ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Stereochemical Criteria for Prediction of the Effects of Proline Mutations on Protein Stability

Bajaj, Kanika and Madhusudhan, MS and Adkar, Bharat V and Chakrabarti, Purbani and Ramakrishnan, C and Sali, Andrej and Varadarajan, Raghavan (2007) Stereochemical Criteria for Prediction of the Effects of Proline Mutations on Protein Stability. In: PLoS Computational Biology, 3 (12). pp. 2465-2475.

[img] PDF
pcbi.0030241.pdf - Published Version
Restricted to Registered users only

Download (529kB) | Request a copy
Official URL: http://www.ploscompbiol.org/article/info%3Adoi%2F1...

Abstract

When incorporated into a polypeptide chain, proline (Pro) differs from all other naturally occurring amino acid residues in two important respects. The \phi dihedral angle of Pro is constrained to values close to −65° and Pro lacks an amide hydrogen. Consequently, mutations which result in introduction of Pro can significantly affect protein stability. In the present work, we describe a procedure to accurately predict the effect of Pro introduction on protein thermodynamic stability. Seventy-seven of the 97 non-Pro amino acid residues in the model protein, CcdB, were individually mutated to Pro, andthe in vivo activity of each mutant was characterized. A decision tree to classify the mutation as perturbing or nonperturbing was created by correlating stereochemical properties of mutants to activity data. The stereochemical properties including main chain dihedral angle \phi and main chain amide H-bonds (hydrogen bonds) were determined from 3D models of the mutant proteins built using MODELLER. We assessed the performance of the decision tree on a large dataset of 163 single-site Pro mutations of T4 lysozyme, 74 nsSNPs, and 52 other Pro substitutions from the literature. The overall accuracy of this algorithm was found to be 81% in the case of CcdB, 77% in the case of lysozyme, 76% in the case of nsSNPs, and 71% in the case of other Pro substitution data. The accuracy of Pro scanning mutagenesis for secondary structure assignment was also assessed and found to be at best 69%. Our prediction procedure will be useful in annotating uncharacterized nsSNPs of disease-associated proteins and for protein engineering and design.

Item Type: Journal Article
Publication: PLoS Computational Biology
Publisher: International Society for Computational Biology
Additional Information: Copyright of this article belongs to International Society for Computational Biology.
Department/Centre: Division of Biological Sciences > Molecular Biophysics Unit
Date Deposited: 28 Feb 2008
Last Modified: 01 Mar 2012 10:06
URI: http://eprints.iisc.ac.in/id/eprint/11222

Actions (login required)

View Item View Item