ArticleBioinformatics (Oxford, England)2013
Predicting protein contact map using evolutionary and physical constraints by integer programming.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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54 citing papers in PubMed, 149 citations in OpenAlex.
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- Evaluation of residue-residue contact prediction methods: From retrospective to prospective.PLoS computational biology · 2021Article
- Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.PLoS computational biology · 2021Article
- Evolution and Diversity of Semaphorins and Plexins in Choanoflagellates.Genome biology and evolution · 2021Article
- Structural Insights on the SARS-CoV-2 Variants of Concern Spike Glycoprotein: A Computational Study With Possible Clinical Implications.Frontiers in genetics · 2021Article
- Evaluating the significance of contact maps in low-homology protein modeling using contact-assisted threading.Scientific reports · 2020Article
- DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment.BMC bioinformatics · 2020Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationProtein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains challenging to predict contact map using only sequence information. Most existing methods predict the contact map matrix element-by-element, ignoring correlation among contacts and physical feasibility of the whole-contact map. A couple of recent methods predict contact map by using mutual information, taking into consideration contact correlation and enforcing a sparsity restraint, but these methods demand for a very large number of sequence homologs for the protein under consideration and the resultant contact map may be still physically infeasible.
resultsThis article presents a novel method PhyCMAP for contact map prediction, integrating both evolutionary and physical restraints by machine learning and integer linear programming. The evolutionary restraints are much more informative than mutual information, and the physical restraints specify more concrete relationship among contacts than the sparsity restraint. As such, our method greatly reduces the solution space of the contact map matrix and, thus, significantly improves prediction accuracy. Experimental results confirm that PhyCMAP outperforms currently popular methods no matter how many sequence homologs are available for the protein under consideration. AVAILABILITY: http://raptorx.uchicago.edu.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.