKHFEIKPQLNESVNLAGYDETCGLWKVKTVSEINGSTSEYMCKWLIVATGENAEMIVPEFEGLQDFGGQVIHACEYKTGEYYTGENVLAVGCGNSGIDISLDLSQHNANPFMVVRSSVQGRNFPEEINIVPAIKKFTQGKVEFVNGQILEIDSVILATGYTSNVTSWLMESEFFSREGCPKSPFPNGWKGEDGLYAVGFTGIGLFGASIDATNVAQDIAKIWKEQM
      >positive56|1
      MKLLSKTFLILTLTFFFFGIALAKQSFEPEIEALKSFKNGISNDPLGVLSDWTIIGSLRHCNWTGITCDSTGHVVSVSLLEKQLEGVLSPAIANLTYLQVLDLTSNSFTGKIPAEIGKLTELNQLILYLNYFSGSIPSGIWELKNIFYLDLRNNLLSGDVPEEICKTSSLVLIGFDYNNLTGKIPECLGDLVHLQMFVAAGNHLTGSIPVSIGTLANLTDLDLSGNQLTGKIPRDFGNLLNLQSLVLTENLLEGDIPAEIGNCSSLVQLELYDNQLTGKIPAELGNLVQLQALRIYKNKLTSSIPSSLFRLTQLTHLGLSENHLVGPISEEIGFLESLEVLTLHSNNFTGEFPQSITNLRNLTVLTVGFNNISGELPADLGLLTNLRNLSAHDNLLTGPIPSSISNCTGLKLLDLSHNQMTGEIPRGFGRMNLTFISIGRNHFTGEIPDDIFNCSNLETLSVADNNLTGTLKPLIGKLQKLRILQVSYNSLTGPIPREIGNLKDLNILYLHSNGFTGRIPREMSNLTLLQGLRMYSNDLEGPIPEEMFDMKLLSVLDLSNNKFSGQIPALFSKLESLTYLSLQGNKFNGSIPASLKSLSLLNTFDISDNLLTGTIPGELLASLKNMQLYLNFSNNLLTGTIPKELGKLEMVQEIDLSNNLFSGSIPRSLQACKNVFTLDFSQNNLSGHIPDEVFQGMDMIISLNLSRNSFSGEIPQSFGNMTHLVSLDLSSNNLTGEIPESLANLSTLKHLKLASNNLKGHVPESGVFKNINASDLMGNTDLCGSKKPLKPCTIKQKSSHFSKRTRVILIILGSAAALLLVLLLVLILTCCKKKEKKIENSSESSLPDLDSALKLKRFEPKELEQATDSFNSANIIGSSSLSTVYKGQLEDGTVIAVKVLNLKEFSAESDKWFYTEAKTLSQLKHRNLVKILGFAWESGKTKALVLPFMENGNLEDTIHGSAAPIGSLLEKIDLCVHIASGIDYLHSGYGFPIVHCDLKPANILLDSDRVAHVSDFGTARILGFREDGSTTASTSAFEGTIGYLAPEFAYMRKVTTKADVFSFGIIMMELMTKQRPTSLNDEDSQDMTLRQLVEKSIGNGRKGMVRVLDMELGDSIVSLKQEEAIEDFLKLCLFCTSSRPEDRPDMNEILTHLMKLRGKANSFREDRNEDREV
      >positive68|1
      MASSSSSPSSRRYDVFPSFSGVDVRKTFLSHLIEALDRRSINTFMDHGIVRSCIIADALITAIREARISIVIFSENYASSTWCLNELVEIHKCYKKGEQMVIPVFYGVDPSHVRKQIGGFGDVFKKTCEDKPEDQKQRWVKALTDISNLAGEDLRNGPTEAFMVKKIANDVSNKLFPLPKGFGDFVGIEDHIKAIKSILCLESKEARIMVGIWGQSGIGKSTIGRALFSQLSSQFHHRAFITYKSTSGSDVSGMKLSWEKELLSEILGQKDIKIDHFGVVEQRLKHKKVLILLDDVDNLEFLKTLVGKAEWFGSGSRIIVITQDKQLLKAHEIDLVYEVELPSQGLALKMISQYAFGKDSPPDDFKELAFEVAELVGSLPLGLSVLGSSLKGRDKDEWVKMMPRLRNDSDDKIEETLRVGYDRLNKKNRELFKCIACFFNGFKVSNVKELLEDDVGLTMLADKSLIRITPDGDIEMHNLLEKLGREIDRAKSKGNPAKRQFLTNFEDIQEVVTEKTGTETVLGIRVPPTVLFSTRPLLVINEESFKGMRNLQYLEIGHWSEIGLWSEIGLWSKIDLPQGLVYLPLKLKLLKWNYCPLKSLPSTFKAEYLVNLIMKYSKLEKLWEGTLPLGSLKKMDLGCSNNLKEIPDLSLAINLEELNLSKCESLVTLPSSIQNAIKLRTLYCSGVLLIDLKSLEGMCNLEYLSVDWSSMEGTQGLIYLPRKLKRLWWDYCPVKRLPSNFKAEYLVELRMENSDLEKLWDGTQPLGSLKEMYLHGSKYLKEIPDLSLAINLERLYLFGCESLVTLPSSIQNATKLINLDMRDCKKLESFPTDLNLESLEYLNLTGCPNLRNFPAIKMGCSYFEILQDRNEIEVEDCFWNKNLPAGLDYLDCLMRCMPCEFRPEYLTFLDVSGCKHEKLWEGIQSLGSLKRMDLSESENLTEIPDLSKATNLKRLYLNGCKSLVTLPSTIGNLHRLVRLEMKECTGLELLPTDVNLSSLIILDLSGCSSLRTFPLISTRIECLYLENTAIEEVPCCIEDLTRLSVLLMYCCQRLKNISPNIFRLTSLMVADFTDCRGVIKALSDATVVATMEDHVSCVPLSENIEYTCERFWDELYERNSRSIFSYKDEDGDVYWVNWDLMMMLMLI
      >positive70|1
      MIAEVAAGGALGLALSVLHEAVKRAKDRSVTTRFILHRLEATIDSITPLVVQIDKFSEEMEDSTSRKVNKRLKLLLENAVSLVEENAELRRRNVRKKFRYMRDIKEFEAKLRWVVDVDVQVNQLADIKELKAKMSEISTKLDKIMPQPKFEIHIGWCSGKTNRAIRFTFCSDDS
      >positive75|1
      MDIVTGAISNLIPKLGELLTEEFKLHKGVKKNIEDLGKELESMNAALIKIGEVPREQLDSQDKLWADEVRELSYVIEDVVDKFLVQVDGIQSDDNNNKFKGLMKRTTELLKKVKHKHGIAHAIKDIQEQLQKVADRRDRNKVFVPHPTRPIAIDPCLRALYAEATELVGIYGKRDQDLMRLLSMEGDDASNKRLKKVSIVGFGGLGKTTLARAVYEKIKGDFDCRAFVPVGQNPDMKKVLRDILIDLGNPHSDLAMLDANQLIKKLHEFLENKRYLVIIDDIWDEKLWEGINFAFSNRNNLGSRLITTTRIVSVSNSCCSSDGDSVYQMEPLSVDDSRMLFSKRIFPDENGCINEFEQVSRDILKKCGGVPLAIITIASALAGDQKMKPKCEWDILLRSLGSGLTEDNSLEEMRRILSFSYSNLPSHLKTCLLYLCVYPEDSMISRDKLIWKWVAEGFVHHENQGNSLYLLGLNYFNQLINRSMIQPIYNYSGEAYACRVHDMVLDLICNLSYEAKFVNLLDGTGNSMSSQSNCRRLSLQKRNEDHQVRPFTDIKSMSRVRSITIFPSAIEVMPSLSRFDVLRVLDLSRCNLGENSSLQLNLKDVGHLTHLRYLGLEGTNISKLPAEIGKLQFLEVLDLGNNRNIKELPSTVCNFRRLIYLNLVGCQVVPPVGLLQNLTAIEVLRGILVSLNIIAQELGKLKSMRELEIRFNDGSLDLYEGFVKSLCNLHHIESLIIGCNSRETSSFEVMDLLGERWVPPVHLREFESSMPSQLSALRGWIKRDPSHLSNLSDLVLPVKEVQQDDVEIIGGLLALRRLWIKSNHQTQRLLVIPVDGFHCIVDFQLDCGSATQILFEPGALPRAESVVISLGVRVAKEDGNRGFDLGLQGNLLSLRRHVFVLIYCGGARVGEAKEAKAALRRAQEAHPDHLRIYIDMRPCIAEGAHDDDLCEGEEEN
      >positive81|1
      MAPCLVSASTGAMGSLLTKLETMLDDEYILLNVRRDIKFVIHELAMWQSFLLDVADTEEPGQHDKSCADLVRELSYDIEDKIDNSMSLMLHHACPKSGIKKHMSKFKNLLPVKIPYQIAKDIKDIKSQILEVSNRCERYRFEDVCLARTEFVDPRLCTVDTCAADLVGIDGPKHELVKWLRNGEDESVHQQKVVSIVGCAGLGKTTLAKQVYDELRINFEYRAFVSISRSPNMATILKCVLSQFHAQDYSSDESEIPKLVDQIRDLLQDKRYFVIIDDIWDMKTWDVLKCALCKNSCGSVIMTTTRIYDVAKSCCSSNGDLVYNIQPLSVADSEELFLNRVFGHEKGFPPELKEVSKDVLRKCGGLPLAINAISSLLAAEKIEEWDRVGLSNVFAQGEKSDIDAMKYKLSLCYFDLPLHLRSCLLYLIMFPEDCLIEKERLVHRWISEGFIRNEDGEDLVEVGERYLYELVNRSLIESVGVPYDGKARFYRVHNVILDFLMIKSMEENFCTLTSNQSRLDYKVRRLSLFANKDPSCIAQLDLSHARSLGASGHLGQLISSVKSNALRVLDVQDCSELGNHHVKDIGRNPLLRYLNISGTDVTELPIQIGDMGFLETLDASFTELVEMPGSITRLRQLQRLFVSDETKLPDEIGNMKRLQELGDINAFKQSVNFLNELGKLTGLRKLGIIWDTNDILKSGKGSSKEKRLVSSLSKLDAGRLSNLYVTFYLREKDGFIGHPFLPALNSIREVYLRRGRMCWMNKWLLSLANLEKLYISGGDEIEQDDLRTVGSIPTLVEFKLYSGCLGPIIISSGFEQLERLELKFSFSQLTFEVGAMPNLKKLDLHVYLSKFKSAGAGFDFGIQHLSSLACVSIVIFCEGVSAAYVEAAEGAFKSMVNAHPNPNRPMLEMTRESADFMSQDE
      >positive88|1
      MKTNFVILLLLLCVFAISPSQQEEINQHNPGIYHQKLLYKVQQWRTSLKESNSVELKLSLAAIVAGVLYFLAALISSACGIGSGGLFIPITTLVSRLDLKTGKRFLGQYLIWVILLLGQLHECKSCIEKERVALLDFKKYWMSITQESDLDYVFPTWNNDTKSDCCQWESIMCNPTSGRLIRLHVGASNLKENSLLNISLLHPFEEVRSLELSAGLNGFVDNVEGYKSLRKLKNLEILDLSYNNRFNNNILPFINAATSLTSLSLQNNSMEGPFPFEEIKDLTNLKLLDLSRNILKGPMQGLTHLKKLKALDLSNNVFSSIMELQVVCEMKNLWELDLRENKFVGQLPLCLGRLNKLRVLDLSSNQLNGNLPSTFNRLESLEYLSLLDNNFTGFFSFDPLANLTKLKVFKLSSTSDMLQIKTESEPKYQFQLSVVVIRVCSLEKIPSFLEYQKNLRLVDLSNNRLSGNLPTWLLANNPELKVLQLQDNLFTIFQMPATIVHELQFLDFSVNDISGLLPDNIGYALPNLLRMNGSRNGFQGHLPSSMGEMVNITSLDLSYNNFSGKLPRRFVTGCFSLKHLKLSHNNFSGHFLPRETSFTSLEELRVDSNSFTGKIGVGLLSSNTTLSVLDMSNNFLTGDIPSWMSNLSGLTILSISNNFLEGTIPPSLLAIGFLSLIDLSGNLLSGSLPSRVGGEFGIKLFLHDNMLTGPIPDTLLEKVQILDLRYNQLSGSIPQFVNTESIYILLMKGNNLTGSMSRQLCDLRNIRLLDLSDNKLNGFIPSCLYNLSFGPEDTNSYVGTAITKITPFKFYESTFVVEDFVVISSSFQEIEIKFSMKRRYDSYFGATEFNNDVLDYMYGMDLSSNELSGVIPAELGSLSKLRVMNLSCNFLSSSIPSSFSNLKDIESLDLSHNMLQGSIPQQLTNLSSLVVFDVSYNNLSGIIPQGRQFNTFDEKSYLGNPLLCGPPTNRSCDAKKTSDESENGGEEEDDEAPVDMLAFYFSSASTYVTTLIGIFILMCFDCPLRRAWLRIVDASIASVKSMLP
      >positive91|1
      MADWAMHHYLLLANQQRHRALADVAVRRRQLLLDSGRVFMLLGAVILMHMLTTTGGGASSGCTRGAEPCVALLLWLLGAALAMLSLVAGRFPVLAAAIAEELGDHLLGGLWSL
      >positive98|1
      MAEGVVGSLIVKLGDALASEAVEVAKSLLGLEGSALKRLFSEIREVKGELESIHAFLQAAERFKDADETTSAFVKQVRSLALSIEDVVDEFTYELGEGDGRMGMAVALKRMCKMGTWSRLAGNLQDIKVNLKNAAERRIRYDLKGVERGAKSMAGRRSSNWRSDSVLFKREDELVGIEKKRDLLMKWVKDEEQRRMVVSVWGMSGIGKTALVANVYNAIKADFDTCAWITVSQSYEADDLLRRTAQEFRKNDRKKDFPIDVDITNYRGLVETTRSYLENKRYVLVLDDVWNANVWFDSKDAFEDGNIGRIILTSRNYDVALLAHETHIINLQPLEKHHAWDLFCKEAFWKNEIRNCPPELQPWANNFVDKCNGLPIAIVCIGRLLSFQGSTYSDWEKVYKNLEMQLTNNSIMDMMNIILKISLEDLPHNIKNCFLYCSMFPENYVMKRKSLVRLWVAEGFIEETEHRTLEEVAEHYLTELVNRCLLLLVKRNEAGHVHEVQMHDILRVLALSKAHEQNFCIVVNHSRSTHLIGEARRLSIQRGDFAQLADHAPHLRSLLLFQSSPNVSSLQSLPKSMKLLSVLDLTDSSVDRLPKEVFGLFNLRFLGLRRTKISKLPSSIGRLKILLVLDAWKCKIVKLPLAITKLQKLTHLIVTSKAVVVSKQFVPSFDVPAPLRICSMTTLQTLLLMEASSQMVHHLGSLVELRTFRISKVRSCHCEQLFMAITNMIHLTRLGI
      >positive101|1
      MGTVLDALAWKFLEKLGQLIEDEVIMTLSVKRGIESLKKNLEFFNAVHEDAEALAMEDPGIDSWWKNMRDVMFDVDDIVDLFMVHSQKLLLPPRPVCCNQPLFSSFAKFSFDHMIAKRIDNINEKFEEIKMNKEMFGLERTNRQQIQITIVDRSQTSPVDELEVVGEDIRRAIDDMVKMIVSSNYNESRSTVFGIQGMGGIGKTTLAQKIYNEQRIREKFQVHIWLCISQNYTETSLLKQAIRMAGGICDQLETKTELLPLLVDTIRGKSVFLVLDDVWKSDVWIDLLRLPFLRGLNSHILVTSRNLDVLVEMHATYTHKVNKMNDCDGLELLMKMSLGPYEQSREFSGVGYQIVKKCDGLPLAIKVVAGVLSTKRTRAEWESIRDSKWSIHGLPRELGGPLYLSYSNLPPELKQCFLWCALLPSNFVIRRDAVAYWWVAEGFVTEVHGYSIHEVAEEYYHELIRRNLLQPRPEFVDKGESTMHDLLRSLGQFLTKDHSIFMNMEYSKALPNLRHLCISNDVEEIPAIEKQKCLRSLLVFDNKNFMKINKDIFRELKHIRVLVLSGTSIQIIPESVGNFLLLRLLDLSYTKIQKLPESIGKLTSLEYLSLHGCIHLDSLPDSLMRLSNISFLELEQTAIDHVPKGVAKLQQLYNLRGVFDSGTGFRLDELQCLSNIQRLRIVKLEKAAPGGSFVLKNCLHLRELWLGCTIGGHDKTYYQANEIERIQQVYELLIPSPSLLYIFLVGFPGVRFPDWLCSEPERKMPNLGHMHLNDCTSCSMLPPAGQMPELLVFKIKGADAIVNMGAELLGKGVNSAKHITIFPKLELLLITNMSNLESWSLNTWNLCGKSEQLVLMPCLKRLFLNDCPKLRALPEDLHRIANLRRIHIEGAHTLQEVDNLPSVLWLKVKNNRCLRRISNLCNLKDLLAQDCPALYQAENLISLKRLYMVDCHNAKQFRMSLLEDQQLAVHVVTVGADGRDIFPDESLYN
      >positive111|1
      MEGLARETNPSSHHQDFASCASDERPDEPELELASRRRQNGAGNNEHVSENMLLDSSKFGALKRREFFNNLLKNLEDDHPRFLRRQKERIDRVDVKLPAIEVRYNNLFVEAECRVTKGNHLPSLWNSTKGAFSGLVKLLGFETERAKTNVLEDVSGIIKPCRLTLLLGPPGCGKSTLLRALAGKLDKSLKVTGDISYNCYELHEFVPEKTAVYINQHDLHIAEMTVRETLDFSAQCQGVGRRPKILKEVNTRESVAGIIPDADIDLYMKVVAVEASERSLQTDYILKIMGLETCADTMVGDAMRRGISGGQKKRLTTAEMIVGPAKAYFMDEISNGLDSSTTFQIINCFQQLTNISEYTMVISLLQPTPEVFDLFDDLILMAEGKIIYHGPRNEALNFFEECGFKCPERKAAADFLQEILSRKDQEQYWLGPHESYRYISPHELSSMFKENHRGRKLHEQSVPPKSQFGKEALAFNKYSLRKLEMFKACGAREALLMKRNMFVYVFKTGQLAIIALVTMSVFLRTRMTISFTHANYYMGALFFSIFMIMLNGIPEMSMQIGRLPSFYKQKSYYFYSSWAYAIPASVLKVPVSILDSLVWISITYYGIGYTPTVSRFFCQFLILCLLHHSVTSQYRFIASYFQTPIVSFFYLFLALTVFLTFGGFILPKTSMPEWLNWGFWISPMAYAEISIVINEFLAPRWQKESIQNITIGNQILVNHGLYYSWHFYWISFGALLGSILLFYIAFGLALDYRTPTEEYHGSRPTKSLCQQQEKDSTIQNESDDQSNISKAKMTIPTMHLPITFHNLNYYIDTPPEMLKQGYPTRRLRLLNNITGALRPGVLSALMGVSGAGKTTLLDVLAGRKTGGYIEGDIRIGGYPKVQETFVRILGYCEQVDIHSPQLTVEESVTYSAWLRLPSHVDKQTRSKFVAEVLETVELDQIKDVLVGSPQKNGLSMEQRKRLTIAVELVSNPSIILMDEPTTGLDTRSAAIVIRAVKNICETGRTVVCTIHQPSTEIFEAFDELILMKTGGKTIYNGPIGERSCKVIEYFEKISGVPKIKSNCNPATWMMDVTSTSMEVQHNMDFAILYEESSLHREAEDLVEQLSIPLPNSENLRFSHSFAQNGWIQLKACLWKQNITYWRSPQYNLRRIMMTVISALIYGVLFWKHAKVLNNEQDMLSVFGAMYLGFTTIGAYNDQTIIPFSTTERIVMYREKFAGMYSSWSYSFAQAFIEIPYVFIQVVLYTLIVYPSTGYYWTAHKFLWFFYTTFCSILSYVYVGLLLVSITPNVQVATILASFFNTMQTLFSGFILPAPQIPKWWTWLYYLTPTSWALNALLTSQYGNIEKEVKAFGETKSVSIFLNDYFGFHQDKLSIVATVLVAFPFVLIILFSLSIEKLNFQKR
      >positive112|1
      MDILISVTAKIAEYTVEPVGRQLGYVFFIRSNFQKLKTQVEKLKITRESVQHKIHSARRNAEDIKPAVEEWLKKVDDFVRESDEILANEGGHGGLCSTYLVQRHKLSRKASKMVDEVLEMKNEGESFDMVSYKSVIPSVDCSLPKVPDFLDFESRKSIMEQIMDALSDGNVHRIGVYGMGGVGKTMLVKDILRKIVESKKPFDEVVTSTISQTPDFRSIQGQLADKLGLKFEQETIEGRATILRKRLKMERSILVVLDDVWEYIDLETIGIPSVEDHTGCKILFTTRIKHLISNQMCANKIFEIKVLGKDESWNLFKAMAGDIVDASDLKPIAIRIVRECAGLPIAITTVAKALRNKPSDIWNDALDQLKTVDVGMANIGEMEKKVYLSLKLSYDCLGYEEVKLLFLLCSMFPEDFSIDVEGLHVYAMGMGFLHGVDTVVKGRRRIKKLVDDLISSSLLQQYSEYGCNYVKMHDMVRDVALLIASKNEHVRTLSYVKRSNEEWEEEKLLGNHTAVFIDGLHYPLPKLTLPKVQLLRLVAKYCWEHNKRVSVVETFFEEMKELKGLVVENVNISLMQRPSDVYSLANIRVLRLERCQLLGSIDWIGELKKLEILDFSESNITQIPTTMSQLTQLKVLNLSSCEQLEVIPPNILSKLTKLEELDLETFDGWEGEEWYEGRKNASLSELKCLRHLYALNLTIQDEEIMPENLFLVGKLKLQKFNICIGCESKLKYTFAYKNRIKNFIGIKMESGRCLDDWIKNLLKRSDNVLLEGSVCSKVLHSELVGANNFVSLPNLEKLEIVNAKSLKMIWSNNVPILNSFSKLEEIKIYSCNNLQKVLFPPNMMDILTCLKVLEIKNCDLLEGIFEAQEPISVVESNNLPILNSFSKLEEIRIWSCNNLQKVLFPSNMMGILPCLKVLDIRGCELLEGIFEVQEPISVVESNSVPILNSFSKLEKIRIWSCNNLQKILFPSNMMGILTCLKVLEIRDCELLEGIFEVQEPISVVESNNLPILNSFSKLEEIRIGSCNNLQKVLFPPNMMGILTCLKVLEIRHCNLLEGIFEVQEPISIVEASPILLQNLSSLMLCNLPNLEYVWSKNPYELLSLENIKSLTIDKCPRLRREYSVKILKQLEDVSIDIKQLMKVIEKEKSAHHNMLESKQWETSSSSKDGVLRLGDGSKLFPNLKSLKLYGFVDYNSTHLPMEMLQILFQLVVFELEGAFLEEIFPSNILIPSYMVLRRLALSKLPKLKHLWSEECSQNNITSVLQHLISLRISECGRLSSLLSSIVCFTNLKHLRVYKCDGLTHLLNPSVATTLVQLESLTIEECKRMSSVIEGGSTEEDGNDEMVVFNNLQHLYIFNCSNLTSFYCGRCIIKFPCLRQVDIWNCSEMKVFSLGIVSTPRLKYENFSLKNDYDDERCHPKYPKDMLVEDMNVITREYWEDNVDTGIPNLFAEQSLEENRSENSSSSKNNVEKE
      >positive116|1
      MEVVTGAMSTLLPLLGDLLKEEYNLQKSTKGEIKFLKAELESMEAALIKISEAPLDQPPNIQVKLWARDVKDLSYEIEDGIDKFRVHLECRQQKKPHSFMGFIHKSMDMLTKGKIRHKIGIDIKDIKSRIKEVSDRRERYKVDSVAPKPTGTSTDTLRQLALFKKAEELIGTKEKSLDIVKMLTEGDEVFKKHLKMVSIVGFGGLGKTTLANVVYEKLRGDFDCAAFVSVSLNPDMKKLFKCLLHQLDKGEYKNIMDESAWSETQLISEIRDFLRDKRYFILIDDIWDKSVWNNIRCALIENECGSRVIATTRILDVAKEVGGVYQLKPLSTSDSGQLFYQRIFGIGDKRPPIQLAEVSEKILGKCGGVPLAIITLASMLAGKKEHENTYTYWYKVYQSMGSGLENNPGLMDMRRILHVSYYDLPPNLKTCLLYLSLYPEDYNIETKELIWKWIGEGFIHEEQGKSLYEVGEDYIAELINKSLVQPMYINIANKASSVRVHDMVLDLITSLSNEENFLATLGGQQTRSLPRKIRRLSLQSSNEEDVQPMPTMSSLSHVRSLTVFSKDLSLLSALSGFLVLRALDLSGCEEVGNHHMKDICNLFHLRYLSLEGTSITEIPKEISNLRLLQLLVIRSTKMKKFPSTFVQLGQLVFIDMGNREVSRLLLKSMSTLPSLSSLAIGIGELREEDLQILGSMPSLHDLSIDVGYWERGRDKRLVIDSGSPFRSLTRFSIKGCGFIDFMFAQGTLQKLQILELSIFGKAIKDRFGDFQFGLENLSSLEHVYVDARGRGIIPSQEAELSGALEKELDINPNKPTLTVKVTPR
      >positive118|1
      MAEAFIQVLLDNLTSFLKGELVLLFGFQDEFQRLSSMFSTIQAVLEDAQEKQLNNKPLENWLQKLNAATYEVDDILDEYKTKATRFSQSEYGRYHPKVIPFRHKVGKRMDQVMKKLKAIAEERKNFHLHEKIVERQAVRRETGSVLTEPQVYGRDKEKDEIVKILINNVSDAQHLSVLPILGMGGLGKTTLAQMVFNDQRVTEHFHSKIWICVSEDFDEKRLIKAIVESIEGRPLLGEMDLAPLQKKLQELLNGKRYLLVLDDVWNEDQQKWANLRAVLKVGASGASVLTTTRLEKVGSIMGTLQPYELSNLSQEDCWLLFMQRAFGHQEEINPNLVAIGKEIVKKSGGVPLAAKTLGGILCFKREERAWEHVRDSPIWNLPQDESSILPALRLSYHQLPLDLKQCFAYCAVFPKDAKMEKEKLISLWMAHGFLLSKGNMELEDVGDEVWKELYLRSFFQEIEVKDGKTYFKMHDLIHDLATSLFSANTSSSNIREINKHSYTHMMSIGFAEVVFFYTLPPLEKFISLRVLNLGDSTFNKLPSSIGDLVHLRYLNLYGSGMRSLPKQLCKLQNLQTLDLQYCTKLCCLPKETSKLGSLRNLLLDGSQSLTCMPPRIGSLTCLKTLGQFVVGRKKGYQLGELGNLNLYGSIKISHLERVKNDKDAKEANLSAKGNLHSLSMSWNNFGPHIYESEEVKVLEALKPHSNLTSLKIYGFRGIHLPEWMNHSVLKNIVSILISNFRNCSCLPPFGDLPCLESLELHWGSADVEYVEEVDIDVHSGFPTRIRFPSLRKLDIWDFGSLKGLLKKEGEEQFPVLEEMIIHECPFLTLSSNLRALTSLRICYNKVATSFPEEMFKNLANLKYLTISRCNNLKELPTSLASLNALKSLKIQLCCALESLPEEGLEGLSSLTELFVEHCNMLKCLPEGLQHLTTLTSLKIRGCPQLIKRCEKGIGEDWHKISHIPNVNIYI
      >positive120|1
      MAEVVLAGLRLAATPICVKLLCNASTCLGVDMTRELHELETIIIPQFELVIEAAEKGNHRAKLDRWLRELKQAFYNAEDLLDEHEYNILKCKAKHKDSLVKDSTQVHDSSISNILKQPMRAVSSRMSNLRPENRKILCQLNELKTMLEKAKEFRELIHLPAGNSLEGPSVPTIVVPVVTSLLPPRVFGRNMDRDRIIHLLTKPMATVSSSVGYSGLAIVAHGGAGKSTLAQCVYNDKRAQEHFDVRMWVCISRKLDVHRHTREIIESATNGECPRVDNLDTLQCRLKDIMQKSEKFLLVLDDVWFDESVNEREWDQLLDPLVSQQEGSRVLVTSRRDVLPAALHCKDVVHLENMEDAEFLALFKYHAFSGTEIRNPQLHARLEEVAEKIAKRLGQSPLAARTVGSQLSRNKDIAIWKSALNIENLSEPMKALLWSYNKLDSRLQRCFLYCSLFPKGHKYKIDEMVDLWVAEGLVDSRNQGDKRIEDIGRDYFNEMVSGSFFQPVSERYMGTWYIMHDLLHDLAESLTKEDCFRLEDDGVKEIPATVRHLSICVDSMKFHKQKICKLRYLRTVICIDPLMDDGDDIFNQLLKNLKKLRVLHLSFYNSSSLPECIGELKHLRYLSIISTLISELPRSLCTLFHLELLHLNDKVKNLPDRLCNLRKLRRLEAYDDRNRMYKLYRAALPQIPYIGKLSLLQDIDGFCVQKQKGYELRQLRDMNKLGGNLRVVNLENVTGKDEASESKLHQKTHLRGLHLSWNDVDDMDVSHLEILEGLRPPSQLEDLTIEGYKSTMYPSWLLDGSYFENLESFTLANCCVIGSLPPNTEIFRHCMTLTLENVPNMKTLPFLPEGLTSLSIEGCPLLVFTTNNDELEHHDYRESITRANNLETQLVLIWEANSDSDIRSTLSSEHSSMKKLTELMDTDMSGNLQTIESALEIERDEALVKEDIIKVWLCCHEERMRFIYSRKAGLPLVLPSGLCVLSLSSCSITDGALAICLGGLTSLRNLFLTEIMTLTTLPPEEVFQHLGNLRYLVIRSCWCLRSFGGLRSATSLSEIRLFSCPSLQLARGAEFMQMSLEKLCVYNCVLSADFFCGDWPHLDDILLSGCRSSSSLHVGDLTSLESFSLYHFPDLCTLEGLSSLQLHHVHLIDVPKLTTESISQFRVQRSLYISSSVMLNHMLSAEGFVVPEFLSLESCKEPSVSFEESANFTSVKCLRLCNCEMRSPPGNMKCLSSLTKLDIYDCPNISSIPDLPSSLQHICIWGCELLKESCRAPEGESWPKIAHIRWKEFR
      >positive125|1
      MSFDSFISFRGEDTRNTFTGHLYKELVGLGITTFMDDKKLLIGDSLSEKLIKAIENSDSFIVVLSENYASSKWCLRELAKIIDCTDEQKHRVLLPVFYHVNPRDVRRQSGCFENSFRLHEELLRELDHMERDKYMEEVQQWRRAFTKVGDLTGVVVTKDCVEVDSIGKITNQLLDMLLHHQKLVPWDELTKLVDIERQLFKMEKLNDLEPNVVRFIGIIGMGGIGKTTIAEVFYDRVARFFGKNRCFLRIYEHTTLLSLQQQLLSQLLQTKDLIINNENEGARMIGSRLKDKRVLIVLDGVKEKTQLEQLVGNPNWFGSGSKIIITTRNRDVLRQPNYKDKMVEYSVEFLDTKSAMTLFCKHAFGCGFPSKNFEDFSKEIVERVEGHPQALIQIGSSLYDKGIEIWKEELKSLEEDYNNRIFKTLKISFDDLGKTSQEVFLDLACFFNEKTKEKVIEILKSFDYRPHSEIQLLQDRCLIEVRSDNTILMPKCIQTMGQQIEREADKRSRIWLPKDAQDVFDEPHQRVKDIKGVVLKLEEKQDEIELEGKVFEDMRSLKILEIGNVEVSGDFTHLSKQLRLLNWHSYPSQCLPLSFESRYLFQLLLPLSQTRQLWNGQKVGFEKLKVINVSGSKNLRETPNFTKVPNLESLDLSNCTRLWKIDSSIIRLNRLTLLDITCCITLKNLPFSRSCKSLITINYVGSGLEEKGTCNFHYGK
      >positive127|1
      MRRCGYSLGLGEPNLDGKPNLDYDAVCRPSELHALKKGALDYIQNSENQILFTIHQIFESWIFSSKKLLDRISERISKEEFTKAADDCWILEKIWKLLEEIENLHLLMDPDDFLHLKTQLRMKTVADSETFCFRSKGLIEVTKLSKDLRHKVPKILGVEVDPMGGPVIQESAMELYREKRRYEKIHLLQAFQGVESAVKGFFFNYKQLLVIMMGSLEAKANFAVIGGSTESSDLLAQLFLEPTYYPSLDGAKTFIGDCWEHDQAVGSGLDCRHHRKNRTAKQ
      >positive129|1
      MERRLMIPCFFWLILVLDLVLRVSGNAEGDALSALKNSLADPNKVLQSWDATLVTPCTWFHVTCNSDNSVTRVDLGNANLSGQLVMQLGQLPNLQYLELYSNNITGTIPEQLGNLTELVSLDLYLNNLSGPIPSTLGRLKKLRFLRLNNNSLSGEIPRSLTAVLTLQVLDLSNNPLTGDIPVNGSFSLFTPISFANTKLTPLPASPPPPISPTPPSPAGSNRITGAIAGGVAAGAALLFAVPAIALAWWRRKKPQDHFFDVPAEEDPEVHLGQLKRFSLRELQVASDNFSNKNILGRGGFGKVYKGRLADGTLVAVKRLKEERTQGGELQFQTEVEMISMAVHRNLLRLRGFCMTPTERLLVYPYMANGSVASCLRERPESQPPLDWPKRQRIALGSARGLAYLHDHCDPKIIHRDVKAANILLDEEFEAVVGDFGLAKLMDYKDTHVTTAVRGTIGHIAPEYLSTGKSSEKTDVFGYGVMLLELITGQRAFDLARLANDDDVMLLDWVKGLLKEKKLEALVDVDLQGNYKDEEVEQLIQVALLCTQSSPMERPKMSEVVRMLEGDGLAERWEEWQKEEMFRQDFNYPTHHPAVSGWIIGDSTSQIENEYPSGPR
      >positive131|1
      MAASFCGSRRYDVFPSFSKVDVRRSFLAHLLKELDRRLINTFTDHGMERNLPIDAELLSAIAESRISIVIFSKNYASSTWCLDELVEIHTCYKELAQIVVPVFFNVHPSQVKKQTGEFGKVFGKTCKGKPENRKLRWMQALAAVANIAGYDLQNWPDEAVMIEMVADDVSKKLFKSSNDFSDIVGIEAHLEAMSSILRLKSEKARMVGISGPSGIGKTTIAKALFSKLSPQFHLRAFVTYKRTNQDDYDMKLCWIEKFLSEILGQKDLKVLDLGAVEQSLMHKKVLIILDDVDDLELLKTLVGQTGWFGFGSRIVVITQDRQLLKAHDINLIYEVAFPSAHLALEIFCQSAFGKIYPPSDFRELSVEFAYLAGNLPLDLRVLGLAMKGKHREEWIEMLPRLRNDLDGKFKKTLRNYLPVIRKRVSNEEGGREKLKKGNKKLDLDEEFPGGEIYSDEIPSPTSNWKDTDDFDSGDIIPIIADKSTTIIPNRRHSNDDWCSFCEFLRNRIPPLNPFKCSANDVIDFLRTRQVLGSTEALVDRLIFSSEAFGIKPEENPFRSQAVTSYLKAARDMTREKECILVFSCHDNLDVDETSFIEAISKELHKQGFIPLTYNLLGRENLDEEMLYGSRVGIMILSSSYVSSRQSLDHLVAVMEHWKTTDLVIIPIYFKVRLSDICGLKGRFEAAFLQLHMSLQEDRVQKWKAAMSEIVSIGGHEWTKGSQFILAEEVVRNASLRLYLKSSKNLLGILALLNHSQSTDVEIMGIWGIAGIGKTSIAREIFELHAPHYDFCYFLQDFHLMCQMKRPRQLREDFISKLFGEEKGLGASDVKPSFMRDWFHKKTILLVLDDVSNARDAEAVIGGFGWFSHGHRIILTSRSKQVLVQCKVKKPYEIQKLSDFESFRLCKQYLDGENPVISELISCSSGIPLALKLLVSSVSKQYITNMKDHLQSLRKDPPTQIQEAFRRSFDGLDENEKNIFLDLACFFRGQSKDYAVLLLDACGFFTYMGICELIDESLISLVDNKIEMPIPFQDMGRIIVHEEDEDPCERSRLWDSKDIVDVLTNNSGTEAIEGIFLDASDLTCELSPTVFGKMYNLRLLKFYCSTSGNQCKLTLPHGLDTLPDELSLLHWENYPLVYLPQKFNPVNLVELNMPYSNMEKLWEGKKNLEKLKNIKLSHSRELTDILMLSEALNLEHIDLEGCTSLIDVSMSIPCCGKLVSLNMKDCSRLRSLPSMVDLTTLKLLNLSGCSEFEDIQDFAPNLEEIYLAGTSIRELPLSIRNLTELVTLDLENCERLQEMPSLPVEIIRRT
      >positive135|1
      MAEIAVLLVLKKIAIALAGETLSFAKPLLAKKSESVAALPDDMKLISNELELIRAFLKEIGRKGWKSEVIETWIGQVRRLAYDMEDTVDHFIYVVGTHDQMGSCWDYMKKIAKKPRRLVSLDEIASEIKKIKQELKQLSESRDRWTKPLDGGSGIPAGSYETEKEMYLPGHDYTISDEELAGIDENKQTLISSLKFEDPSLRIIAVWGMGGVGKSTLVNNVYKNEGSNFDCRAWVSISQSYRLEDIWKKMLTDLIGKDKIEFDLGTMDSAELREQLTKTLDKRQYLIILDDVWMANVFFKIKEVLVDNGLGSRVIITTRIEEVASLAKGSCKIKVEPLGVDDSWHVFCRKAFLKDENHICPPELRQCGINIVEKCDGLPLALVAIGSILSLRPKNVDEWKLFYDQLIWELHNNENLNRVEKIMNLSYKYLPDYLKNCFLYCAMFPEDYLIHRKRLIRLWIAEGFIEQKGACSLEDTAESYLKELIRRSMLHVAERNCFGRIKCIRMHDLVRELAIFQSKREGFSTTYGGNNEAVLVGSYSRRVAVLQCSKGIPSTIDPSRLRTLITFDTSRALSVWYSSISSKPKYLAVLDLSSLPIETIPNSIGELFNLRLLCLNKTKVKELPKSITKLQNLQTMSLENGELVKFPQGFSKLKKLRHLMVSRLQDVTFSGFKSWEAVEPFKGLWTLIELQTLYAITASEVLVAKLGNLSQLRRLIICDVRSNLCAQLCGSLSKLCQLSRLTIRACNEDEVLQLDHLTFPNPLQTLSLDGRLSEGTFKSPFFLNHGNGLLRLMLFYSQLSENPVPHLSELSNLTRLSLIKAYTGQELYFQAGWFLNLKELYLKNLSRLNQIDIQEGALASLERITMKHLPELREVPVGFRFLKSLKTIFFSDMHPEFESSFQKEM
      >positive140|1
      MGSKYSKATNSINDASNLSYGVPFENYRVPFVDLEEATNNFDDNFFIGEGGFGKVYRGVLRDGTKVALKKHKPESSQGIEEFETEIEILSFCSHPHLVSLIGFCDERNEMILIYDYMENGNLKSHLYGSDLPSMSWEQRLEICIGAARGLHYLHKNAVIHRDVKCTNILLDENFVPKITDFGISKTMPELDQTHLSTVVRGNIGYIAPEYALWGQLTEKSDVYSFGVVLFEVLCARPALDRSEIMSLDDETQKMGQLEQIVDPTIAAKIRPESLRMFGETAIKCLAPSSKNRPSMGDVLWKLEYALCLQEPTIQDDPE
      >positive149|1
      MAAAEMERTMSFDAAEKLKAADGGGGEVDDELEEGEIVEESNDTASYLGKEITVKHPLEHSWTFWFDNPTTKSRQTAWGSSLRNVYTFSTVEDFWGAYNNIHHPSKLIMGADFHCFKHKIEPKWEDPVCANGGTWKMSFSKGKSDTSWLYTLLAMIGHQFDHGDEICGAVVSVRAKGEKIALWTKNAANETAQVSIGKQWKQFLDYSDSVGFIFHDDAKRLDRNAKNRYTV
      >positive152|1
      MELPRNKVAGSNQGNENLKAKAKWGSDIKKFSEHQIKRITKNYSTHVGKGAFGEVFRGFLDDGSPVAVKKYIHQNMKEWFDKEITIHCQVNHKNIVKLLGYCSEENALMMLTEYIPRGNLKDLLHGSDDPISFEARLCIAIDCAEALAFMHSMSPPIIHGDIKPDNILLDDNLGAKLADFGISRLLSMDNTHFTMNVIGSRGYMDPEHIETGRVDPKIDVYSFGVVLVELVTRDMASQNGICNGLARNFIGASLTKNNFFSEAFGKQKKAREMFDIQIANMSNMEVLDKFGELAVECLRRDIKKRPEMNHVLERLRMLGKDHEKGQDRVKEHGVLPPFSSSQPKGRLETGRKSSSSDHERLFSQEVVEQEEKNQKYFTWRTSIANGPPESFYDWIRGNDLEIPNQRSPDEVFSRGRWRLLTCQNGLRIFEVLEPAVYLARAIGKAMKAVGVIDASSEAIFQLVMSMDDTRHKWDCSYKYGSLVEEVDGHTAILYHRLRLDWFLTFVWPRDLCYVRHWRRYYDGSYVVLFQSREHPNCGPQPGFVRAHVEIGGFRISPLKSHEGRPRTQVQYLMQIDLKGWGVGYLSSFQQHCVLRMLNTIAELREWFSRSDDRPISAKASLTMDQSKCTTILEEEFDEDEWLSQSDESQ
      dataset/test_data.fasta 0 → 100644
      View file @ 80dfc643
      >negative212|0
      MGSQAGREASIFDSAVKGDTTILDRANIAKYGESSYLSHTPSLGNNIIHIAARRGKTTFVDAAIRLFPDLLWQKNNNDNTVVHEAAAEMGTKDCVRLLISYLKSSMSSSTTSSVSDHNRSNATAIAVPFLLERNCDGETPFHVALRCRNLAAAEELFADVKTHQQILLIKNNSGETPLHLYARYCAGGTFPKDLEHDDNHNVVDKPKFIDALIISNSAAIFEQDSDGFIPVMRAAQYGRVFAVLRMLLSYKQSTECRDLKGMNVLHHLRLRIADLNARDKDYTFWICEKILELHGVDTLIFLQDKDGNTPLHLAIMDRDYDIAELILKRYVEAYLKERKELIVSIKNKEGKTVLDLITFVPDIPITLRKLMEQSSMGCMMPEELYEAAKMGYVGVFGPQTSEVSNLSQETGSTTKNTQDDEYFLSQDVDGRNILHIALEHEREVFITTAIKSFPNLMYHKDSNGDTPLHIAARLRSNHSAYELLRESFHYWYANNRQSYDVTLRVPPWKVTNSRSNTPLHEAARTSNYSSLYTFIANCITPEIRSEAMSDVNEDGETVLHLIARYGQKLDAVLDHEHQHSLSVTEVLRESITSVYMRDRDGLTPILRAAHCGQIWVVARIGKMYPKSIYISDYKGKTVLHHLCSSALDVVNELEIVLQLWEQVSETFPNGDDLMFSQDHDGNTPLHLAIMAQNFRVVHYFIRYYIKVKSSLRNELMGLMNNDGKSIMNLITCGSDIPPNLKKLIKQASMETPMEDDIYEAATKGDINVINEHNRLQCQAYDGSNILHIAVRHRKLSLLMAVVEKNYLYHLIYWEDSKGDTPLHVAAGIQAPEALQFVEQCVKKWKPFYGLLPWTVRNMKGNTVLHEAARFCNYEVILSALKYATDINDVKLNQKIIKDVNDDGETVLHIIARYATYKAKDFLEKVSRELKSLVYMRDYEGFTPVLRAVQCGRLGVARLLVQRYPQSVEIADNKGRTILHHMRSLVVDLVDDLKDIVPVWKDILNYPEADNLRNAQDEDGNTPLHLSIADANVAKAKFLIERCLESTNKQELDINNNDGHTVYDLLSSRSHIPATKELQQLVSRSYVKERALVYDMMEDDLYNAAVKGDVQIFVKVPMDLESQPPTSVEAYFCRQTPGGSNIIHIALRHGSPKVKQFVKTALTHYPILSMRPDNNGDTPLHLAAKWKTGLSSVEVLIEASKCFINELGESDKAFYVAPWKVKNYKGNFPIHEALQSNNLKAAENLLGCDVEATSRVNDLGETPLHAFAKNGFAINNKKEAEKFVEKLIIAKTKEDESSNTASYIRDDEGLNQNAYNSAYIQDDEGLSQNAYNSAYIRDDEGLTPLLRAARSGRLEVVRAILTHCPQSAYLRDPWGRTFLHLLRFTGEDIDESLDGNFQKTGKELFVLREADSQRLVQDYEGNTPLHYAIKTQNSIAAIVLTQRCLEDEEHKELGLVNRDGQTVLDLLALHDVPSEIIQQIRKKLPKEVYLARSSYGIRNTETNQSANALSVVAALLATITFAAGLQVPGGFDSDDGSPVLLKNAVFAAFMVANTIAMCCSMLCLFLLLWVGIRKSHGSLMILDISIILLEFSFYLTMLAFTLGVFVVTLQKSLWLAVLVCVLSFITFLLTWKCSIKLVIKFVESVVAAGKKLASCCASKSADTEGACVGDNRTEK
      >positive29|1
      MAEILLTSVINKSVEIAGNLLIQEGKRLYWLKEDIDWLQREMRHIRSYVDNAKAKEAGGDSRVKNLLKDIQELAGDVEDLLDDFLPKIQQSNKFNYCLKRSSFADEFAMEIEKIKRRVVDIDRIRKTYNIIDTDNNNDDCVLLDRRRLFLHADETEIIGLDDDFNMLQAKLLNQDLHYGVVSIVGMPGLGKTTLAKKLYRLIRDQFECSGLVYVSQQPRASEILLDIAKQIGLTEQKMKENLEDNLRSLLKIKRYVFLLDDIWDVEIWDDLKLVLPECDSKVGSRIIITSRNSNVGRYIGGESSLHALQPLESEKSFELFTKKIFNFDDNNSWANASPDLVNIGRNIVGRCGGIPLAIVVTAGMLRARERTEHAWNRVLESMGHKVQDGCAKVLALSYNDLPIASRPCFLYFGLYPEDHEIRAFDLINMWIAEKFIVVNSGNRREAEDLAEDVLNDLVSRNLIQLAKRTYNGRISSCRIHDLLHSLCVDLAKESNFFHTAHDAFGDPGNVARLRRITFYSDNVMIEFFRSNPKLEKLRVLFCFAKDPSIFSHMAYFDFKLLHTLVVVMSQSFQAYVTIPSKFGNMTCLRYLRLEGNICGKLPNSIVKLTRLETIDIDRRSLIQPPSGVWESKHLRHLCYRDYGQACNSCFSISSFYPNIYSLHPNNLQTLMWIPDKFFEPRLLHRLINLRKLGILGVSNSTVKMLSIFSPVLKALEVLKLSFSSDPSEQIKLSSYPHIAKLHLNVNRTMALNSQSFPPNLIKLTLANFTVDRYILAVLKTFPKLRKLKMFICKYNEEKMDLSGEANGYSFPQLEVLHIHSPNGLSEVTCTDDVSMPKLKKLLLTGFHCRISLSERLKKLSK
      dataset/train.fasta 0 → 100644
      View file @ 80dfc643
      This source diff could not be displayed because it is too large. You can view the blob instead.
      dataset/unirep_feature.csv 0 → 100644
      View file @ 80dfc643
      This source diff could not be displayed because it is too large. You can view the blob instead.
      prPred-DRLF.py 0 → 100644
      View file @ 80dfc643
      import joblib
      import pandas as pd
      from prediction.get_feature import fasta,GnerateFeatures
      import argparse
      def predict(inputfasta,outfile):
      seq_dict,id_list,seq_list = fasta(inputfasta)
      feature_sel= GnerateFeatures(seq_dict)
      model = joblib.load(open('./prediction/BiLSTM_unirep_model_lgb.pkl', 'rb'))
      x=pd.read_csv("./prediction/BiLSTM_unirep_lgbm_feature.csv")
      x2=x.iloc[:,1:]
      y=pd.read_csv("./prediction/train_label.csv")
      y2=y.iloc[:,1:]
      model.fit(x2,y2)
      pred_proba = model.predict_proba(feature_sel)
      id3=[]
      for i in range(len(id_list)):
      id2 = []
      id2.append(str(id_list[i]))
      id2.append(str(round(float(pred_proba[:,1][i]),3)))
      id3.append(id2)
      col = ["Sequence_ID", "R protein possibility"]
      result2=pd.DataFrame(data=id3,columns=col)
      result2.to_csv(outfile)
      if __name__=="__main__":
      parser = argparse.ArgumentParser(
      'Script for predicting plant R protein using deep representation learning features')
      parser.add_argument('-i', type=str, help='input sequences in Fasta format')
      parser.add_argument('-o', type=str, help='path to saved CSV file')
      args = parser.parse_args()
      inputfasta= args.i
      outfile = args.o
      predict(inputfasta,outfile)
      prediction/BiLSTM_unirep_lgbm_feature.csv 0 → 100644
      View file @ 80dfc643
      This source diff could not be displayed because it is too large. You can view the blob instead.
      prediction/feature_id.csv 0 → 100644
      View file @ 80dfc643
      UniRep_F232,BiLSTM_F3253,BiLSTM_F231,UniRep_F1029,UniRep_F1338,UniRep_F747,BiLSTM_F750,BiLSTM_F2662,UniRep_F774,UniRep_F1757,UniRep_F650,UniRep_F1574,BiLSTM_F1420,BiLSTM_F77,UniRep_F662,BiLSTM_F1270,BiLSTM_F1404,BiLSTM_F1775,BiLSTM_F213,UniRep_F57,BiLSTM_F101,UniRep_F1800,UniRep_F1827,UniRep_F682,BiLSTM_F1758,UniRep_F204,UniRep_F861,BiLSTM_F2557,UniRep_F1793,BiLSTM_F655,BiLSTM_F1786,BiLSTM_F853,BiLSTM_F1448,UniRep_F4,BiLSTM_F671,UniRep_F510,BiLSTM_F1116,UniRep_F1754,BiLSTM_F482,UniRep_F212,BiLSTM_F1526,UniRep_F945,UniRep_F1573,BiLSTM_F1912,UniRep_F1241,BiLSTM_F1677,BiLSTM_F74,BiLSTM_F1696,BiLSTM_F524,UniRep_F814,BiLSTM_F1124,BiLSTM_F528,UniRep_F1299,UniRep_F408,UniRep_F1736,BiLSTM_F415,UniRep_F868,BiLSTM_F596,UniRep_F1818,UniRep_F1369,UniRep_F529,UniRep_F472,UniRep_F165,UniRep_F860,UniRep_F1376,BiLSTM_F3362,UniRep_F1214,BiLSTM_F22,UniRep_F1187,UniRep_F1182,UniRep_F1576,UniRep_F156,UniRep_F1095,UniRep_F1082,BiLSTM_F2843,UniRep_F1780,UniRep_F322,UniRep_F1791,BiLSTM_F154,BiLSTM_F252,UniRep_F51,BiLSTM_F3119,UniRep_F1843,UniRep_F660,UniRep_F1459,BiLSTM_F3485,BiLSTM_F2284,BiLSTM_F974,UniRep_F1336,BiLSTM_F1002,BiLSTM_F3578,BiLSTM_F865,BiLSTM_F969,BiLSTM_F3463,BiLSTM_F1066,UniRep_F657,BiLSTM_F1233,BiLSTM_F2256,UniRep_F538,UniRep_F1389,BiLSTM_F1481,BiLSTM_F1997,UniRep_F72,UniRep_F314,UniRep_F288,UniRep_F1625,UniRep_F97,BiLSTM_F1631,UniRep_F237,BiLSTM_F1705,BiLSTM_F1707,BiLSTM_F1740,BiLSTM_F1755,UniRep_F201,UniRep_F1423,UniRep_F183,BiLSTM_F2003,UniRep_F337,BiLSTM_F1424,UniRep_F49,BiLSTM_F1096,BiLSTM_F2179,BiLSTM_F1186,UniRep_F1711,UniRep_F1709,UniRep_F25,UniRep_F32,UniRep_F1581,UniRep_F1615,UniRep_F409,BiLSTM_F2134,BiLSTM_F2117,BiLSTM_F1339,UniRep_F362,BiLSTM_F2097,BiLSTM_F1414,UniRep_F1335,UniRep_F1229,UniRep_F1024,BiLSTM_F75,UniRep_F980,UniRep_F1877,BiLSTM_F2869,UniRep_F1840,BiLSTM_F87,BiLSTM_F525,BiLSTM_F2725,BiLSTM_F336,UniRep_F1065,BiLSTM_F2711,UniRep_F879,UniRep_F1817,UniRep_F889,BiLSTM_F3019,UniRep_F1090,BiLSTM_F3181,UniRep_F902,UniRep_F904,BiLSTM_F370,UniRep_F1832,UniRep_F1093,UniRep_F925,BiLSTM_F110,BiLSTM_F2747,BiLSTM_F2877,BiLSTM_F273,BiLSTM_F2879,BiLSTM_F166,BiLSTM_F794,UniRep_F703,UniRep_F704,BiLSTM_F2,BiLSTM_F3382,UniRep_F749,UniRep_F770,UniRep_F1215,BiLSTM_F2837,BiLSTM_F38,UniRep_F919,UniRep_F790,UniRep_F1539,BiLSTM_F3312,BiLSTM_F696,BiLSTM_F2839,BiLSTM_F677,UniRep_F1478,UniRep_F1149,BiLSTM_F53,BiLSTM_F3114,BiLSTM_F3051,BiLSTM_F3053,UniRep_F62,UniRep_F73,BiLSTM_F3423,UniRep_F92,UniRep_F129,UniRep_F155,UniRep_F152,BiLSTM_F2987,BiLSTM_F2991
      prediction/preprocessing/alphabets.py 0 → 100644
      View file @ 80dfc643
      from __future__ import print_function, division
      import numpy as np
      class Alphabet:
      def __init__(self, chars, encoding=None, mask=False, missing=255):
      self.chars = np.frombuffer(chars, dtype=np.uint8)
      self.encoding = np.zeros(256, dtype=np.uint8) + missing
      if encoding is None:
      self.encoding[self.chars] = np.arange(len(self.chars))
      self.size = len(self.chars)
      else:
      self.encoding[self.chars] = encoding
      self.size = encoding.max() + 1
      self.mask = mask
      if mask:
      self.size -= 1
      def __len__(self):
      return self.size
      def __getitem__(self, i):
      return chr(self.chars[i])
      def encode(self, x):
      """ encode a byte string into alphabet indices """
      x = np.frombuffer(x, dtype=np.uint8)
      return self.encoding[x]
      def decode(self, x):
      """ decode index array, x, to byte string of this alphabet """
      string = self.chars[x]
      return string.tobytes()
      def unpack(self, h, k):
      """ unpack integer h into array of this alphabet with length k """
      n = self.size
      kmer = np.zeros(k, dtype=np.uint8)
      for i in reversed(range(k)):
      c = h % n
      kmer[i] = c
      h = h // n
      return kmer
      def get_kmer(self, h, k):
      """ retrieve byte string of length k decoded from integer h """
      kmer = self.unpack(h, k)
      return self.decode(kmer)
      DNA = Alphabet(b'ACGT')
      class Uniprot21(Alphabet):
      def __init__(self, mask=False):
      chars = alphabet = b'ARNDCQEGHILKMFPSTWYVXOUBZ'
      encoding = np.arange(len(chars))
      encoding[21:] = [11,4,20,20] # encode 'OUBZ' as synonyms
      super(Uniprot21, self).__init__(chars, encoding=encoding, mask=mask, missing=20)
      class SDM12(Alphabet):
      """
      A D KER N TSQ YF LIVM C W H G P
      See https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732308/#B33
      "Reduced amino acid alphabets exhibit an improved sensitivity and selectivity in fold assignment"
      Peterson et al. 2009. Bioinformatics.
      """
      def __init__(self, mask=False):
      chars = alphabet = b'ADKNTYLCWHGPXERSQFIVMOUBZ'
      groups = [b'A',b'D',b'KERO',b'N',b'TSQ',b'YF',b'LIVM',b'CU',b'W',b'H',b'G',b'P',b'XBZ']
      groups = {c:i for i in range(len(groups)) for c in groups[i]}
      encoding = np.array([groups[c] for c in chars])
      super(SDM12, self).__init__(chars, encoding=encoding, mask=mask)
      SecStr8 = Alphabet(b'HBEGITS ')
      prediction/src/.idea/.gitignore 0 → 100644
      View file @ 80dfc643
      # Default ignored files
      /shelf/
      /workspace.xml
      prediction/src/.idea/inspectionProfiles/profiles_settings.xml 0 → 100644
      View file @ 80dfc643
      <component name="InspectionProjectProfileManager">
      <settings>
      <option name="USE_PROJECT_PROFILE" value="false" />
      <version value="1.0" />
      </settings>
      </component>
      \ No newline at end of file
      prediction/src/.idea/misc.xml 0 → 100644
      View file @ 80dfc643
      <?xml version="1.0" encoding="UTF-8"?>
      <project version="4">
      <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
      </project>
      \ No newline at end of file
      prediction/src/.idea/modules.xml 0 → 100644
      View file @ 80dfc643
      <?xml version="1.0" encoding="UTF-8"?>
      <project version="4">
      <component name="ProjectModuleManager">
      <modules>
      <module fileurl="file://$PROJECT_DIR$/.idea/src.iml" filepath="$PROJECT_DIR$/.idea/src.iml" />
      </modules>
      </component>
      </project>
      \ No newline at end of file
      prediction/src/.idea/src.iml 0 → 100644
      View file @ 80dfc643
      <?xml version="1.0" encoding="UTF-8"?>
      <module type="PYTHON_MODULE" version="4">
      <component name="NewModuleRootManager">
      <content url="file://$MODULE_DIR$" />
      <orderEntry type="inheritedJdk" />
      <orderEntry type="sourceFolder" forTests="false" />
      </component>
      </module>
      \ No newline at end of file
      prediction/src/SSA_embedding.py 0 → 100644
      View file @ 80dfc643
      from __future__ import print_function,division
      import sys
      import os
      #sys.path.append('./embedding_model/')
      sys.path.append(os.path.join(os.pardir, os.pardir))
      import pandas as pd
      import torch
      import torch.nn as nn
      import time
      import logging
      DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
      logging.info(DEVICE)
      from preprocessing.alphabets import Uniprot21
      import warnings
      warnings.filterwarnings('ignore')
      def unstack_lstm(lstm):
      device = next(iter(lstm.parameters())).device
      in_size = lstm.input_size
      hidden_dim = lstm.hidden_size
      layers = []
      for i in range(lstm.num_layers):
      layer = nn.LSTM(in_size, hidden_dim, batch_first=True, bidirectional=True)
      layer.to(device)
      attributes = ['weight_ih_l', 'weight_hh_l', 'bias_ih_l', 'bias_hh_l']
      for attr in attributes:
      dest = attr + '0'
      src = attr + str(i)
      getattr(layer, dest).data[:] = getattr(lstm, src)
      dest = attr + '0_reverse'
      src = attr + str(i) + '_reverse'
      getattr(layer, dest).data[:] = getattr(lstm, src)
      layer.flatten_parameters()
      layers.append(layer)
      in_size = 2*hidden_dim
      return layers
      def embed_stack(x, lm_embed, lstm_stack, proj, include_lm=True, final_only=False):
      zs = []
      x_onehot = x.new(x.size(0),x.size(1), 21).float().zero_()
      x_onehot.scatter_(2,x.unsqueeze(2),1)
      zs.append(x_onehot)
      h = lm_embed(x)
      if include_lm and not final_only:
      zs.append(h)
      if lstm_stack is not None:
      for lstm in lstm_stack:
      h,_ = lstm(h)
      if not final_only:
      zs.append(h)
      if proj is not None:
      h = proj(h.squeeze(0)).unsqueeze(0)
      zs.append(h)
      z = torch.cat(zs, 2)
      return z
      def embed_sequence(x, lm_embed, lstm_stack, proj, include_lm=True, final_only=False
      , pool='none', use_cuda=False):
      if len(x) == 0:
      return None
      alphabet = Uniprot21()
      x = x.upper()
      x = alphabet.encode(x)
      x = torch.from_numpy(x)
      if use_cuda:
      x = x.to(DEVICE)
      with torch.no_grad():
      x = x.long().unsqueeze(0)
      z = embed_stack(x, lm_embed, lstm_stack, proj
      , include_lm=include_lm, final_only=final_only)
      z = z.squeeze(0)
      if pool == 'sum':
      z = z.sum(0)
      elif pool == 'max':
      z,_ = z.max(0)
      elif pool == 'avg':
      z = z.mean(0)
      z = z.cpu().numpy()
      return z
      def load_model(path, use_cuda=False):
      encoder = torch.load(path)
      encoder.eval()
      if use_cuda:
      encoder=encoder.to(DEVICE)
      encoder = encoder.embedding
      lm_embed = encoder.embed
      lstm_stack = unstack_lstm(encoder.rnn)
      proj = encoder.proj
      return lm_embed, lstm_stack, proj
      def SSA_Embed(input_seq):
      T0 = time.time()
      SSAEMB_ = []
      PID_ =[]
      # inData = fasta.fasta2csv(fastaFile)
      # SEQ_ = inData["Seq"]
      # PID_ = inData["PID"]
      # CLASS_ = inData["Class"]
      logging.info("SSA Embedding...")
      print("Loading SSA Model...", file=sys.stderr, end='\r')
      lm_embed, lstm_stack, proj = load_model("./prediction/embbed_models/SSA_embed.model", use_cuda=True)
      include_lm = True
      final_only = True
      for key, value in input_seq.items():
      PID_.append(key)
      sequence = str(value).encode("utf-8")
      z = embed_sequence(sequence, lm_embed, lstm_stack, proj
      , include_lm=include_lm, final_only=final_only
      , pool='avg', use_cuda=True)
      SSAEMB_.append(z)
      # count += 1
      # print('#{}$'.format(count), file=sys.stderr, end='\r')
      ssa_feature = pd.DataFrame(SSAEMB_)
      col = ["SSA_F" + str(i + 1) for i in range(0, 121)]
      ssa_feature.columns = col
      ssa_feature = pd.concat([ssa_feature], axis=1)
      ssa_feature.index = PID_
      logging.info(ssa_feature.shape)
      # ssa_feature.to_csv("./feature_vectors/SSA_feature.csv")
      logging.info("SSA embedding finished@￥￥￥￥￥")
      logging.info("it took %0.3f mins.\n" % ((time.time() - T0) / 60))
      return ssa_feature
      def BiLSTM_Embed(input_seq):
      T0=time.time()
      BiLSTMEMB_=[]
      PID=[]
      logging.info("\nBiLSTM Embedding...")
      print("Loading BiLSTM Model...", file=sys.stderr, end='\r')
      lm_embed, lstm_stack, proj = load_model("./prediction/embbed_models/SSA_embed.model", use_cuda=True)
      proj = None
      for key,value in input_seq.items():
      PID.append(key)
      sequence = str(value).encode("utf-8")
      z = embed_sequence(sequence, lm_embed, lstm_stack, proj
      , final_only=False,include_lm = True
      , pool='avg', use_cuda=True)
      BiLSTMEMB_.append(z)
      bilstm_feature=pd.DataFrame(BiLSTMEMB_)
      col=["BiLSTM_F"+str(i+1) for i in range(0,3605)]
      bilstm_feature.columns=col
      bilstm_feature=pd.concat([bilstm_feature],axis=1)
      bilstm_feature.index=PID
      # bilstm_feature.to_csv("./feature_vectors/bilstm_feature.csv")
      logging.info("BiLSTM embedding finished@@￥￥￥￥￥")
      logging.info("it took %0.3f mins.\n"%((time.time()-T0)/60))
      return bilstm_feature
      prediction/src/UniRep_emb.py 0 → 100644
      View file @ 80dfc643
      from __future__ import print_function,division
      import sys
      import os
      sys.path.append(os.pardir)
      sys.path.append(os.path.join(os.pardir, os.pardir))
      import time
      import pandas as pd
      import torch
      import warnings
      warnings.filterwarnings('ignore')
      DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
      from tape import UniRepModel,TAPETokenizer
      def UniRep_Embed(input_seq):
      T0=time.time()
      UNIREPEB_=[]
      PID = []
      print("UniRep Embedding...")
      model = UniRepModel.from_pretrained('babbler-1900')
      model=model.to(DEVICE)
      tokenizer = TAPETokenizer(vocab='unirep')
      for key,value in input_seq.items():
      PID.append(key)
      sequence = value
      if len(sequence) == 0:
      print('# WARNING: sequence', PID, 'has length=0. Skipping.', file=sys.stderr)
      continue
      with torch.no_grad():
      token_ids = torch.tensor([tokenizer.encode(sequence)])
      token_ids = token_ids.to(DEVICE)
      output = model(token_ids)
      unirep_output = output[0]
      unirep_output=torch.squeeze(unirep_output)
      unirep_output= unirep_output.mean(0)
      unirep_output = unirep_output.cpu().numpy()
      UNIREPEB_.append(unirep_output.tolist())
      unirep_feature=pd.DataFrame(UNIREPEB_)
      col=["UniRep_F"+str(i+1) for i in range(0,1900)]
      unirep_feature.columns=col
      unirep_feature=pd.concat([unirep_feature],axis=1)
      unirep_feature.index=PID
      # print(unirep_feature.shape)
      unirep_feature.to_csv("./dataset/unirep_feature.csv")
      print("Getting Deep Representation Learning Features with UniRep is done.")
      print("it took %0.3f mins.\n"%((time.time()-T0)/60))
      return unirep_feature
      \ No newline at end of file
      prediction/src/models/comparison.py 0 → 100644
      View file @ 80dfc643
      from __future__ import print_function,division
      import torch
      import torch.nn as nn
      import torch.nn.functional as F
      class L1(nn.Module):
      def forward(self, x, y):
      return -torch.sum(torch.abs(x.unsqueeze(1)-y), -1)
      class L2(nn.Module):
      def forward(self, x, y):
      return -torch.sum((x.unsqueeze(1)-y)**2, -1)
      class DotProduct(nn.Module):
      def forward(self, x, y):
      return torch.mm(x, y.t())
      def pad_gap_scores(s, gap):
      col = gap.expand(s.size(0), 1)
      s = torch.cat([s, col], 1)
      row = gap.expand(1, s.size(1))
      s = torch.cat([s, row], 0)
      return s
      class OrdinalRegression(nn.Module):
      def __init__(self, embedding, n_classes, compare=L1()
      , align_method='ssa', beta_init=10
      , allow_insertions=False, gap_init=-10
      ):
      super(OrdinalRegression, self).__init__()
      self.embedding = embedding
      self.n_out = n_classes
      self.compare = compare
      self.align_method = align_method
      self.allow_insertions = allow_insertions
      self.gap = nn.Parameter(torch.FloatTensor([gap_init]))
      self.theta = nn.Parameter(torch.ones(1,n_classes-1))
      self.beta = nn.Parameter(torch.zeros(n_classes-1)+beta_init)
      self.clip()
      def forward(self, x):
      return self.embedding(x)
      def clip(self):
      # clip the weights of ordinal regression to be non-negative
      self.theta.data.clamp_(min=0)
      def score(self, z_x, z_y):
      if self.align_method == 'ssa':
      s = self.compare(z_x, z_y)
      if self.allow_insertions:
      s = pad_gap_scores(s, self.gap)
      a = F.softmax(s, 1)
      b = F.softmax(s, 0)
      if self.allow_insertions:
      index = s.size(0)-1
      index = s.data.new(1).long().fill_(index)
      a = a.index_fill(0, index, 0)
      index = s.size(1)-1
      index = s.data.new(1).long().fill_(index)
      b = b.index_fill(1, index, 0)
      a = a + b - a*b
      c = torch.sum(a*s)/torch.sum(a)
      elif self.align_method == 'ua':
      s = self.compare(z_x, z_y)
      c = torch.mean(s)
      elif self.align_method == 'me':
      z_x = z_x.mean(0)
      z_y = z_y.mean(0)
      c = self.compare(z_x.unsqueeze(0), z_y.unsqueeze(0)).squeeze(0)
      else:
      raise Exception('Unknown alignment method: ' + self.align_method)
      logits = c*self.theta + self.beta
      return logits.view(-1)
      prediction/src/models/embedding.py 0 → 100644
      View file @ 80dfc643
      from __future__ import print_function,division
      import torch
      import torch.nn as nn
      import torch.nn.functional as F
      from torch.nn.utils.rnn import PackedSequence
      class LMEmbed(nn.Module):
      def __init__(self, nin, nout, lm, padding_idx=-1, transform=nn.ReLU()
      , sparse=False):
      super(LMEmbed, self).__init__()
      if padding_idx == -1:
      padding_idx = nin-1
      self.lm = lm
      self.embed = nn.Embedding(nin, nout, padding_idx=padding_idx, sparse=sparse)
      self.proj = nn.Linear(lm.hidden_size(), nout)
      self.transform = transform
      self.nout = nout
      def forward(self, x):
      packed = type(x) is PackedSequence
      h_lm = self.lm.encode(x)
      # embed and unpack if packed
      if packed:
      h = self.embed(x.data)
      h_lm = h_lm.data
      else:
      h = self.embed(x)
      # project
      h_lm = self.proj(h_lm)
      h = self.transform(h + h_lm)
      # repack if needed
      if packed:
      h = PackedSequence(h, x.batch_sizes)
      return h
      class Linear(nn.Module):
      def __init__(self, nin, nhidden, nout, padding_idx=-1,
      sparse=False, lm=None):
      super(Linear, self).__init__()
      if padding_idx == -1:
      padding_idx = nin-1
      if lm is not None:
      self.embed = LMEmbed(nin, nhidden, lm, padding_idx=padding_idx, sparse=sparse)
      self.proj = nn.Linear(self.embed.nout, nout)
      self.lm = True
      else:
      self.proj = nn.Embedding(nin, nout, padding_idx=padding_idx, sparse=sparse)
      self.lm = False
      self.nout = nout
      def forward(self, x):
      if self.lm:
      h = self.embed(x)
      if type(h) is PackedSequence:
      h = h.data
      z = self.proj(h)
      z = PackedSequence(z, x.batch_sizes)
      else:
      h = h.view(-1, h.size(2))
      z = self.proj(h)
      z = z.view(x.size(0), x.size(1), -1)
      else:
      if type(x) is PackedSequence:
      z = self.embed(x.data)
      z = PackedSequence(z, x.batch_sizes)
      else:
      z = self.embed(x)
      return z
      class StackedRNN(nn.Module):
      def __init__(self, nin, nembed, nunits, nout, nlayers=2, padding_idx=-1, dropout=0,
      rnn_type='lstm', sparse=False, lm=None):
      super(StackedRNN, self).__init__()
      if padding_idx == -1:
      padding_idx = nin-1
      if lm is not None:
      self.embed = LMEmbed(nin, nembed, lm, padding_idx=padding_idx, sparse=sparse)
      nembed = self.embed.nout
      self.lm = True
      else:
      self.embed = nn.Embedding(nin, nembed, padding_idx=padding_idx, sparse=sparse)
      self.lm = False
      if rnn_type == 'lstm':
      RNN = nn.LSTM
      elif rnn_type == 'gru':
      RNN = nn.GRU
      self.dropout = nn.Dropout(p=dropout)
      if nlayers == 1:
      dropout = 0
      self.rnn = RNN(nembed, nunits, nlayers, batch_first=True
      , bidirectional=True, dropout=dropout)
      self.proj = nn.Linear(2*nunits, nout)
      self.nout = nout
      def forward(self, x):
      if self.lm:
      h = self.embed(x)
      else:
      if type(x) is PackedSequence:
      h = self.embed(x.data)
      h = PackedSequence(h, x.batch_sizes)
      else:
      h = self.embed(x)
      h,_ = self.rnn(h)
      if type(h) is PackedSequence:
      h = h.data
      h = self.dropout(h)
      z = self.proj(h)
      z = PackedSequence(z, x.batch_sizes)
      else:
      h = h.view(-1, h.size(2))
      h = self.dropout(h)
      z = self.proj(h)
      z = z.view(x.size(0), x.size(1), -1)
      return z
      prediction/src/models/multitask.py 0 → 100644
      View file @ 80dfc643
      from __future__ import print_function,division
      import torch
      import torch.nn as nn
      import torch.nn.functional as F
      from .comparison import L1, pad_gap_scores
      class SCOPCM(nn.Module):
      def __init__(self, embedding, similarity_kwargs={},
      cmap_kwargs={}):
      super(SCOPCM, self).__init__()
      self.embedding = embedding
      embed_dim = embedding.nout
      self.scop_predict = OrdinalRegression(5, **similarity_kwargs)
      self.cmap_predict = ConvContactMap(embed_dim, **cmap_kwargs)
      def clip(self):
      self.scop_predict.clip()
      self.cmap_predict.clip()
      def forward(self, x):
      return self.embedding(x)
      def score(self, z_x, z_y):
      return self.scop_predict(z_x, z_y)
      def predict(self, z):
      return self.cmap_predict(z)
      class ConvContactMap(nn.Module):
      def __init__(self, embed_dim, hidden_dim=50, width=7, act=nn.ReLU()):
      super(ConvContactMap, self).__init__()
      self.hidden = nn.Conv2d(2*embed_dim, hidden_dim, 1)
      self.act = act
      self.conv = nn.Conv2d(hidden_dim, 1, width, padding=width//2)
      self.clip()
      def clip(self):
      # force the conv layer to be transpose invariant
      w = self.conv.weight
      self.conv.weight.data[:] = 0.5*(w + w.transpose(2,3))
      def forward(self, z):
      return self.predict(z)
      def predict(self, z):
      # z is (b,L,d)
      z = z.transpose(1, 2) # (b,d,L)
      z_dif = torch.abs(z.unsqueeze(2) - z.unsqueeze(3))
      z_mul = z.unsqueeze(2)*z.unsqueeze(3)
      z = torch.cat([z_dif, z_mul], 1)
      # (b,2d,L,L)
      h = self.act(self.hidden(z))
      logits = self.conv(h).squeeze(1)
      return logits
      class OrdinalRegression(nn.Module):
      def __init__(self, n_classes, compare=L1()
      , align_method='ssa', beta_init=10
      , allow_insertions=False, gap_init=-10
      ):
      super(OrdinalRegression, self).__init__()
      self.n_out = n_classes
      self.compare = compare
      self.align_method = align_method
      self.allow_insertions = allow_insertions
      self.gap = nn.Parameter(torch.FloatTensor([gap_init]))
      self.theta = nn.Parameter(torch.ones(1,n_classes-1))
      self.beta = nn.Parameter(torch.zeros(n_classes-1)+beta_init)
      self.clip()
      def clip(self):
      # clip the weights of ordinal regression to be non-negative
      self.theta.data.clamp_(min=0)
      def forward(self, z_x, z_y):
      if self.align_method == 'ssa':
      s = self.compare(z_x, z_y)
      if self.allow_insertions:
      s = pad_gap_scores(s, self.gap)
      a = F.softmax(s, 1)
      b = F.softmax(s, 0)
      if self.allow_insertions:
      index = s.size(0)-1
      index = s.data.new(1).long().fill_(index)
      a = a.index_fill(0, index, 0)
      index = s.size(1)-1
      index = s.data.new(1).long().fill_(index)
      b = b.index_fill(1, index, 0)
      a = a + b - a*b
      c = torch.sum(a*s)/torch.sum(a)
      elif self.align_method == 'ua':
      s = self.compare(z_x, z_y)
      c = torch.mean(s)
      elif self.align_method == 'me':
      z_x = z_x.mean(0)
      z_y = z_y.mean(0)
      c = self.compare(z_x.unsqueeze(0), z_y.unsqueeze(0)).squeeze(0)
      else:
      raise Exception('Unknown alignment method: ' + self.align_method)
      logits = c*self.theta + self.beta
      return logits.view(-1)
      prediction/src/models/sequence.py 0 → 100644
      View file @ 80dfc643
      from __future__ import print_function,division
      import torch
      import torch.nn as nn
      import torch.nn.functional as F
      from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence, pad_packed_sequence
      class BiLM(nn.Module):
      def __init__(self, nin, nout, embedding_dim, hidden_dim, num_layers
      , tied=True, mask_idx=None, dropout=0):
      super(BiLM, self).__init__()
      if mask_idx is None:
      mask_idx = nin-1
      self.mask_idx = mask_idx
      self.embed = nn.Embedding(nin, embedding_dim, padding_idx=mask_idx)
      self.dropout = nn.Dropout(p=dropout)
      self.tied = tied
      if tied:
      layers = []
      nin = embedding_dim
      for _ in range(num_layers):
      layers.append(nn.LSTM(nin, hidden_dim, 1, batch_first=True))
      nin = hidden_dim
      self.rnn = nn.ModuleList(layers)
      else:
      layers = []
      nin = embedding_dim
      for _ in range(num_layers):
      layers.append(nn.LSTM(nin, hidden_dim, 1, batch_first=True))
      nin = hidden_dim
      self.lrnn = nn.ModuleList(layers)
      layers = []
      nin = embedding_dim
      for _ in range(num_layers):
      layers.append(nn.LSTM(nin, hidden_dim, 1, batch_first=True))
      nin = hidden_dim
      self.rrnn = nn.ModuleList(layers)
      self.linear = nn.Linear(hidden_dim, nout)
      def hidden_size(self):
      h = 0
      if self.tied:
      for layer in self.rnn:
      h += 2*layer.hidden_size
      else:
      for layer in self.lrnn:
      h += layer.hidden_size
      for layer in self.rrnn:
      h += layer.hidden_size
      return h
      def reverse(self, h):
      packed = type(h) is PackedSequence
      if packed:
      h,batch_sizes = pad_packed_sequence(h, batch_first=True)
      h_rvs = h.clone().zero_()
      for i in range(h.size(0)):
      n = batch_sizes[i]
      idx = [j for j in range(n-1, -1, -1)]
      idx = torch.LongTensor(idx).to(h.device)
      h_rvs[i,:n] = h[i].index_select(0, idx)
      # repack h_rvs
      h_rvs = pack_padded_sequence(h_rvs, batch_sizes, batch_first=True)
      else:
      idx = [i for i in range(h.size(1)-1, -1, -1)]
      idx = torch.LongTensor(idx).to(h.device)
      h_rvs = h.index_select(1, idx)
      return h_rvs
      def transform(self, z_fwd, z_rvs, last_only=False):
      # sequences are flanked by the start/stop token as:
      # [stop, x, stop]
      # z_fwd should be [stop,x]
      # z_rvs should be [x,stop] reversed
      # first, do the forward direction
      if self.tied:
      layers = self.rnn
      else:
      layers = self.lrnn
      h_fwd = []
      h = z_fwd
      for rnn in layers:
      h,_ = rnn(h)
      if type(h) is PackedSequence:
      h = PackedSequence(self.dropout(h.data), h.batch_sizes)
      else:
      h = self.dropout(h)
      if not last_only:
      h_fwd.append(h)
      if last_only:
      h_fwd = h
      # now, do the reverse direction
      if self.tied:
      layers = self.rnn
      else:
      layers = self.rrnn
      # we'll need to reverse the direction of these
      # hidden states back to match forward direction
      h_rvs = []
      h = z_rvs
      for rnn in layers:
      h,_ = rnn(h)
      if type(h) is PackedSequence:
      h = PackedSequence(self.dropout(h.data), h.batch_sizes)
      else:
      h = self.dropout(h)
      if not last_only:
      h_rvs.append(self.reverse(h))
      if last_only:
      h_rvs = self.reverse(h)
      return h_fwd,h_rvs
      def embed_and_split(self, x, pad=False):
      packed = type(x) is PackedSequence
      if packed:
      x,batch_sizes = pad_packed_sequence(x, batch_first=True)
      if pad:
      # pad x with the start/stop token
      x = x + 1
      ## append start/stop tokens to x
      x_ = x.data.new(x.size(0), x.size(1)+2).zero_()
      if packed:
      for i in range(len(batch_sizes)):
      n = batch_sizes[i]
      x_[i,1:n+1] = x[i,:n]
      batch_sizes = [s+2 for s in batch_sizes]
      else:
      x_[:,1:-1] = x
      x = x_
      # sequences x are flanked by the start/stop token as:
      # [stop, x, stop]
      # now, encode x as distributed vectors
      z = self.embed(x)
      # to pass to transform, we discard the last element for z_fwd and the first element for z_rvs
      z_fwd = z[:,:-1]
      z_rvs = z[:,1:]
      if packed:
      lengths = [s-1 for s in batch_sizes]
      z_fwd = pack_padded_sequence(z_fwd, lengths, batch_first=True)
      z_rvs = pack_padded_sequence(z_rvs, lengths, batch_first=True)
      # reverse z_rvs
      z_rvs = self.reverse(z_rvs)
      return z_fwd, z_rvs
      def encode(self, x):
      z_fwd,z_rvs = self.embed_and_split(x, pad=True)
      h_fwd_layers,h_rvs_layers = self.transform(z_fwd, z_rvs)
      # concatenate hidden layers together
      packed = type(z_fwd) is PackedSequence
      concat = []
      for h_fwd,h_rvs in zip(h_fwd_layers,h_rvs_layers):
      if packed:
      h_fwd,batch_sizes = pad_packed_sequence(h_fwd, batch_first=True)
      h_rvs,batch_sizes = pad_packed_sequence(h_rvs, batch_first=True)
      # discard last element of h_fwd and first element of h_rvs
      h_fwd = h_fwd[:,:-1]
      h_rvs = h_rvs[:,1:]
      # accumulate for concatenation
      concat.append(h_fwd)
      concat.append(h_rvs)
      h = torch.cat(concat, 2)
      if packed:
      batch_sizes = [s-1 for s in batch_sizes]
      h = pack_padded_sequence(h, batch_sizes, batch_first=True)
      return h
      def forward(self, x):
      # x's are already flanked by the star/stop token as:
      # [stop, x, stop]
      z_fwd,z_rvs = self.embed_and_split(x, pad=False)
      h_fwd,h_rvs = self.transform(z_fwd, z_rvs, last_only=True)
      packed = type(z_fwd) is PackedSequence
      if packed:
      h_flat = h_fwd.data
      logp_fwd = self.linear(h_flat)
      logp_fwd = PackedSequence(logp_fwd, h_fwd.batch_sizes)
      h_flat = h_rvs.data
      logp_rvs = self.linear(h_flat)
      logp_rvs = PackedSequence(logp_rvs, h_rvs.batch_sizes)
      logp_fwd,batch_sizes = pad_packed_sequence(logp_fwd, batch_first=True)
      logp_rvs,batch_sizes = pad_packed_sequence(logp_rvs, batch_first=True)
      else:
      b = h_fwd.size(0)
      n = h_fwd.size(1)
      h_flat = h_fwd.contiguous().view(-1, h_fwd.size(2))
      logp_fwd = self.linear(h_flat)
      logp_fwd = logp_fwd.view(b, n, -1)
      h_flat = h_rvs.contiguous().view(-1, h_rvs.size(2))
      logp_rvs = self.linear(h_flat)
      logp_rvs = logp_rvs.view(b, n, -1)
      # prepend forward logp with zero
      # postpend reverse logp with zero
      b = h_fwd.size(0)
      zero = h_fwd.data.new(b,1,logp_fwd.size(2)).zero_()
      logp_fwd = torch.cat([zero, logp_fwd], 1)
      logp_rvs = torch.cat([logp_rvs, zero], 1)
      logp = F.log_softmax(logp_fwd + logp_rvs, dim=2)
      if packed:
      batch_sizes = [s+1 for s in batch_sizes]
      logp = pack_padded_sequence(logp, batch_sizes, batch_first=True)
      return logp
      prediction/train_label.csv 0 → 100644
      View file @ 80dfc643
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      requirements.txt 0 → 100644
      View file @ 80dfc643
      joblib==1.0.1
      tape_proteins==0.4
      torch==1.2.0+cpu
      numpy==1.19.2
      pandas==1.2.0
      Bio==0.4.1
      tape==1.0
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