ArticleBioinformatics (Oxford, England)2015
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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57 citing papers in PubMed.
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- LPTD: a novel linear programming-based topology determination method for cryo-EM maps.Bioinformatics (Oxford, England) · 2022Article
- A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.Briefings in bioinformatics · 2022Review
- Customized chitooligosaccharide production-controlling their lengthFrontiers in bioengineering and biotechnology · 2022Article
- GWAS to Identify Novel QTNs for WSCs Accumulation in Wheat Peduncle Under Different Water Regimes.Frontiers in plant science · 2022Article
- Hotspots for mutations in the SARS-CoV-2 spike glycoprotein: a correspondence analysis.Scientific reports · 2021Article
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- Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps.Biomolecules · 2021Article
- Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations.Journal of chemical theory and computation · 2021Article
- Toward the solution of the protein structure prediction problem.The Journal of biological chemistry · 2021Review
- COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization.Membranes · 2021Article
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- The whole is greater than its parts: ensembling improves protein contact prediction.Scientific reports · 2021Article
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4 authors.
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Abstract
motivationProtein contact prediction is important for protein structure and functional study. Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed, making use of different information sources. However, contact prediction is still challenging especially for proteins without a large number of sequence homologs.
resultsThis article presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning to improve accuracy on proteins without many sequence homologs. Different from existing single-family EC analysis that uses residue coevolution information in only the target protein family, our joint EC analysis uses residue coevolution in both the target family and its related families, which may have divergent sequences but similar folds. To implement this, we model a set of related protein families using Gaussian graphical models and then coestimate their parameters by maximum-likelihood, subject to the constraint that these parameters shall be similar to some degree. Our GGL method can also integrate supervised learning methods to further improve accuracy. Experiments show that our method outperforms existing methods on proteins without thousands of sequence homologs, and that our method performs better on both conserved and family-specific contacts. AVAILABILITY AND IMPLEMENTATION: See http://raptorx.uchicago.edu/ContactMap/ for a web server implementing the method. CONTACT: j3xu@ttic.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.