Packpred: Predicting the Functional Effect of Missense Mutations.

ABSTRACT: Predicting the functional consequences of single point mutations has relevance to protein function annotation and to clinical analysis/diagnosis. We developed and tested Packpred that makes use of a multi-body clique statistical potential in combination with a depth-dependent amino acid substitution matrix (FADHM) and positional Shannon entropy to predict the functional consequences of point mutations in proteins. Parameters were trained over a saturation mutagenesis data set of T4-lysozyme (1,966 mutations). The method was tested over another saturation mutagenesis data set (CcdB; 1,534 mutations) and the Missense3D data set (4,099 mutations). The performance of Packpred was compared against those of six other contemporary methods. With MCC values of 0.42, 0.47, and 0.36 on the training a

SUBMITTER: Tan KP 

PROVIDER: S-EPMC8417552 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature