Literature DB >> 27115648

Protein Residue Contacts and Prediction Methods.

Badri Adhikari1, Jianlin Cheng2.   

Abstract

In the field of computational structural proteomics, contact predictions have shown new prospects of solving the longstanding problem of ab initio protein structure prediction. In the last few years, application of deep learning algorithms and availability of large protein sequence databases, combined with improvement in methods that derive contacts from multiple sequence alignments, have shown a huge increase in the precision of contact prediction. In addition, these predicted contacts have also been used to build three-dimensional models from scratch.In this chapter, we briefly discuss many elements of protein residue-residue contacts and the methods available for prediction, focusing on a state-of-the-art contact prediction tool, DNcon. Illustrating with a case study, we describe how DNcon can be used to make ab initio contact predictions for a given protein sequence and discuss how the predicted contacts may be analyzed and evaluated.

Entities:  

Keywords:  Deep learning; Protein contact prediction methods

Mesh:

Substances:

Year:  2016        PMID: 27115648      PMCID: PMC4894841          DOI: 10.1007/978-1-4939-3572-7_24

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


  53 in total

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6.  Using inferred residue contacts to distinguish between correct and incorrect protein models.

Authors:  Christopher S Miller; David Eisenberg
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6.  MISTIC2: comprehensive server to study coevolution in protein families.

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Authors:  Zhengping Hu; Issahy Cano; Kahira L Saez-Torres; Michelle E LeBlanc; Magali Saint-Geniez; Yin-Shan Ng; Pablo Argüeso; Patricia A D'Amore
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