Literature DB >> 31400199

Impact of In Vivo Protein Folding Probability on Local Fitness Landscapes.

Matthew S Faber1, Emily E Wrenbeck2, Laura R Azouz2, Paul J Steiner3, Timothy A Whitehead2,3,4.   

Abstract

It is incompletely understood how biophysical properties like protein stability impact molecular evolution and epistasis. Epistasis is defined as specific when a mutation exclusively influences the phenotypic effect of another mutation, often at physically interacting residues. In contrast, nonspecific epistasis results when a mutation is influenced by a large number of nonlocal mutations. As most mutations are pleiotropic, the in vivo folding probability-governed by basal protein stability-is thought to determine activity-enhancing mutational tolerance, implying that nonspecific epistasis is dominant. However, evidence exists for both specific and nonspecific epistasis as the prevalent factor, with limited comprehensive data sets to support either claim. Here, we use deep mutational scanning to probe how in vivo enzyme folding probability impacts local fitness landscapes. We computationally designed two different variants of the amidase AmiE with statistically indistinguishable catalytic efficiencies but lower probabilities of folding in vivo compared with wild-type. Local fitness landscapes show slight alterations among variants, with essentially the same global distribution of fitness effects. However, specific epistasis was predominant for the subset of mutations exhibiting positive sign epistasis. These mutations mapped to spatially distinct locations on AmiE near the initial mutation or proximal to the active site. Intriguingly, the majority of specific epistatic mutations were codon dependent, with different synonymous codons resulting in fitness sign reversals. Together, these results offer a nuanced view of how protein folding probability impacts local fitness landscapes and suggest that transcriptional-translational effects are as important as stability in determining evolutionary outcomes.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  deep mutational scanning; enzyme evolvability; enzyme stability; protein fitness landscapes

Mesh:

Substances:

Year:  2019        PMID: 31400199     DOI: 10.1093/molbev/msz184

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  8 in total

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Journal:  ACS Synth Biol       Date:  2020-01-07       Impact factor: 5.110

2.  A Method for User-defined Mutagenesis by Integrating Oligo Pool Synthesis Technology with Nicking Mutagenesis.

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Review 3.  Evolution, folding, and design of TIM barrels and related proteins.

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4.  Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation.

Authors:  Liskin Swint-Kruse; Tyler A Martin; Braelyn M Page; Tiffany Wu; Paige M Gerhart; Larissa L Dougherty; Qingling Tang; Daniel J Parente; Brian R Mosier; Leonidas E Bantis; Aron W Fenton
Journal:  Protein Sci       Date:  2021-06-11       Impact factor: 6.993

5.  Allostery and Epistasis: Emergent Properties of Anisotropic Networks.

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Journal:  Entropy (Basel)       Date:  2020-06-16       Impact factor: 2.524

Review 6.  Oligo Pools as an Affordable Source of Synthetic DNA for Cost-Effective Library Construction in Protein- and Metabolic Pathway Engineering.

Authors:  Bastiaan P Kuiper; Rianne C Prins; Sonja Billerbeck
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7.  Structural changes and adaptative evolutionary constraints in FLOWERING LOCUS T and TERMINAL FLOWER1-like genes of flowering plants.

Authors:  Deivid Almeida de Jesus; Darlisson Mesquista Batista; Elton Figueira Monteiro; Shayla Salzman; Lucas Miguel Carvalho; Kauê Santana; Thiago André
Journal:  Front Genet       Date:  2022-09-29       Impact factor: 4.772

Review 8.  High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering.

Authors:  Rosario Vanella; Gordana Kovacevic; Vanni Doffini; Jaime Fernández de Santaella; Michael A Nash
Journal:  Chem Commun (Camb)       Date:  2022-02-17       Impact factor: 6.222

  8 in total

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