Literature DB >> 11707608

A cross-section of the fitness landscape of dihydrofolate reductase.

T Aita1, M Iwakura, Y Husimi.   

Abstract

In vitro molecular evolution is regarded as a hill-climbing on a fitness landscape in sequence space, where the 'fitness' is a quantitative measure of a certain physicochemical property of a biopolymer. We analyzed a 'cross-section' of the enzymatic activity landscape of dihydrofolate reductase (DHFR) by using a method of analysis of a fitness landscape. We limited the sequence space of interest to the five-dimensional sequence space, where the coordinate corresponds to the 1st, 16th, 20th, 42nd and 92nd site in the DHFR sequence. Thirty six mutants mapped into the limited sequence space were taken in the analysis. As a result, the cross-section is of the rough Mt Fuji type based on the mutational additivity. The ratio of the mean slope to the roughness is 2.8 and the Z-score of the original ratio against a distribution of random references is 7.0, which indicates a large statistical significance. The existence of such a cross-section was discussed in terms of the occurrence probability of sets of five sites distantly separated from each other on the DHFR 3D structure. Our results support the effectiveness of the evolution strategy which exploits the accumulation of advantageous single point mutations in such a cross-section.

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Year:  2001        PMID: 11707608     DOI: 10.1093/protein/14.9.633

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  18 in total

Review 1.  Colloquium papers: Adaptive landscapes and protein evolution.

Authors:  Maurício Carneiro; Daniel L Hartl
Journal:  Proc Natl Acad Sci U S A       Date:  2009-09-30       Impact factor: 11.205

2.  On the (un)predictability of a large intragenic fitness landscape.

Authors:  Claudia Bank; Sebastian Matuszewski; Ryan T Hietpas; Jeffrey D Jensen
Journal:  Proc Natl Acad Sci U S A       Date:  2016-11-18       Impact factor: 11.205

Review 3.  Empirical fitness landscapes and the predictability of evolution.

Authors:  J Arjan G M de Visser; Joachim Krug
Journal:  Nat Rev Genet       Date:  2014-06-10       Impact factor: 53.242

4.  Effect of Host Species on Topography of the Fitness Landscape for a Plant RNA Virus.

Authors:  Héctor Cervera; Jasna Lalić; Santiago F Elena
Journal:  J Virol       Date:  2016-10-28       Impact factor: 5.103

5.  Selection Limits to Adaptive Walks on Correlated Landscapes.

Authors:  Jorge Pérez Heredia; Barbora Trubenová; Dirk Sudholt; Tiago Paixão
Journal:  Genetics       Date:  2016-11-23       Impact factor: 4.562

Review 6.  What can we learn from fitness landscapes?

Authors:  Daniel L Hartl
Journal:  Curr Opin Microbiol       Date:  2014-10-13       Impact factor: 7.934

7.  The fitness landscape of the codon space across environments.

Authors:  Inês Fragata; Sebastian Matuszewski; Mark A Schmitz; Thomas Bataillon; Jeffrey D Jensen; Claudia Bank
Journal:  Heredity (Edinb)       Date:  2018-08-20       Impact factor: 3.821

8.  Unbiased inference of the fitness landscape ruggedness from imprecise fitness estimates.

Authors:  Siliang Song; Jianzhi Zhang
Journal:  Evolution       Date:  2021-10-07       Impact factor: 3.694

9.  The peaks and geometry of fitness landscapes.

Authors:  Kristina Crona; Devin Greene; Miriam Barlow
Journal:  J Theor Biol       Date:  2012-10-02       Impact factor: 2.691

10.  Selecting among three basic fitness landscape models: Additive, multiplicative and stickbreaking.

Authors:  Craig R Miller; James T Van Leuven; Holly A Wichman; Paul Joyce
Journal:  Theor Popul Biol       Date:  2017-12-02       Impact factor: 1.570

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