A new parameter-rich structure-aware mechanistic model for amino acid substitution during evolution.
Peter B Chi, Dohyup Kim, Jason K Lai, Nadia Bykova, Claudia C Weber, Jan Kubelka, David A Liberles
Author Information
Peter B Chi: Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, Pennsylvania, 19122.
Dohyup Kim: Department of Molecular Biology, University of Wyoming, Laramie, Wyoming, 82071.
Jason K Lai: Department of Molecular Biology, University of Wyoming, Laramie, Wyoming, 82071.
Nadia Bykova: Department of Molecular Biology, University of Wyoming, Laramie, Wyoming, 82071.
Claudia C Weber: Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, Pennsylvania, 19122.
Jan Kubelka: Department of Chemistry, University of Wyoming, Laramie, Wyoming, 82071.
David A Liberles: Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, Pennsylvania, 19122. ORCID
Improvements in the description of amino acid substitution are required to develop better pseudo-energy-based protein structure-aware models for use in phylogenetic studies. These models are used to characterize the probabilities of amino acid substitution and enable better simulation of protein sequences over a phylogeny. A better characterization of amino acid substitution probabilities in turn enables numerous downstream applications, like detecting positive selection, ancestral sequence reconstruction, and evolutionarily-motivated protein engineering. Many existing Markov models for amino acid substitution in molecular evolution disregard molecular structure and describe the amino acid substitution process over longer evolutionary periods poorly. Here, we present a new model upgraded with a site-specific parameterization of pseudo-energy terms in a coarse-grained force field, which describes local heterogeneity in physical constraints on amino acid substitution better than a previous pseudo-energy-based model with minimum cost in runtime. The importance of each weight term parameterization in characterizing underlying features of the site, including contact number, solvent accessibility, and secondary structural elements was evaluated, returning both expected and biologically reasonable relationships between model parameters. This results in the acceptance of proposed amino acid substitutions that more closely resemble those observed site-specific frequencies in gene family alignments. The modular site-specific pseudo-energy function is made available for download through the following website: https://liberles.cst.temple.edu/Software/CASS/index.html.
