Literature DB >> 20981889

Relative von Neumann entropy for evaluating amino acid conservation.

Fredrik Johansson1, Hiroyuki Toh.   

Abstract

The Shannon entropy is a common way of measuring conservation of sites in multiple sequence alignments, and has also been extended with the relative Shannon entropy to account for background frequencies. The von Neumann entropy is another extension of the Shannon entropy, adapted from quantum mechanics in order to account for amino acid similarities. However, there is yet no relative von Neumann entropy defined for sequence analysis. We introduce a new definition of the von Neumann entropy for use in sequence analysis, which we found to perform better than the previous definition. We also introduce the relative von Neumann entropy and a way of parametrizing this in order to obtain the Shannon entropy, the relative Shannon entropy and the von Neumann entropy at special parameter values. We performed an exhaustive search of this parameter space and found better predictions of catalytic sites compared to any of the previously used entropies.

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Year:  2010        PMID: 20981889     DOI: 10.1142/s021972001000494x

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  6 in total

1.  Sequence conservation in the prediction of catalytic sites.

Authors:  Yongchao Dou; Xingbo Geng; Hongyun Gao; Jialiang Yang; Xiaoqi Zheng; Jun Wang
Journal:  Protein J       Date:  2011-04       Impact factor: 2.371

2.  A comparative study of conservation and variation scores.

Authors:  Fredrik Johansson; Hiroyuki Toh
Journal:  BMC Bioinformatics       Date:  2010-07-21       Impact factor: 3.169

3.  H2rs: deducing evolutionary and functionally important residue positions by means of an entropy and similarity based analysis of multiple sequence alignments.

Authors:  Jan-Oliver Janda; Ajmal Popal; Jochen Bauer; Markus Busch; Michael Klocke; Wolfgang Spitzer; Jörg Keller; Rainer Merkl
Journal:  BMC Bioinformatics       Date:  2014-04-27       Impact factor: 3.169

4.  Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming.

Authors:  Mehmet Gültas; Güncel Düzgün; Sebastian Herzog; Sven Joachim Jäger; Cornelia Meckbach; Edgar Wingender; Stephan Waack
Journal:  BMC Bioinformatics       Date:  2014-04-03       Impact factor: 3.169

Review 5.  Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.

Authors:  Jeffrey R Wagner; Christopher T Lee; Jacob D Durrant; Robert D Malmstrom; Victoria A Feher; Rommie E Amaro
Journal:  Chem Rev       Date:  2016-04-13       Impact factor: 60.622

6.  Molecular phylogeny and missense mutations of envelope proteins across coronaviruses.

Authors:  Sk Sarif Hassan; Pabitra Pal Choudhury; Bidyut Roy
Journal:  Genomics       Date:  2020-09-11       Impact factor: 5.736

  6 in total

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