STRUM: structure-based prediction of protein stability changes upon single-point mutation

Motivation: Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from exp...

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Published in:Bioinformatics (Oxford, England) Vol. 32; no. 19; pp. 2936 - 2946
Main Authors: Quan, Lijun, Lv, Qiang, Zhang, Yang
Format: Journal Article
Language:English
Published: England Oxford University Press 01.10.2016
Subjects:
Algorithms
Humans
Models, Molecular
Original Papers
Point Mutation
Protein Stability
Proteins
Software
Structure-Activity Relationship
ISSN:1367-4803, 1367-4811, 1367-4811
Online Access:Get full text
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Summary:Motivation: Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling. Results: We developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations. Availability and Implementation:  http://zhanglab.ccmb.med.umich.edu/STRUM/ Contact:  qiang@suda.edu.cn and zhng@umich.edu Supplementary information:  Supplementary data are available at Bioinformatics online.
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Associate Editor: Anna Tramontano
ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btw361