Literature DB >> 21458982

Protein sequence comparison and fold recognition: progress and good-practice benchmarking.

Johannes Söding1, Michael Remmert.   

Abstract

Protein sequence comparison methods have grown increasingly sensitive during the last decade and can often identify distantly related proteins sharing a common ancestor some 3 billion years ago. Although cellular function is not conserved so long, molecular functions and structures of protein domains often are. In combination with a domain-centered approach to function and structure prediction, modern remote homology detection methods have a great and largely underexploited potential for elucidating protein functions and evolution. Advances during the last few years include nonlinear scoring functions combining various sequence features, the use of sequence context information, and powerful new software packages. Since progress depends on realistically assessing new and existing methods and published benchmarks are often hard to compare, we propose 10 rules of good-practice benchmarking.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21458982     DOI: 10.1016/j.sbi.2011.03.005

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   6.809


  27 in total

1.  HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment.

Authors:  Michael Remmert; Andreas Biegert; Andreas Hauser; Johannes Söding
Journal:  Nat Methods       Date:  2011-12-25       Impact factor: 28.547

Review 2.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

3.  Rapid search for tertiary fragments reveals protein sequence-structure relationships.

Authors:  Jianfu Zhou; Gevorg Grigoryan
Journal:  Protein Sci       Date:  2014-12-31       Impact factor: 6.725

4.  CSI 2.0: a significantly improved version of the Chemical Shift Index.

Authors:  Noor E Hafsa; David S Wishart
Journal:  J Biomol NMR       Date:  2014-10-02       Impact factor: 2.835

Review 5.  A guide to machine learning for biologists.

Authors:  Joe G Greener; Shaun M Kandathil; Lewis Moffat; David T Jones
Journal:  Nat Rev Mol Cell Biol       Date:  2021-09-13       Impact factor: 94.444

6.  Constructing benchmark test sets for biological sequence analysis using independent set algorithms.

Authors:  Samantha Petti; Sean R Eddy
Journal:  PLoS Comput Biol       Date:  2022-03-07       Impact factor: 4.475

7.  Respiratory chain Complex I of unparalleled divergence in diplonemids.

Authors:  Matus Valach; Alexandra Léveillé-Kunst; Michael W Gray; Gertraud Burger
Journal:  J Biol Chem       Date:  2018-08-30       Impact factor: 5.157

Review 8.  Too Many False Targets for MicroRNAs: Challenges and Pitfalls in Prediction of miRNA Targets and Their Gene Ontology in Model and Non-model Organisms.

Authors:  Arie Fridrich; Yael Hazan; Yehu Moran
Journal:  Bioessays       Date:  2019-04       Impact factor: 4.345

9.  Predictive sequence analysis of the Candidatus Liberibacter asiaticus proteome.

Authors:  Qian Cong; Lisa N Kinch; Bong-Hyun Kim; Nick V Grishin
Journal:  PLoS One       Date:  2012-07-18       Impact factor: 3.240

10.  Bioinformatics analysis identify novel OB fold protein coding genes in C. elegans.

Authors:  Daryanaz Dargahi; David Baillie; Frederic Pio
Journal:  PLoS One       Date:  2013-04-25       Impact factor: 3.240

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