Pycofitness—evaluating the fitness landscape of RNA and protein sequences
Fabrizio Pucci, Mehari B Zerihun, Marianne Rooman, Alexander Schug- Computational Mathematics
- Computational Theory and Mathematics
- Computer Science Applications
- Molecular Biology
- Biochemistry
- Statistics and Probability
Abstract
Motivation
The 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.
Results
We 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
https://github.com/KIT-MBS/pycofitness
Supplementary information
Supplementary data is available at Bioinformatics online.