├── LICENSE ├── PABP_YEAST_Fields2013-doubles.csv ├── PABP_YEAST_Fields2013-singles.csv ├── PABP_YEAST_hmmerbit_plmc_n5_m30_f50_t0.2_r115-210_id100_b48.a2m ├── PABP_YEAST_hmmerbit_t0.2_r50000.reweight ├── README.md ├── VAE_for_protein_function_prediction.ipynb ├── compute_reweights.py ├── helper_tools.py ├── helper_tools_for_plotting.py └── other_datasets.csv /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Sam Sinai 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /PABP_YEAST_Fields2013-singles.csv: -------------------------------------------------------------------------------- 1 | # Reference: Melamed et al., RNA 2013 (Supplementary Table 2), PMID: 24064791 2 | # Experimental data columns: linear 3 | mutant;effect_prediction_epistatic;effect_prediction_independent;linear 4 | G126A;-2.6106144744;0.406489950981;0.711743232 5 | G126C;-5.66363767697;-0.0276019332916;0.449026715 6 | G126E;-6.61106177093;-1.82761221989;0.588928368 7 | G126D;-7.27057669693;-1.18009385683;0.229852843 8 | G126N;-5.80916681373;0.387442738678;0.679434676 9 | G126S;-4.61724799831;0.661685767522;0.721788343 10 | G126R;-5.58238082565;1.14414833612;0.313689694 11 | G126W;-8.07990067202;-2.05239053473;0.226031523 12 | G126V;-5.43563147262;-0.800307436137;0.230314981 13 | N127A;-4.98720616469;-2.25182271844;0.062650042 14 | N127C;-8.04418135138;-2.21563884182;0.024995925 15 | N127E;-7.60525847657;-1.43770923094;0.027818045 16 | N127D;-8.19616061676;-3.15252542616;0.092973688 17 | N127G;-7.14667977358;-3.59254334169;0.062339233 18 | N127F;-9.10837932164;-4.55434367895;0.040344299 19 | N127I;-6.9905979106;-1.80615370742;0.059962776 20 | N127H;-6.23133972846;-2.99947633964;0.047049254 21 | N127K;-5.72093126969;0.499716763069;0.106342179 22 | N127L;-10.6068525729;-3.32752515234;0.029995109 23 | N127Q;-8.90309358994;-1.91573605317;0.135272062 24 | N127P;-8.62985067698;-4.50703121999;0.042361514 25 | N127S;-5.26112256572;-0.279307020127;0.090002529 26 | N127R;-7.96217455412;0.132131497942;0.07300397 27 | N127T;-5.62266072386;0.550327446518;0.044739787 28 | N127V;-6.88994216884;-1.22039135848;0.022768565 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F129G;-6.37614134506;-4.35706882166;0.040109739 48 | F129I;-3.99244210559;-2.37610425165;0.153378856 49 | F129H;-3.12819404357;-3.05497156234;0.185828287 50 | F129M;-5.38534175527;-4.26689683301;0.021292825 51 | F129L;-2.96224180998;-2.90656565107;0.096322196 52 | F129N;-5.82414108903;-6.06873070118;0.100860748 53 | F129S;-4.8211035758;-4.85724479353;0.061435844 54 | F129R;-3.97332018962;-3.534019009;0.051102779 55 | F129T;-5.09132353564;-5.66700070011;0.13853163 56 | F129W;-6.80389308707;-2.97707636346;0.045387337 57 | F129V;-4.06238647403;-3.22767239323;0.058542172 58 | F129Y;-1.19199826771;-0.380223416591;1.039766256 59 | I130A;-6.02787943741;-2.91467500008;0.717802041 60 | I130C;-6.7321838117;-2.89997279712;0.781004736 61 | I130D;-10.2884918718;-8.97428955349;0.032851787 62 | I130G;-9.80901469338;-8.33444376551;0.065703573 63 | I130F;-6.11297785535;-3.7140479574;0.461896214 64 | I130H;-10.8330812711;-10.8364299079;0.071227325 65 | I130K;-10.8405047118;-10.8931437298;0.106464123 66 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L186S;-3.18368980021;-3.49698216409;0.719068969 968 | L186R;-2.30210723472;-3.85500630418;0.609304468 969 | L186T;-0.979339221201;-2.89370464342;0.753716955 970 | L186W;-5.1020951026;-4.74840800264;0.57760906 971 | L186V;0.450453379017;-2.57079826212;1.042199053 972 | L186Y;-2.38059384393;-2.59802995079;0.505133773 973 | N187A;-6.70168010054;-3.45014332551;1.408195559 974 | N187C;-8.94619045415;-5.43720845701;0.974205714 975 | N187E;-6.86188566771;-3.08398644785;0.96757416 976 | N187D;-1.65639301436;-0.897647946432;0.962531812 977 | N187G;-8.23379176599;-3.23112404863;0.958689329 978 | N187F;-6.80067286863;-5.58322536421;0.924012367 979 | N187I;-7.20852258359;-5.67694720239;0.769871911 980 | N187H;-3.77885667083;-1.53537138661;0.992637585 981 | N187K;-4.74684317177;-2.62747270724;1.034804345 982 | N187M;-7.43704888399;-5.67284297782;1.171809215 983 | N187L;-7.94760546973;-5.18463479246;0.96846812 984 | N187Q;-5.21105236141;-1.97945497779;1.000988138 985 | N187P;-7.84973866714;-4.0009399604;0.464477724 986 | N187S;-4.87855596398;-2.16263332449;1.039508749 987 | N187R;-5.40107353171;-2.91443265777;1.027414547 988 | N187T;-5.22430142443;-3.27971057612;1.0405576 989 | N187V;-7.71155727399;-5.62869440048;0.81084922 990 | N187Y;-7.58566967596;-5.66637969212;0.955870023 991 | G188A;-7.26107847341;-3.83511521777;0.827014353 992 | G188C;-7.04428812789;-5.31189057017;0.810427711 993 | G188E;-6.24115681718;-2.45285224913;0.949142115 994 | G188D;-4.88274131878;-2.36404887528;0.773161816 995 | G188F;-8.13779081521;-3.75207448935;0.574623075 996 | G188I;-9.11248456175;-5.40855649964;0.129914782 997 | G188H;-8.18892151932;-3.11552967972;0.863766081 998 | G188L;-5.86672639032;-3.86217143169;0.592232492 999 | G188N;-3.46621841882;-1.60399772372;0.829428563 1000 | G188P;-8.24147685606;-3.38501145693;0.192219046 1001 | G188S;-7.64363836986;-3.1192317156;0.937625076 1002 | G188R;-6.47630701528;-2.65456482246;1.279470658 1003 | G188T;-7.84931288648;-4.06484652019;0.863398621 1004 | G188W;-9.80329049169;-7.21146845674;0.347385768 1005 | G188V;-8.88208533803;-4.70604916069;0.352351137 1006 | G188Y;-7.42266358456;-3.66759638072;0.540353386 1007 | M189A;-1.04445159342;0.701675848376;0.778353075 1008 | M189E;-4.5782579853;-0.263610397108;0.716211855 1009 | M189G;-6.25568951666;-0.817072066636;0.801629647 1010 | M189F;-3.40952824667;0.524496873681;0.871993096 1011 | M189I;-3.53217276942;0.129365851268;0.923263263 1012 | M189K;-3.19980209216;1.56534832524;0.761294156 1013 | M189L;-3.84184991138;0.209950856831;1.04303602 1014 | M189N;-4.05585678213;-0.800620674966;0.80054805 1015 | M189Q;-1.77051986579;0.747338781464;0.890765183 1016 | M189P;-7.02204967711;-1.66715317429;0.270893736 1017 | M189S;-0.536915310076;1.08219810973;0.617171502 1018 | M189R;-3.92343728209;0.505147916749;0.696916156 1019 | M189T;-2.26907551707;1.46213535688;0.780911993 1020 | M189W;-6.56583181827;-2.98786545208;0.860378775 1021 | M189V;-2.92210254923;0.419771619048;0.940465321 1022 | L190A;-4.44301362359;-1.45082756463;1.089639727 1023 | L190E;-3.13696802843;1.13088814578;0.991558888 1024 | L190G;-4.17879003473;-1.8329147284;0.736799305 1025 | L190F;-4.2281133814;-0.771474082821;1.020502945 1026 | L190I;-2.38446376775;-0.241490833701;0.995115482 1027 | L190H;-3.75281565019;-1.10523366573;0.909375494 1028 | L190K;-2.90311155526;0.192013769244;0.798423642 1029 | L190M;-1.90076209756;-0.413002841896;0.98449501 1030 | L190Q;-3.46046812121;-0.18761196797;0.930791897 1031 | L190P;-5.79789508926;-0.627521226294;0.954500969 1032 | L190S;-3.05936565169;-0.386102168379;0.792113094 1033 | L190R;-4.17141968815;-0.572092974861;0.932539433 1034 | L190T;-2.98343534507;0.0204205729599;1.143818115 1035 | L190W;-6.54714155523;-2.49780715286;0.784803056 1036 | L190V;-3.15829834191;0.117333006169;1.021934262 1037 | L191A;-4.41221599084;-3.6037006015;0.853018048 1038 | L191C;-4.04128285393;-3.75712756442;1.021809582 1039 | L191E;-7.05300269777;-6.17325024428;0.796305074 1040 | L191G;-8.05615465448;-6.36650086743;0.578387847 1041 | L191F;-2.90128186171;-0.815681761869;1.02186369 1042 | L191I;-1.70599189832;-0.435417445506;1.155247436 1043 | L191H;-5.22389659685;-4.4281090266;0.583242422 1044 | L191K;-7.17622652677;-5.32968663788;0.644121407 1045 | L191M;-2.91888836004;-2.23396992807;1.023319734 1046 | L191Q;-5.58256758878;-5.12037685134;0.64116662 1047 | L191P;-8.13699339005;-4.00866904126;0.520881349 1048 | L191S;-7.07583545082;-5.02207222367;0.917796963 1049 | L191R;-7.71482123144;-5.44000578641;0.825313557 1050 | L191T;-7.93073026057;-3.40686094765;0.809682619 1051 | L191W;-6.77110565245;-3.13620678598;0.646856373 1052 | L191V;-1.97711394262;-1.08404557169;1.030155189 1053 | L191Y;-5.55465993786;-2.42349035857;0.831808743 1054 | N192A;-2.45702339942;-0.888936815157;0.989202983 1055 | N192C;-3.86404870567;-1.52294582243;1.028335879 1056 | N192E;-2.52594390465;-0.202302077879;1.229206413 1057 | N192D;-3.08493190166;0.798311031169;0.995612316 1058 | N192G;-2.919587526;0.347322700441;0.920909087 1059 | N192F;-2.69467603089;-0.911219230191;0.781487383 1060 | N192I;-5.78150937613;-3.55928054926;0.937524048 1061 | N192H;-2.44379157986;-0.796102506442;0.771378206 1062 | N192K;-3.03390018514;-0.18485486619;0.925284516 1063 | N192M;-3.49830380912;-0.666384938751;0.703740782 1064 | N192L;-2.16752612207;-0.453701773383;0.803772765 1065 | N192Q;-3.90559939179;-0.596753141122;0.988747982 1066 | N192P;-5.98638215632;-2.25960720313;0.167194545 1067 | N192S;-3.99809965241;-1.41790687408;0.968018551 1068 | N192R;-2.24619030615;-0.69854451349;0.866139172 1069 | N192T;-5.30696935044;-3.39525692067;1.00384307 1070 | N192W;-6.70887591178;-3.98292500695;0.551095202 1071 | N192V;-4.50310763152;-3.21953210695;1.13128918 1072 | N192Y;-4.72880169121;-1.11872173233;0.69238734 1073 | G193A;-6.03444281197;-4.29780855382;0.994089168 1074 | G193C;-5.43504358572;-6.48536145156;0.933225191 1075 | G193E;-2.35842875636;-2.92204676852;0.782166483 1076 | G193D;-0.437098237424;-2.07269661096;1.022131157 1077 | G193F;-7.90488881036;-6.51122711975;0.814528209 1078 | G193I;-7.64193408858;-7.75107742022;0.448511108 1079 | G193H;-4.54327521008;-4.352319637;1.206708458 1080 | G193L;-6.61975130549;-6.55898887459;0.968613516 1081 | G193N;-1.14969330886;-2.16070977577;1.013348646 1082 | G193P;-7.11594785436;-5.15414896529;0.203456659 1083 | G193S;-3.8966126325;-2.67145452254;1.048120097 1084 | G193R;-5.49820301938;-4.18919018266;0.997456334 1085 | G193T;-4.8838790555;-3.59457919063;0.789845536 1086 | G193W;-9.27181864725;-9.29782127174;0.645337128 1087 | G193V;-5.5060183754;-6.40040905954;0.645759188 1088 | G193Y;-6.05177965132;-5.51229493498;0.637007453 1089 | Q194A;-3.85511621085;-1.36684459475;1.091049501 1090 | Q194C;-2.49414626669;-1.77151435384;2.868778599 1091 | Q194E;-1.69031690838;-1.27948696349;1.078387558 1092 | Q194D;-5.9793217093;-2.50480775228;1.020224754 1093 | Q194G;-5.29061167629;-1.83016238629;1.018160262 1094 | Q194I;-4.01712226917;-2.51951038664;0.940740064 1095 | Q194H;-1.50213038955;-0.290977054669;1.106452862 1096 | Q194K;-0.34400618248;1.23931846634;1.048921589 1097 | Q194L;-3.49002625336;-1.34033868459;1.068201167 1098 | Q194N;-2.15717490469;-0.513052006267;1.171251225 1099 | Q194P;-5.51215151296;-4.02839870653;0.387851124 1100 | Q194S;-2.15307357287;-0.17838787682;1.153527183 1101 | Q194R;0.911428596417;2.18926965752;1.128068907 1102 | Q194T;-2.98340891712;-1.5336380502;1.06879892 1103 | Q194V;-5.07699507223;-1.64870899761;0.973517143 1104 | Q194Y;-2.7623902999;-1.67189174974;1.016358362 1105 | E195A;-1.07406251968;-0.161845144935;0.963037742 1106 | E195D;-4.10167956838;-1.68305759923;0.986846317 1107 | E195G;-3.78770990074;-3.01149657191;0.982113709 1108 | E195F;-3.1951843706;-2.21281802587;0.734518192 1109 | E195I;-2.19855999351;-0.401623596459;0.998389223 1110 | E195H;-2.40749883289;-1.32020247759;1.073649351 1111 | E195K;-0.270748179966;0.685888097488;1.072498521 1112 | E195L;-2.21830890628;-0.560276069945;0.922013168 1113 | E195N;0.00387948532443;-0.410983613679;0.982742093 1114 | E195Q;-0.315606326891;-0.0233079436803;0.976396087 1115 | E195P;-0.207563787619;0.93445454086;0.891029452 1116 | E195S;-2.71065474637;-0.475041067589;1.053873928 1117 | E195R;-1.21469402881;0.595553258909;0.959095343 1118 | E195T;-1.80856919054;0.473991296523;1.177805262 1119 | E195V;-1.34386337305;-0.0750611443339;0.939699592 1120 | E195Y;-3.73467684093;-1.63988255939;0.950719637 1121 | I196A;-3.92491664003;-3.45683416956;0.841103269 1122 | I196C;-5.78881653245;-2.93801735488;0.825232881 1123 | I196D;-9.81370435611;-10.5862867782;0.098324393 1124 | I196G;-8.31827747326;-7.75908139449;0.036021148 1125 | I196F;-4.46283889269;-4.1922362693;0.813812083 1126 | I196H;-8.51748742994;-8.35305673113;0.152393594 1127 | I196K;-6.8459934127;-6.73680288918;0.038926079 1128 | I196M;-1.76578125601;-1.75481716489;1.050810932 1129 | I196L;-1.16959421719;0.11726015775;1.106330975 1130 | I196N;-6.98607656689;-6.61466801185;0.087992657 1131 | I196P;-8.0235703865;-5.44564443679;0.269063372 1132 | I196S;-6.00568350875;-5.91865188266;0.278667843 1133 | I196R;-7.55461275535;-7.35525921126;0.07021443 1134 | I196T;-5.29626427566;-4.26211347008;0.830127581 1135 | I196V;0.42855865242;-0.893819663543;1.049993656 1136 | I196Y;-6.78465228395;-6.47142356947;0.308585751 1137 | Y197A;-5.00338889565;-0.424905119091;0.839083323 1138 | Y197C;-5.24128567404;-2.63639001198;0.670965463 1139 | Y197E;-5.67275276489;0.424674209085;0.153338644 1140 | Y197D;-5.36526705138;-0.138666967621;0.258099142 1141 | Y197G;-5.81928197347;-1.48418189688;0.522133468 1142 | Y197F;-0.606410145992;-0.223825903594;0.896740007 1143 | Y197I;-5.96686269194;-0.30111763282;0.790602093 1144 | Y197H;-5.08256390993;-0.14839173821;0.675273848 1145 | Y197K;-4.93882670905;1.29832200142;0.718390639 1146 | Y197L;-5.88571672793;-0.620745567221;0.630036085 1147 | Y197N;-5.30815639067;0.133421445601;0.523818869 1148 | Y197Q;-4.0187035884;-0.0598886743743;0.61331057 1149 | Y197P;-8.28037678043;-3.9568562711;0.040416073 1150 | Y197S;-3.60408925056;0.232865575977;0.527856762 1151 | Y197R;-5.48688041871;1.67161348681;0.819824831 1152 | Y197T;-4.07961256616;0.37116642899;0.596723963 1153 | Y197W;-6.55990709539;-3.570259982;0.483217325 1154 | Y197V;-5.07092241838;0.543643711982;0.706631079 1155 | V198A;-5.66518665745;-3.38304217287;1.085770394 1156 | V198C;-6.51790799561;-2.72178021408;0.927632561 1157 | V198D;-9.8900890254;-8.60181854683;0.313196428 1158 | V198G;-7.33662935847;-6.60822278829;0.3420948 1159 | V198F;-6.34481428284;-5.46138035007;0.202743853 1160 | V198I;-3.28509867168;-1.49402462466;1.170472458 1161 | V198H;-10.0434272544;-8.41809666663;0.050987681 1162 | V198M;-4.93253334588;-4.39154394804;0.830848447 1163 | V198L;-3.62396425728;-1.9414113364;1.043956695 1164 | V198P;-8.03980894529;-4.11354586575;0.078357692 1165 | V198S;-9.1064490045;-5.27044001263;0.586686985 1166 | V198R;-9.05525088764;-7.30114194695;0.091263665 1167 | V198T;-6.40460463916;-4.03791571543;0.793452137 1168 | A199C;-4.52951102972;-1.9588907559;1.162015553 1169 | A199E;-2.99391671061;1.04992950751;0.293941804 1170 | A199D;-3.96985025681;0.990890468661;0.234994571 1171 | A199G;0.791757181054;0.0406924827628;1.099738901 1172 | A199F;-6.6506366072;-2.37032397877;0.219401538 1173 | A199H;-5.71596096741;-1.34261318446;0.951443208 1174 | A199L;-4.5166374878;-0.832965215923;0.464970232 1175 | A199P;-8.07605238899;-5.27230849998;0.069190814 1176 | A199S;-2.46590250722;0.852430305497;0.820620117 1177 | A199T;-3.81892280981;-0.190837932114;0.822250097 1178 | A199V;-5.41147655656;-1.20177179178;0.867020788 1179 | P200A;-3.29388163122;-1.04599574323;1.022150666 1180 | P200D;-5.03323230881;-2.95294442775;0.338725181 1181 | P200G;-5.66996178846;-3.40485217328;1.33915207 1182 | P200F;-0.277521105949;0.735737464466;1.162015553 1183 | P200I;-1.83991687174;0.0675055045423;1.02570219 1184 | P200H;-0.877076584613;-0.93302410526;1.030841169 1185 | P200L;-0.609710023738;0.668356551761;0.990326208 1186 | P200N;-1.62115836982;-2.68573797587;1.05266057 1187 | P200Q;-1.65487499244;-1.35275072944;1.379698133 1188 | P200S;-2.50857417169;-1.91843110215;1.052319688 1189 | P200R;-0.580024396535;0.43116169506;1.133948359 1190 | P200T;-3.02039329975;-1.95517896214;1.005246804 1191 | P200V;-3.10254485719;0.0513057468011;0.976336844 1192 | -------------------------------------------------------------------------------- /PABP_YEAST_hmmerbit_t0.2_r50000.reweight: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/samsinai/VAE_protein_function/000dfc0597063fa07e308b13953bcc830467cd7b/PABP_YEAST_hmmerbit_t0.2_r50000.reweight -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # EVOVAE: Variational Auto-encoding of protein sequences 2 | 3 | Protein function prediction using a variational autoencoder. You can find the short [paper](https://arxiv.org/pdf/1712.03346.pdf) which was an oral presentation at [NIPS MLCB workshop 2017](https://mlcb.github.io/). 4 | 5 | This repository contains the [detailed workflow](https://github.com/samsinai/VAE_protein_function/blob/master/VAE_for_protein_function_prediction.ipynb), and is associated with this [blog post](https://samsinai.github.io/jekyll/update/2017/08/14/Using-a-Variational-Autoencoder-to-predict-protein-function.html). 6 | 7 | You can find more alignments/validation data to analyze [from Marks' lab](https://marks.hms.harvard.edu/evmutation/downloads.html). Also, their group has done a [comprehensive analysis on the application of VAE](https://arxiv.org/abs/1712.06527) for all of this data independently. 8 | -------------------------------------------------------------------------------- /compute_reweights.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import sys 4 | import time 5 | 6 | #Invariants 7 | ORDER_KEY="XILVAGMFYWEDQNHCRKSTPBZ-"[::-1] 8 | ORDER_LIST=list(ORDER_KEY) 9 | 10 | #Drop columns that are not part of the alignment 11 | def prune_seq(sequence): 12 | output="" 13 | for s in sequence: 14 | if s!="." and not (s.islower()): 15 | output+=s 16 | return output 17 | 18 | #Find the indices of aligned columns, Note that indices are 0-indexed 19 | def index_of_non_lower_case_dot(sequence): 20 | output=[] 21 | for s in range(len(sequence)): 22 | if sequence[s]!="." and not (sequence[s].islower()): 23 | output.append(s) 24 | return output 25 | 26 | #Helper function to translate string to one_hot 27 | def translate_string_to_one_hot(sequence,order_list): 28 | out=np.zeros((len(order_list),len(sequence))) 29 | for i in range(len(sequence)): 30 | out[order_list.index(sequence[i])][i]=1 31 | return out 32 | 33 | #generate single mutants for those positions that experimental data and alignment are available 34 | def mutate_single(wt,mutation_data,offset=0,index=0): 35 | mutants=[] 36 | prev=int(mutation_data[0][1])-offset 37 | 38 | for md in mutation_data: 39 | if prev!=int(md[1])-offset: 40 | index+=1 41 | prev=int(md[1])-offset 42 | mutant=[md[2] if i==index else wt[i] for i in range (len(wt))] 43 | mutants.append(mutant) 44 | 45 | return mutants 46 | 47 | #generate single mutants for those positions that experimental data and alignment are available 48 | def mutate_double(wt,mutation_data1,mutation_data2,offset=0,index=0): 49 | mutants=[] 50 | mutants_double=[] 51 | index2=int(mutation_data2[0][1])-int(mutation_data1[0][1])+index 52 | 53 | #make first mutation 54 | prev=int(mutation_data1[0][1])-offset 55 | for md in mutation_data1: 56 | if prev!=int(md[1])-offset: #new 57 | index+=int(md[1])-offset-prev 58 | prev=int(md[1])-offset 59 | mutant=[md[2] if i==index else wt[i] for i in range (len(wt))] 60 | mutants.append(mutant) 61 | 62 | #make second mutation 63 | prev=int(mutation_data2[0][1])-offset 64 | for md,mutant in zip(mutation_data2,mutants): 65 | if prev!=int(md[1])-offset: #new 66 | index2+=int(md[1])-offset-prev 67 | prev=int(md[1])-offset 68 | mutant=[md[2] if i==index2 else mutant[i] for i in range (len(mutant))] 69 | mutants_double.append(mutant) 70 | 71 | 72 | return mutants_double 73 | 74 | #generate a pandas dataframe from an alignment file 75 | def pdataframe_from_alignment_file(filename,num_reads=200000): 76 | 77 | data=pd.DataFrame(columns=["name","sequence"]) 78 | with open(filename) as datafile: 79 | serotype="" 80 | sequence="" 81 | dump=False 82 | count=0 83 | dataf=datafile.readlines() 84 | for line in dataf: 85 | if line.startswith(">"): 86 | if count>=num_reads: 87 | break 88 | if dump: 89 | row=pd.DataFrame([[serotype,sequence]],columns=["name","sequence"]) 90 | data=data.append(row,ignore_index=True) 91 | dump=True 92 | serotype=line[1:].strip("\n") 93 | sequence="" 94 | count+=1 95 | else: 96 | sequence+=line.strip("\n") 97 | row=pd.DataFrame([[serotype,sequence]],columns=["name","sequence"]) 98 | data=data.append(row,ignore_index=True) 99 | return data 100 | 101 | 102 | #Compute log probability of a particular mutant sequence from a pwm and a one-hot encoding 103 | def compute_log_probability(one_hot_seq,pwm): 104 | prod_mat=np.matmul(one_hot_seq.T,pwm) 105 | log_prod_mat=np.log(prod_mat) 106 | sum_diag=np.trace(log_prod_mat) 107 | return sum_diag 108 | 109 | #Compute the most likely protein sequence given a position weight matrix 110 | def most_likely_seq(pwm): 111 | most_likely=np.argmax(pwm,axis=0) 112 | out_seq="" 113 | for m in most_likely: 114 | out_seq+=ORDER_LIST[m] 115 | return out_seq 116 | 117 | 118 | #compute distance between two aligned sequences 119 | def aligned_dist(s1,s2): 120 | count=0 121 | for i,j in zip(s1,s2): 122 | if i!=j: 123 | count+=1 124 | return count 125 | 126 | 127 | #Compute a new weight for sequence based on similarity threshold theta 128 | def reweight_sequences(dataset,theta): 129 | weights=[1.0 for i in range(len(dataset))] 130 | start = time.process_time() 131 | 132 | for i in range(len(dataset)): 133 | 134 | if i%250==0: 135 | print(str(i)+" took "+str(time.process_time()-start) +" s ") 136 | start = time.process_time() 137 | 138 | for j in range(i+1,len(dataset)): 139 | if aligned_dist(dataset[i],dataset[j])*1./len(dataset[i]) "): 84 | if count>=num_reads: 85 | break 86 | if dump: 87 | row=pd.DataFrame([[serotype,sequence]],columns=["name","sequence"]) 88 | data=data.append(row,ignore_index=True) 89 | dump=True 90 | serotype=line[1:].strip("\n") 91 | sequence="" 92 | count+=1 93 | else: 94 | sequence+=line.strip("\n") 95 | row=pd.DataFrame([[serotype,sequence]],columns=["name","sequence"]) 96 | data=data.append(row,ignore_index=True) 97 | return data 98 | 99 | 100 | #Compute log probability of a particular mutant sequence from a pwm and a one-hot encoding 101 | def compute_log_probability(one_hot_seq,pwm): 102 | prod_mat=np.matmul(one_hot_seq.T,pwm) 103 | log_prod_mat=np.log(prod_mat) 104 | sum_diag=np.trace(log_prod_mat) 105 | return sum_diag 106 | 107 | #Compute the most likely protein sequence given a position weight matrix 108 | def most_likely_seq(pwm): 109 | most_likely=np.argmax(pwm,axis=0) 110 | out_seq="" 111 | for m in most_likely: 112 | out_seq+=ORDER_LIST[m] 113 | return out_seq 114 | 115 | 116 | #compute distance between two aligned sequences 117 | def aligned_dist(s1,s2): 118 | count=0 119 | for i,j in zip(s1,s2): 120 | if i!=j: 121 | count+=1 122 | return count 123 | 124 | 125 | #Compute a new weight for sequence based on similarity threshold theta 126 | def reweight_sequences(dataset,theta): 127 | weights=[1.0 for i in range(len(dataset))] 128 | start = time.process_time() 129 | 130 | for i in range(len(dataset)): 131 | 132 | if i%250==0: 133 | print(str(i)+" took "+str(time.process_time()-start) +" s ") 134 | start = time.process_time() 135 | 136 | for j in range(i+1,len(dataset)): 137 | if aligned_dist(dataset[i],dataset[j])*1./len(dataset[i])