Literature DB >> 15064085

A new criterion and method for amino acid classification.

Carolin Kosiol1, Nick Goldman, Nigel H Buttimore.   

Abstract

It is accepted that many evolutionary changes of amino acid sequence in proteins are conservative: the replacement of one amino acid by another residue has a far greater chance of being accepted if the two residues have similar properties. It is difficult, however, to identify relevant physicochemical properties that capture this similarity. In this paper we introduce a criterion that determines similarity from an evolutionary point of view. Our criterion is based on the description of protein evolution by a Markov process and the corresponding matrix of instantaneous replacement rates. It is inspired by the conductance, a quantity that reflects the strength of mixing in a Markov process. Furthermore we introduce a method to divide the 20 amino acid residues into subsets that achieve good scores with our criterion. The criterion has the time-invariance property that different time distances of the same amino acid replacement rate matrix lead to the same grouping; but different rate matrices lead to different groupings. Therefore it can be used as an automated method to compare matrices derived from consideration of different types of proteins, or from parts of proteins sharing different structural or functional features. We present the groupings resulting from two standard matrices used in sequence alignment and phylogenetic tree estimation.

Mesh:

Year:  2004        PMID: 15064085     DOI: 10.1016/j.jtbi.2003.12.010

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  17 in total

1.  A method for computing the inter-residue interaction potentials for reduced amino acid alphabet.

Authors:  Abhinav Luthra; Anupam Nath Jha; G K Ananthasuresh; Saraswathi Vishveswara
Journal:  J Biosci       Date:  2007-08       Impact factor: 1.826

2.  Geometry-based distance for clustering amino acids.

Authors:  Samira F Abushilah; Charles C Taylor; Arief Gusnanto
Journal:  J Appl Stat       Date:  2019-10-03       Impact factor: 1.416

3.  BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data.

Authors:  Jacob L Steenwyk; Thomas J Buida; Carla Gonçalves; Dayna C Goltz; Grace Morales; Matthew E Mead; Abigail L LaBella; Christina M Chavez; Jonathan E Schmitz; Maria Hadjifrangiskou; Yuanning Li; Antonis Rokas
Journal:  Genetics       Date:  2022-07-04       Impact factor: 4.402

4.  A model-independent approach to infer hierarchical codon substitution dynamics.

Authors:  Olof Görnerup; Martin Nilsson Jacobi
Journal:  BMC Bioinformatics       Date:  2010-04-23       Impact factor: 3.169

5.  Six-State Amino Acid Recoding is not an Effective Strategy to Offset Compositional Heterogeneity and Saturation in Phylogenetic Analyses.

Authors:  Alexandra M Hernandez; Joseph F Ryan
Journal:  Syst Biol       Date:  2021-10-13       Impact factor: 15.683

6.  Evolutionary models for insertions and deletions in a probabilistic modeling framework.

Authors:  Elena Rivas
Journal:  BMC Bioinformatics       Date:  2005-03-21       Impact factor: 3.169

7.  Statistical tests to identify appropriate types of nucleotide sequence recoding in molecular phylogenetics.

Authors:  Victor A Vera-Ruiz; Kwok W Lau; John Robinson; Lars S Jermiin
Journal:  BMC Bioinformatics       Date:  2014-01-31       Impact factor: 3.169

8.  A generalized mechanistic codon model.

Authors:  Maryam Zaheri; Linda Dib; Nicolas Salamin
Journal:  Mol Biol Evol       Date:  2014-06-23       Impact factor: 16.240

9.  Probabilistic phylogenetic inference with insertions and deletions.

Authors:  Elena Rivas; Sean R Eddy
Journal:  PLoS Comput Biol       Date:  2008-09-19       Impact factor: 4.475

10.  Amyloidogenic motifs revealed by n-gram analysis.

Authors:  Michał Burdukiewicz; Piotr Sobczyk; Stefan Rödiger; Anna Duda-Madej; Paweł Mackiewicz; Małgorzata Kotulska
Journal:  Sci Rep       Date:  2017-10-11       Impact factor: 4.379

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