{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T09:28:50Z","timestamp":1768296530868,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011958","name":"Danmarks Frie Forskningsfond","doi-asserted-by":"publisher","award":["8022-00041B"],"award-info":[{"award-number":["8022-00041B"]}],"id":[{"id":"10.13039\/501100011958","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Prediction of the change in fold stability (\u0394\u0394G) of a protein upon mutation is of major importance to protein engineering and screening of disease-causing variants. Many prediction methods can use 3D structural information to predict \u0394\u0394G. While the performance of these methods has been extensively studied, a new problem has arisen due to the abundance of crystal structures: How precise are these methods in terms of structure input used, which structure should be used, and how much does it matter? Thus, there is a need to quantify the structural sensitivity of protein stability prediction methods.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We computed the structural sensitivity of six widely-used prediction methods by use of saturated computational mutagenesis on a diverse set of 87 structures of 25 proteins. Our results show that structural sensitivity varies massively and surprisingly falls into two very distinct groups, with methods that take detailed account of the local environment showing a sensitivity of\u2009~\u20090.6 to 0.8\u00a0kcal\/mol, whereas machine-learning methods display much lower sensitivity (~\u20090.1\u00a0kcal\/mol). We also observe that the precision correlates with the accuracy for mutation-type-balanced data sets but not generally reported accuracy of the methods, indicating the importance of mutation-type balance in both contexts.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The structural sensitivity of stability prediction methods varies greatly and is caused mainly by the models and less by the actual protein structural differences. As a new recommended standard, we therefore suggest that \u0394\u0394G values are evaluated on three protein structures when available and the associated standard deviation reported, to emphasize not just the accuracy but also the precision of the method in a specific study. Our observation that machine-learning methods deemphasize structure may indicate that folded wild-type structures alone, without the folded mutant and unfolded structures, only add modest value for assessing protein stability effects, and that side-chain-sensitive methods overstate the significance of the folded wild-type structure.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04030-w","type":"journal-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T14:09:41Z","timestamp":1614262181000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A base measure of precision for protein stability predictors: structural sensitivity"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5900-6568","authenticated-orcid":false,"given":"Octav","family":"Caldararu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2708-8992","authenticated-orcid":false,"given":"Tom L.","family":"Blundell","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6754-7348","authenticated-orcid":false,"given":"Kasper P.","family":"Kepp","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"4030_CR1","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1038\/nature19946","volume":"537","author":"P-S Huang","year":"2016","unstructured":"Huang P-S, Boyken SE, Baker D. The coming of age of de novo protein design. Nature. 2016;537:320.","journal-title":"Nature"},{"key":"4030_CR2","doi-asserted-by":"publisher","first-page":"R105","DOI":"10.1016\/S0969-2126(99)80062-8","volume":"7","author":"AG Street","year":"1999","unstructured":"Street AG, Mayo SL. Computational protein design. Structure. 1999;7:R105\u20139.","journal-title":"Structure"},{"key":"4030_CR3","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1038\/nature08620","volume":"462","author":"N Yeung","year":"2009","unstructured":"Yeung N, Lin Y-W, Gao Y-G, Zhao X, Russell BS, Lei L, et al. Rational design of a structural and functional nitric oxide reductase. Nature. 2009;462:1079\u201382.","journal-title":"Nature"},{"key":"4030_CR4","doi-asserted-by":"publisher","first-page":"16152","DOI":"10.1073\/pnas.0705366104","volume":"104","author":"KB Zeldovich","year":"2007","unstructured":"Zeldovich KB, Chen P, Shakhnovich EI. Protein stability imposes limits on organism complexity and speed of molecular evolution. Proc Natl Acad Sci USA. 2007;104:16152\u20137. https:\/\/doi.org\/10.1073\/pnas.0705366104.","journal-title":"Proc Natl Acad Sci USA"},{"key":"4030_CR5","doi-asserted-by":"publisher","first-page":"3023","DOI":"10.1007\/s00018-017-2519-8","volume":"74","author":"P Dasmeh","year":"2017","unstructured":"Dasmeh P, Kepp KP. Superoxide dismutase 1 is positively selected to minimize protein aggregation in great apes. Cell Mol Life Sci. 2017;74:3023\u201337. https:\/\/doi.org\/10.1007\/s00018-017-2519-8.","journal-title":"Cell Mol Life Sci"},{"key":"4030_CR6","doi-asserted-by":"publisher","first-page":"2956","DOI":"10.1093\/gbe\/evu223","volume":"6","author":"P Dasmeh","year":"2014","unstructured":"Dasmeh P, Serohijos AWR, Kepp KP, Shakhnovich EI. The influence of selection for protein stability on dN\/dS estimations. Genome Biol Evol. 2014;6:2956\u201367. https:\/\/doi.org\/10.1093\/gbe\/evu223.","journal-title":"Genome Biol Evol"},{"key":"4030_CR7","doi-asserted-by":"publisher","first-page":"e90504","DOI":"10.1371\/journal.pone.0090504","volume":"9","author":"KP Kepp","year":"2014","unstructured":"Kepp KP, Dasmeh P. A model of proteostatic energy cost and its use in analysis of proteome trends and sequence evolution. PLoS ONE. 2014;9:e90504. https:\/\/doi.org\/10.1371\/journal.pone.0090504.","journal-title":"PLoS ONE"},{"key":"4030_CR8","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.jmb.2003.12.048","volume":"336","author":"R Godoy-Ruiz","year":"2004","unstructured":"Godoy-Ruiz R, Perez-Jimenez R, Ibarra-Molero B, Sanchez-Ruiz JM. Relation between protein stability, evolution and structure, as probed by carboxylic acid mutations. J Mol Biol. 2004;336:313\u20138.","journal-title":"J Mol Biol"},{"key":"4030_CR9","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1002\/prot.22964","volume":"79","author":"RA Goldstein","year":"2011","unstructured":"Goldstein RA. The evolution and evolutionary consequences of marginal thermostability in proteins. Proteins. 2011;79:1396\u2013407. https:\/\/doi.org\/10.1002\/prot.22964.","journal-title":"Proteins"},{"key":"4030_CR10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.sbi.2015.01.003","volume":"32","author":"TG Kucukkal","year":"2015","unstructured":"Kucukkal TG, Petukh M, Li L, Alexov E. Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins. Curr Opin Struct Biol. 2015;32:18\u201324.","journal-title":"Curr Opin Struct Biol"},{"key":"4030_CR11","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1002\/humu.22770","volume":"36","author":"M Petukh","year":"2015","unstructured":"Petukh M, Kucukkal TG, Alexov E. On human disease-causing amino acid variants: statistical study of sequence and structural patterns. Hum Mutat. 2015;36:524\u201334. https:\/\/doi.org\/10.1002\/humu.22770.","journal-title":"Hum Mutat"},{"key":"4030_CR12","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.jmb.2005.08.020","volume":"353","author":"P Yue","year":"2005","unstructured":"Yue P, Li Z, Moult J. Loss of protein structure stability as a major causative factor in monogenic disease. J Mol Biol. 2005;353:459\u201373.","journal-title":"J Mol Biol"},{"key":"4030_CR13","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1093\/protein\/10.1.7","volume":"10","author":"CM Topham","year":"1997","unstructured":"Topham CM, Srinivasan N, Blundell TL. Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitution and propensity tables. Protein Eng. 1997;10:7\u201321.","journal-title":"Protein Eng"},{"key":"4030_CR14","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s10822-016-9914-3","volume":"30","author":"S Kulshreshtha","year":"2016","unstructured":"Kulshreshtha S, Chaudhary V, Goswami GK, Mathur N. Computational approaches for predicting mutant protein stability. J Comput Aided Mol Des. 2016;30:401\u201312.","journal-title":"J Comput Aided Mol Des"},{"key":"4030_CR15","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1093\/bioinformatics\/bty979","volume":"35","author":"L Montanucci","year":"2019","unstructured":"Montanucci L, Savojardo C, Martelli PL, Casadio R, Fariselli P. On the biases in predictions of protein stability changes upon variations: the INPS test case. Bioinformatics. 2019;35:2525\u20137.","journal-title":"Bioinformatics"},{"key":"4030_CR16","doi-asserted-by":"publisher","first-page":"3659","DOI":"10.1093\/bioinformatics\/bty348","volume":"34","author":"F Pucci","year":"2018","unstructured":"Pucci F, Bernaerts KV, Kwasigroch JM, Rooman M. Quantification of biases in predictions of protein stability changes upon mutations. Bioinformatics. 2018;34:3659\u201365.","journal-title":"Bioinformatics"},{"key":"4030_CR17","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1093\/protein\/gzp030","volume":"22","author":"V Potapov","year":"2009","unstructured":"Potapov V, Cohen M, Schreiber G. Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng Des Sel. 2009;22:553\u201360. https:\/\/doi.org\/10.1093\/protein\/gzp030.","journal-title":"Protein Eng Des Sel"},{"issue":"Web Server issu","key":"4030_CR18","doi-asserted-by":"publisher","first-page":"W215","DOI":"10.1093\/nar\/gkr363","volume":"39","author":"CL Worth","year":"2011","unstructured":"Worth CL, Preissner R, Blundell TL. SDM\u2014a server for predicting effects of mutations on protein stability and malfunction. Nucl Acids Res. 2011;39(Web Server issue):W215\u201322. https:\/\/doi.org\/10.1093\/nar\/gkr363.","journal-title":"Nucl Acids Res"},{"key":"4030_CR19","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1093\/bioinformatics\/btt691","volume":"30","author":"DEV Pires","year":"2014","unstructured":"Pires DEV, Ascher DB, Blundell TL. MCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics. 2014;30:335\u201342.","journal-title":"Bioinformatics"},{"key":"4030_CR20","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1093\/protein\/13.12.849","volume":"13","author":"D Gilis","year":"2000","unstructured":"Gilis D, Rooman M. PoPMuSiC, an algorithm for predicting protein mutant stability changes: application to prion proteins. Protein Eng. 2000;13:849\u201356.","journal-title":"Protein Eng"},{"key":"4030_CR21","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1093\/bioinformatics\/btp445","volume":"25","author":"Y Dehouck","year":"2009","unstructured":"Dehouck Y, Grosfils A, Folch B, Gilis D, Bogaerts P, Rooman M. Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0. Bioinformatics. 2009;25:2537\u201343.","journal-title":"Bioinformatics"},{"key":"4030_CR22","doi-asserted-by":"publisher","first-page":"W306","DOI":"10.1093\/nar\/gki375","volume":"33","author":"E Capriotti","year":"2005","unstructured":"Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucl Acids Res. 2005;33:W306\u201310.","journal-title":"Nucl Acids Res"},{"key":"4030_CR23","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1002\/humu.21242","volume":"31","author":"S Khan","year":"2010","unstructured":"Khan S, Vihinen M. Performance of protein stability predictors. Hum Mutat. 2010;31:675\u201384. https:\/\/doi.org\/10.1002\/humu.21242.","journal-title":"Hum Mutat"},{"key":"4030_CR24","doi-asserted-by":"publisher","first-page":"R213","DOI":"10.1016\/S0969-2126(00)00524-4","volume":"8","author":"YW Chen","year":"2000","unstructured":"Chen YW, Dodson EJ, Kleywegt GJ. Does NMR mean \u201cnot for molecular replacement\u201d? Using NMR-based search models to solve protein crystal structures. Structure. 2000;8:R213\u201320.","journal-title":"Structure"},{"key":"4030_CR25","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1093\/bioinformatics\/btm625","volume":"24","author":"K Hinsen","year":"2008","unstructured":"Hinsen K. Structural flexibility in proteins: impact of the crystal environment. Bioinformatics. 2008;24:521\u20138.","journal-title":"Bioinformatics"},{"issue":"Suppl_1","key":"4030_CR26","doi-asserted-by":"publisher","first-page":"D120","DOI":"10.1093\/nar\/gkh082","volume":"32","author":"KA Bava","year":"2004","unstructured":"Bava KA, Gromiha MM, Uedaira H, Kitajima K, Sarai A. ProTherm, version 4.0: thermodynamic database for proteins and mutants. Nucl Acids Res. 2004;32(Suppl_1):D120\u20131.","journal-title":"Nucl Acids Res"},{"key":"4030_CR27","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1002\/humu.22204","volume":"34","author":"P Sasidharan Nair","year":"2013","unstructured":"Sasidharan Nair P, Vihinen M. VariBench: a benchmark database for variations. Hum Mutat. 2013;34:42\u20139.","journal-title":"Hum Mutat"},{"key":"4030_CR28","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1093\/nar\/28.1.235","volume":"28","author":"HM Berman","year":"2000","unstructured":"Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Wessig H, et al. The protein data bank. Nucl Acids Res. 2000;28:235\u201342. https:\/\/doi.org\/10.1093\/nar\/28.1.235.","journal-title":"Nucl Acids Res"},{"key":"4030_CR29","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/S0022-2836(02)00442-4","volume":"320","author":"R Guerois","year":"2002","unstructured":"Guerois R, Nielsen JE, Serrano L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol. 2002;320:369\u201387.","journal-title":"J Mol Biol"},{"key":"4030_CR30","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1002\/prot.22921","volume":"79","author":"EH Kellogg","year":"2011","unstructured":"Kellogg EH, Leaver-Fay A, Baker D. Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins Struct Funct Bioinforma. 2011;79:830\u20138.","journal-title":"Proteins Struct Funct Bioinforma"},{"key":"4030_CR31","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.1021\/jp4119138","volume":"118","author":"KP Kepp","year":"2014","unstructured":"Kepp KP. Computing stability effects of mutations in human superoxide dismutase 1. J Phys Chem B. 2014;118:1799\u2013812. https:\/\/doi.org\/10.1021\/jp4119138.","journal-title":"J Phys Chem B"},{"key":"4030_CR32","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1016\/j.bbapap.2015.06.002","volume":"1854","author":"KP Kepp","year":"2015","unstructured":"Kepp KP. Towards a \u201cGolden Standard\u201d for computing globin stability: stability and structure sensitivity of myoglobin mutants. Biochim Biophys Acta Proteins Proteomics. 2015;1854:1239\u201348.","journal-title":"Biochim Biophys Acta Proteins Proteomics"},{"key":"4030_CR33","doi-asserted-by":"publisher","first-page":"4772","DOI":"10.1021\/acs.jcim.0c00591","volume":"60","author":"O Caldararu","year":"2020","unstructured":"Caldararu O, Mehra R, Blundell TL, Kepp KP. Systematic investigation of the data set dependency of protein stability predictors. J Chem Inf Model. 2020;60:4772\u201384.","journal-title":"J Chem Inf Model"},{"key":"4030_CR34","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1002\/humu.22987","volume":"37","author":"A Niroula","year":"2016","unstructured":"Niroula A, Vihinen M. Variation interpretation predictors: principles, types, performance, and choice. Hum Mutat. 2016;37:579\u201397.","journal-title":"Hum Mutat"},{"key":"4030_CR35","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1002\/cbic.201100051","volume":"12","author":"A Fischer","year":"2011","unstructured":"Fischer A, Seitz T, Lochner A, Sterner R, Merkl R, Bocola M. A fast and precise approach for computational saturation mutagenesis and its experimental validation by using an artificial (\u03b2\u03b1)8-barrel protein. ChemBioChem. 2011;12:1544\u201350.","journal-title":"ChemBioChem"},{"key":"4030_CR36","doi-asserted-by":"publisher","first-page":"W382","DOI":"10.1093\/nar\/gki387","volume":"33","author":"J Schymkowitz","year":"2005","unstructured":"Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. The FoldX web server: an online force field. Nucl Acids Res. 2005;33:W382\u20138. https:\/\/doi.org\/10.1093\/nar\/gki387.","journal-title":"Nucl Acids Res"},{"issue":"Suppl 2","key":"4030_CR37","doi-asserted-by":"publisher","first-page":"S6","DOI":"10.1186\/1471-2105-9-S2-S6","volume":"9","author":"E Capriotti","year":"2008","unstructured":"Capriotti E, Fariselli P, Rossi I, Casadio R. A three-state prediction of single point mutations on protein stability changes. BMC Bioinform. 2008;9(Suppl 2):S6. https:\/\/doi.org\/10.1186\/1471-2105-9-S2-S6.","journal-title":"BMC Bioinform"},{"key":"4030_CR38","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1186\/1471-2105-12-151","volume":"12","author":"Y Dehouck","year":"2011","unstructured":"Dehouck Y, Kwasigroch JM, Gilis D, Rooman M. PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinform. 2011;12:151.","journal-title":"BMC Bioinform"},{"key":"4030_CR39","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1186\/s12859-015-0548-6","volume":"16","author":"J Laimer","year":"2015","unstructured":"Laimer J, Hofer H, Fritz M, Wegenkittl S, Lackner P. MAESTRO\u2014multi agent stability prediction upon point mutations. BMC Bioinform. 2015;16:116.","journal-title":"BMC Bioinform"},{"issue":"Pt 6","key":"4030_CR40","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1042\/BST0351569","volume":"35","author":"MM Gromiha","year":"2007","unstructured":"Gromiha MM. Prediction of protein stability upon point mutations. Biochem Soc Trans. 2007;35(Pt 6):1569\u201373.","journal-title":"Biochem Soc Trans"},{"key":"4030_CR41","doi-asserted-by":"publisher","first-page":"3028","DOI":"10.1021\/ci300398z","volume":"52","author":"NJ Christensen","year":"2012","unstructured":"Christensen NJ, Kepp KP. Accurate stabilities of laccase mutants predicted with a modified FoldX protocol. J Chem Inf Model. 2012;52:3028\u201342.","journal-title":"J Chem Inf Model"},{"key":"4030_CR42","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1186\/1479-7364-4-3-207","volume":"4","author":"M Knudsen","year":"2010","unstructured":"Knudsen M, Wiuf C. The CATH database. Hum Genomics. 2010;4:207\u201312.","journal-title":"Hum Genomics"},{"key":"4030_CR43","first-page":"82","volume":"40","author":"WL DeLano","year":"2002","unstructured":"DeLano WL. Pymol: an open-source molecular graphics tool. CCP4 Newsl Protein Crystallogr. 2002;40:82\u201392.","journal-title":"CCP4 Newsl Protein Crystallogr"},{"key":"4030_CR44","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1002\/bip.360221211","volume":"22","author":"W Kabsch","year":"1983","unstructured":"Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers. 1983;22:2577\u2013637.","journal-title":"Biopolymers"},{"key":"4030_CR45","unstructured":"Hubbard S, Thornton J. NACCESS; 1993."},{"key":"4030_CR46","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/0022-2836(71)90324-X","volume":"55","author":"B Lee","year":"1971","unstructured":"Lee B, Richards FM. The interpretation of protein structures: estimation of static accessibility. J Mol Biol. 1971;55:379\u2013400.","journal-title":"J Mol Biol"},{"key":"4030_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-14-S19-S1","volume":"14","author":"DB Craig","year":"2013","unstructured":"Craig DB, Dombkowski AA. Disulfide by design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinform. 2013;14:1\u20137.","journal-title":"BMC Bioinform"},{"key":"4030_CR48","doi-asserted-by":"publisher","first-page":"1852","DOI":"10.1093\/bioinformatics\/btg231","volume":"19","author":"AA Dombkowski","year":"2003","unstructured":"Dombkowski AA. Disulfide by DesignTM: a computational method for the rational design of disulfide bonds in proteins. Bioinformatics. 2003;19:1852\u20133.","journal-title":"Bioinformatics"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04030-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-021-04030-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04030-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T14:09:51Z","timestamp":1614262191000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04030-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,25]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["4030"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04030-w","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,25]]},"assertion":[{"value":"30 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Professor Tom L. Blundell is one of the authors of the mCSM method studied in this work.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"88"}}