Literature DB >> 29178386

A new parameter-rich structure-aware mechanistic model for amino acid substitution during evolution.

Peter B Chi1,2, Dohyup Kim3, Jason K Lai3, Nadia Bykova3,4, Claudia C Weber1, Jan Kubelka5, David A Liberles1,3.   

Abstract

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.
© 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  SH2 domain; coarse-grained force field; macromolecular structure; mathematical model; protein evolution; sequence analysis

Mesh:

Substances:

Year:  2017        PMID: 29178386      PMCID: PMC5897152          DOI: 10.1002/prot.25429

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  31 in total

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Journal:  Mol Biol Evol       Date:  1994-09       Impact factor: 16.240

Review 5.  Determinants of the rate of protein sequence evolution.

Authors:  Jianzhi Zhang; Jian-Rong Yang
Journal:  Nat Rev Genet       Date:  2015-06-09       Impact factor: 53.242

6.  Spin glasses and the statistical mechanics of protein folding.

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Journal:  Proc Natl Acad Sci U S A       Date:  1987-11       Impact factor: 11.205

7.  Non-homogeneous models of sequence evolution in the Bio++ suite of libraries and programs.

Authors:  Julien Dutheil; Bastien Boussau
Journal:  BMC Evol Biol       Date:  2008-09-22       Impact factor: 3.260

8.  On the need for mechanistic models in computational genomics and metagenomics.

Authors:  David A Liberles; Ashley I Teufel; Liang Liu; Tanja Stadler
Journal:  Genome Biol Evol       Date:  2013       Impact factor: 3.416

9.  GenBank.

Authors:  Dennis A Benson; Mark Cavanaugh; Karen Clark; Ilene Karsch-Mizrachi; David J Lipman; James Ostell; Eric W Sayers
Journal:  Nucleic Acids Res       Date:  2016-11-28       Impact factor: 16.971

10.  The Pfam protein families database: towards a more sustainable future.

Authors:  Robert D Finn; Penelope Coggill; Ruth Y Eberhardt; Sean R Eddy; Jaina Mistry; Alex L Mitchell; Simon C Potter; Marco Punta; Matloob Qureshi; Amaia Sangrador-Vegas; Gustavo A Salazar; John Tate; Alex Bateman
Journal:  Nucleic Acids Res       Date:  2015-12-15       Impact factor: 16.971

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  3 in total

1.  mtProtEvol: the resource presenting molecular evolution analysis of proteins involved in the function of Vertebrate mitochondria.

Authors:  Anastasia A Kuzminkova; Anastasia D Sokol; Kristina E Ushakova; Konstantin Yu Popadin; Konstantin V Gunbin
Journal:  BMC Evol Biol       Date:  2019-02-26       Impact factor: 3.260

2.  Ancestral Sequence Reconstruction: From Chemical Paleogenetics to Maximum Likelihood Algorithms and Beyond.

Authors:  Avery G A Selberg; Eric A Gaucher; David A Liberles
Journal:  J Mol Evol       Date:  2021-01-24       Impact factor: 2.395

3.  Characterizing lineage-specific evolution and the processes driving genomic diversification in chordates.

Authors:  David E Northover; Stephen D Shank; David A Liberles
Journal:  BMC Evol Biol       Date:  2020-02-11       Impact factor: 3.260

  3 in total

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