2e:null 2f:["$","title",null,{"children":"(Preprint) From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences | CVR Bioinformatics | Observable"}] 34:T205b,viewof form = Inputs.form({ ref:Inputs.select([null].concat(data.map(d => d.ref)), {value:null,label: "Select Reference Amino Acid",unique:true,sort:true}), alt:Inputs.select([null].concat(data.map(d => d.alt)), {value:null,label: "Select Mutant Amino Acid",unique:true,sort:true}), subunit:Inputs.select([null].concat(data.map(d => d.subunit)), {label: "Subunit",unique:true}), domain:Inputs.select([null].concat(data.map(d => d.domain)), {label: "Domain",unique:true}), voc_mutation : Inputs.radio(["true","false"], {label: "Filter by presence in VOC",unique:true,value:"false"}), highlight_voc_mutations:Inputs.radio(["True", "False"], {label: "Highlight VOC Mutations", value: "False"}), x_axis:Inputs.select( ['label', 'semantic_score', 'grammaticality', 'relative_grammaticality', 'sequence_grammaticality', 'relative_sequence_grammaticality', 'probability', 'ref', 'alt', 'position', 'semantic_rank', 'grammatical_rank', 'acquisition_priority', 'sequence_grammatical_rank', 'sequence_acquisition_priority', 'subunit', 'domain', 'reference_amino_acid_property_basic', 'reference_amino_acid_property_complex', 'mutant_amino_acid_property_basic', 'mutant_amino_acid_property_complex', 'bloom_bind_avg', 'bloom_expr_avg', 'voc_mutation', 'positional_semantic_rank', 'Count', 'entropy','Beta_starr_bind', 'Beta_starr_delta_bind', 'Beta_starr_expr', 'Beta_starr_delta_expr', 'Wuhan-Hu-1_starr_bind', 'Wuhan-Hu-1_starr_delta_bind', 'Wuhan-Hu-1_starr_expr', 'Wuhan-Hu-1_starr_delta_expr', 'Delta_starr_bind', 'Delta_starr_delta_bind', 'Delta_starr_expr', 'Delta_starr_delta_expr', 'Alpha_starr_bind', 'Alpha_starr_delta_bind', 'Alpha_starr_expr', 'Alpha_starr_delta_expr', 'Omicron_XBB15_starr_bind', 'Omicron_XBB15_starr_delta_bind', 'Omicron_XBB15_starr_expr', 'Omicron_XBB15_starr_delta_expr', 'Omicron_BQ11_starr_bind', 'Omicron_BQ11_starr_delta_bind', 'Omicron_BQ11_starr_expr', 'Omicron_BQ11_starr_delta_expr', 'Omicron_BA2_starr_bind', 'Omicron_BA2_starr_delta_bind', 'Omicron_BA2_starr_expr', 'Omicron_BA2_starr_delta_expr', 'Eta_starr_bind', 'Eta_starr_delta_bind', 'Eta_starr_expr', 'Eta_starr_delta_expr', 'Omicron_BA1_starr_bind', 'Omicron_BA1_starr_delta_bind', 'Omicron_BA1_starr_expr', 'Omicron_BA1_starr_delta_expr', 'Cao_mut_escape'], {value:"position",label: "Select X-Axis",unique:true}), y_axis:Inputs.select( ['label', 'semantic_score', 'grammaticality', 'relative_grammaticality', 'sequence_grammaticality', 'relative_sequence_grammaticality', 'probability', 'ref', 'alt', 'position', 'semantic_rank', 'grammatical_rank', 'acquisition_priority', 'sequence_grammatical_rank', 'sequence_acquisition_priority', 'subunit', 'domain', 'reference_amino_acid_property_basic', 'reference_amino_acid_property_complex', 'mutant_amino_acid_property_basic', 'mutant_amino_acid_property_complex', 'bloom_bind_avg', 'bloom_expr_avg', 'voc_mutation', 'positional_semantic_rank', 'Count', 'entropy','Beta_starr_bind', 'Beta_starr_delta_bind', 'Beta_starr_expr', 'Beta_starr_delta_expr', 'Wuhan-Hu-1_starr_bind', 'Wuhan-Hu-1_starr_delta_bind', 'Wuhan-Hu-1_starr_expr', 'Wuhan-Hu-1_starr_delta_expr', 'Delta_starr_bind', 'Delta_starr_delta_bind', 'Delta_starr_expr', 'Delta_starr_delta_expr', 'Alpha_starr_bind', 'Alpha_starr_delta_bind', 'Alpha_starr_expr', 'Alpha_starr_delta_expr', 'Omicron_XBB15_starr_bind', 'Omicron_XBB15_starr_delta_bind', 'Omicron_XBB15_starr_expr', 'Omicron_XBB15_starr_delta_expr', 'Omicron_BQ11_starr_bind', 'Omicron_BQ11_starr_delta_bind', 'Omicron_BQ11_starr_expr', 'Omicron_BQ11_starr_delta_expr', 'Omicron_BA2_starr_bind', 'Omicron_BA2_starr_delta_bind', 'Omicron_BA2_starr_expr', 'Omicron_BA2_starr_delta_expr', 'Eta_starr_bind', 'Eta_starr_delta_bind', 'Eta_starr_expr', 'Eta_starr_delta_expr', 'Omicron_BA1_starr_bind', 'Omicron_BA1_starr_delta_bind', 'Omicron_BA1_starr_expr', 'Omicron_BA1_starr_delta_expr', 'Cao_mut_escape'], {value:"relative_grammaticality",label: "Select Y-Axis",unique:true}), color_on:Inputs.select( ['label', 'semantic_score', 'grammaticality', 'relative_grammaticality', 'sequence_grammaticality', 'relative_sequence_grammaticality', 'probability', 'ref', 'alt', 'position', 'semantic_rank', 'grammatical_rank', 'acquisition_priority', 'sequence_grammatical_rank', 'sequence_acquisition_priority', 'subunit', 'domain', 'reference_amino_acid_property_basic', 'reference_amino_acid_property_complex', 'mutant_amino_acid_property_basic', 'mutant_amino_acid_property_complex', 'bloom_bind_avg', 'bloom_expr_avg', 'voc_mutation', 'positional_semantic_rank', 'Count', 'entropy','Beta_starr_bind', 'Beta_starr_delta_bind', 'Beta_starr_expr', 'Beta_starr_delta_expr', 'Wuhan-Hu-1_starr_bind', 'Wuhan-Hu-1_starr_delta_bind', 'Wuhan-Hu-1_starr_expr', 'Wuhan-Hu-1_starr_delta_expr', 'Delta_starr_bind', 'Delta_starr_delta_bind', 'Delta_starr_expr', 'Delta_starr_delta_expr', 'Alpha_starr_bind', 'Alpha_starr_delta_bind', 'Alpha_starr_expr', 'Alpha_starr_delta_expr', 'Omicron_XBB15_starr_bind', 'Omicron_XBB15_starr_delta_bind', 'Omicron_XBB15_starr_expr', 'Omicron_XBB15_starr_delta_expr', 'Omicron_BQ11_starr_bind', 'Omicron_BQ11_starr_delta_bind', 'Omicron_BQ11_starr_expr', 'Omicron_BQ11_starr_delta_expr', 'Omicron_BA2_starr_bind', 'Omicron_BA2_starr_delta_bind', 'Omicron_BA2_starr_expr', 'Omicron_BA2_starr_delta_expr', 'Eta_starr_bind', 'Eta_starr_delta_bind', 'Eta_starr_expr', 'Eta_starr_delta_expr', 'Omicron_BA1_starr_bind', 'Omicron_BA1_starr_delta_bind', 'Omicron_BA1_starr_expr', 'Omicron_BA1_starr_delta_expr', 'Cao_mut_escape'], {value:"semantic_rank",label: "Colour Mutations On Property",unique:true}), color:Inputs.select(new Map([ ["Blues (sequential, single-hue)", "blues"], ["Greens (sequential, single-hue)", "greens"], ["Greys (sequential, single-hue)", "greys"], ["Purples (sequential, single-hue)", "purples"], ["Reds (sequential, single-hue)", "reds"], ["Oranges (sequential, single-hue)", "oranges"], ["Turbo (sequential, multi-hue)", "turbo"], ["Viridis (sequential, multi-hue)", "viridis"], ["Magma (sequential, multi-hue)", "magma"], ["Inferno (sequential, multi-hue)", "inferno"], ["Plasma (sequential, multi-hue)", "plasma"], ["Cividis (sequential, multi-hue)", "cividis"], ["Cubehelix (sequential, multi-hue)", "cubehelix"], ["Warm (sequential, multi-hue)", "warm"], ["Cool (sequential, multi-hue)", "cool"], ["BuGn (sequential, multi-hue)", "bugn"], ["BuPu (sequential, multi-hue)", "bupu"], ["GnBu (sequential, multi-hue)", "gnbu"], ["OrRd (sequential, multi-hue)", "orrd"], ["PuBuGn (sequential, multi-hue)", "pubugn"], ["PuBu (sequential, multi-hue)", "pubu"], ["PuRd (sequential, multi-hue)", "purd"], ["RdPu (sequential, multi-hue)", "rdpu"], ["YlGnBu (sequential, multi-hue)", "ylgnbu"], ["YlGn (sequential, multi-hue)", "ylgn"], ["YlOrBr (sequential, multi-hue)", "ylorbr"], ["YlOrRd (sequential, multi-hue)", "ylorrd"], ["BrBG (diverging)", "brbg"], ["PRGn (diverging)", "prgn"], ["PiYG (diverging)", "piyg"], ["PuOr (diverging)", "puor"], ["RdBu (diverging)", "rdbu"], ["RdGy (diverging)", "rdgy"], ["RdYlBu (diverging)", "rdylbu"], ["RdYlGn (diverging)", "rdylgn"], ["Spectral (diverging)", "spectral"], ["BuRd (diverging)", "burd"], ["BuYlRd (diverging)", "buylrd"], ["Rainbow (cyclical)", "rainbow"], ["Sinebow (cylical)", "sinebow"]]), {label: "Color scheme", value: "turbo"}), log_y:Inputs.select( [true,false], {value:false,label: "Log Y-axis"}), // log_colour:Inputs.select( ['linear','log'], {value:false,label: "Log colour scale"}), })35:T645,dms_plot = Plot.plot({ height: 800, width:2500, x:{nice:true,grid:true}, y:{nice:true,grid:true,transform: (d) => {if(form.log_y == true){return Math.log(d)}else{return d}}}, style: {fontSize: "28px"}, marginLeft:60, marginRight:50, marginTop:40, marginBottom:50, marks: [ Plot.dot(filtered_data, {y: form.y_axis, x: form.x_axis, stroke:'black', strokeWidth:(d) => {if (form.highlight_voc_mutations == "True" && d.voc_mutation == 'False'){return 0}else if (form.highlight_voc_mutations == "True" && d.voc_mutation == 'True'){return 1}else{return 0.1}}, r:2.5, tip:true, // r:point_radius(form.opacity,filtered_data), sort:form.color_on, fillOpacity:(d) => {if (form.highlight_voc_mutations == "True" && d.voc_mutation == 'False'){return 0.1}else if (form.highlight_voc_mutations == "True" && d.voc_mutation == 'True'){return 1}else{return 1}}, fill:form.color_on, title: (d) => [ "Mutation: "+d.label, form.x_axis+": "+d[form.x_axis], form.y_axis+": "+d[form.y_axis], form.color_on+": "+d[form.color_on], ].join("\n\n")}), ], color: { scheme: form.color, // type:'log', legend: true, nice:true }, })36:T9fd,Plot.plot({ height: 800, width: 1100, marginBottom:50, marginTop:20, style: {fontSize: "17px"}, marginLeft: 60, x:{grid:true}, y:{grid:true}, color: { scheme: "turbo", // type: "log", legend:true }, marks: [ Plot.rect(logit_consistent_sites, Plot.bin({fill: "count"}, {x: "consistent_position", y: "mutated_position", thresholds:150,tip:true})), //NA Plot.rect([{x1: 0,y1: 0, x2: 13, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //NTD Plot.rect([{x1: 13,y1: 0, x2: 305, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "forestgreen",fill:"forestgreen",opacity:0.3}), //NA Plot.rect([{x1: 306,y1: 0, x2: 318, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //RBD Plot.rect([{x1: 319,y1: 0, x2: 541, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "red",fill:"red",opacity:0.3}), //NA Plot.rect([{x1: 542,y1: 0, x2: 787, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //FP Plot.rect([{x1: 788,y1: 0, x2: 806, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "blue",fill:"blue",opacity:0.4}), //NA Plot.rect([{x1: 807,y1: 0, x2: 814, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //FCS Plot.ruleX([681,682,683,684],{strokeWidth:3,stroke:'orange',opacity:0.7}), //S2' Plot.ruleX([815],{strokeWidth:3,stroke:'orange',opacity:1}), //NA Plot.rect([{x1: 816,y1: 0, x2: 911, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //HR1 Plot.rect([{x1: 912,y1: 0, x2: 984, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "cornflowerblue",fill:"cornflowerblue",opacity:0.3}), //NA Plot.rect([{x1: 985,y1: 0, x2: 1162, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //HR2 Plot.rect([{x1: 1163,y1: 0, x2: 1213, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "cyan",fill:"cyan",opacity:0.5}), //TM Plot.rect([{x1: 1213,y1: 0, x2: 1237, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "brown",fill:"brown",opacity:0.3}), //CTD Plot.rect([{x1: 1237,y1: 0, x2: 1273, y2: 1300,}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "purple",fill:"purple",opacity:0.4}), ] })37:T9d1,Plot.plot({ height: 400, width: 1200, marginBottom:50, marginTop:20, x:{grid:false,tickRotate: -90,}, y:{grid:true}, style: {fontSize: "17px"}, color: { legend:true, scheme: 'turbo', }, marks: [ Plot.cell(consistent_site_amino_acids, { x: 'pos', y: 'amino_acid', fill: "amino_acid", tip:true }), //FCS Plot.rect([{x1:681,y1: 'Y', x2: 684, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"orange",opacity:1}), //S2' Plot.rect([{x1:815,y1: 'Y', x2: 815, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"orange",opacity:1}), //NA Plot.rect([{x1: 9,y1: 'Y', x2: 9, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //NTD Plot.rect([{x1: 15,y1: 'Y', x2: 291, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "forestgreen",fill:"forestgreen",opacity:0.3}), //RBD Plot.rect([{x1: 330,y1: 'Y', x2: 541, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "red",fill:"red",opacity:0.3}), //NA Plot.rect([{x1: 544,y1: 'Y', x2: 788, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //FP Plot.rect([{x1: 789,y1: 'Y', x2: 789, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "blue",fill:"blue",opacity:0.4}), //NA Plot.rect([{x1: 807,y1: 'Y', x2: 814, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //NA Plot.rect([{x1: 816,y1: 'Y', x2: 911, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //HR1 Plot.rect([{x1: 917,y1: 'Y', x2: 976, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "cornflowerblue",fill:"cornflowerblue",opacity:0.3}), //NA Plot.rect([{x1: 1005,y1: 'Y', x2: 1159, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //HR2 Plot.rect([{x1: 1167,y1: 'Y', x2: 1202, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "cyan",fill:"cyan",opacity:0.5}), //TM Plot.rect([{x1: 1228,y1: 'Y', x2: 1228, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "brown",fill:"brown",opacity:0.3}), //CTD Plot.rect([{x1: 1239,y1: 'Y', x2: 1245, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "purple",fill:"purple",opacity:0.4}), ] })38:T4d6,Plot.plot({ width:1700, height:600, marginLeft:130, marginBottom:220, marginTop:40, style: {fontSize: "15px"}, x: { axis: null, domain:['semantic_score','relative_grammaticality','relative_sequence_grammaticality'] }, y: { grid:true, nice:true, // tickFormat: "s" label:"Correlation" , domain:[-1,1] }, color: { domain:['semantic_score','relative_grammaticality','relative_sequence_grammaticality'], legend:true, scheme: 'Tableau10' }, fx: { tickRotate: -30, label:'', domain:[ 'Escape Dadonaite et al. (2023)', 'RBD Escape Yisimayi et al. (2024)', 'Entry Dadonaite et al. (2023)', 'Binding Dadonaite et al. (2023)', 'RBD Wuhan-Hu-1 Binding Starr et al. (2022)', 'RBD Variant Average Binding Starr et al. (2022)', 'RBD Wuhan-Hu-1 Expression Starr et al. (2022)', 'RBD Variant Average Expression Starr et al. (2022)', 'Accessibility BEpro', 'B-Factor PDB:6VXX', 'ΔΔG FoldX', 'PSSM DELTA-BLAST', 'ESST CRESCENDO', 'Entropy Alignment' ] }, marks: [ Plot.barY(single_value_correlations, {fx: "predictor", x: "feature", y: "correlation", fill: "feature", title: ""}), Plot.ruleY([0]), ] })39:T7fc,Plot.plot({ facet: { data:updated_linear_correlations, x: "predictor", marginRight: 60,grid:true }, width:1500, height:600, marginLeft:130, marginBottom:190, marginTop:40, style: {fontSize: "14px"}, x: { axis: null, domain:['semantic_score','relative grammaticality','relative_sequence_grammaticality','semantic_score/relative grammaticality','semantic_score/relative_sequence_grammaticality','Mean_Embedding','sequence_logits'] }, y: { grid: true, nice:true, // tickFormat: "s" label:'Correlation', domain:[0,1] }, color: { domain:['semantic_score','relative grammaticality','relative_sequence_grammaticality','semantic_score/relative grammaticality','semantic_score/relative_sequence_grammaticality','Mean_Embedding','sequence_logits'], legend:true, scheme: 'Tableau10' }, fx: { tickRotate: -30, label:'', domain:[ 'Escape Dadonaite et al. (2023)', 'RBD Escape Yisimayi et al. (2024)', 'Entry Dadonaite et al. (2023)', 'Binding Dadonaite et al. (2023)', 'RBD Wuhan-Hu-1 Binding Starr et al. (2022)', 'RBD Variant Average Binding Starr et al. (2022)', 'RBD Wuhan-Hu-1 Expression Starr et al. (2022)', 'RBD Variant Average Expression Starr et al. (2022)', 'Accessibility BEpro', 'B-Factor PDB:6VXX', 'ΔΔG FoldX', 'PSSM DELTA-BLAST', 'ESST CRESCENDO', 'Entropy Alignment' ] }, marks: [ Plot.ruleY([0]), Plot.barY( updated_linear_correlations, Plot.groupX({ y: "mean" }, { x: "feature", y: "correlation", fill: "feature", tip:true, }) ), Plot.link( updated_linear_correlations, Plot.groupX( { y1: (d) => d3.mean(d) - d3.deviation(d), y2: (d) => d3.mean(d) + d3.deviation(d) }, { x: "feature", y: "correlation", // stroke: "black", strokeWidth: 3 } ) ) ] })3a:T7de,Plot.plot({ facet: { data:rbf_svr_correlations, x: "predictor", marginRight: 60 }, width:1500, height:600, marginLeft:130, marginBottom:190, marginTop:40, style: {fontSize: "14px"}, x: { axis: null, domain:['semantic_score','relative grammaticality','relative_sequence_grammaticality','semantic_score/relative grammaticality','semantic_score/relative_sequence_grammaticality','Mean_Embedding','sequence_logits'], }, y: { grid: true, nice:true, // tickFormat: "s" label:'Correlation', domain:[0,1] }, color: { domain:['semantic_score','relative grammaticality','relative_sequence_grammaticality','semantic_score/relative grammaticality','semantic_score/relative_sequence_grammaticality','Mean_Embedding','sequence_logits'], legend:true, scheme: 'Tableau10' }, fx: { tickRotate: -30, label:'', domain:[ 'Escape Dadonaite et al. (2023)', 'RBD Escape Yisimayi et al. (2024)', 'Entry Dadonaite et al. (2023)', 'Binding Dadonaite et al. (2023)', 'RBD Wuhan-Hu-1 Binding Starr et al. (2022)', 'RBD Variant Average Binding Starr et al. (2022)', 'RBD Wuhan-Hu-1 Expression Starr et al. (2022)', 'RBD Variant Average Expression Starr et al. (2022)', 'Accessibility BEpro', 'B-Factor PDB:6VXX', 'ΔΔG FoldX', 'PSSM DELTA-BLAST', 'ESST CRESCENDO', 'Entropy Alignment' ] }, marks: [ Plot.ruleY([0]), Plot.barY( rbf_svr_correlations, Plot.groupX({ y: "mean", }, { x: "feature", y: "correlation", fill: "feature",tip:true, }) ), Plot.link( rbf_svr_correlations, Plot.groupX( { y1: (d) => d3.mean(d) - d3.deviation(d), y2: (d) => d3.mean(d) + d3.deviation(d) }, { x: "feature", y: "correlation", // stroke: "black", strokeWidth: 3 } ) ) ] })3b:T5a9,lineage_plot = Plot.plot({ width:1900, height:800, marginRight:30, marginBottom:80, // style: {fontSize: "17px"}, x:{nice:true}, y:{nice:true}, style: {fontSize: "25px"}, marginLeft:70, marginTop:40, // marginBottom:50, grid:{x:true}, marks: [ Plot.dot(initial_lineage_sequences, {y: lineage_form.y_axis, x: lineage_form.x_axis, stroke:'black', strokeWidth:1, fill:'Voc', // r:(d)=>{if(lineage_form.opacity_and_fill == false){return 3}else{return Math.log(d['cluster_size'])}}, r:4.5, fillOpacity:1, sort: 'cluster_size', }), Plot.tip(initial_lineage_sequences, Plot.pointer({ y: lineage_form.y_axis, x: lineage_form.x_axis, // filter: (d) => d.info, title: (d) => ["Lineage: "+d.lineage, lineage_form.x_axis+": "+d[lineage_form.x_axis], lineage_form.y_axis+": "+d[lineage_form.y_axis], lineage_form.color_on+": "+d[lineage_form.color_on], ].join("\n\n") })), ], // color: { // scheme: form.color, // legend: true, // nice:true // }, color: { type: "categorical", range:['#1877B5','#FF7F09','#369D33','#D52627','#9667BC','#8C5D54','#FF8AB7'], // range:['#1877B5','#FF7F09','#369D33','Black','Black','#D52627','Black','Black','Black','Black','#9667BC','#8C5D54','Black'], legend:true }, })3c:T674,haplotype_lineage_plot = Plot.plot({ width:1900, height:800, marginRight:30, marginBottom:80, // style: {fontSize: "17px"}, x:{nice:true}, y:{nice:true}, style: {fontSize: "25px"}, marginLeft:70, marginTop:40, // marginBottom:50, grid:{x:true}, marks: [ Plot.dot(haplotype_sequences, {y: haplotype_lineage_form.y_axis, x: haplotype_lineage_form.x_axis, stroke:'black', strokeWidth:1, fill:'Variant', // r:(d)=>{if(lineage_form.opacity_and_fill == false){return 3}else{return Math.log(d['cluster_size'])}}, r:4.5, fillOpacity:1, sort: 'cluster_size', }), Plot.tip(haplotype_sequences, Plot.pointer({ y: haplotype_lineage_form.y_axis, x: haplotype_lineage_form.x_axis, // filter: (d) => d.info, title: (d) => ["Lineage: "+d.lineage, haplotype_lineage_form.x_axis+": "+d[haplotype_lineage_form.x_axis], haplotype_lineage_form.y_axis+": "+d[haplotype_lineage_form.y_axis], haplotype_lineage_form.color_on+": "+d[haplotype_lineage_form.color_on], ].join("\n\n") })), ], color: { type: "categorical", range:[...['#9667BC','#1877B5','#FF7F09','#369D33','#D52627','#8C5D54'],...d3.quantize(d3.interpolateSpectral, 11).reverse(),...['Pink','Pink','Pink','Pink','Pink','Pink','Pink',]], // range:['#1877B5','#FF7F09','#369D33','#D52627','#9667BC','#8C5D54','#FF8AB7'], // range:['#1877B5','#FF7F09','#369D33','Black','Black','#D52627','Black','Black','Black','Black','#9667BC','#8C5D54','Black'], legend:true }, })3d:T502,spike ="MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT"3e:T1257,function filter_data(uploaded_spike,data){ return data.filter((element, index) => { if (form.ref === null && form.alt === null){ return element.position >=slider_form.position[0] && element.position <=slider_form.position[1] && element["semantic_score"] >= slider_form.semantic[0] && element["semantic_score"] <= slider_form.semantic[1] && element["relative_grammaticality"] >= slider_form.ei[0] && element["relative_grammaticality"] <= slider_form.ei[1] && element["relative_sequence_grammaticality"] >= slider_form.relative_sequence_grammaticality[0] && element["relative_sequence_grammaticality"] <= slider_form.relative_sequence_grammaticality[1] && element["positional_semantic_rank"] >= slider_form.positional_semantic_rank[0] && element["positional_semantic_rank"] <= slider_form.positional_semantic_rank[1] && element["Count"] >= slider_form.count }else if (form.ref !== null && form.alt === null){ return element.position >=slider_form.position[0] && element.position <=slider_form.position[1] && element.ref == form.ref && element["semantic_score"] >= slider_form.semantic[0] && element["semantic_score"] <= slider_form.semantic[1] && element["relative_grammaticality"] >= slider_form.ei[0] && element["relative_grammaticality"] <= slider_form.ei[1] && element["relative_sequence_grammaticality"] >= slider_form.relative_sequence_grammaticality[0] && element["relative_sequence_grammaticality"] <= slider_form.relative_sequence_grammaticality[1] && element["positional_semantic_rank"] >= slider_form.positional_semantic_rank[0] && element["positional_semantic_rank"] <= slider_form.positional_semantic_rank[1] && element["Count"] >= slider_form.count }else if (form.ref === null && form.alt !== null){ return element.position >=slider_form.position[0] && element.position <=slider_form.position[1] && element.alt == form.alt && element["semantic_score"] >= slider_form.semantic[0] && element["semantic_score"] <= slider_form.semantic[1] && element["relative_grammaticality"] >= slider_form.ei[0] && element["relative_grammaticality"] <= slider_form.ei[1] && element["relative_sequence_grammaticality"] >= slider_form.relative_sequence_grammaticality[0] && element["relative_sequence_grammaticality"] <= slider_form.relative_sequence_grammaticality[1] && element["positional_semantic_rank"] >= slider_form.positional_semantic_rank[0] && element["positional_semantic_rank"] <= slider_form.positional_semantic_rank[1] && element["Count"] >= slider_form.count }else{ return element.position >=slider_form.position[0] && element.position <=slider_form.position[1] && element.ref == form.ref && element.alt == form.alt && element["semantic_score"] >= slider_form.semantic[0] && element["semantic_score"] <= slider_form.semantic[1] && element["relative_grammaticality"] >= slider_form.ei[0] && element["relative_grammaticality"] <= slider_form.ei[1] && element["relative_sequence_grammaticality"] >= slider_form.relative_sequence_grammaticality[0] && element["relative_sequence_grammaticality"] <= slider_form.relative_sequence_grammaticality[1] && element["positional_semantic_rank"] >= slider_form.positional_semantic_rank[0] && element["positional_semantic_rank"] <= slider_form.positional_semantic_rank[1] && element["Count"] >= slider_form.count } }).filter((element, index) => { if(form.subunit === null && form.domain === null){ return element }else if(form.subunit !== null && form.domain ===null){ return element.subunit == form.subunit }else if(form.subunit === null && form.domain !==null){ return element.domain == form.domain }else{ return element.domain == form.domain && element.subunit == form.subunit } }).filter((element, index) => { if(form.real === null){ return element }else{ return element.real == form.real } }).filter((element, index) => { if(form.convergent === null){ return element }else{ return element.convergent == form.convergent } }).filter((element, index) => { if(form.voc_mutation === 'false'){ return element }else{ return element.voc_mutation == 'True' } }).filter((element, index) => { if(form.deletions === "True"){ return element }else{ return element.alt != "-" } }) }3f:[{"id":"498fcae781f82d86f225ea0393c07422355d739fdd5344b3c738a39e7beb690cd7ac83e3c856a59951b4bd28e6aff9444cff369d674b63b90f9872bbce380680","href":"https://static.observableusercontent.com/files/498fcae781f82d86f225ea0393c07422355d739fdd5344b3c738a39e7beb690cd7ac83e3c856a59951b4bd28e6aff9444cff369d674b63b90f9872bbce380680","name":"Epistasis_1d_3d.csv","type":"text/csv","lastModified":1728316357333.445,"size":26403},{"id":"463516092fe02d765ce27ec06f80f4da42b2330014bf1a429bd6deb641c4fd13e56e462ecc42de338fba023d1e9d17a6006ed538c05a1b8457cfb797d7815a3e","href":"https://static.observableusercontent.com/files/463516092fe02d765ce27ec06f80f4da42b2330014bf1a429bd6deb641c4fd13e56e462ecc42de338fba023d1e9d17a6006ed538c05a1b8457cfb797d7815a3e","name":"BA1_Epistasis_Distances_Counts.csv","type":"text/csv","lastModified":1728316496218.263,"size":676},{"id":"f5e4a8a61424c5fa824d0d250001e03d3241d1f9fdeec38ec846e5be96f3158f753604e64985a8c6dac229b2a46d614c6d5dec73fea667ef5ad6cd68c6b2e007","href":"https://static.observableusercontent.com/files/f5e4a8a61424c5fa824d0d250001e03d3241d1f9fdeec38ec846e5be96f3158f753604e64985a8c6dac229b2a46d614c6d5dec73fea667ef5ad6cd68c6b2e007","name":"Fig_2C.png","type":"image/png","lastModified":1728317929965.831,"size":698542},{"id":"be6487846e1e03d2c4d4d307dc29651fb42d3f21135ca9e71b954f245fd654a7c38d261fc028489b1c27810fdc7a46e55b2ae70a7aacc29cdc7eeadf33ae02b4","href":"https://static.observableusercontent.com/files/be6487846e1e03d2c4d4d307dc29651fb42d3f21135ca9e71b954f245fd654a7c38d261fc028489b1c27810fdc7a46e55b2ae70a7aacc29cdc7eeadf33ae02b4","name":"Fig_3A.png","type":"image/png","lastModified":1728318109433.172,"size":651771},{"id":"e86c457e851a38d399d246e7e35c7a963aceccfd069f8a70c00fae2556c6384ac03f9d428efd038f59002135a95790a2f164ad47204c0e694ee7f223e85afd7d","href":"https://static.observableusercontent.com/files/e86c457e851a38d399d246e7e35c7a963aceccfd069f8a70c00fae2556c6384ac03f9d428efd038f59002135a95790a2f164ad47204c0e694ee7f223e85afd7d","name":"esm2_sarscov2_s_dms_scores_annotated-1.csv.zip","type":"application/zip","lastModified":1728301931291.908,"size":1975781},{"id":"a2d51b6ad794395600b2a3f0a3e6f2426c7107efbec03428e9078738023b7be21cb75bfb1a5839b300454e0b6154f463ac0b3a6e2f5933a91c165a6c7475b549","href":"https://static.observableusercontent.com/files/a2d51b6ad794395600b2a3f0a3e6f2426c7107efbec03428e9078738023b7be21cb75bfb1a5839b300454e0b6154f463ac0b3a6e2f5933a91c165a6c7475b549","name":"Fig_3C.png","type":"image/png","lastModified":1728318700792.563,"size":809575},{"id":"c02c182e9064f2aba9058b1570eb92a7a1fbb720789925bf0e420192e719fe2f24eeffbf0efa93cf3729015ae13a1a7624c17d57a9a24af9f3f6daaed62d7483","href":"https://static.observableusercontent.com/files/c02c182e9064f2aba9058b1570eb92a7a1fbb720789925bf0e420192e719fe2f24eeffbf0efa93cf3729015ae13a1a7624c17d57a9a24af9f3f6daaed62d7483","name":"Fig_3C@1.png","type":"image/png","lastModified":1728318927264.933,"size":288210},{"id":"968835c2b615fa24a8464a302bb337058bf3b93e1958709ad51fd13092877e61d628fa68730cb6b36db0a161d387381e241082226302cbbc267e3a5ed80c6bdf","href":"https://static.observableusercontent.com/files/968835c2b615fa24a8464a302bb337058bf3b93e1958709ad51fd13092877e61d628fa68730cb6b36db0a161d387381e241082226302cbbc267e3a5ed80c6bdf","name":"initial_sequences_with_sliding_windows-4.csv","type":"text/csv","lastModified":1728299940288.504,"size":1152819},{"id":"02a3bbe05b0b1211f1d4f4b0acee4f430ee4a4fb65758065013267b61243215454cc4ed1618a5570ed340d78240d80cfc976b850866a498ec0924b9f04fa82bb","href":"https://static.observableusercontent.com/files/02a3bbe05b0b1211f1d4f4b0acee4f430ee4a4fb65758065013267b61243215454cc4ed1618a5570ed340d78240d80cfc976b850866a498ec0924b9f04fa82bb","name":"updated_90_proper_Mafft_Haplotype_Spikes_Annotated 2-5.tsv.zip","type":"application/zip","lastModified":1728300749798.823,"size":866597},{"id":"98f901572e11c13f0476f20016f496ab1ee2f05b486843dc4d7ae82bdee5289a0e8fa99e3c87087459381aacccbd3a1222b89674cc34e51d173c67f8a06352d5","href":"https://static.observableusercontent.com/files/98f901572e11c13f0476f20016f496ab1ee2f05b486843dc4d7ae82bdee5289a0e8fa99e3c87087459381aacccbd3a1222b89674cc34e51d173c67f8a06352d5","name":"significant_single_value_correlations-2@3.csv","type":"text/csv","lastModified":1728305319404.059,"size":4101},{"id":"68d8b75832d6a5454c8aa8c28375a04ddb645626f1b02398421ab255976bc3f23548ee9a5b6fd508c576bb71bfea81ec6d09254627ae2261e287fe32f9e6972e","href":"https://static.observableusercontent.com/files/68d8b75832d6a5454c8aa8c28375a04ddb645626f1b02398421ab255976bc3f23548ee9a5b6fd508c576bb71bfea81ec6d09254627ae2261e287fe32f9e6972e","name":"linear_svr_significant_correlations_with_dms (1)@3.csv","type":"text/csv","lastModified":1728311319169.38,"size":60541},{"id":"296b57e96fd06287001612b71fa96dca4113ffd5bc42869af5d6deb306ba4821bdbfed738afa8455a7bf238a7b47cc5607698a42d8125c392f78d8788ff0bc84","href":"https://static.observableusercontent.com/files/296b57e96fd06287001612b71fa96dca4113ffd5bc42869af5d6deb306ba4821bdbfed738afa8455a7bf238a7b47cc5607698a42d8125c392f78d8788ff0bc84","name":"svr_significant_correlations_with_dms (1)@4.csv","type":"text/csv","lastModified":1728314097252.629,"size":61033},{"id":"781ac2716be2d28b4d1e0fef825e913ad46ecb98e93b1703c00479cd90edc3ccfd493a29370c036a6fbf09ddfdd737f28477714fafcaa5363c50c5c8b64b8f24","href":"https://static.observableusercontent.com/files/781ac2716be2d28b4d1e0fef825e913ad46ecb98e93b1703c00479cd90edc3ccfd493a29370c036a6fbf09ddfdd737f28477714fafcaa5363c50c5c8b64b8f24","name":"logit_consistent_links.csv.zip","type":"application/zip","lastModified":1728314938910.238,"size":735775}] 32:{"id":"07fdc4d42e2ee11f","slug":"from-a-single-sequence-to-evolutionary-trajectorie","title":"(Preprint) From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences","thumbnail":"60572e3c6331a1657d51b70557c1f1a2f4bf0f179557e07f2d9d6818b45bff33","access_level":"public","create_time":"$D2024-10-07T11:14:17.874Z","update_time":"$D2025-03-22T16:25:06.990Z","view_time":null,"first_public_time":"$D2024-10-07T16:25:06.379Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"1157d8a0e7ace7c6","login":"cvr-bioinfo","name":"CVR Bioinformatics","avatar_url":"https://avatars-next.observableusercontent.com/022d43de01273d3d19941f51b9b2c412d919f4a74262d41e14c5379b08c7d5f2","tier":"pro_2024"},"version":551,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"1c0f023b5e4ac4b5","login":"kieran12lamb","name":"Kieran Lamb","avatar_url":"https://avatars-next.observableusercontent.com/01b5f491b7dc6ccd2a81b4681d30c9693b427ea0f1022b2764d0156e67e53309","tier":null}],"members":[{"type":"individual","id":"1c0f023b5e4ac4b5","login":"kieran12lamb","name":"Kieran Lamb","avatar_url":"https://avatars-next.observableusercontent.com/01b5f491b7dc6ccd2a81b4681d30c9693b427ea0f1022b2764d0156e67e53309","tier":null,"role":"editor"},{"type":"team","id":"1157d8a0e7ace7c6","login":"cvr-bioinfo","name":"CVR Bioinformatics","avatar_url":"https://avatars-next.observableusercontent.com/022d43de01273d3d19941f51b9b2c412d919f4a74262d41e14c5379b08c7d5f2","tier":"pro_2024","role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"(Preprint) From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences","cells":[{"id":0,"value":"# (Preprint) From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":472,"value":"## Introduction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":474,"value":"### Figure 1\nSchematic summary of our methodology. Deep mutational scanning involves taking a sequence and mutating every position to each of the possible alternative amino acids. These are then passed to the PLM to produce embeddings and logits which can be used for downstream tasks or to produce the metrics relative grammaticality and semantic score. Identifying epistatic interactions involves reverting the mutations from a SARS-CoV-2 variant (here BA.1) and measuring the effect this has on the other likelihoods. Embeddings and log-likelihoods (logits) can be used for surveillance by producing metrics over timescales for newly emergent protein sequences of concern, or by looking at evolutionary trajectories such as with evo-velocity.\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":469,"value":"![Graphical Paper Abstract](https://www.biorxiv.org/content/biorxiv/early/2024/09/18/2024.07.05.602129/F1.large.jpg?width=800&height=600&carousel=1)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":313,"value":"## PLMs capture evolutionary potential","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":218,"value":"### Figure 2A\nGraph of the amino acid sequence alignment entropy at each position in the SARS-CoV-2 spike protein. S1 contains the majority of the sites with high entropy, while S2 contains only a few. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":220,"value":"Plot.plot({\n height: 300,\n width:1200,\n x:{nice:true,},\n y:{nice:true,transform: (d) => {if(form.log_y == true){return Math.log(d)}else{return d}}},\n grid:{x:true},\n style: {fontSize: \"14px\"},\n marginLeft:85,\n marginTop:40,\n marginBottom:50,\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(filtered_data, {x: \"position\", y: \"entropy\",stroke:'subunit',tip:true,})\n ],\n color: {\n range:['salmon','cornflowerblue'],\n legend: true,\n nice:true\n },\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":336,"value":"### Figure 2B \nScatter plot of spike protein DMS. Relative grammaticality is shown on the y-axis, with the amino acid position on the x-axis. Points are coloured on the semantic rank of each change\n\n Use X-axis \"position\" and Y-axis \"relative_grammaticality\".\n\n### Supplementary Figure 2\nDMS plot with only the substitutions observed in SARS-CoV-2 VOC sequences highlighted. Most of the mutations occur in the spike protein S1 region which the model predicts has a higher likelihood of mutations, i.e., a higher grammaticality.\n\n Use X-axis \"position\" and Y-axis \"relative_grammaticality\".\n\n Also select \"highlight VOC mutations\".\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":172,"value":"#### Upload Spike Sequence as filter\nSpike sequences must be 1273 in length (i.e a complete spike sequence). Spike Sequences will be checked for mutations, and the following plots will then filter and display only mutations present in the uploaded spike. Alternatively the Pango lineage can be entered to display a lineage from the initial spike sequences.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":86,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"viewof slider_form = Inputs.form({ \n \n position: rangeSlider({step:1,min: 1,max: 1273,value: this ? this.value : [1, 1273],title: 'Position Filter',description: '',}),\n \n semantic: rangeSlider({step:0.5,min:0 ,max: 20,value: this ? this.value : [0,20],title: 'Semantic Score Filter',description: '',}),\n \n ei: rangeSlider({step:1,min: -35,max: 5,value: this ? this.value : [-35,5],title: 'Relative Grammatically',description: '',}),\n \n relative_sequence_grammaticality: rangeSlider({step:1,min:-40,max:10,value: this ? this.value : [ -40,10],title: 'Relative Sequence Grammaticality Filter',description: '',}),\n \n positional_semantic_rank: rangeSlider({step:1,min:1,max:20,value: this ? this.value : [ 1,20],title: 'Semantic Rank Filter',description: 'Filter by Count',}),\ncount:Inputs.range([0, 15000000], {step: 1, value: 0,}),\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":121,"value":"viewof uploaded_spike= Inputs.text({label: \"Submit Spike Sequence Here\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":123,"value":"viewof search = Inputs.text({label: 'Or select a lineage to use', placeholder: \"Type a lineage here\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":166,"value":"{\n if (uploaded_spike.length == 0 & lineage_mutations.length == 0){\n return 'Spike not uploaded'\n }else if (uploaded_spike.length != 1273 & lineage_mutations.length == 0){\n return \"Invalid spike, must be of length 1273\"\n }else if (uploaded_spike.length == 1273){\n return uploaded_spike\n }else if(uploaded_spike.length != 1273 & lineage_mutations.length > 0){\n return \"Using Spike protein from lineage \"+search\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":169,"value":"Inputs.table([selected_lineage_entry],{})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":542,"value":"spike","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"wuhan_esm2_data = dms_zip.file(\"esm2_sarscov2_s_dms_scores_annotated@1.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":106,"value":"dms_zip = FileAttachment(\"esm2_sarscov2_s_dms_scores_annotated-1.csv.zip\").zip()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":479,"value":"### Figure 2C \nAverage relative grammaticality at each position on the spike protein plotted on three structures, the full Spike protein, the spike monomer, and the spike receptor binding domain (RBD) bound to the ACE-2 receptor in purple.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":477,"value":"FileAttachment(\"Fig_2C.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":309,"value":"## PLMS Identify epistatic interaction sites","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":489,"value":"### Figure 3A\nMonomeric structures showing the changes in probabilities for three example substitutions in SARS-CoV-2’s spike protein: E484A, D614G and N969K. The mutation site is coloured yellow, blue sites increase in probability while red sites decrease. Mutation probabilities were only shown if they were outside two standard deviations of the mean change.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":486,"value":"FileAttachment(\"Fig_3A.png\").image({height:800})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":361,"value":"### Figure 3C\nThe significantly changed logits across the whole DMS were identified, with positions that were repeatedly identified (called critical sites) as being affected by the in-silico mutations counted. These were then mapped onto the spike structure and coloured on their domains. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":311,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":539,"value":"logit_consistent_sites","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":502,"value":"FileAttachment(\"Fig_3C@1.png\").image({width: 500})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":343,"value":"consistent_sites_zip = FileAttachment(\"logit_consistent_links.csv.zip\").zip()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":341,"value":"logit_consistent_sites = consistent_sites_zip.file(\"logit_consistent_links.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":364,"value":"### Figure 3D\nAmino acids of each consistently affected reference residues from the DMS data. The NTD and RBD contain mostly Prolines(P) and Cystines(C), while the rest of spike has a wider distribution of amino acids.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":355,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":357,"value":"consistent_site_amino_acids = [...new Set(logit_consistent_sites.map(d=>{return d['consistent_position']}))].map(d=>{return {'pos':d,'amino_acid':spike[d-1]}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":227,"value":"# PLMs provide new metrics for assessing mutational impact","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":492,"value":"### Figure 4A\n Spearman’s Rank correlations between the semantic score, grammaticality and relative sequence grammaticality and traditional metrics. Bars not present in a metric category means the correlation was not found to be significant after a Bonferroni correction.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":233,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":238,"value":"single_value_correlations = FileAttachment(\"significant_single_value_correlations-2@3.csv\").csv({typed: true})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":494,"value":"### Figure 4B\nSpearman’s Rank correlations between the language model metric and the traditional metric. Each pair was fitted using a grid search and a linear regression model, with 5-fold cross validation. Bars represent the mean of the correlations, with the error bar +/- 1 standard deviation of the correlations. Bars not present in a metric category means the correlation was not found to be significant after a Bonferroni correction. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":231,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":243,"value":"updated_linear_correlations = FileAttachment(\"linear_svr_significant_correlations_with_dms (1)@3.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":497,"value":"### Figure 4B\nSpearman’s Rank correlations with a support vector regression model using an RBF kernel and 5-fold cross validation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":229,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":246,"value":"rbf_svr_correlations = FileAttachment(\"svr_significant_correlations_with_dms (1)@4.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":325,"value":"## Language models recapitulate evolutionary relationships between related sequences","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":72,"value":"### Figure 5D \nShows the spike protein sequences plotted using their sample date and semantic score coloured consistently to Figure 1B.\n\nUse X-axis \"sample_date\" and Y-axis \"semantic_score\"\n\n\n### Supplementary Figure 7\n\nUse X-axis \"semantic_score\" and Y-axis \"relative_grammaticality\"\n\n \n### Supplementary Figure 8\n\nUse X-axis \"sample_date\" and Y-axis \"relative_grammaticality\"\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"viewof lineage_form =Inputs.form({\n x_axis:Inputs.select(initial_lineage_sequences.columns, {value:\"sample_date\",label: \"Select X-Axis\",unique:true}), \n y_axis:Inputs.select(initial_lineage_sequences.columns, {value:\"relative_grammaticality\",label: \"Select Y-Axis\",unique:true}),\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"initial_lineage_sequences = FileAttachment(\"initial_sequences_with_sliding_windows-4.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":329,"value":"### Figure 6\n(A) UK SARS-CoV-2 spike sequences through the pandemic. Each point represents a sequence cluster with 99.9% sequence similarity. Dynamic semantic scores were calculated for each sequence cluster, with the black line showing the mean sliding score. \n\nUse X-axis \"sample_date\" and Y-axis \"sliding_semantic_score\"\n\n(B) Relative sequence grammaticalities for each of the haplotype spikes.\n\nUse X-axis \"sample_date\" and Y-axis \"relative_sequence_grammaticality\"\n\n(C) Semantic scores for each of the haplotype spikes.\n\nUse X-axis \"sample_date\" and Y-axis \"semantic_score\"\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":57,"value":"viewof haplotype_lineage_form =Inputs.form({\n x_axis:Inputs.select(haplotype_sequences.columns, {value:\"sample_date\",label: \"Select X-Axis\",unique:true}), \n y_axis:Inputs.select(haplotype_sequences.columns, {value:\"relative_sequence_grammaticality\",label: \"Select Y-Axis\",unique:true}),\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"haplotype_sequences = haplotype_sequences_zip.file('updated_90_proper_Mafft_Haplotype_Spikes_Annotated.tsv').tsv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"haplotype_sequences_zip = FileAttachment(\"updated_90_proper_Mafft_Haplotype_Spikes_Annotated 2-5.tsv.zip\").zip()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":368,"value":"## Supplementary Figures","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":366,"value":"vegalite(({hconcat:[one_dimensional_boxplot,three_dimensional_boxplot]}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":426,"value":"### Supplementary Figure 3(A)\nBoxplots showing the distribution of distances between mutations and the positions affected by mutations in each subunit. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":370,"value":"vegalite(({vconcat:[mutation_one_dimensional_boxplot,mutation_three_dimensional_boxplot]}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":423,"value":"### Supplementary Figure 3(B) \n Boxplots showing the distribution of distances between mutations and the positions for each mutation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":393,"value":"distances = FileAttachment(\"Epistasis_1d_3d.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":372,"value":"Plot.plot({\n marginLeft:80,\n width:1100,\n height:400,\n // marginBottom:30,\n // marginTop:10,\n x:{tickRotate:0, label:'Mutation',nice:true},\n y:{tickRotate:0, label:'Sites\\n Affected',nice:true,grid:true},\n marks: [\n Plot.barY(epistasis_distance_counts, {x: \"mutation\", y: \"count\",fill:'color', sort: {x: \"y\", reverse: false}}),\n Plot.ruleY([0])\n ],\n\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":418,"value":"### Supplementary Figure 4\nNumber of sites with a significant (+/-2 deviations from mean) change in probability for each SARS-CoV-2 BA.1 reversion change.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":409,"value":"epistasis_distance_counts = FileAttachment(\"BA1_Epistasis_Distances_Counts.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":131,"value":"# Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":202,"value":"## DMS Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":150,"value":"function get_lineage_mutations(search,data){\n for (const lineage of data) {\n if (lineage.lineage == search){\n\n if(lineage.mutations !=null){\n return lineage.mutations.replaceAll(\"'\",\"\").replaceAll(\"[\",\"\").replaceAll(\"]\",\"\").split(\", \")\n }else{\n return []\n }\n }\n }\n return []\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":144,"value":"function mutate_spike(lineage_mutations,spike){\n if (lineage_mutations.length>0){\n let mutated_spike = spike.split(\"\")\n for (let mutation of lineage_mutations){\n let ref = mutation.slice(0,1)\n let alt = mutation.slice(-1)\n let pos = parseInt(mutation.slice(1,-1))\n console.log(ref)\n console.log(pos)\n console.log(alt)\n \n mutated_spike[pos-1] = alt\n }\n return mutated_spike.join(\"\")\n }\n return \"\"\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":147,"value":"mutated_lineage_spike = mutate_spike(lineage_mutations,spike)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":139,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"selected_lineage_entry = {\n if(search !=\"\"){\n for (const lineage of initial_lineage_sequences) {\n if (lineage.lineage == search){\n return lineage\n }\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":153,"value":"lineage_mutations = get_lineage_mutations(search,initial_lineage_sequences)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":100,"value":"dms_spike_upload_filter = {\n return wuhan_esm2_data.filter((element, index) => {\n if (uploaded_mutations.length > 0){\n return uploaded_mutations.includes(element.label)\n }\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":98,"value":"uploaded_mutations_positions = uploaded_mutations.map((d)=>parseInt(d.slice(1,-1)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"uploaded_mutations = { \n if (uploaded_spike.split(\"\").length == 1273) {\n return spike.split(\"\").map((d, i) => {\n if(d != uploaded_spike[i]){\n return d+(i+1)+uploaded_spike[i]\n }else{\n return false\n } \n }).filter((d)=>d!=false)\n }else if(mutated_lineage_spike.split(\"\").length == 1273){\n return spike.split(\"\").map((d, i) => {\n if(d != mutated_lineage_spike[i]){\n return d+(i+1)+mutated_lineage_spike[i]\n }else{\n return false\n }\n }).filter((d)=>d!=false)\n }else{\n console.log(mutated_lineage_spike.length)\n return []\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"filtered_data = { \n if (uploaded_spike.split(\"\").length ==1273 ){ \n return filter_data(uploaded_spike,dms_spike_upload_filter)\n }else if(mutated_lineage_spike.split(\"\").length == 1273){\n return filter_data(uploaded_spike,dms_spike_upload_filter)\n }\n return filter_data(uploaded_spike,data)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"data = wuhan_esm2_data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":128,"value":"import {rangeSlider} from '@mootari/range-slider@1326'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":385,"value":"# Boxplots","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":396,"value":"vegalite = require(\"@observablehq/vega-lite@0.2\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":387,"value":"one_dimensional_boxplot = ({\n width:450,\n height:300,\n \"title\": \"1D Distances\",\n labelFontSize:20,\n titleFontSize:20,\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:distances\n },\n encoding:{\n y:{field:\"1d_distance\",type:\"quantitative\",\"title\": \"Distance\",},\n x:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\"},\n color:{field:\"subunit\",type:\"nominal\", scale:{range:['#E74421','#4068FE']}}\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":390,"value":"three_dimensional_boxplot = ({\n width:500,\n height:300,\n \"title\": \"3D Distances\",\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:distances\n },\n encoding:{\n y:{field:\"3d_distance\",type:\"quantitative\",\"title\": \" \",},\n x:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\"},\n color:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\", scale:{range:['#E74421','#4068FE']}}\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":402,"value":"mutation_three_dimensional_boxplot = ({\n width:1000,\n height:150,\n \"title\": \"3D Distances\",\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:distances\n },\n encoding:{\n y:{field:\"3d_distance\",type:\"quantitative\",\"title\": \" Distance\",},\n x:{field:\"mutation\",\n type:\"ordinal\",\n \"title\": \"Mutation\",\n scale: {\"domain\": ['67V', '95I', '142D', '212I', '339D', '371L', '373P', '375F',\n '417N', '440K', '446S', '477N', '478K', '484A', '493R', '496S',\n '498R', '501Y', '505H', '547K', '614G', '655Y', '679K', '681H',\n '764K', '796Y', '856K', '954H', '969K', '981F']}\n },\n color:{field:\"subunit\",\n type:\"nominal\",\n title: \"Subunit\", \n scale:{range:['#E74421','#4068FE']}\n }\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":400,"value":"mutation_one_dimensional_boxplot = ({\n width:1000,\n height:150,\n \"title\": \"1D Distances\",\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:distances\n },\n encoding:{\n y:{field:\"1d_distance\",type:\"quantitative\",\"title\": \"Distance\",},\n x:{field:\"mutation\",type:\"ordinal\",\"title\": \"Mutation\",\n scale: {\"domain\": ['67V', '95I', '142D', '212I', '339D', '371L', '373P', '375F',\n '417N', '440K', '446S', '477N', '478K', '484A', '493R', '496S',\n '498R', '501Y', '505H', '547K', '614G', '655Y', '679K', '681H',\n '764K', '796Y', '856K', '954H', '969K', '981F']}},\n color:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\", scale:{range:['#E74421','#4068FE']}}\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3f","stdlib":"1","titling":"auto"}} 33:"$32:body"