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Structure-based prediction of the effects of a missense variant on protein stability

In English

Författare

Summary, in English

Predicting the effects of amino acid substitutions on protein stability provides invaluable information for protein design, the assignment of biological function, and for understanding disease-associated variations. To understand the effects of substitutions, computational models are preferred to time-consuming and expensive experimental methods. Several methods have been proposed for this task including machine learning-based approaches. However, models trained using limited data have performance problems and many model parameters tend to be over-fitted. To decrease the number of model parameters and to improve the generalization potential, we calculated the amino acid contact energy change for point variations using a structure-based coarse-grained model. Based on the structural properties including contact energy (CE) and further physicochemical properties of the amino acids as input features, we developed two support vector machine classifiers. M47 predicted the stability of variant proteins with an accuracy of 87 % and a Matthews correlation coefficient of 0.68 for a large dataset of 1925 variants, whereas M8 performed better when a relatively small dataset of 388 variants was used for 20-fold cross-validation. The performance of the M47 classifier on all six tested contingency table evaluation parameters is better than that of existing machine learning-based models or energy function-based protein stability classifiers.

Publiceringsår

2013

Språk

Engelska

Sidor

847-855

Publikation/Tidskrift/Serie

Amino Acids

Volym

44

Issue

3

Dokumenttyp

Artikel i tidskrift

Förlag

Springer

Ämne

  • Medical Genetics

Nyckelord

  • Amino acid mutation
  • Physicochemical properties
  • Residue-residue contact
  • energy
  • Support vector machine
  • Protein stability prediction

Status

Published

Forskningsgrupp

  • Protein Bioinformatics

ISBN/ISSN/Övrigt

  • ISSN: 0939-4451