Literature DB >> 29120346

Inverse statistical physics of protein sequences: a key issues review.

Simona Cocco1, Christoph Feinauer, Matteo Figliuzzi, Rémi Monasson, Martin Weigt.   

Abstract

In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e. evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.

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Year:  2018        PMID: 29120346     DOI: 10.1088/1361-6633/aa9965

Source DB:  PubMed          Journal:  Rep Prog Phys        ISSN: 0034-4885


  39 in total

1.  Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction.

Authors:  Susann Vorberg; Stefan Seemayer; Johannes Söding
Journal:  PLoS Comput Biol       Date:  2018-11-05       Impact factor: 4.475

2.  Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation.

Authors:  Allan Haldane; Ronald M Levy
Journal:  Phys Rev E       Date:  2019-03       Impact factor: 2.529

3.  Learning protein constitutive motifs from sequence data.

Authors:  Jérôme Tubiana; Simona Cocco; Rémi Monasson
Journal:  Elife       Date:  2019-03-12       Impact factor: 8.140

4.  Computationally Aided Discovery of LysEFm5 Variants with Improved Catalytic Activity and Stability.

Authors:  Tsvetelina H Baryakova; Seth C Ritter; Daniel T Tresnak; Benjamin J Hackel
Journal:  Appl Environ Microbiol       Date:  2020-02-03       Impact factor: 4.792

5.  Field-theoretic density estimation for biological sequence space with applications to 5' splice site diversity and aneuploidy in cancer.

Authors:  Wei-Chia Chen; Juannan Zhou; Jason M Sheltzer; Justin B Kinney; David M McCandlish
Journal:  Proc Natl Acad Sci U S A       Date:  2021-10-05       Impact factor: 11.205

6.  Global analysis of more than 50,000 SARS-CoV-2 genomes reveals epistasis between eight viral genes.

Authors:  Hong-Li Zeng; Vito Dichio; Edwin Rodríguez Horta; Kaisa Thorell; Erik Aurell
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-17       Impact factor: 11.205

7.  Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks.

Authors:  Purushottam D Dixit; Eugenia Lyashenko; Mario Niepel; Dennis Vitkup
Journal:  Cell Syst       Date:  2019-12-18       Impact factor: 10.304

8.  Mi3-GPU: MCMC-based Inverse Ising Inference on GPUs for protein covariation analysis.

Authors:  Allan Haldane; Ronald M Levy
Journal:  Comput Phys Commun       Date:  2020-04-17       Impact factor: 4.390

9.  MPL resolves genetic linkage in fitness inference from complex evolutionary histories.

Authors:  Muhammad Saqib Sohail; Raymond H Y Louie; Matthew R McKay; John P Barton
Journal:  Nat Biotechnol       Date:  2020-11-30       Impact factor: 54.908

10.  Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment.

Authors:  Akira R Kinjo
Journal:  Biophys Physicobiol       Date:  2017-07-12
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