Literature DB >> 33067388

Funneled energy landscape unifies principles of protein binding and evolution.

Zhiqiang Yan1, Jin Wang2.   

Abstract

Most proteins have evolved to spontaneously fold into native structure and specifically bind with their partners for the purpose of fulfilling biological functions. According to Darwin, protein sequences evolve through random mutations, and only the fittest survives. The understanding of how the evolutionary selection sculpts the interaction patterns for both biomolecular folding and binding is still challenging. In this study, we incorporated the constraint of functional binding into the selection fitness based on the principle of minimal frustration for the underlying biomolecular interactions. Thermodynamic stability and kinetic accessibility were derived and quantified from a global funneled energy landscape that satisfies the requirements of both the folding into the stable structure and binding with the specific partner. The evolution proceeds via a bowl-like evolution energy landscape in the sequence space with a closed-ring attractor at the bottom. The sequence space is increasingly reduced until this ring attractor is reached. The molecular-interaction patterns responsible for folding and binding are identified from the evolved sequences, respectively. The residual positions participating in the interactions responsible for folding are highly conserved and maintain the hydrophobic core under additional evolutionary constraints of functional binding. The positions responsible for binding constitute a distributed network via coupling conservations that determine the specificity of binding with the partner. This work unifies the principles of protein binding and evolution under minimal frustration and sheds light on the evolutionary design of proteins for functions.

Keywords:  energy landscape; minimal frustration; protein binding; protein evolution; protein folding

Mesh:

Substances:

Year:  2020        PMID: 33067388      PMCID: PMC7959555          DOI: 10.1073/pnas.2013822117

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


  51 in total

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  1 in total

Review 1.  Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems.

Authors:  Jin Wang
Journal:  J Biol Phys       Date:  2021-11-25       Impact factor: 1.365

  1 in total

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