Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations

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dc.contributor.authorIqbal, S.
dc.contributor.authorLi, F.
dc.contributor.authorAkutsu, T.
dc.contributor.authorAscher, D.B.
dc.contributor.authorWebb, G.I.
dc.contributor.authorSong, J.
dc.date.issued2021
dc.description.abstractUnderstanding how a mutation might affect protein stability is of significant importance to protein engineering and for understanding protein evolution genetic diseases. While a number of computational tools have been developed to predict the effect of missense mutations on protein stability protein stability upon mutations, they are known to exhibit large biases imparted in part by the data used to train and evaluate them. Here, we provide a comprehensive overview of predictive tools, which has provided an evolving insight into the importance and relevance of features that can discern the effects of mutations on protein stability. A diverse selection of these freely available tools was benchmarked using a large mutation-level blind dataset of 1342 experimentally characterised mutations across 130 proteins from ThermoMutDB, a second test dataset encompassing 630 experimentally characterised mutations across 39 proteins from iStable2.0 and a third blind test dataset consisting of 268 mutations in 27 proteins from the newly published ProThermDB. The performance of the methods was further evaluated with respect to the site of mutation, type of mutant residue and by ranging the pH and temperature. Additionally, the classification performance was also evaluated by classifying the mutations as stabilizing (∆∆G ≥ 0) or destabilizing (∆∆G < 0). The results reveal that the performance of the predictors is affected by the site of mutation and the type of mutant residue. Further, the results show very low performance for pH values 6-8 and temperature higher than 65 for all predictors except iStable2.0 on the S630 dataset. To illustrate how stability and structure change upon single point mutation, we considered four stabilizing, two destabilizing and two stabilizing mutations from two proteins, namely the toxin protein and bovine liver cytochrome. Overall, the results on S268, S630 and S1342 datasets show that the performance of the integrated predictors is better than the mechanistic or individual machine learning predictors. We expect that this paper will provide useful guidance for the design and development of next-generation bioinformatic tools for predicting protein stability changes upon mutations.
dc.description.statementofresponsibilityShahid Iqbal, Fuyi Li, Tatsuya Akutsu, David B. Ascher, Geoffrey I. Webb and Jiangning Song
dc.identifier.citationBriefings in Bioinformatics, 2021; 22(6):1-23
dc.identifier.doi10.1093/bib/bbab184
dc.identifier.issn1467-5463
dc.identifier.issn1477-4054
dc.identifier.orcidLi, F. [0000-0001-5216-3213]
dc.identifier.urihttps://hdl.handle.net/2440/139705
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1127948
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1174405
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1144652
dc.rights© The Author(s) 2021. Published by Oxford University Press. All rights reserved.
dc.source.urihttps://doi.org/10.1093/bib/bbab184
dc.subjectprotein stability change; bioinformatics; machine learning; deep learning; feature engineering; predictors
dc.subject.meshProteins
dc.subject.meshReproducibility of Results
dc.subject.meshComputational Biology
dc.subject.meshEvolution, Molecular
dc.subject.meshProtein Conformation
dc.subject.meshStructure-Activity Relationship
dc.subject.meshMutation, Missense
dc.subject.meshAlgorithms
dc.subject.meshModels, Molecular
dc.subject.meshSoftware
dc.subject.meshDatabases, Protein
dc.subject.meshProtein Stability
dc.subject.meshMachine Learning
dc.titleAssessing the performance of computational predictors for estimating protein stability changes upon missense mutations
dc.typeJournal article
pubs.publication-statusPublished

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