diff --git a/extras/baseline/rbd_distance_baseline_predictor.ipynb b/extras/baseline/rbd_distance_baseline_predictor.ipynb index 3b89e8c6..2cfd338f 100644 --- a/extras/baseline/rbd_distance_baseline_predictor.ipynb +++ b/extras/baseline/rbd_distance_baseline_predictor.ipynb @@ -2,18 +2,20 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 28, "id": "4c34e438", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "import numpy as np" + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 29, "id": "c92f77bd", "metadata": {}, "outputs": [ @@ -155,57 +157,57 @@ "" ], "text/plain": [ - " subtype ha_accession na_accession \\\n", + " subtype ha_accession na_accession \n", "id \n", - "A/swine/Shandong/1207/2016 H1N1 EPI1751427 EPI1751500 \n", + "A/swine/Shandong/1207/2016 H1N1 EPI1751427 EPI1751500 \\\n", "A/Ohio/13/2017 H3N2 EPI1056653 EPI1056652 \n", "A/Hong Kong/125/2017 H7N9 EPI977395 EPI977394 \n", "A/Shanghai/02/2013 H7N9 EPI448936 EPI448938 \n", "A/Anhui-Lujiang/39/2018 H9N2 EPI1315830 EPI1315828 \n", "\n", - " ha \\\n", + " ha \n", "id \n", - "A/swine/Shandong/1207/2016 MEARLFVLFCAFTTLKADTICVGYHANNSTDTVDTILEKNVTVTHS... \n", + "A/swine/Shandong/1207/2016 MEARLFVLFCAFTTLKADTICVGYHANNSTDTVDTILEKNVTVTHS... \\\n", "A/Ohio/13/2017 MKTIIALSHILCLVFAQKLPGNDNNMATLCLGHHAVPNGTIVKTIT... \n", "A/Hong Kong/125/2017 MNTQILVFALIAIIPTNADKICLGHHAVSNGTKVNTLTERGVEVVN... \n", "A/Shanghai/02/2013 MNTQILVFALIAIIPTNADKICLGHHAVSNGTKVNTLTERGVEVVN... \n", "A/Anhui-Lujiang/39/2018 METVSLITILLVATASNADKICIGYQSTNSTETVDTLTENNVPVTH... \n", "\n", - " na \\\n", + " na \n", "id \n", - "A/swine/Shandong/1207/2016 MNPNQKIITIGSICMTIGIASLILQIGNIISIWISHSIQIENQNQS... \n", + "A/swine/Shandong/1207/2016 MNPNQKIITIGSICMTIGIASLILQIGNIISIWISHSIQIENQNQS... \\\n", "A/Ohio/13/2017 MNPNQKIITIGSVSLIIATICFLMQIAILVTTITLHFKQHNCDSSP... \n", "A/Hong Kong/125/2017 MNPNQKILCTSATAITIGAIAVLIGIANLGLNIGLHLKPGCNCSHS... \n", "A/Shanghai/02/2013 MNPNQKILCTSATAIIIGAIAVLIGMANLGLNIGLHLKPGCNCSHS... \n", "A/Anhui-Lujiang/39/2018 MNPNQKITAIGSVSLIIAIICLLMQIAILTTTMTLHFGQKECSNPS... \n", "\n", - " ha_risk \\\n", + " ha_risk \n", "id \n", - "A/swine/Shandong/1207/2016 [0.059268942695459764, 0.059207549599012387, 0... \n", + "A/swine/Shandong/1207/2016 [0.059268942695459764, 0.059207549599012387, 0... \\\n", "A/Ohio/13/2017 [0.007443436845452373, 0.007528108280118501, 0... \n", "A/Hong Kong/125/2017 [0.008053012400112526, 0.011702039255706398, 0... \n", "A/Shanghai/02/2013 [0.0029756019607934496, 0.003893470493957003, ... \n", "A/Anhui-Lujiang/39/2018 [0.013913606506215747, 0.014682700295856159, 0... \n", "\n", - " na_risk \\\n", + " na_risk \n", "id \n", - "A/swine/Shandong/1207/2016 [0.04153677158036683, 0.04121188550974603, 0.0... \n", + "A/swine/Shandong/1207/2016 [0.04153677158036683, 0.04121188550974603, 0.0... \\\n", "A/Ohio/13/2017 [0.0684551151913223, 0.06848866622907772, 0.06... \n", "A/Hong Kong/125/2017 [0.001702640937331002, 0.0015986998079791897, ... \n", "A/Shanghai/02/2013 [0.0014946170772583095, 0.0014623661892494138,... \n", "A/Anhui-Lujiang/39/2018 [0.025868069685588593, 0.04350993746295142, 0.... \n", "\n", - " geometric_mean_risk \\\n", + " geometric_mean_risk \n", "id \n", - "A/swine/Shandong/1207/2016 [0.04961693798040306, 0.04939691038300986, 0.0... \n", + "A/swine/Shandong/1207/2016 [0.04961693798040306, 0.04939691038300986, 0.0... \\\n", "A/Ohio/13/2017 [0.02257302209884124, 0.0227066090672604, 0.02... \n", "A/Hong Kong/125/2017 [0.0037028892207661, 0.004325280096255358, 0.0... \n", "A/Shanghai/02/2013 [0.00210888252534967, 0.002386143249933443, 0.... \n", "A/Anhui-Lujiang/39/2018 [0.018971508708604274, 0.025275351068975476, 0... \n", "\n", - " emergence_risk emergence_risk_var impact_risk \\\n", + " emergence_risk emergence_risk_var impact_risk \n", "id \n", - "A/swine/Shandong/1207/2016 7.5 0.001861 6.9 \n", + "A/swine/Shandong/1207/2016 7.5 0.001861 6.9 \\\n", "A/Ohio/13/2017 6.6 0.071931 5.8 \n", "A/Hong Kong/125/2017 6.5 0.057740 7.5 \n", "A/Shanghai/02/2013 6.4 0.025769 7.2 \n", @@ -220,7 +222,7 @@ "A/Anhui-Lujiang/39/2018 0.013857 1.0 " ] }, - "execution_count": 2, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -233,390 +235,15 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "id": "cf9b9f93", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
emergence_riskimpact_riskis_iratha_riskgeometric_mean_risk
id
A/swine/Shandong/1207/20167.56.91.0[0.059268942695459764, 0.059207549599012387, 0...[0.04961693798040306, 0.04939691038300986, 0.0...
A/Ohio/13/20176.65.81.0[0.007443436845452373, 0.007528108280118501, 0...[0.02257302209884124, 0.0227066090672604, 0.02...
A/Hong Kong/125/20176.57.51.0[0.008053012400112526, 0.011702039255706398, 0...[0.0037028892207661, 0.004325280096255358, 0.0...
A/Shanghai/02/20136.47.21.0[0.0029756019607934496, 0.003893470493957003, ...[0.00210888252534967, 0.002386143249933443, 0....
A/Anhui-Lujiang/39/20186.25.91.0[0.013913606506215747, 0.014682700295856159, 0...[0.018971508708604274, 0.025275351068975476, 0...
A/Indiana/08/20116.04.51.0[0.015833187603574352, 0.01579698562348106, 0....[0.016902504332314258, 0.01683599355230769, 0....
A/California/62/20185.85.71.0[0.25421003694129446, 0.17137241238504203, 0.1...[0.11064301441040252, 0.08010185536798671, 0.0...
A/Bangladesh/0994/20115.65.41.0[0.047962278609110426, 0.047376021553330504, 0...[0.1509708016952567, 0.14755990149418968, 0.15...
A/Sichuan/06681/20215.36.31.0[0.3401659563640977, 0.4372884849313668, 0.335...[0.14098323610429828, 0.16479919492131653, 0.1...
A/Vietnam/1203/20045.26.61.0[0.12775494527491757, 0.11190370497308454, 0.1...[0.07357578392227798, 0.06512340773617227, 0.0...
A/Yunnan/14564/20155.06.61.0[0.20080863109685176, 0.10301992847449411, 0.2...[0.08744456695565636, 0.05634300538887098, 0.0...
A/Astrakhan/3212/20204.65.21.0[0.2452941610025089, 0.2335184366024988, 0.221...[0.3660547134708487, 0.352999397804964, 0.3433...
A/Netherlands/219/20034.65.81.0[0.16431983904493178, 0.16426481855673325, 0.1...[0.2735882731677115, 0.27353672735391277, 0.27...
A/American wigeon/South Carolina/AH0195145/20214.45.11.0[0.22507345650411203, 0.253385372610634, 0.250...[0.2618009640888491, 0.28041946959711944, 0.27...
A/Jiangxi-Donghu/346/20134.36.01.0[0.03929273071556801, 0.3559593557688923, 0.15...[0.027090727115239115, 0.1774459269887038, 0.0...
A/gyrfalcon/Washington/41088/20144.24.61.0[0.24718594726896237, 0.2361702553946631, 0.22...[0.36678073190475535, 0.35436513753260845, 0.3...
A/Northern pintail/Washington/40964/20143.84.11.0[0.2410948922603147, 0.22971447028474637, 0.21...[0.349366042786461, 0.34039602323151935, 0.331...
A/canine/Illinois/12191/20153.73.71.0[0.01616230298118454, 0.016067588755834316, 0....[0.024453287740176615, 0.024374121053616235, 0...
A/American green-winged teal/Washington/1957050/20143.64.11.0[0.22532859992890514, 0.2523992705219045, 0.24...[0.2592089036823605, 0.2766319981079351, 0.273...
A/turkey/Indiana/1573-2/20163.43.91.0[0.042531258378212616, 0.046850178126532895, 0...[0.13246011857462825, 0.13903537495472926, 0.1...
A/chicken/Tennessee/17-007431-3/20173.13.51.0[0.032175814200484966, 0.03682965073244792, 0....[0.12816247069024164, 0.1371356157354049, 0.12...
A/chicken/Tennessee/17-007147-2/20172.83.51.0[0.082518541655176, 0.08595812903880762, 0.083...[0.20524466180722442, 0.20950526315825033, 0.2...
\n", - "
" - ], - "text/plain": [ - " emergence_risk \\\n", - "id \n", - "A/swine/Shandong/1207/2016 7.5 \n", - "A/Ohio/13/2017 6.6 \n", - "A/Hong Kong/125/2017 6.5 \n", - "A/Shanghai/02/2013 6.4 \n", - "A/Anhui-Lujiang/39/2018 6.2 \n", - "A/Indiana/08/2011 6.0 \n", - "A/California/62/2018 5.8 \n", - "A/Bangladesh/0994/2011 5.6 \n", - "A/Sichuan/06681/2021 5.3 \n", - "A/Vietnam/1203/2004 5.2 \n", - "A/Yunnan/14564/2015 5.0 \n", - "A/Astrakhan/3212/2020 4.6 \n", - "A/Netherlands/219/2003 4.6 \n", - "A/American wigeon/South Carolina/AH0195145/2021 4.4 \n", - "A/Jiangxi-Donghu/346/2013 4.3 \n", - "A/gyrfalcon/Washington/41088/2014 4.2 \n", - "A/Northern pintail/Washington/40964/2014 3.8 \n", - "A/canine/Illinois/12191/2015 3.7 \n", - "A/American green-winged teal/Washington/1957050... 3.6 \n", - "A/turkey/Indiana/1573-2/2016 3.4 \n", - "A/chicken/Tennessee/17-007431-3/2017 3.1 \n", - "A/chicken/Tennessee/17-007147-2/2017 2.8 \n", - "\n", - " impact_risk is_irat \\\n", - "id \n", - "A/swine/Shandong/1207/2016 6.9 1.0 \n", - "A/Ohio/13/2017 5.8 1.0 \n", - "A/Hong Kong/125/2017 7.5 1.0 \n", - "A/Shanghai/02/2013 7.2 1.0 \n", - "A/Anhui-Lujiang/39/2018 5.9 1.0 \n", - "A/Indiana/08/2011 4.5 1.0 \n", - "A/California/62/2018 5.7 1.0 \n", - "A/Bangladesh/0994/2011 5.4 1.0 \n", - "A/Sichuan/06681/2021 6.3 1.0 \n", - "A/Vietnam/1203/2004 6.6 1.0 \n", - "A/Yunnan/14564/2015 6.6 1.0 \n", - "A/Astrakhan/3212/2020 5.2 1.0 \n", - "A/Netherlands/219/2003 5.8 1.0 \n", - "A/American wigeon/South Carolina/AH0195145/2021 5.1 1.0 \n", - "A/Jiangxi-Donghu/346/2013 6.0 1.0 \n", - "A/gyrfalcon/Washington/41088/2014 4.6 1.0 \n", - "A/Northern pintail/Washington/40964/2014 4.1 1.0 \n", - "A/canine/Illinois/12191/2015 3.7 1.0 \n", - "A/American green-winged teal/Washington/1957050... 4.1 1.0 \n", - "A/turkey/Indiana/1573-2/2016 3.9 1.0 \n", - "A/chicken/Tennessee/17-007431-3/2017 3.5 1.0 \n", - "A/chicken/Tennessee/17-007147-2/2017 3.5 1.0 \n", - "\n", - " ha_risk \\\n", - "id \n", - "A/swine/Shandong/1207/2016 [0.059268942695459764, 0.059207549599012387, 0... \n", - "A/Ohio/13/2017 [0.007443436845452373, 0.007528108280118501, 0... \n", - "A/Hong Kong/125/2017 [0.008053012400112526, 0.011702039255706398, 0... \n", - "A/Shanghai/02/2013 [0.0029756019607934496, 0.003893470493957003, ... \n", - "A/Anhui-Lujiang/39/2018 [0.013913606506215747, 0.014682700295856159, 0... \n", - "A/Indiana/08/2011 [0.015833187603574352, 0.01579698562348106, 0.... \n", - "A/California/62/2018 [0.25421003694129446, 0.17137241238504203, 0.1... \n", - "A/Bangladesh/0994/2011 [0.047962278609110426, 0.047376021553330504, 0... \n", - "A/Sichuan/06681/2021 [0.3401659563640977, 0.4372884849313668, 0.335... \n", - "A/Vietnam/1203/2004 [0.12775494527491757, 0.11190370497308454, 0.1... \n", - "A/Yunnan/14564/2015 [0.20080863109685176, 0.10301992847449411, 0.2... \n", - "A/Astrakhan/3212/2020 [0.2452941610025089, 0.2335184366024988, 0.221... \n", - "A/Netherlands/219/2003 [0.16431983904493178, 0.16426481855673325, 0.1... \n", - "A/American wigeon/South Carolina/AH0195145/2021 [0.22507345650411203, 0.253385372610634, 0.250... \n", - "A/Jiangxi-Donghu/346/2013 [0.03929273071556801, 0.3559593557688923, 0.15... \n", - "A/gyrfalcon/Washington/41088/2014 [0.24718594726896237, 0.2361702553946631, 0.22... \n", - "A/Northern pintail/Washington/40964/2014 [0.2410948922603147, 0.22971447028474637, 0.21... \n", - "A/canine/Illinois/12191/2015 [0.01616230298118454, 0.016067588755834316, 0.... \n", - "A/American green-winged teal/Washington/1957050... [0.22532859992890514, 0.2523992705219045, 0.24... \n", - "A/turkey/Indiana/1573-2/2016 [0.042531258378212616, 0.046850178126532895, 0... \n", - "A/chicken/Tennessee/17-007431-3/2017 [0.032175814200484966, 0.03682965073244792, 0.... \n", - "A/chicken/Tennessee/17-007147-2/2017 [0.082518541655176, 0.08595812903880762, 0.083... \n", - "\n", - " geometric_mean_risk \n", - "id \n", - "A/swine/Shandong/1207/2016 [0.04961693798040306, 0.04939691038300986, 0.0... \n", - "A/Ohio/13/2017 [0.02257302209884124, 0.0227066090672604, 0.02... \n", - "A/Hong Kong/125/2017 [0.0037028892207661, 0.004325280096255358, 0.0... \n", - "A/Shanghai/02/2013 [0.00210888252534967, 0.002386143249933443, 0.... \n", - "A/Anhui-Lujiang/39/2018 [0.018971508708604274, 0.025275351068975476, 0... \n", - "A/Indiana/08/2011 [0.016902504332314258, 0.01683599355230769, 0.... \n", - "A/California/62/2018 [0.11064301441040252, 0.08010185536798671, 0.0... \n", - "A/Bangladesh/0994/2011 [0.1509708016952567, 0.14755990149418968, 0.15... \n", - "A/Sichuan/06681/2021 [0.14098323610429828, 0.16479919492131653, 0.1... \n", - "A/Vietnam/1203/2004 [0.07357578392227798, 0.06512340773617227, 0.0... \n", - "A/Yunnan/14564/2015 [0.08744456695565636, 0.05634300538887098, 0.0... \n", - "A/Astrakhan/3212/2020 [0.3660547134708487, 0.352999397804964, 0.3433... \n", - "A/Netherlands/219/2003 [0.2735882731677115, 0.27353672735391277, 0.27... \n", - "A/American wigeon/South Carolina/AH0195145/2021 [0.2618009640888491, 0.28041946959711944, 0.27... \n", - "A/Jiangxi-Donghu/346/2013 [0.027090727115239115, 0.1774459269887038, 0.0... \n", - "A/gyrfalcon/Washington/41088/2014 [0.36678073190475535, 0.35436513753260845, 0.3... \n", - "A/Northern pintail/Washington/40964/2014 [0.349366042786461, 0.34039602323151935, 0.331... \n", - "A/canine/Illinois/12191/2015 [0.024453287740176615, 0.024374121053616235, 0... \n", - "A/American green-winged teal/Washington/1957050... [0.2592089036823605, 0.2766319981079351, 0.273... \n", - "A/turkey/Indiana/1573-2/2016 [0.13246011857462825, 0.13903537495472926, 0.1... \n", - "A/chicken/Tennessee/17-007431-3/2017 [0.12816247069024164, 0.1371356157354049, 0.12... \n", - "A/chicken/Tennessee/17-007147-2/2017 [0.20524466180722442, 0.20950526315825033, 0.2... " - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_h1n1=df#[df.subtype=='H1N1']\n", - "df_=df_h1n1[['emergence_risk','impact_risk','is_irat','ha_risk','geometric_mean_risk']]\n", - "df_[df_.is_irat==1]" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "523e82f7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['subtype', 'ha_accession', 'na_accession', 'ha', 'na', 'ha_risk',\n", - " 'na_risk', 'geometric_mean_risk', 'emergence_risk',\n", - " 'emergence_risk_var', 'impact_risk', 'impact_risk_var', 'is_irat'],\n", - " dtype='object')" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "be2e6e33", - "metadata": {}, "outputs": [], - "source": [ - "RBD=range(63,286)\n", - "R=range(150,236)\n", - "R=range(50,336)\n", - "RBD=range(90,240)\n", - "binding_pocket=range(190,200)\n", - "\n", - "R=[90, 91,92,93,94,155,156,157,158,159,160,190,191,192,193,194,195,220,221,222,223,224,225,226]\n", - "df_['frag_rbd']=[''.join(np.array(list(x))[RBD]) for x in df_h1n1.ha.values]\n", - "df_['frag_sel']=[''.join(np.array(list(x))[R]) for x in df_h1n1.ha.values]\n", - "df_['frag_pocket']=[''.join(np.array(list(x))[binding_pocket]) for x in df_h1n1.ha.values]\n", - "df_['mean_geom_risk'] = [np.array(eval(x)).mean() for x in df_.geometric_mean_risk]\n", - "df_['mean_ha_risk'] = [np.array(eval(x)).mean() for x in df_.ha_risk]\n", - "df_['max_ha_risk'] = [np.array(eval(x)).max() for x in df_.ha_risk]\n", - "df_['min_ha_risk'] = [np.array(eval(x)).min() for x in df_.ha_risk]" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 30, "id": "f424b5a0", "metadata": {}, "outputs": [], @@ -666,7 +293,59 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 31, + "id": "523e82f7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['subtype', 'ha_accession', 'na_accession', 'ha', 'na', 'ha_risk',\n", + " 'na_risk', 'geometric_mean_risk', 'emergence_risk',\n", + " 'emergence_risk_var', 'impact_risk', 'impact_risk_var', 'is_irat'],\n", + " dtype='object')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "id": "be2e6e33", + "metadata": {}, + "outputs": [], + "source": [ + "RBD=range(63,286)\n", + "#R=range(150,236)\n", + "#R=range(50,336)\n", + "#RBD=range(90,240)\n", + "binding_pocket=range(210,240)\n", + "df_h1n1=df#[df.subtype=='H3N2']\n", + "df_=df_h1n1[['emergence_risk','impact_risk','is_irat','ha_risk','geometric_mean_risk']]\n", + "R=[130,131,132,133,134,135,150,151,152,153,154,155,156,157,158,159,160,190,191,192,193,194,195,220,221,222,223,224,225,226]\n", + "df_['frag_rbd']=[''.join(np.array(list(x))[RBD]) for x in df_h1n1.ha.values]\n", + "df_['frag_sel']=[''.join(np.array(list(x))[R]) for x in df_h1n1.ha.values]\n", + "df_['frag_pocket']=[''.join(np.array(list(x))[binding_pocket]) for x in df_h1n1.ha.values]\n", + "df_['mean_geom_risk'] = [np.array(eval(x)).mean() for x in df_.geometric_mean_risk]\n", + "df_['mean_ha_risk'] = [np.array(eval(x)).mean() for x in df_.ha_risk]\n", + "df_['max_ha_risk'] = [np.array(eval(x)).max() for x in df_.ha_risk]\n", + "df_['min_ha_risk'] = [np.array(eval(x)).min() for x in df_.ha_risk]\n", + "df_['max_geom_risk'] = [np.array(eval(x)).max() for x in df_.geometric_mean_risk]\n", + "df_['min_geom_risk'] = [np.array(eval(x)).min() for x in df_.geometric_mean_risk]\n", + "df_=df_.drop(['ha_risk',\n", + " 'geometric_mean_risk'],axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 211, "id": "33d74ba3", "metadata": {}, "outputs": [], @@ -678,24 +357,58 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 212, "id": "65b0cf9d", "metadata": {}, "outputs": [], "source": [ - "high_risk_rbd = [getLav(x,seq0) for x in df_.frag_rbd.values]\n", + "high_risk_rbd = [getLav(x,seq0[:6]) for x in df_.frag_rbd.values]\n", "df_['hr_rbd'] = high_risk_rbd\n", - "high_risk_rbd = [getLav(x,seq0) for x in df_.frag_sel.values]\n", + "high_risk_rbd = [getLav(x,seq0[:6]) for x in df_.frag_sel.values]\n", "df_['hr_sel'] = high_risk_rbd\n", - "high_risk_rbd = [getLav(x,seq0) for x in df_.frag_pocket.values]\n", + "high_risk_rbd = [getLav(x,seq0[:6]) for x in df_.frag_pocket.values]\n", "df_['hr_pocket'] = high_risk_rbd" ] }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 213, "id": "096ff036", "metadata": {}, + "outputs": [], + "source": [ + "tf=df_[df_.is_irat==1].drop('is_irat',axis=1).corr(numeric_only=True).loc[['hr_rbd','hr_sel','hr_pocket'],:]\n", + "tf.index=['On RBD (63-286)','On seleted residues on RBD$^\\\\star$',\"Encompassing 220 loop (210-240)\"]\n", + "tf.index.name='edit distance from high fitness human strains'\n", + "tf=tf.drop(['hr_rbd','hr_sel'],axis=1).round(3)\n", + "#tf=tf[['mean_ha_risk','mean_geom_risk']]\n", + "tf['HA \\\\qdist (IRAT sequences)']=['$'+(str(x)+'\\pm'+str(abs(y-x)))[:13]+'$' for x,y in zip(tf.mean_ha_risk.values,tf.min_ha_risk.values)]\n", + "tf['geometric mean of HA and NA \\\\qdist (IRAT sequences)']=['$'+(str(x)+'\\pm'+str(abs(y-x)))[:13]+'$' for x,y in zip(tf.mean_geom_risk.values,tf.min_geom_risk.values)]\n", + "tf0=tf[['HA \\\\qdist (IRAT sequences)', 'geometric mean of HA and NA \\\\qdist (IRAT sequences)']]" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "id": "c67f743a", + "metadata": {}, + "outputs": [], + "source": [ + "tf=df_[df_.is_irat==0].drop('is_irat',axis=1).corr(numeric_only=True).loc[['hr_rbd','hr_sel','hr_pocket'],:]\n", + "tf.index=['On RBD (63-286)','On seleted residues on RBD$^\\\\star$',\"Encompassing 220 loop (210-240)\"]\n", + "tf.index.name='edit distance from high fitness human strains'\n", + "tf=tf.drop(['hr_rbd','hr_sel'],axis=1).round(3)\n", + "tf['HA \\\\qdist (2020-2022 sequences)']=['$'+(str(x)+'\\pm'+str(abs(y-x)))[:13]+'$' for x,y in zip(tf.mean_ha_risk.values,tf.min_ha_risk.values)]\n", + "tf['geometric mean of HA and NA \\\\qdist (2020-2022 sequences)']=['$'+(str(x)+'\\pm'+str(abs(y-x)))[:13]+'$' for x,y in zip(tf.mean_geom_risk.values,tf.min_geom_risk.values)]\n", + "tf=tf[['HA \\\\qdist (2020-2022 sequences)', 'geometric mean of HA and NA \\\\qdist (2020-2022 sequences)']]\n", + "tf=tf.join(tf0)" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "id": "81387c94", + "metadata": {}, "outputs": [ { "data": { @@ -718,19 +431,13 @@ " \n", " \n", " \n", - " emergence_risk\n", - " impact_risk\n", - " mean_geom_risk\n", - " mean_ha_risk\n", - " max_ha_risk\n", - " min_ha_risk\n", - " hr_pocket\n", + " HA \\qdist (2020-2022 sequences)\n", + " geometric mean of HA and NA \\qdist (2020-2022 sequences)\n", + " HA \\qdist (IRAT sequences)\n", + " geometric mean of HA and NA \\qdist (IRAT sequences)\n", " \n", " \n", - " edit distance\n", - " \n", - " \n", - " \n", + " edit distance from high fitness human strains\n", " \n", " \n", " \n", @@ -739,833 +446,138 @@ " \n", " \n", " \n", - " RBD\n", - " -0.379037\n", - " 0.045975\n", - " 0.325985\n", - " 0.169715\n", - " 0.161580\n", - " 0.114448\n", - " NaN\n", + " On RBD (63-286)\n", + " $0.671\\pm0.005$\n", + " $0.773\\pm0.007$\n", + " $0.214\\pm0.061$\n", + " $0.376\\pm0.071$\n", " \n", " \n", - " On seleted residues\n", - " -0.112898\n", - " 0.035949\n", - " -0.148383\n", - " -0.461506\n", - " -0.399043\n", - " -0.509472\n", - " NaN\n", + " On seleted residues on RBD$^\\star$\n", + " $-0.21\\pm0.007$\n", + " $-0.203\\pm0.01$\n", + " $-0.545\\pm0.02$\n", + " $-0.234\\pm0.03$\n", " \n", " \n", - " pocket\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " Encompassing 220 loop (210-240)\n", + " $-0.383\\pm0.00$\n", + " $-0.368\\pm0.00$\n", + " $-0.432\\pm0.03$\n", + " $-0.426\\pm0.04$\n", " \n", " \n", "\n", "" ], "text/plain": [ - " emergence_risk impact_risk mean_geom_risk \\\n", - "edit distance \n", - "RBD -0.379037 0.045975 0.325985 \n", - "On seleted residues -0.112898 0.035949 -0.148383 \n", - "pocket NaN NaN NaN \n", + " HA \\qdist (2020-2022 sequences) \n", + "edit distance from high fitness human strains \n", + "On RBD (63-286) $0.671\\pm0.005$ \\\n", + "On seleted residues on RBD$^\\star$ $-0.21\\pm0.007$ \n", + "Encompassing 220 loop (210-240) $-0.383\\pm0.00$ \n", + "\n", + " geometric mean of HA and NA \\qdist (2020-2022 sequences) \n", + "edit distance from high fitness human strains \n", + "On RBD (63-286) $0.773\\pm0.007$ \\\n", + "On seleted residues on RBD$^\\star$ $-0.203\\pm0.01$ \n", + "Encompassing 220 loop (210-240) $-0.368\\pm0.00$ \n", + "\n", + " HA \\qdist (IRAT sequences) \n", + "edit distance from high fitness human strains \n", + "On RBD (63-286) $0.214\\pm0.061$ \\\n", + "On seleted residues on RBD$^\\star$ $-0.545\\pm0.02$ \n", + "Encompassing 220 loop (210-240) $-0.432\\pm0.03$ \n", "\n", - " mean_ha_risk max_ha_risk min_ha_risk hr_pocket \n", - "edit distance \n", - "RBD 0.169715 0.161580 0.114448 NaN \n", - "On seleted residues -0.461506 -0.399043 -0.509472 NaN \n", - "pocket NaN NaN NaN NaN " + " geometric mean of HA and NA \\qdist (IRAT sequences) \n", + "edit distance from high fitness human strains \n", + "On RBD (63-286) $0.376\\pm0.071$ \n", + "On seleted residues on RBD$^\\star$ $-0.234\\pm0.03$ \n", + "Encompassing 220 loop (210-240) $-0.426\\pm0.04$ " ] }, - "execution_count": 117, + "execution_count": 215, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tf=df_[df_.is_irat==1].drop('is_irat',axis=1).corr().loc[['hr_rbd','hr_sel','hr_pocket'],:]\n", - "tf.index=['RBD','On seleted residues',\"pocket\"]\n", - "tf.index.name='edit distance'\n", - "tf.drop(['hr_rbd','hr_sel'],axis=1)" + "tf" ] }, { "cell_type": "code", - "execution_count": 112, - "id": "b685a5a6", + "execution_count": 216, + "id": "a805cdf6", "metadata": {}, + "outputs": [], + "source": [ + "from zedstat.textable import textable\n", + "textable(tf,tabname='../../tex/overleaf/Figures/tabdata/baselinetab.tex',\n", + " INDEX=True,LNTERM='\\\\\\\\\\\\hline\\n',TABFORMAT='L{1.2in}|L{1.2in}|L{1.2in}|L{1.2in}|L{1.2in}') \n" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "scrolled": false + }, "outputs": [ { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
emergence_riskimpact_riskha_riskgeometric_mean_riskfrag_rbdfrag_selmean_geom_riskmean_ha_riskmax_ha_riskmin_ha_riskhr_rbdhr_selfrag_pocket
id
A/swine/Shandong/1207/20167.56.9[0.059268942695459764, 0.059207549599012387, 0...[0.04961693798040306, 0.04939691038300986, 0.0...NSWSYIIETSNSKNGACYPGEFADYEELKEQLSTVSSFERFEIFPK...NSWSYSGANSFLVIWGVSKYYKRF0.0495310.0593250.0598090.059069450541LVIWGVHHPP
A/Ohio/13/20176.65.8[0.007443436845452373, 0.007528108280118501, 0...[0.02257302209884124, 0.0227066090672604, 0.02...QCDGFQNNKWDLFVERSKAHSNCYPYDVPDYASLRSLVASSGTLEF...QCDGFKRRSSNDKLYIWSTKRNQQ0.0237800.0084550.0174830.007363435541DKLYIWGVHH
A/Hong Kong/125/20176.57.5[0.008053012400112526, 0.011702039255706398, 0...[0.0037028892207661, 0.004325280096255358, 0.0...SADLIIERREGSDVCYPGKFVNEEALRQILRESGGIDKETMGFTYN...SADLIAEMKWLIHHSVSSFVPSPG0.0054810.0097870.0124530.005927466542IHHSVSTAEQ
A/Shanghai/02/20136.47.2[0.0029756019607934496, 0.003893470493957003, ...[0.00210888252534967, 0.002386143249933443, 0....SADLIIERREGSDVCYPGKFVNEEALRQILRESGGIDKEAMGFTYS...SADLIAEMKWLIHHSVSSFVPSPG0.0028460.0031310.0038930.002597468542IHHSVSTAEQ
A/Anhui-Lujiang/39/20186.25.9[0.013913606506215747, 0.014682700295856159, 0...[0.018971508708604274, 0.025275351068975476, 0...GREWSYIVERPSAVNGLCYPGNVENLEELRSLFSSARSYQRIQIFP...GREWSYRSMRWNHPPTDFKPLIGP0.0220340.0146390.0176850.012083467542NHPPTDDTQR
A/Indiana/08/20116.04.5[0.015833187603574352, 0.01579698562348106, 0....[0.016902504332314258, 0.01683599355230769, 0....HCDDFQNKEWDLFVERSTAYSNCYPYYVPDYATLRSLVASSGNLEF...HCDDFRRGSVNDKLYIWSTKRSQQ0.0173260.0167890.0256310.015772443541DKLYIWGVHH
A/California/62/20185.85.7[0.25421003694129446, 0.17137241238504203, 0.1...[0.11064301441040252, 0.08010185536798671, 0.0...ESWSYIVETSNPENGTCYPGYFEDYEELREQLSSVSSFKKFEIFPK...ESWSYGNSSFYVLWGVHHYSRRFT0.0940640.1961000.2542100.153140452541VLWGVHHPSN
A/Bangladesh/0994/20115.65.4[0.047962278609110426, 0.047376021553330504, 0...[0.1509708016952567, 0.14755990149418968, 0.15...GREWSYIVERPSAVNGTCYPGNVENLEELRTLFSSSSSYQRIQIFP...GREWSYRNMRWHHPPTDFKPLIGP0.1483340.0471220.0487920.044856468542HHPPTDTAQT
A/Sichuan/06681/20215.36.3[0.3401659563640977, 0.4372884849313668, 0.335...[0.14098323610429828, 0.16479919492131653, 0.1...EWSYIVERANPANDLCYPGSLNDYEELKHLLSRINHFEKILIIPKG...EWSYIAPSFFRLWGIHHLNQRLVP0.1443360.3409750.4372880.293157460541LWGIHHSNNA
A/Vietnam/1203/20045.26.6[0.12775494527491757, 0.11190370497308454, 0.1...[0.07357578392227798, 0.06512340773617227, 0.0...EWSYIVEKANPVNDLCYPGDFNDYEELKHLLSRINHFEKIQIIPKS...EWSYIKSSFFRLWGIHHLNQRLVP0.0709490.1287440.1570390.107543465541LWGIHHPNDA
A/Yunnan/14564/20155.06.6[0.20080863109685176, 0.10301992847449411, 0.2...[0.08744456695565636, 0.05634300538887098, 0.0...EWSYIVERANPANDLCYPGNLNDYEELKHLLSRINHFEKTLIIPKS...EWSYIPSFFRNWGIHHSNQRLEPK0.0904330.2062270.2502670.103020462541WGIHHSNNAA
A/Astrakhan/3212/20204.65.2[0.2452941610025089, 0.2335184366024988, 0.221...[0.3660547134708487, 0.352999397804964, 0.3433...EWSYIVERANPANDLCYPGSLNDYEELKHLLSRINHFEKILIIPKS...EWSYIAPSFFRLWGIHHLNQRLVP0.3499130.2267280.2452940.196373460541LWGIHHSNNA
A/Netherlands/219/20034.65.8[0.16431983904493178, 0.16426481855673325, 0.1...[0.2735882731677115, 0.27353672735391277, 0.27...SADLIIERREGSDVCYPGKFVNEEALRQILRESGGIDKETMGFTYS...SADLIAEMKWLIHHSGSSFVPSPG0.2735390.1643360.1645750.163875466542IHHSGSTTEQ
A/American wigeon/South Carolina/AH0195145/20214.45.1[0.22507345650411203, 0.253385372610634, 0.250...[0.2618009640888491, 0.28041946959711944, 0.27...EWSYIVERANPANDLCYPGSLNDYEELKHMLSRINHFEKILIIPKS...EWSYIAPSFFRLWGIHHLNQRLAP0.2665840.2310730.2541590.212808460541LWGIHHSNNA
A/Jiangxi-Donghu/346/20134.36.0[0.03929273071556801, 0.3559593557688923, 0.15...[0.027090727115239115, 0.1774459269887038, 0.0...WDTLIERENAIAYCYPGATVNVEALRQKIMESGGINKISTGFTYGS...WDTLIYAELKWGIHHPSNNFVPVV0.2021980.2097160.3683740.039293468542GIHHPSSTQE
A/gyrfalcon/Washington/41088/20144.24.6[0.24718594726896237, 0.2361702553946631, 0.22...[0.36678073190475535, 0.35436513753260845, 0.3...EWSYIVERANPANDLCYPGTLNDYEELKHLLSRINHFEKTLIIPRS...EWSYIASSFFRLWGIHHLNQRLVP0.3511170.2289410.2471860.198971462541LWGIHHSNNA
A/Northern pintail/Washington/40964/20143.84.1[0.2410948922603147, 0.22971447028474637, 0.21...[0.349366042786461, 0.34039602323151935, 0.331...EWSYIVERANPANDLCYPGTLNDYEELKHLLSRINHFEKNLIIPRS...EWSYIASSFFRLWGIHHLNQRLVP0.3354840.2232690.2410950.195036462541LWGIHHSNNA
A/canine/Illinois/12191/20153.73.7[0.01616230298118454, 0.016067588755834316, 0....[0.024453287740176615, 0.024374121053616235, 0...HCDVFQNETWDLFVERSNAFSNCYPYDVPDYASLRSIVASSGTLEF...HCDVFKRGPANDKLYIWSTRRSQQ0.0250450.0170880.0259810.016030447541DKLYIWGVHH
A/American green-winged teal/Washington/1957050/20143.64.1[0.22532859992890514, 0.2523992705219045, 0.24...[0.2592089036823605, 0.2766319981079351, 0.273...EWSYIVERANPANDLCYPGTLNDYEELKHLLSRINHFEKTLIIPRS...EWSYIASSFFRLWGIHHLNQRLVP0.2633590.2307410.2530980.213461462541LWGIHHSNNA
A/turkey/Indiana/1573-2/20163.43.9[0.042531258378212616, 0.046850178126532895, 0...[0.13246011857462825, 0.13903537495472926, 0.1...DADLIIERREGTDVCYPGKFTNKESLRQILRGSGGIDKESMGFTYS...DADLIAEMKWLVHHSGSSFTPSPG0.1350010.0441940.0468500.041007468542VHHSGSVTEQ
A/chicken/Tennessee/17-007431-3/20173.13.5[0.032175814200484966, 0.03682965073244792, 0....[0.12816247069024164, 0.1371356157354049, 0.12...DADLIIERREGTDVCYPGKFTNEESLRQILRGSGGIDKESMGFTYS...DADLIAEMKWLVHHSGSSFTPSPG0.1314200.0339370.0368300.030574466542VHHSGSADEQ
A/chicken/Tennessee/17-007147-2/20172.83.5[0.082518541655176, 0.08595812903880762, 0.083...[0.20524466180722442, 0.20950526315825033, 0.2...DADLIIERREGTDVCYPGKFTNEESLRQILRGSGGIDKESMGFTYS...DADLIAEMKWLVHHSGSSFTPSPG0.2066110.0838170.0859580.081610468542VHHSGSAAEQ
\n", - "
" - ], - "text/plain": [ - " emergence_risk \\\n", - "id \n", - "A/swine/Shandong/1207/2016 7.5 \n", - "A/Ohio/13/2017 6.6 \n", - "A/Hong Kong/125/2017 6.5 \n", - "A/Shanghai/02/2013 6.4 \n", - "A/Anhui-Lujiang/39/2018 6.2 \n", - "A/Indiana/08/2011 6.0 \n", - "A/California/62/2018 5.8 \n", - "A/Bangladesh/0994/2011 5.6 \n", - "A/Sichuan/06681/2021 5.3 \n", - "A/Vietnam/1203/2004 5.2 \n", - "A/Yunnan/14564/2015 5.0 \n", - "A/Astrakhan/3212/2020 4.6 \n", - "A/Netherlands/219/2003 4.6 \n", - "A/American wigeon/South Carolina/AH0195145/2021 4.4 \n", - "A/Jiangxi-Donghu/346/2013 4.3 \n", - "A/gyrfalcon/Washington/41088/2014 4.2 \n", - "A/Northern pintail/Washington/40964/2014 3.8 \n", - "A/canine/Illinois/12191/2015 3.7 \n", - "A/American green-winged teal/Washington/1957050... 3.6 \n", - "A/turkey/Indiana/1573-2/2016 3.4 \n", - "A/chicken/Tennessee/17-007431-3/2017 3.1 \n", - "A/chicken/Tennessee/17-007147-2/2017 2.8 \n", - "\n", - " impact_risk \\\n", - "id \n", - "A/swine/Shandong/1207/2016 6.9 \n", - "A/Ohio/13/2017 5.8 \n", - "A/Hong Kong/125/2017 7.5 \n", - "A/Shanghai/02/2013 7.2 \n", - "A/Anhui-Lujiang/39/2018 5.9 \n", - "A/Indiana/08/2011 4.5 \n", - "A/California/62/2018 5.7 \n", - "A/Bangladesh/0994/2011 5.4 \n", - "A/Sichuan/06681/2021 6.3 \n", - "A/Vietnam/1203/2004 6.6 \n", - "A/Yunnan/14564/2015 6.6 \n", - "A/Astrakhan/3212/2020 5.2 \n", - "A/Netherlands/219/2003 5.8 \n", - "A/American wigeon/South Carolina/AH0195145/2021 5.1 \n", - "A/Jiangxi-Donghu/346/2013 6.0 \n", - "A/gyrfalcon/Washington/41088/2014 4.6 \n", - "A/Northern pintail/Washington/40964/2014 4.1 \n", - "A/canine/Illinois/12191/2015 3.7 \n", - "A/American green-winged teal/Washington/1957050... 4.1 \n", - "A/turkey/Indiana/1573-2/2016 3.9 \n", - "A/chicken/Tennessee/17-007431-3/2017 3.5 \n", - "A/chicken/Tennessee/17-007147-2/2017 3.5 \n", - "\n", - " ha_risk \\\n", - "id \n", - "A/swine/Shandong/1207/2016 [0.059268942695459764, 0.059207549599012387, 0... \n", - "A/Ohio/13/2017 [0.007443436845452373, 0.007528108280118501, 0... \n", - "A/Hong Kong/125/2017 [0.008053012400112526, 0.011702039255706398, 0... \n", - "A/Shanghai/02/2013 [0.0029756019607934496, 0.003893470493957003, ... \n", - "A/Anhui-Lujiang/39/2018 [0.013913606506215747, 0.014682700295856159, 0... \n", - "A/Indiana/08/2011 [0.015833187603574352, 0.01579698562348106, 0.... \n", - "A/California/62/2018 [0.25421003694129446, 0.17137241238504203, 0.1... \n", - "A/Bangladesh/0994/2011 [0.047962278609110426, 0.047376021553330504, 0... \n", - "A/Sichuan/06681/2021 [0.3401659563640977, 0.4372884849313668, 0.335... \n", - "A/Vietnam/1203/2004 [0.12775494527491757, 0.11190370497308454, 0.1... \n", - "A/Yunnan/14564/2015 [0.20080863109685176, 0.10301992847449411, 0.2... \n", - "A/Astrakhan/3212/2020 [0.2452941610025089, 0.2335184366024988, 0.221... \n", - "A/Netherlands/219/2003 [0.16431983904493178, 0.16426481855673325, 0.1... \n", - "A/American wigeon/South Carolina/AH0195145/2021 [0.22507345650411203, 0.253385372610634, 0.250... \n", - "A/Jiangxi-Donghu/346/2013 [0.03929273071556801, 0.3559593557688923, 0.15... \n", - "A/gyrfalcon/Washington/41088/2014 [0.24718594726896237, 0.2361702553946631, 0.22... \n", - "A/Northern pintail/Washington/40964/2014 [0.2410948922603147, 0.22971447028474637, 0.21... \n", - "A/canine/Illinois/12191/2015 [0.01616230298118454, 0.016067588755834316, 0.... \n", - "A/American green-winged teal/Washington/1957050... [0.22532859992890514, 0.2523992705219045, 0.24... \n", - "A/turkey/Indiana/1573-2/2016 [0.042531258378212616, 0.046850178126532895, 0... \n", - "A/chicken/Tennessee/17-007431-3/2017 [0.032175814200484966, 0.03682965073244792, 0.... \n", - "A/chicken/Tennessee/17-007147-2/2017 [0.082518541655176, 0.08595812903880762, 0.083... \n", - "\n", - " geometric_mean_risk \\\n", - "id \n", - "A/swine/Shandong/1207/2016 [0.04961693798040306, 0.04939691038300986, 0.0... \n", - "A/Ohio/13/2017 [0.02257302209884124, 0.0227066090672604, 0.02... \n", - "A/Hong Kong/125/2017 [0.0037028892207661, 0.004325280096255358, 0.0... \n", - "A/Shanghai/02/2013 [0.00210888252534967, 0.002386143249933443, 0.... \n", - "A/Anhui-Lujiang/39/2018 [0.018971508708604274, 0.025275351068975476, 0... \n", - "A/Indiana/08/2011 [0.016902504332314258, 0.01683599355230769, 0.... \n", - "A/California/62/2018 [0.11064301441040252, 0.08010185536798671, 0.0... \n", - "A/Bangladesh/0994/2011 [0.1509708016952567, 0.14755990149418968, 0.15... \n", - "A/Sichuan/06681/2021 [0.14098323610429828, 0.16479919492131653, 0.1... \n", - "A/Vietnam/1203/2004 [0.07357578392227798, 0.06512340773617227, 0.0... \n", - "A/Yunnan/14564/2015 [0.08744456695565636, 0.05634300538887098, 0.0... \n", - "A/Astrakhan/3212/2020 [0.3660547134708487, 0.352999397804964, 0.3433... \n", - "A/Netherlands/219/2003 [0.2735882731677115, 0.27353672735391277, 0.27... \n", - "A/American wigeon/South Carolina/AH0195145/2021 [0.2618009640888491, 0.28041946959711944, 0.27... \n", - "A/Jiangxi-Donghu/346/2013 [0.027090727115239115, 0.1774459269887038, 0.0... \n", - "A/gyrfalcon/Washington/41088/2014 [0.36678073190475535, 0.35436513753260845, 0.3... \n", - "A/Northern pintail/Washington/40964/2014 [0.349366042786461, 0.34039602323151935, 0.331... \n", - "A/canine/Illinois/12191/2015 [0.024453287740176615, 0.024374121053616235, 0... \n", - "A/American green-winged teal/Washington/1957050... [0.2592089036823605, 0.2766319981079351, 0.273... \n", - "A/turkey/Indiana/1573-2/2016 [0.13246011857462825, 0.13903537495472926, 0.1... \n", - "A/chicken/Tennessee/17-007431-3/2017 [0.12816247069024164, 0.1371356157354049, 0.12... \n", - "A/chicken/Tennessee/17-007147-2/2017 [0.20524466180722442, 0.20950526315825033, 0.2... \n", - "\n", - " frag_rbd \\\n", - "id \n", - "A/swine/Shandong/1207/2016 NSWSYIIETSNSKNGACYPGEFADYEELKEQLSTVSSFERFEIFPK... \n", - "A/Ohio/13/2017 QCDGFQNNKWDLFVERSKAHSNCYPYDVPDYASLRSLVASSGTLEF... \n", - "A/Hong Kong/125/2017 SADLIIERREGSDVCYPGKFVNEEALRQILRESGGIDKETMGFTYN... \n", - "A/Shanghai/02/2013 SADLIIERREGSDVCYPGKFVNEEALRQILRESGGIDKEAMGFTYS... \n", - "A/Anhui-Lujiang/39/2018 GREWSYIVERPSAVNGLCYPGNVENLEELRSLFSSARSYQRIQIFP... \n", - "A/Indiana/08/2011 HCDDFQNKEWDLFVERSTAYSNCYPYYVPDYATLRSLVASSGNLEF... \n", - "A/California/62/2018 ESWSYIVETSNPENGTCYPGYFEDYEELREQLSSVSSFKKFEIFPK... \n", - "A/Bangladesh/0994/2011 GREWSYIVERPSAVNGTCYPGNVENLEELRTLFSSSSSYQRIQIFP... \n", - "A/Sichuan/06681/2021 EWSYIVERANPANDLCYPGSLNDYEELKHLLSRINHFEKILIIPKG... \n", - "A/Vietnam/1203/2004 EWSYIVEKANPVNDLCYPGDFNDYEELKHLLSRINHFEKIQIIPKS... \n", - "A/Yunnan/14564/2015 EWSYIVERANPANDLCYPGNLNDYEELKHLLSRINHFEKTLIIPKS... \n", - "A/Astrakhan/3212/2020 EWSYIVERANPANDLCYPGSLNDYEELKHLLSRINHFEKILIIPKS... \n", - "A/Netherlands/219/2003 SADLIIERREGSDVCYPGKFVNEEALRQILRESGGIDKETMGFTYS... \n", - "A/American wigeon/South Carolina/AH0195145/2021 EWSYIVERANPANDLCYPGSLNDYEELKHMLSRINHFEKILIIPKS... \n", - "A/Jiangxi-Donghu/346/2013 WDTLIERENAIAYCYPGATVNVEALRQKIMESGGINKISTGFTYGS... \n", - "A/gyrfalcon/Washington/41088/2014 EWSYIVERANPANDLCYPGTLNDYEELKHLLSRINHFEKTLIIPRS... \n", - "A/Northern pintail/Washington/40964/2014 EWSYIVERANPANDLCYPGTLNDYEELKHLLSRINHFEKNLIIPRS... \n", - "A/canine/Illinois/12191/2015 HCDVFQNETWDLFVERSNAFSNCYPYDVPDYASLRSIVASSGTLEF... \n", - "A/American green-winged teal/Washington/1957050... EWSYIVERANPANDLCYPGTLNDYEELKHLLSRINHFEKTLIIPRS... \n", - "A/turkey/Indiana/1573-2/2016 DADLIIERREGTDVCYPGKFTNKESLRQILRGSGGIDKESMGFTYS... \n", - "A/chicken/Tennessee/17-007431-3/2017 DADLIIERREGTDVCYPGKFTNEESLRQILRGSGGIDKESMGFTYS... \n", - "A/chicken/Tennessee/17-007147-2/2017 DADLIIERREGTDVCYPGKFTNEESLRQILRGSGGIDKESMGFTYS... \n", - "\n", - " frag_sel \\\n", - "id \n", - "A/swine/Shandong/1207/2016 NSWSYSGANSFLVIWGVSKYYKRF \n", - "A/Ohio/13/2017 QCDGFKRRSSNDKLYIWSTKRNQQ \n", - "A/Hong Kong/125/2017 SADLIAEMKWLIHHSVSSFVPSPG \n", - "A/Shanghai/02/2013 SADLIAEMKWLIHHSVSSFVPSPG \n", - "A/Anhui-Lujiang/39/2018 GREWSYRSMRWNHPPTDFKPLIGP \n", - "A/Indiana/08/2011 HCDDFRRGSVNDKLYIWSTKRSQQ \n", - "A/California/62/2018 ESWSYGNSSFYVLWGVHHYSRRFT \n", - "A/Bangladesh/0994/2011 GREWSYRNMRWHHPPTDFKPLIGP \n", - "A/Sichuan/06681/2021 EWSYIAPSFFRLWGIHHLNQRLVP \n", - "A/Vietnam/1203/2004 EWSYIKSSFFRLWGIHHLNQRLVP \n", - "A/Yunnan/14564/2015 EWSYIPSFFRNWGIHHSNQRLEPK \n", - "A/Astrakhan/3212/2020 EWSYIAPSFFRLWGIHHLNQRLVP \n", - "A/Netherlands/219/2003 SADLIAEMKWLIHHSGSSFVPSPG \n", - "A/American wigeon/South Carolina/AH0195145/2021 EWSYIAPSFFRLWGIHHLNQRLAP \n", - "A/Jiangxi-Donghu/346/2013 WDTLIYAELKWGIHHPSNNFVPVV \n", - "A/gyrfalcon/Washington/41088/2014 EWSYIASSFFRLWGIHHLNQRLVP \n", - "A/Northern pintail/Washington/40964/2014 EWSYIASSFFRLWGIHHLNQRLVP \n", - "A/canine/Illinois/12191/2015 HCDVFKRGPANDKLYIWSTRRSQQ \n", - "A/American green-winged teal/Washington/1957050... EWSYIASSFFRLWGIHHLNQRLVP \n", - "A/turkey/Indiana/1573-2/2016 DADLIAEMKWLVHHSGSSFTPSPG \n", - "A/chicken/Tennessee/17-007431-3/2017 DADLIAEMKWLVHHSGSSFTPSPG \n", - "A/chicken/Tennessee/17-007147-2/2017 DADLIAEMKWLVHHSGSSFTPSPG \n", - "\n", - " mean_geom_risk \\\n", - "id \n", - "A/swine/Shandong/1207/2016 0.049531 \n", - "A/Ohio/13/2017 0.023780 \n", - "A/Hong Kong/125/2017 0.005481 \n", - "A/Shanghai/02/2013 0.002846 \n", - "A/Anhui-Lujiang/39/2018 0.022034 \n", - "A/Indiana/08/2011 0.017326 \n", - "A/California/62/2018 0.094064 \n", - "A/Bangladesh/0994/2011 0.148334 \n", - "A/Sichuan/06681/2021 0.144336 \n", - "A/Vietnam/1203/2004 0.070949 \n", - "A/Yunnan/14564/2015 0.090433 \n", - "A/Astrakhan/3212/2020 0.349913 \n", - "A/Netherlands/219/2003 0.273539 \n", - "A/American wigeon/South Carolina/AH0195145/2021 0.266584 \n", - "A/Jiangxi-Donghu/346/2013 0.202198 \n", - "A/gyrfalcon/Washington/41088/2014 0.351117 \n", - "A/Northern pintail/Washington/40964/2014 0.335484 \n", - "A/canine/Illinois/12191/2015 0.025045 \n", - "A/American green-winged teal/Washington/1957050... 0.263359 \n", - "A/turkey/Indiana/1573-2/2016 0.135001 \n", - "A/chicken/Tennessee/17-007431-3/2017 0.131420 \n", - "A/chicken/Tennessee/17-007147-2/2017 0.206611 \n", - "\n", - " mean_ha_risk max_ha_risk \\\n", - "id \n", - "A/swine/Shandong/1207/2016 0.059325 0.059809 \n", - "A/Ohio/13/2017 0.008455 0.017483 \n", - "A/Hong Kong/125/2017 0.009787 0.012453 \n", - "A/Shanghai/02/2013 0.003131 0.003893 \n", - "A/Anhui-Lujiang/39/2018 0.014639 0.017685 \n", - "A/Indiana/08/2011 0.016789 0.025631 \n", - "A/California/62/2018 0.196100 0.254210 \n", - "A/Bangladesh/0994/2011 0.047122 0.048792 \n", - "A/Sichuan/06681/2021 0.340975 0.437288 \n", - "A/Vietnam/1203/2004 0.128744 0.157039 \n", - "A/Yunnan/14564/2015 0.206227 0.250267 \n", - "A/Astrakhan/3212/2020 0.226728 0.245294 \n", - "A/Netherlands/219/2003 0.164336 0.164575 \n", - "A/American wigeon/South Carolina/AH0195145/2021 0.231073 0.254159 \n", - "A/Jiangxi-Donghu/346/2013 0.209716 0.368374 \n", - "A/gyrfalcon/Washington/41088/2014 0.228941 0.247186 \n", - "A/Northern pintail/Washington/40964/2014 0.223269 0.241095 \n", - "A/canine/Illinois/12191/2015 0.017088 0.025981 \n", - "A/American green-winged teal/Washington/1957050... 0.230741 0.253098 \n", - "A/turkey/Indiana/1573-2/2016 0.044194 0.046850 \n", - "A/chicken/Tennessee/17-007431-3/2017 0.033937 0.036830 \n", - "A/chicken/Tennessee/17-007147-2/2017 0.083817 0.085958 \n", - "\n", - " min_ha_risk hr_rbd \\\n", - "id \n", - "A/swine/Shandong/1207/2016 0.059069 450 \n", - "A/Ohio/13/2017 0.007363 435 \n", - "A/Hong Kong/125/2017 0.005927 466 \n", - "A/Shanghai/02/2013 0.002597 468 \n", - "A/Anhui-Lujiang/39/2018 0.012083 467 \n", - "A/Indiana/08/2011 0.015772 443 \n", - "A/California/62/2018 0.153140 452 \n", - "A/Bangladesh/0994/2011 0.044856 468 \n", - "A/Sichuan/06681/2021 0.293157 460 \n", - "A/Vietnam/1203/2004 0.107543 465 \n", - "A/Yunnan/14564/2015 0.103020 462 \n", - "A/Astrakhan/3212/2020 0.196373 460 \n", - "A/Netherlands/219/2003 0.163875 466 \n", - "A/American wigeon/South Carolina/AH0195145/2021 0.212808 460 \n", - "A/Jiangxi-Donghu/346/2013 0.039293 468 \n", - "A/gyrfalcon/Washington/41088/2014 0.198971 462 \n", - "A/Northern pintail/Washington/40964/2014 0.195036 462 \n", - "A/canine/Illinois/12191/2015 0.016030 447 \n", - "A/American green-winged teal/Washington/1957050... 0.213461 462 \n", - "A/turkey/Indiana/1573-2/2016 0.041007 468 \n", - "A/chicken/Tennessee/17-007431-3/2017 0.030574 466 \n", - "A/chicken/Tennessee/17-007147-2/2017 0.081610 468 \n", - "\n", - " hr_sel frag_pocket \n", - "id \n", - "A/swine/Shandong/1207/2016 541 LVIWGVHHPP \n", - "A/Ohio/13/2017 541 DKLYIWGVHH \n", - "A/Hong Kong/125/2017 542 IHHSVSTAEQ \n", - "A/Shanghai/02/2013 542 IHHSVSTAEQ \n", - "A/Anhui-Lujiang/39/2018 542 NHPPTDDTQR \n", - "A/Indiana/08/2011 541 DKLYIWGVHH \n", - "A/California/62/2018 541 VLWGVHHPSN \n", - "A/Bangladesh/0994/2011 542 HHPPTDTAQT \n", - "A/Sichuan/06681/2021 541 LWGIHHSNNA \n", - "A/Vietnam/1203/2004 541 LWGIHHPNDA \n", - "A/Yunnan/14564/2015 541 WGIHHSNNAA \n", - "A/Astrakhan/3212/2020 541 LWGIHHSNNA \n", - "A/Netherlands/219/2003 542 IHHSGSTTEQ \n", - "A/American wigeon/South Carolina/AH0195145/2021 541 LWGIHHSNNA \n", - "A/Jiangxi-Donghu/346/2013 542 GIHHPSSTQE \n", - "A/gyrfalcon/Washington/41088/2014 541 LWGIHHSNNA \n", - "A/Northern pintail/Washington/40964/2014 541 LWGIHHSNNA \n", - "A/canine/Illinois/12191/2015 541 DKLYIWGVHH \n", - "A/American green-winged teal/Washington/1957050... 541 LWGIHHSNNA \n", - "A/turkey/Indiana/1573-2/2016 542 VHHSGSVTEQ \n", - "A/chicken/Tennessee/17-007431-3/2017 542 VHHSGSADEQ \n", - "A/chicken/Tennessee/17-007147-2/2017 542 VHHSGSAAEQ " - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{tabular}{L{1in}|L{1in}|L{1in}|L{1in}|L{1in}}\\hline\r\n", + " edit distance & HA risk (2020-2022 sequences) & geometric risk (2020-2022 sequences) & HA risk (IRAT sequences) & geometric risk (IRAT sequences) \\\\\\hline\r\n", + "RBD&$0.62\\pm0.0$&$0.68\\pm0.0$&$0.17\\pm0.060$&$0.33\\pm0.07$\\\\\\hline\r\n", + " On seleted residues &$-0.24\\pm0.01$&$-0.13\\pm0.01$&$-0.46\\pm0.04$&$-0.15\\pm0.05$\\\\\\hline\r\n", + "pocket&$-0.03\\pm0.0$&$-0.02\\pm0.0$&$-0.17\\pm0.02$&$-0.02\\pm0.02$\\\\\\hline\r\n", + "\\hline\\end{tabular}\r\n" + ] } ], "source": [ - "df_[df_.is_irat==1].drop('is_irat',axis=1)" + "! cat tmp.tex" ] }, { "cell_type": "code", - "execution_count": 113, - "id": "c67f743a", + "execution_count": 221, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
emergence_riskimpact_riskmean_geom_riskmean_ha_riskmax_ha_riskmin_ha_risk
hr_rbd-0.781912-0.7915620.6802070.6213450.6259800.616085
hr_sel0.0480720.028948-0.129186-0.244412-0.240427-0.252039
\n", - "
" - ], "text/plain": [ - " emergence_risk impact_risk mean_geom_risk mean_ha_risk \\\n", - "hr_rbd -0.781912 -0.791562 0.680207 0.621345 \n", - "hr_sel 0.048072 0.028948 -0.129186 -0.244412 \n", - "\n", - " max_ha_risk min_ha_risk \n", - "hr_rbd 0.625980 0.616085 \n", - "hr_sel -0.240427 -0.252039 " + "'210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239'" ] }, - "execution_count": 113, + "execution_count": 221, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_.drop('is_irat',axis=1).corr().loc[['hr_rbd','hr_sel'],:].drop(['hr_rbd','hr_sel'],axis=1)" + "#R=[130,131,132,133,134,135,150,151,152,153,154,155,156,157,158,159,160,190,191,192,193,194,195,220,221,222,223,224,225,226]\n", + "' '.join(np.array(range(210,240)).astype(str))" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "81387c94", + "cell_type": "raw", "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a805cdf6", - "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "selected residues\n", + "@article{wu2020influenza,\n", + " title={Influenza hemagglutinin structures and antibody recognition},\n", + " author={Wu, Nicholas C and Wilson, Ian A},\n", + " journal={Cold Spring Harbor perspectives in medicine},\n", + " volume={10},\n", + " number={8},\n", + " pages={a038778},\n", + " year={2020},\n", + " publisher={Cold Spring Harbor Laboratory Press}\n", + "}" + ] } ], "metadata": { @@ -1584,7 +596,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/extras/baseline/tmp.tex b/extras/baseline/tmp.tex new file mode 100644 index 00000000..40c8e52b --- /dev/null +++ b/extras/baseline/tmp.tex @@ -0,0 +1,6 @@ +\begin{tabular}{L{1in}|L{1in}|L{1in}|L{1in}|L{1in}}\hline + edit distance & HA risk (2020-2022 sequences) & geometric risk (2020-2022 sequences) & HA risk (IRAT sequences) & geometric risk (IRAT sequences) \\\hline +RBD&$0.62\pm0.0$&$0.68\pm0.0$&$0.17\pm0.060$&$0.33\pm0.07$\\\hline + On seleted residues &$-0.24\pm0.01$&$-0.13\pm0.01$&$-0.46\pm0.04$&$-0.15\pm0.05$\\\hline +pocket&$-0.03\pm0.0$&$-0.02\pm0.0$&$-0.17\pm0.02$&$-0.02\pm0.02$\\\hline +\hline\end{tabular} diff --git a/extras/ntb/IRAT_RBD_feature_distance.ipynb b/extras/ntb/IRAT_RBD_feature_distance.ipynb index ada3047e..66268756 100644 --- a/extras/ntb/IRAT_RBD_feature_distance.ipynb +++ b/extras/ntb/IRAT_RBD_feature_distance.ipynb @@ -309,7 +309,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/extras/ntb/RBD_shap_figures.ipynb b/extras/ntb/RBD_shap_figures.ipynb index f3c2c30e..49a9cfb8 100644 --- a/extras/ntb/RBD_shap_figures.ipynb +++ b/extras/ntb/RBD_shap_figures.ipynb @@ -882,7 +882,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/extras/ntb/shap_analysis_features_driving_theta_importance.ipynb b/extras/ntb/shap_analysis_features_driving_theta_importance.ipynb index 45c26320..39645c5b 100644 --- a/extras/ntb/shap_analysis_features_driving_theta_importance.ipynb +++ b/extras/ntb/shap_analysis_features_driving_theta_importance.ipynb @@ -274,7 +274,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/extras/ntb/shap_analysis_features_low_risk_vs_high_risk.ipynb b/extras/ntb/shap_analysis_features_low_risk_vs_high_risk.ipynb index 27786074..905a58ca 100644 --- a/extras/ntb/shap_analysis_features_low_risk_vs_high_risk.ipynb +++ b/extras/ntb/shap_analysis_features_low_risk_vs_high_risk.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -247,12 +247,22 @@ ], "source": [ "ax=shp2.sort_index().ewm(alpha=.95).mean().plot(logy=False)\n", - "shp1.sort_index().ewm(alpha=.95).mean().plot(logy=False,ax=ax)" + "shp1.sort_index().ewm(alpha=.95).mean().plot(logy=False,ax=ax)\n" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "shp1=pd.read_csv('lowhighrisk_H1N1.csv',index_col=0)\n", + "shp2=pd.read_csv('lowhighrisk_H3N2.csv',index_col=0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -276,135 +286,123 @@ " \n", " \n", " \n", - " shp_h3n2\n", + " shp_h1n1\n", " \n", " \n", - " H3N2_features\n", + " H1N1_features\n", " \n", " \n", " \n", " \n", " \n", - " 65\n", - " 0.023221\n", - " \n", - " \n", - " 155\n", - " 0.004798\n", + " 145\n", + " 0.004103\n", " \n", " \n", - " 234\n", - " 0.004545\n", + " 292\n", + " 0.004045\n", " \n", " \n", - " 484\n", - " 0.004392\n", + " 123\n", + " 0.003762\n", " \n", " \n", - " 216\n", - " 0.004280\n", + " 227\n", + " 0.003003\n", " \n", " \n", - " ...\n", - " ...\n", + " 275\n", + " 0.002602\n", " \n", " \n", - " 23\n", - " 0.000000\n", + " 70\n", + " 0.002059\n", " \n", " \n", - " 20\n", - " 0.000000\n", + " 408\n", + " 0.002023\n", " \n", " \n", - " 16\n", - " 0.000000\n", + " 10\n", + " 0.001941\n", " \n", " \n", - " 15\n", - " 0.000000\n", + " 278\n", + " 0.001920\n", " \n", " \n", - " 0\n", - " 0.000000\n", + " 276\n", + " 0.001676\n", " \n", " \n", "\n", - "

550 rows × 1 columns

\n", "" ], "text/plain": [ - " shp_h3n2\n", - "H3N2_features \n", - "65 0.023221\n", - "155 0.004798\n", - "234 0.004545\n", - "484 0.004392\n", - "216 0.004280\n", - "... ...\n", - "23 0.000000\n", - "20 0.000000\n", - "16 0.000000\n", - "15 0.000000\n", - "0 0.000000\n", - "\n", - "[550 rows x 1 columns]" + " shp_h1n1\n", + "H1N1_features \n", + "145 0.004103\n", + "292 0.004045\n", + "123 0.003762\n", + "227 0.003003\n", + "275 0.002602\n", + "70 0.002059\n", + "408 0.002023\n", + "10 0.001941\n", + "278 0.001920\n", + "276 0.001676" ] }, - "execution_count": 38, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "shp2" + "shp1.head(10)" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'MKTIIALSYIFCLAFSQDLSGSNNNNTATLCLGHHAVPNGTLVKTITDDQIEVTNATELVQSSSTGKICNNPHRILDGRDCTLIDALLGDPHCDVFQDVTWDLFVERSNALSNCYPYDVPDYASLRSLVASSGTLEFITEGFTWTGVTQNGGSGACKRGPANGFFSRLNWLTKSGSAYPVLNVTMPNNDNFDKLYIWGVHHPSTNQEQTNLYVQASGRVTVSTRRSQQTIIPNIGSRPWVRGQSGRISIYWTVVKPGDVLVINSNGNLIAPRGYFKMRAGKSSIMRSDAPIDTCISECITPNGSIPNDKPFQNVNKITYGACPKYVKQNTLKLATGMRNVPEKQARGLFGAIAGFIENGWEGMIDGWYGFRHQNSEGTGQAADLKSTQAAIDQINGKLNRVIEKTNEKFHQIEKEFSEVEGRIQDLEKYVEDTKIDLWSYNAELLVALENQHTIDLTDSEMNKLFEKTRRQLRENAEDMGNGCFKIYHKCDNACIDSIRNGTYDHDIYRDEALNNRFQIKGVELKSGYKDWILWISFAISCFLLCVVLLG'" + "" ] }, - "execution_count": 59, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "df_=df[df.subtype=='H3N2'][['ha_accession','ha','emergence_risk']].set_index('ha_accession').sort_values('emergence_risk')\n", - "lf=df_[df_.emergence_risk<6]\n", - "lf.head(1).ha.values[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ + }, { "data": { + "image/png": "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\n", "text/plain": [ - "'SSSTGRICNS'" + "
" ] }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "lf.ha.values[0][60:70]" + "s1=shp1.sort_index().ewm(alpha=.85).mean()\n", + "s1=s1/s1.max()\n", + "s2=shp2.sort_index().ewm(alpha=.85).mean()\n", + "s2=s2/s2.max()\n", + "ax=s2.plot(logy=False)\n", + "#ax.set_ylim(0,0.005)\n", + "s1.plot(logy=False,ax=ax)\n" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -437,48 +435,48 @@ " \n", " \n", " \n", - " 145\n", - " 0.004103\n", + " 0\n", + " 0.000000\n", " \n", " \n", - " 292\n", - " 0.004045\n", + " 1\n", + " 0.237298\n", " \n", " \n", - " 123\n", - " 0.003762\n", + " 2\n", + " 0.034912\n", " \n", " \n", - " 227\n", - " 0.003003\n", + " 3\n", + " 0.011454\n", " \n", " \n", - " 275\n", - " 0.002602\n", + " 4\n", + " 0.001717\n", " \n", " \n", " ...\n", " ...\n", " \n", " \n", - " 217\n", - " 0.000000\n", + " 545\n", + " 0.004228\n", " \n", " \n", - " 215\n", - " 0.000000\n", + " 546\n", + " 0.000634\n", " \n", " \n", - " 210\n", - " 0.000000\n", + " 547\n", + " 0.000095\n", " \n", " \n", - " 209\n", - " 0.000000\n", + " 548\n", + " 0.000014\n", " \n", " \n", " 549\n", - " 0.000000\n", + " 0.000002\n", " \n", " \n", "\n", @@ -488,28 +486,253 @@ "text/plain": [ " shp_h1n1\n", "H1N1_features \n", - "145 0.004103\n", - "292 0.004045\n", - "123 0.003762\n", - "227 0.003003\n", - "275 0.002602\n", + "0 0.000000\n", + "1 0.237298\n", + "2 0.034912\n", + "3 0.011454\n", + "4 0.001717\n", "... ...\n", - "217 0.000000\n", - "215 0.000000\n", - "210 0.000000\n", - "209 0.000000\n", - "549 0.000000\n", + "545 0.004228\n", + "546 0.000634\n", + "547 0.000095\n", + "548 0.000014\n", + "549 0.000002\n", "\n", "[550 rows x 1 columns]" ] }, - "execution_count": 61, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s1" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 8, 28, 31, 32, 39, 40, 44, 45, 46, 48, 50, 51, 52,\n", + " 55, 56, 57, 58, 59, 64, 65, 66, 67, 68, 69, 71, 72,\n", + " 73, 74, 80, 82, 94, 95, 96, 98, 102, 103, 107, 109, 110,\n", + " 114, 115, 118, 119, 120, 121, 122, 124, 125, 134, 135, 137, 138,\n", + " 139, 144, 145, 146, 148, 154, 155, 164, 169, 170, 171, 172, 173,\n", + " 175, 176, 177, 182, 183, 188, 194, 195, 198, 200, 201, 202, 204,\n", + " 205, 206, 207, 210, 212, 213, 215, 216, 217, 227, 231, 232, 234,\n", + " 235, 236, 238, 240, 250, 254, 255, 257, 266, 269, 270, 274, 275,\n", + " 277, 278, 279, 280, 284, 285, 287, 288, 290, 291, 294, 296, 298,\n", + " 299, 300, 303, 313, 315, 316, 317, 318, 321, 325, 326, 334, 337,\n", + " 340, 341, 345, 346, 348, 351, 353, 354, 355, 361, 369, 370, 371,\n", + " 372, 373, 374, 376, 386, 387, 390, 393, 401, 405, 407, 410, 411,\n", + " 413, 415, 420, 421, 422, 423, 427, 429, 430, 431, 432, 435, 437,\n", + " 438, 439, 440, 441, 449, 451, 452, 453, 454, 455, 456, 459, 460,\n", + " 462, 470, 472, 473, 476, 477, 479, 481, 484, 486, 488, 489, 490,\n", + " 493, 496, 507, 508, 510, 511, 514, 520, 522, 528, 529, 530, 535,\n", + " 537, 539, 540, 543, 545, 549])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s2[s2>0.05].dropna().index.values" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1 10 11 14 15 64 70 71 110 123 124 136 137 144 145 146 153 158 165 167 177 178 179 180 182 184 195 202 203 205 206 211 227 228 250 266 274 275 276 277 278 279 283 286 287 292 293 299 300 408 409 415 467 470 522 543'" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "' '.join(s1[s1>0.05].dropna().index.values.astype(str))" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2 3 7 12 16 65 67 72 90 111 112 125 141 147 154 155 159 166 168 171 181 183 185 196 201 204 207 212 229 232 238 244 251 267 280 284 285 294 311 337 410 416 434 468 471 523 544'" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "' '.join(s1[s1.shp_h1n1.between(0.01,0.05)].dropna().index.values.astype(str))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'A:American wigeon:South Carolina:AH0195145:2021_ha_SHAP.csv'\r\n", + " A:Anhui-Lujiang:39:2018_ha_SHAP.csv\r\n", + " A:Astrakhan:3212:2020_ha_SHAP.csv\r\n", + " A:California:62:2018_ha_SHAP.csv\r\n", + " A:canine:Illinois:12191:2015_ha_SHAP.csv\r\n", + " A:chicken:Tennessee:17-007147-2:2017_ha_SHAP.csv\r\n", + " A:chicken:Tennessee:17-007431-3:2017_ha_SHAP.csv\r\n", + " A:gyrfalcon:Washington:41088:2014_ha_SHAP.csv\r\n", + " A:Indiana:08:2011_ha_SHAP.csv\r\n", + " A:Netherlands:219:2003_ha_SHAP.csv\r\n", + " A:Ohio:13:2017_ha_SHAP.csv\r\n", + " A:Shanghai:02:2013_ha_SHAP.csv\r\n", + " A:Sichuan:06681:2021_ha_SHAP.csv\r\n", + " A:swine:Shandong:1207:2016_ha_SHAP.csv\r\n", + " A:turkey:Indiana:1573-2:2016_ha_SHAP.csv\r\n", + " A:Vietnam:1203:2004_ha_SHAP.csv\r\n", + " A:Yunnan:14564:2015_ha_SHAP.csv\r\n", + " north_h1n1_ha_SHAP.csv\r\n", + " north_h3n2_ha_SHAP.csv\r\n", + " south_h1n1_ha_SHAP.csv\r\n", + " south_h3n2_ha_SHAP.csv\r\n" + ] + } + ], + "source": [ + "! ls *SHAP*csv\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 70, 141, 153, 155, 182, 184, 187, 202, 203, 222, 223, 227, 231,\n", + " 238, 265, 299, 408])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sf_n_h1n1=pd.read_csv('north_h1n1_ha_SHAP.csv',index_col=0)\n", + "sf_n_h1n1=sf_n_h1n1.sort_index().ewm(alpha=.85).mean()\n", + "sf_n_h1n1=sf_n_h1n1/sf_n_h1n1.max()\n", + "sf_n_h1n1[sf_n_h1n1>0.03].dropna().index.values" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sf_n_h1n1.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 7, 70, 71, 110, 136, 146, 147, 165, 177, 178, 202, 203, 225,\n", + " 232, 240, 266, 415, 522, 523])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sf_s_h1n1=pd.read_csv('south_h1n1_ha_SHAP.csv',index_col=0)\n", + "sf_s_h1n1=sf_s_h1n1.sort_index().ewm(alpha=.85).mean()\n", + "sf_s_h1n1=sf_s_h1n1/sf_s_h1n1.max()\n", + "sf_s_h1n1[sf_s_h1n1>0.03].dropna().index.values" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "shp1" + "sf_s_h1n1.plot()" ] } ], diff --git a/extras/pml/figs/RBDnosurf_all0.png b/extras/pml/figs/RBDnosurf_all0.png new file mode 100644 index 00000000..527e1378 Binary files /dev/null and b/extras/pml/figs/RBDnosurf_all0.png differ diff --git a/extras/pml/figs/RBDnosurf_all1.png b/extras/pml/figs/RBDnosurf_all1.png new file mode 100644 index 00000000..fd13ab0c Binary files /dev/null and b/extras/pml/figs/RBDnosurf_all1.png differ diff --git a/extras/pml/figs/RBDnosurf_all2.png b/extras/pml/figs/RBDnosurf_all2.png new file mode 100644 index 00000000..35e2c247 Binary files /dev/null and b/extras/pml/figs/RBDnosurf_all2.png differ diff --git a/extras/pml/figs/RBDsurf_all0.png b/extras/pml/figs/RBDsurf_all0.png new file mode 100644 index 00000000..82e2c8aa Binary files /dev/null and b/extras/pml/figs/RBDsurf_all0.png differ diff --git a/extras/pml/figs/iratnosurf_all0.png b/extras/pml/figs/iratnosurf_all0.png index 6e2464ba..6d6127bb 100644 Binary files a/extras/pml/figs/iratnosurf_all0.png and b/extras/pml/figs/iratnosurf_all0.png differ diff --git a/extras/pml/figs/iratnosurf_all1.png b/extras/pml/figs/iratnosurf_all1.png index 1caef07d..15050141 100644 Binary files a/extras/pml/figs/iratnosurf_all1.png and b/extras/pml/figs/iratnosurf_all1.png differ diff --git a/extras/pml/figs/iratnosurf_all2.png b/extras/pml/figs/iratnosurf_all2.png index b85cd05e..08791d30 100644 Binary files a/extras/pml/figs/iratnosurf_all2.png and b/extras/pml/figs/iratnosurf_all2.png differ diff --git a/extras/pml/figs/iratsurf_all0.png b/extras/pml/figs/iratsurf_all0.png index 325115a3..e4cac8c5 100644 Binary files a/extras/pml/figs/iratsurf_all0.png and b/extras/pml/figs/iratsurf_all0.png differ diff --git a/extras/pml/figs/nosurf_all0.png b/extras/pml/figs/nosurf_all0.png index 70ff5d59..6e5d3eae 100644 Binary files a/extras/pml/figs/nosurf_all0.png and b/extras/pml/figs/nosurf_all0.png differ diff --git a/extras/pml/figs/nosurf_all1.png b/extras/pml/figs/nosurf_all1.png index 0eef8118..6b707e4c 100644 Binary files a/extras/pml/figs/nosurf_all1.png and b/extras/pml/figs/nosurf_all1.png differ diff --git a/extras/pml/figs/nosurf_all2.png b/extras/pml/figs/nosurf_all2.png index 064372b8..8ca7bb7a 100644 Binary files a/extras/pml/figs/nosurf_all2.png and b/extras/pml/figs/nosurf_all2.png differ diff --git a/extras/pml/figs/surf_all0.png b/extras/pml/figs/surf_all0.png index bc4c1a6d..1ab68769 100644 Binary files a/extras/pml/figs/surf_all0.png and b/extras/pml/figs/surf_all0.png differ diff --git a/extras/pml/script_rbd_irat.pml b/extras/pml/script_rbd_irat.pml index d72bc2d8..44fb4573 100644 --- a/extras/pml/script_rbd_irat.pml +++ b/extras/pml/script_rbd_irat.pml @@ -1,7 +1,7 @@ run ./color_L1.py -color_L1 pdb/1ruz.pdb,63 67 106 107 120 150 155 174 179 180 203 205 208 282 318 415 500, red,1,1 -#color_L1 pdb/1ruz.pdb,8 48 64 146 148 157 167 201 268 272 283 284 299 303 304 421, yellow, 1,0 +color_L1 pdb/1ruz.pdb,1 10 11 14 15 64 70 71 110 123 124 136 137 144 145 146 153 158 165 167 177 178 179 180 182 184 195 202 203 205 206 211 227 228 250 266 274 275 276 277 278 279 283 286 287 292 293 299 300 408 409 415 467 470 522 543, red,1,1 +#color_L1 pdb/1ruz.pdb,2 3 7 12 16 65 67 72 90 111 112 125 141 147 154 155 159 166 168 171 181 183 185 196 201 204 207 212 229 232 238 244 251 267 280 284 285 294 311 337 410 416 434 468 471 523 544, yellow, 1,0 set ray_opaque_background, 0 diff --git a/extras/pml/script_rbd_residue.pml b/extras/pml/script_rbd_residue.pml new file mode 100644 index 00000000..6e480f1d --- /dev/null +++ b/extras/pml/script_rbd_residue.pml @@ -0,0 +1,75 @@ +run ./color_L1.py + +color_L1 pdb/1ruz.pdb,130 131 132 133 134 135 150 151 152 153 154 155 156 157 158 159 160 190 191 192 193 194 195 220 221 222 223 224 225 226, red,1,1 +color_L1 pdb/1ruz.pdb,210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239, yellow, 1,0 + + +set ray_opaque_background, 0 + +#show surface + +### cut below here and paste into script ### +set_view (\ + -0.369117439, 0.502643943, 0.781719923,\ + 0.567749918, -0.543976426, 0.617852807,\ + 0.735801637, 0.671887159, -0.084585808,\ + -0.000552207, -0.000250354, -406.876342773,\ + 26.569091797, 103.128158569, 41.886680603,\ + 350.275970459, 463.393707275, -20.000000000 ) +### cut above here and paste into script ### + +png figs/RBDnosurf_all0.png, dpi=1200, ray=1 + + +### cut below here and paste into script ### +set_view (\ + 0.767445922, 0.590233505, -0.250261575,\ + -0.087211363, -0.290627509, -0.952850759,\ + -0.635140121, 0.753089905, -0.171567246,\ + -0.000552207, -0.000250354, -406.876342773,\ + 26.569091797, 103.128158569, 41.886680603,\ + 350.275970459, 463.393707275, -20.000000000 ) +### cut above here and paste into script ### + + +png figs/RBDnosurf_all1.png, dpi=1200, ray=1 + + +### cut below here and paste into script ### +set_view (\ + 0.783726215, 0.264176100, 0.562107265,\ + 0.075246021, 0.857972443, -0.508150041,\ + -0.616516948, 0.440548718, 0.652538776,\ + -0.000552207, -0.000250354, -406.876342773,\ + 26.569091797, 103.128158569, 41.886680603,\ + 350.275970459, 463.393707275, -20.000000000 ) +### cut above here and paste into script ### + +png figs/RBDnosurf_all2.png, dpi=1200, ray=1 + + +### cut below here and paste into script ### +set_view (\ + 0.360190630, 0.417017072, 0.834467471,\ + 0.804233491, -0.592093527, -0.051255286,\ + 0.472710431, 0.689570665, -0.548648596,\ + -0.000552207, -0.000250354, -406.876342773,\ + 26.569091797, 103.128158569, 41.886680603,\ + 350.275970459, 463.393707275, -20.000000000 ) +### cut above here and paste into script ### + +show surface + + +set_view (\ + 0.437985450, 0.342558742, 0.831153452,\ + 0.825026393, -0.520381212, -0.220283955,\ + 0.357056171, 0.782211483, -0.510542572,\ + 0.000000000, 0.000000000, -438.062805176,\ + 30.206069946, 99.011039734, 44.933494568,\ + 301.992126465, 574.133422852, -20.000000000 ) +### cut above here and paste into script ### + +png figs/RBDsurf_all0.png, dpi=1200, ray=1 + +quit \ No newline at end of file diff --git a/extras/pml/script_rbd_residue.pml~ b/extras/pml/script_rbd_residue.pml~ new file mode 100644 index 00000000..92f9c978 --- /dev/null +++ b/extras/pml/script_rbd_residue.pml~ @@ -0,0 +1,75 @@ +run ./color_L1.py + +color_L1 pdb/1ruz.pdb,68 70 111 119 146 150 171 172 174 175 177 184 201 202 210 222 232 265 291 322 522 537, red,1,1 +#color_L1 pdb/1ruz.pdb,65 71 77 203 205 211 223 266 545, yellow, 1,0 + + +set ray_opaque_background, 0 + +#show surface + +### cut below here and paste into script ### +set_view (\ + -0.369117439, 0.502643943, 0.781719923,\ + 0.567749918, -0.543976426, 0.617852807,\ + 0.735801637, 0.671887159, -0.084585808,\ + -0.000552207, -0.000250354, -406.876342773,\ + 26.569091797, 103.128158569, 41.886680603,\ + 350.275970459, 463.393707275, -20.000000000 ) +### cut above here and paste into script ### + +png figs/nosurf_all0.png, dpi=1200, ray=1 + + +### cut below here and paste into script ### +set_view (\ + 0.767445922, 0.590233505, -0.250261575,\ + -0.087211363, -0.290627509, -0.952850759,\ + -0.635140121, 0.753089905, -0.171567246,\ + -0.000552207, -0.000250354, -406.876342773,\ + 26.569091797, 103.128158569, 41.886680603,\ + 350.275970459, 463.393707275, -20.000000000 ) +### cut above here and paste into script ### + + +png figs/nosurf_all1.png, dpi=1200, ray=1 + + +### cut below here and paste into script ### +set_view (\ + 0.783726215, 0.264176100, 0.562107265,\ + 0.075246021, 0.857972443, -0.508150041,\ + -0.616516948, 0.440548718, 0.652538776,\ + -0.000552207, -0.000250354, -406.876342773,\ + 26.569091797, 103.128158569, 41.886680603,\ + 350.275970459, 463.393707275, -20.000000000 ) +### cut above here and paste into script ### + +png figs/nosurf_all2.png, dpi=1200, ray=1 + + +### cut below here and paste into script ### +set_view (\ + 0.360190630, 0.417017072, 0.834467471,\ + 0.804233491, -0.592093527, -0.051255286,\ + 0.472710431, 0.689570665, -0.548648596,\ + -0.000552207, -0.000250354, -406.876342773,\ + 26.569091797, 103.128158569, 41.886680603,\ + 350.275970459, 463.393707275, -20.000000000 ) +### cut above here and paste into script ### + +show surface + + +set_view (\ + 0.437985450, 0.342558742, 0.831153452,\ + 0.825026393, -0.520381212, -0.220283955,\ + 0.357056171, 0.782211483, -0.510542572,\ + 0.000000000, 0.000000000, -438.062805176,\ + 30.206069946, 99.011039734, 44.933494568,\ + 301.992126465, 574.133422852, -20.000000000 ) +### cut above here and paste into script ### + +png figs/surf_all0.png, dpi=1200, ray=1 + +quit \ No newline at end of file diff --git a/tex/overleaf b/tex/overleaf index 19c7ac9a..7029b218 160000 --- a/tex/overleaf +++ b/tex/overleaf @@ -1 +1 @@ -Subproject commit 19c7ac9a154bb6bcee542c9ce3e270d84a9c7a2a +Subproject commit 7029b21870e1e751d98b7587a7bd596a7464740b