Literature DB >> 27010590

Quantifying and understanding the fitness effects of protein mutations: Laboratory versus nature.

Jeffrey I Boucher1, Daniel N A Bolon1, Dan S Tawfik2.   

Abstract

The last decade has seen a growing number of experiments aimed at systematically mapping the effects of mutations in different proteins, and of attempting to correlate their biophysical and biochemical effects with organismal fitness. While insightful, systematic laboratory measurements of fitness effects present challenges and difficulties. Here, we discuss the limitations associated with such measurements, and in particular the challenge of correlating the effects of mutations at the single protein level ("protein fitness") with their effects on organismal fitness. A variety of experimental setups are used, with some measuring the direct effects on protein function and others monitoring the growth rate of a model organism carrying the protein mutants. The manners by which fitness effects are calculated and presented also vary, and the conclusions, including the derived distributions of fitness effects of mutations, vary accordingly. The comparison of the effects of mutations in the laboratory to the natural protein diversity, namely to amino acid changes that have fixed in the course of millions of years of evolution, is also debatable. The results of laboratory experiments may, therefore, be less relevant to understanding long-term inter-species variations yet insightful with regard to short-term polymorphism, for example, in the study of the effects of human SNPs.
© 2016 The Protein Society.

Entities:  

Keywords:  SNP; mutational scan; polymorphism; protein evolution; systematic mutational mapping

Mesh:

Substances:

Year:  2016        PMID: 27010590      PMCID: PMC4918414          DOI: 10.1002/pro.2928

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  33 in total

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Journal:  Nat Genet       Date:  2000-07       Impact factor: 38.330

2.  Amino acid sequence determinants of beta-lactamase structure and activity.

Authors:  W Huang; J Petrosino; M Hirsch; P S Shenkin; T Palzkill
Journal:  J Mol Biol       Date:  1996-05-17       Impact factor: 5.469

3.  Drift-barrier hypothesis and mutation-rate evolution.

Authors:  Way Sung; Matthew S Ackerman; Samuel F Miller; Thomas G Doak; Michael Lynch
Journal:  Proc Natl Acad Sci U S A       Date:  2012-10-17       Impact factor: 11.205

4.  Systems-level response to point mutations in a core metabolic enzyme modulates genotype-phenotype relationship.

Authors:  Shimon Bershtein; Jeong-Mo Choi; Sanchari Bhattacharyya; Bogdan Budnik; Eugene Shakhnovich
Journal:  Cell Rep       Date:  2015-04-16       Impact factor: 9.423

5.  Pervasive cryptic epistasis in molecular evolution.

Authors:  Mark Lunzer; G Brian Golding; Antony M Dean
Journal:  PLoS Genet       Date:  2010-10-21       Impact factor: 5.917

6.  Analyses of the effects of all ubiquitin point mutants on yeast growth rate.

Authors:  Benjamin P Roscoe; Kelly M Thayer; Konstantin B Zeldovich; David Fushman; Daniel N A Bolon
Journal:  J Mol Biol       Date:  2013-01-30       Impact factor: 5.469

Review 7.  Fitness and its role in evolutionary genetics.

Authors:  H Allen Orr
Journal:  Nat Rev Genet       Date:  2009-08       Impact factor: 53.242

8.  Deep mutational scanning: a new style of protein science.

Authors:  Douglas M Fowler; Stanley Fields
Journal:  Nat Methods       Date:  2014-08       Impact factor: 28.547

9.  High-resolution sequence-function mapping of full-length proteins.

Authors:  Caitlin A Kowalsky; Justin R Klesmith; James A Stapleton; Vince Kelly; Nolan Reichkitzer; Timothy A Whitehead
Journal:  PLoS One       Date:  2015-03-19       Impact factor: 3.240

10.  Selective pressure causes an RNA virus to trade reproductive fitness for increased structural and thermal stability of a viral enzyme.

Authors:  Moshe Dessau; Daniel Goldhill; Robert C McBride; Robert L McBride; Paul E Turner; Yorgo Modis
Journal:  PLoS Genet       Date:  2012-11-29       Impact factor: 5.917

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  35 in total

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Journal:  J Mol Evol       Date:  2021-05-02       Impact factor: 2.395

2.  A Statistical Guide to the Design of Deep Mutational Scanning Experiments.

Authors:  Sebastian Matuszewski; Marcel E Hildebrandt; Ana-Hermina Ghenu; Jeffrey D Jensen; Claudia Bank
Journal:  Genetics       Date:  2016-07-13       Impact factor: 4.562

3.  Mutation effects predicted from sequence co-variation.

Authors:  Thomas A Hopf; John B Ingraham; Frank J Poelwijk; Charlotta P I Schärfe; Michael Springer; Chris Sander; Debora S Marks
Journal:  Nat Biotechnol       Date:  2017-01-16       Impact factor: 54.908

4.  Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations.

Authors:  Manasi A Pethe; Aliza B Rubenstein; Sagar D Khare
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-26       Impact factor: 11.205

5.  Functional Segregation of Overlapping Genes in HIV.

Authors:  Jason D Fernandes; Tyler B Faust; Nicolas B Strauli; Cynthia Smith; David C Crosby; Robert L Nakamura; Ryan D Hernandez; Alan D Frankel
Journal:  Cell       Date:  2016-12-15       Impact factor: 41.582

Review 6.  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

Review 7.  Hierarchical design of artificial proteins and complexes toward synthetic structural biology.

Authors:  Ryoichi Arai
Journal:  Biophys Rev       Date:  2017-12-14

Review 8.  Engineered control of enzyme structural dynamics and function.

Authors:  David D Boehr; Rebecca N D'Amico; Kathleen F O'Rourke
Journal:  Protein Sci       Date:  2018-02-16       Impact factor: 6.725

Review 9.  Deep sequencing methods for protein engineering and design.

Authors:  Emily E Wrenbeck; Matthew S Faber; Timothy A Whitehead
Journal:  Curr Opin Struct Biol       Date:  2016-11-22       Impact factor: 6.809

10.  Molecular Origins of Complex Heritability in Natural Genotype-to-Phenotype Relationships.

Authors:  Christopher M Jakobson; Daniel F Jarosz
Journal:  Cell Syst       Date:  2019-05-01       Impact factor: 10.304

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