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Computational Mutagenesis Models for Protein Activity and Stability Analysis

Date

2008-01-30T19:11:57Z

Authors

Zhan, Bill

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Abstract

Missense mutations may cause structural alterations of a protein and lead to a loss/gain of activity and stability. Studies of missense mutations in proteins are important for understanding protein structure-function relationships, analyzing the function of gene variations, and designing new proteins. In this dissertation, we have developed computational mutagenesis models to predict the changes of stability and activity of protein mutants using the four-body statistical potential derived from Delaunay tessellations of protein structures. First, our results show that a strong correlation exists between the mean residual scores of mutants and the change of mutant stability in 18 proteins extracted from ProTherm database. Second, we developed robust and accurate machine-learning models based on the residual score profiles of protein mutants to predict the sign of mutant stability change. Third, we have demonstrated a correlation between changes of four-body statistical potential and activity alternation in human p53 and rabbit sarcoendo plasmic reticulum calcium-ATPases (SERCA1) mutants. The supervised machine-learning models based on the residual score profiles of protein mutants were also developed to predict the activity changes in p53 and SERCA1 mutants. Fourth, a highly significant correlation between changes in four-body statistical potential with conservation of amino-acid substitutions was observed. Finally, a novel statistical matrix based on the mean residual scores of all 380 types of mutations in 700 proteins was developed and a statistically significant correlation is revealed between the novel matrix and PAM/BLOSUM matrices. Overall, these conclusions support our hypothesis that computational mutagenesis models using four-body statistical potential present a powerful approach for predicting the changes of activity and stability in protein mutants.

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Keywords

Delaunay tessellation, Computational mutagenesis, Structure-function correlation, Conservative substitution, Functional prediction, Stability prediction

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