ArticleBMC bioinformatics2017
Improving prediction of burial state of residues by exploiting correlation among residues.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Cancer-associated TRF1 mutations alter PARP1 interaction dynamics: an in silico study.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Investigating the Structural and Functional Consequences of Pathogenic SNPs on Human VEGFA Dimer: Insights from Molecular Dynamics Study.ACS omega · 2025Article
- Understanding the HIV-CA protein and the ligands that bind at the N-terminal domain (NTD) - C-terminal domain (CTD) interface.RSC medicinal chemistry · 2025Review
- A quantitative comparison of the deleteriousness of missense and nonsense mutations using the structurally resolved human protein interactome.Protein science : a publication of the Protein Society · 2025Article
- Comprehensive computational analysis of deleterious nsSNPs inMolecular biology research communications · 2025Article
- A computational approach for structural and functional analyses of disease-associated mutations in the human CYLD gene.Genomics & informatics · 2024Article
- Insilico prediction and functional analysis of nonsynonymous SNPs in human CTLA4 gene.Scientific reports · 2022Article
- Structural and Functional Differences between Homologous Bacterial Ribonucleases.International journal of molecular sciences · 2022Article
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Authors and funding
7 authors.
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Abstract
backgroundResidues in a protein might be buried inside or exposed to the solvent surrounding the protein. The buried residues usually form hydrophobic cores to maintain the structural integrity of proteins while the exposed residues are tightly related to protein functions. Thus, the accurate prediction of solvent accessibility of residues will greatly facilitate our understanding of both structure and functionalities of proteins. Most of the state-of-the-art prediction approaches consider the burial state of each residue independently, thus neglecting the correlations among residues.
resultsIn this study, we present a high-order conditional random field model that considers burial states of all residues in a protein simultaneously. Our approach exploits not only the correlation among adjacent residues but also the correlation among long-range residues. Experimental results showed that by exploiting the correlation among residues, our approach outperformed the state-of-the-art approaches in prediction accuracy. In-depth case studies also showed that by using the high-order statistical model, the errors committed by the bidirectional recurrent neural network and chain conditional random field models were successfully corrected.
conclusionsOur methods enable the accurate prediction of residue burial states, which should greatly facilitate protein structure prediction and evaluation.
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