Journal Article

FZJ-2024-01756

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png pycofitness—Evaluating the fitness landscape of RNA and protein sequences

Pucci, F. ; Zerihun, M. B. ; Rooman, M. ; Schug, A. (Corresponding author)FZJ*

2024
Oxford Univ. Press Oxford

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Please use a persistent id in citations: doi:10.1093/bioinformatics/btae074  doi:10.34734/FZJ-2024-01756

Abstract: MotivationThe accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue.ResultsWe present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences. It is based on coevolution and, more specifically, on a popular inverse statistical approach, namely direct coupling analysis by pseudo-likelihood maximization. Its efficient implementation and user-friendly command line interface make it an easy-to-use tool even for researchers with no bioinformatics background. To illustrate its strengths, we present three applications in which pycofitness efficiently predicts the deleteriousness of genetic variants and the effect of mutations on protein fitness and thermodynamic stability.Availability and implementationhttps://github.com/KIT-MBS/pycofitness.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2024

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