Bioinformatics Advance Access originally published online on July 16, 2008
Bioinformatics 2008 24(18):2002-2009; doi:10.1093/bioinformatics/btn353
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis
Laboratory for Structural Bioinformatics, Department of Bioinformatics and Computational Biology, George Mason University, 10900 University Blvd., MSN 5B3, Manassas, VA 20110, USA
*To whom correspondence should be protect
 |
Abstract |
|---|
Motivation: Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (
G) and denaturant (GH2O) denaturations, as well as mutant thermal stability (Tm), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal.
Results: We detail a computational mutagenesis technique based on a four-body, knowledge-based, statistical contact potential. For any mutation due to a single amino acid replacement in a protein, the method provides an empirical normalized measure of the ensuing environmental perturbation occurring at every residue position. A feature vector is generated for the mutant by considering perturbations at the mutated position and it's ordered six nearest neighbors in the 3-dimensional (3D) protein structure. These predictors of stability change are evaluated by applying machine learning tools to large training sets of mutants derived from diverse proteins that have been experimentally studied and described. Predictive models based on our combined approach are either comparable to, or in many cases significantly outperform, previously published results.
Availability: A web server with supporting documentation is available at http://proteins.gmu.edu/automute
Contact: ivaisman{at}gmu.edu
Associate Editor: Anna Tramontano
Received on November 26, 2007; revised on June 1, 2008; accepted on June 9, 2008
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  M. Masso, E. Mathe, N. Parvez, K. Hijazi, and I. I. Vaisman Modeling the functional consequences of single residue replacements in bacteriophage f1 gene V protein Protein Eng. Des. Sel., November 1, 2009; 22(11): 665 - 671. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Dehouck, A. Grosfils, B. Folch, D. Gilis, P. Bogaerts, and M. Rooman Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0 Bioinformatics, October 1, 2009; 25(19): 2537 - 2543. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L.-T. Huang and M. M. Gromiha Reliable prediction of protein thermostability change upon double mutation from amino acid sequence Bioinformatics, September 1, 2009; 25(17): 2181 - 2187. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Lonquety, Z. Lacroix, N. Papandreou, and J. Chomilier SPROUTS: a database for the evaluation of protein stability upon point mutation Nucleic Acids Res., January 1, 2009; 37(suppl_1): D374 - D379. [Abstract] [Full Text] [PDF]
|
|