Literature DB >> 10799982

Analysis of a local fitness landscape with a model of the rough Mt. Fuji-type landscape: application to prolyl endopeptidase and thermolysin.

T Aita1, H Uchiyama, T Inaoka, M Nakajima, T Kokubo, Y Husimi.   

Abstract

A method of analysis of a local fitness landscape for a current biopolymer is presented. Based on the assumption of additivity of mutational effects in the biopolymer, we assigned a site-fitness to each residue at each site. The assigned values of site-fitnesses were obtained by the least-squares method to minimize discrepancies between experimental fitnesses and theoretical ones. As test cases, we analyzed a section of a local landscape for the thermostability of prolyl endopeptidase and that for the enzymatic activity of thermolysin. These sections were proved to be of the rough Mt. Fuji-type with straight theta values of larger than 1.0, where straight theta is defined as the ratio of the "mean slope" to the "degree of roughness" on the fitness surface. Furthermore, we theoretically explained discrepancies between the fitnesses of multiple mutants and those predicted based on strict additivity of the component mutations by using a model of the rough Mt. Fuji-type landscape. According to this model, the discrepancies depend on the local landscape property (such as the straight theta value) and the location of the wild type on the landscape and the mean change in fitness by the component mutations. Our results suggest that this model may provide a good approximation of real sections of local landscapes for current biopolymers phenomenologically. Copyright 2000 John Wiley & Sons, Inc.

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Year:  2000        PMID: 10799982     DOI: 10.1002/(SICI)1097-0282(200007)54:1<64::AID-BIP70>3.0.CO;2-R

Source DB:  PubMed          Journal:  Biopolymers        ISSN: 0006-3525            Impact factor:   2.505


  23 in total

1.  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

2.  Evolutionary constraints in fitness landscapes.

Authors:  Luca Ferretti; Daniel Weinreich; Fumio Tajima; Guillaume Achaz
Journal:  Heredity (Edinb)       Date:  2018-07-11       Impact factor: 3.821

3.  Predictability of evolution depends nonmonotonically on population size.

Authors:  Ivan G Szendro; Jasper Franke; J Arjan G M de Visser; Joachim Krug
Journal:  Proc Natl Acad Sci U S A       Date:  2012-12-24       Impact factor: 11.205

4.  Correlated flexible molecular coding and molecular evolvability.

Authors:  Y Husimi; T Aita; I Tabuchi
Journal:  J Biol Phys       Date:  2002-09       Impact factor: 1.365

5.  Correlation between the conformation space and the sequence space of Peptide chain.

Authors:  T N Sasaki; M Sasai
Journal:  J Biol Phys       Date:  2002-09       Impact factor: 1.365

6.  Adaptation in tunably rugged fitness landscapes: the rough Mount Fuji model.

Authors:  Johannes Neidhart; Ivan G Szendro; Joachim Krug
Journal:  Genetics       Date:  2014-08-13       Impact factor: 4.562

7.  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

8.  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 9.  Modeling Tumor Clonal Evolution for Drug Combinations Design.

Authors:  Boyang Zhao; Michael T Hemann; Douglas A Lauffenburger
Journal:  Trends Cancer       Date:  2016-03

10.  Quantifying the similarity of monotonic trajectories in rough and smooth fitness landscapes.

Authors:  Alexander E Lobkovsky; Yuri I Wolf; Eugene V Koonin
Journal:  Mol Biosyst       Date:  2013-03-04
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