Literature DB >> 18075167

The pros and cons of predicting protein contact maps.

Lisa Bartoli1, Emidio Capriotti, Piero Fariselli, Pier Luigi Martelli, Rita Casadio.   

Abstract

Is there any reason why we should predict contact maps (CMs)? The question is one of the several 'NP-hard' questions that arise when striving for feasible solutions of the protein folding problem. At some point, theoreticians started thinking that a possible alternative to an unsolvable problem was to predict a simplified version of the protein structure: a CM. In this chapter, we will clarify that whenever problems are difficult they remain at least as difficult in the process of finding approximate solutions or heuristic approaches. However, humans rarely give up, as it is stimulating to find solutions in the face of difficulties. CMs of proteins are an interesting and useful representation of protein structures. These two-dimensional representations capture all the important features of a protein fold. We will review the general characteristics of CMs and the methods developed to study and predict them, and we will highlight some new ideas on how to improve CM predictions.

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Year:  2008        PMID: 18075167     DOI: 10.1007/978-1-59745-574-9_8

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  7 in total

1.  Atomic interaction networks in the core of protein domains and their native folds.

Authors:  Venkataramanan Soundararajan; Rahul Raman; S Raguram; V Sasisekharan; Ram Sasisekharan
Journal:  PLoS One       Date:  2010-02-23       Impact factor: 3.240

2.  CNNcon: improved protein contact maps prediction using cascaded neural networks.

Authors:  Wang Ding; Jiang Xie; Dongbo Dai; Huiran Zhang; Hao Xie; Wu Zhang
Journal:  PLoS One       Date:  2013-04-23       Impact factor: 3.240

3.  Optimal contact definition for reconstruction of contact maps.

Authors:  Jose M Duarte; Rajagopal Sathyapriya; Henning Stehr; Ioannis Filippis; Michael Lappe
Journal:  BMC Bioinformatics       Date:  2010-05-27       Impact factor: 3.169

4.  Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure.

Authors:  Marco Vassura; Pietro Di Lena; Luciano Margara; Maria Mirto; Giovanni Aloisio; Piero Fariselli; Rita Casadio
Journal:  BioData Min       Date:  2011-01-13       Impact factor: 2.522

5.  Alignments of biomolecular contact maps.

Authors:  Peter F Stadler
Journal:  Interface Focus       Date:  2021-06-11       Impact factor: 4.661

6.  ConSole: using modularity of contact maps to locate solenoid domains in protein structures.

Authors:  Thomas Hrabe; Adam Godzik
Journal:  BMC Bioinformatics       Date:  2014-04-27       Impact factor: 3.169

Review 7.  Deep learning methods in protein structure prediction.

Authors:  Mirko Torrisi; Gianluca Pollastri; Quan Le
Journal:  Comput Struct Biotechnol J       Date:  2020-01-22       Impact factor: 7.271

  7 in total

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