Literature DB >> 25114242

Coevolutionary information, protein folding landscapes, and the thermodynamics of natural selection.

Faruck Morcos1, Nicholas P Schafer2, Ryan R Cheng1, José N Onuchic3, Peter G Wolynes4.   

Abstract

The energy landscape used by nature over evolutionary timescales to select protein sequences is essentially the same as the one that folds these sequences into functioning proteins, sometimes in microseconds. We show that genomic data, physical coarse-grained free energy functions, and family-specific information theoretic models can be combined to give consistent estimates of energy landscape characteristics of natural proteins. One such characteristic is the effective temperature T(sel) at which these foldable sequences have been selected in sequence space by evolution. T(sel) quantifies the importance of folded-state energetics and structural specificity for molecular evolution. Across all protein families studied, our estimates for T(sel) are well below the experimental folding temperatures, indicating that the energy landscapes of natural foldable proteins are strongly funneled toward the native state.

Keywords:  elastic effects; energy landscape theory; funneled landscapes; information theory; selection temperature

Mesh:

Substances:

Year:  2014        PMID: 25114242      PMCID: PMC4151759          DOI: 10.1073/pnas.1413575111

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  30 in total

1.  Modeling evolutionary landscapes: mutational stability, topology, and superfunnels in sequence space.

Authors:  E Bornberg-Bauer; H S Chan
Journal:  Proc Natl Acad Sci U S A       Date:  1999-09-14       Impact factor: 11.205

2.  Gradual development of protein-like global structures through functional selection.

Authors:  T Yomo; S Saito; M Sasai
Journal:  Nat Struct Biol       Date:  1999-08

3.  Computational analysis of sequence selection mechanisms.

Authors:  Leonid Meyerguz; Catherine Grasso; Jon Kleinberg; Ron Elber
Journal:  Structure       Date:  2004-04       Impact factor: 5.006

4.  Genomics-aided structure prediction.

Authors:  Joanna I Sułkowska; Faruck Morcos; Martin Weigt; Terence Hwa; José N Onuchic
Journal:  Proc Natl Acad Sci U S A       Date:  2012-06-12       Impact factor: 11.205

Review 5.  An integrated view of molecular coevolution in protein-protein interactions.

Authors:  Simon C Lovell; David L Robertson
Journal:  Mol Biol Evol       Date:  2010-06-14       Impact factor: 16.240

Review 6.  Understanding protein evolution: from protein physics to Darwinian selection.

Authors:  Konstantin B Zeldovich; Eugene I Shakhnovich
Journal:  Annu Rev Phys Chem       Date:  2008       Impact factor: 12.703

7.  Structure, dynamics and binding characteristics of the second PDZ domain of PTP-BL.

Authors:  Tine Walma; Christian A E M Spronk; Marco Tessari; Jan Aelen; Jan Schepens; Wiljan Hendriks; Geerten W Vuister
Journal:  J Mol Biol       Date:  2002-03-08       Impact factor: 5.469

8.  Toward an outline of the topography of a realistic protein-folding funnel.

Authors:  J N Onuchic; P G Wolynes; Z Luthey-Schulten; N D Socci
Journal:  Proc Natl Acad Sci U S A       Date:  1995-04-11       Impact factor: 11.205

9.  The Pfam protein families database.

Authors:  Robert D Finn; Jaina Mistry; John Tate; Penny Coggill; Andreas Heger; Joanne E Pollington; O Luke Gavin; Prasad Gunasekaran; Goran Ceric; Kristoffer Forslund; Liisa Holm; Erik L L Sonnhammer; Sean R Eddy; Alex Bateman
Journal:  Nucleic Acids Res       Date:  2009-11-17       Impact factor: 16.971

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

Authors:  J D Bryngelson; P G Wolynes
Journal:  Proc Natl Acad Sci U S A       Date:  1987-11       Impact factor: 11.205

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  48 in total

1.  Constructing sequence-dependent protein models using coevolutionary information.

Authors:  Ryan R Cheng; Mohit Raghunathan; Jeffrey K Noel; José N Onuchic
Journal:  Protein Sci       Date:  2015-08-10       Impact factor: 6.725

2.  Surveying the Energy Landscapes of Aβ Fibril Polymorphism.

Authors:  Mingchen Chen; Nicholas P Schafer; Peter G Wolynes
Journal:  J Phys Chem B       Date:  2018-10-01       Impact factor: 2.991

3.  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

4.  Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis.

Authors:  Guido Uguzzoni; Shalini John Lovis; Francesco Oteri; Alexander Schug; Hendrik Szurmant; Martin Weigt
Journal:  Proc Natl Acad Sci U S A       Date:  2017-03-13       Impact factor: 11.205

5.  On the Natural Structure of Amino Acid Patterns in Families of Protein Sequences.

Authors:  Pablo Turjanski; Diego U Ferreiro
Journal:  J Phys Chem B       Date:  2018-10-08       Impact factor: 2.991

6.  Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme.

Authors:  Ryan L Hayes; Jonah Z Vilseck; Charles L Brooks
Journal:  Protein Sci       Date:  2018-11       Impact factor: 6.725

7.  Biophysics of protein evolution and evolutionary protein biophysics.

Authors:  Tobias Sikosek; Hue Sun Chan
Journal:  J R Soc Interface       Date:  2014-11-06       Impact factor: 4.118

8.  Contact Statistics Highlight Distinct Organizing Principles of Proteins and RNA.

Authors:  Lei Liu; Changbong Hyeon
Journal:  Biophys J       Date:  2016-06-07       Impact factor: 4.033

9.  Structural propensities of kinase family proteins from a Potts model of residue co-variation.

Authors:  Allan Haldane; William F Flynn; Peng He; R S K Vijayan; Ronald M Levy
Journal:  Protein Sci       Date:  2016-06-26       Impact factor: 6.725

Review 10.  Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness.

Authors:  Ronald M Levy; Allan Haldane; William F Flynn
Journal:  Curr Opin Struct Biol       Date:  2016-11-18       Impact factor: 6.809

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