Literature DB >> 15777696

Looking at structure, stability, and evolution of proteins through the principal eigenvector of contact matrices and hydrophobicity profiles.

Ugo Bastolla1, Markus Porto, H Eduardo Roman, Michele Vendruscolo.   

Abstract

We review and further develop an analytical model that describes how thermodynamic constraints on the stability of the native state influence protein evolution in a site-specific manner. To this end, we represent both protein sequences and protein structures as vectors: structures are represented by the principal eigenvector (PE) of the protein contact matrix, a quantity that resembles closely the effective connectivity of each site; sequences are represented through the "interactivity" of each amino acid type, using novel parameters that are correlated with hydropathy scales. These interactivity parameters are more strongly correlated than the other hydropathy scales that we examine with: (1) the change upon mutations of the unfolding free energy of proteins with two-states thermodynamics; (2) genomic properties as the genome-size and the genome-wide GC content; (3) the main eigenvectors of the substitution matrices. The evolutionary average of the interactivity vector correlates very strongly with the PE of a protein structure. Using this result, we derive an analytic expression for site-specific distributions of amino acids across protein families in the form of Boltzmann distributions whose "inverse temperature" is a function of the PE component. We show that our predictions are in agreement with site-specific amino acid distributions obtained from the Protein Data Bank, and we determine the mutational model that best fits the observed site-specific amino acid distributions. Interestingly, the optimal model almost minimizes the rate at which deleterious mutations are eliminated by natural selection.

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Year:  2005        PMID: 15777696     DOI: 10.1016/j.gene.2004.12.015

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  8 in total

1.  A nonadaptive origin of a beneficial trait: in silico selection for free energy of folding leads to the neutral emergence of mutational robustness in single domain proteins.

Authors:  Rafael F Pagan; Steven E Massey
Journal:  J Mol Evol       Date:  2013-12-21       Impact factor: 2.395

2.  Protein evolution along phylogenetic histories under structurally constrained substitution models.

Authors:  Miguel Arenas; Helena G Dos Santos; David Posada; Ugo Bastolla
Journal:  Bioinformatics       Date:  2013-09-12       Impact factor: 6.937

3.  A protein evolution model with independent sites that reproduces site-specific amino acid distributions from the Protein Data Bank.

Authors:  Ugo Bastolla; Markus Porto; H Eduardo Roman; Michele Vendruscolo
Journal:  BMC Evol Biol       Date:  2006-05-31       Impact factor: 3.260

4.  Positive and negative design in stability and thermal adaptation of natural proteins.

Authors:  Igor N Berezovsky; Konstantin B Zeldovich; Eugene I Shakhnovich
Journal:  PLoS Comput Biol       Date:  2007-02-01       Impact factor: 4.475

5.  Efficient Parameter Estimation of Generalizable Coarse-Grained Protein Force Fields Using Contrastive Divergence: A Maximum Likelihood Approach.

Authors:  Csilla Várnai; Nikolas S Burkoff; David L Wild
Journal:  J Chem Theory Comput       Date:  2013-11-15       Impact factor: 6.006

6.  A first-principles model of early evolution: emergence of gene families, species, and preferred protein folds.

Authors:  Konstantin B Zeldovich; Peiqiu Chen; Boris E Shakhnovich; Eugene I Shakhnovich
Journal:  PLoS Comput Biol       Date:  2007-07       Impact factor: 4.475

Review 7.  Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.

Authors:  Andrew Currin; Neil Swainston; Philip J Day; Douglas B Kell
Journal:  Chem Soc Rev       Date:  2015-03-07       Impact factor: 54.564

8.  Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness.

Authors:  Martin Schwersensky; Marianne Rooman; Fabrizio Pucci
Journal:  BMC Biol       Date:  2020-10-20       Impact factor: 7.431

  8 in total

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