ArticleBMC bioinformatics2011
Predicting disease-associated substitution of a single amino acid by analyzing residue interactions.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 51 citations in OpenAlex.
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- Role of a high centrality residue in protein dynamics and thermal stability.Journal of structural biology · 2021Article
- Variation benchmark datasets: update, criteria, quality and applications.Database : the journal of biological databases and curation · 2020Article
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- Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations.PLoS computational biology · 2015Article
- Systematic Analysis of the Genetic Variability That Impacts SUMO Conjugation and Their Involvement in Human Diseases.Scientific reports · 2015Article
- Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association.Proteins · 2015Article
- Principal component analysis of binding energies for single-point mutants of hT2R16 bound to an agonist correlate with experimental mutant cell response.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- VERMONT: Visualizing mutations and their effects on protein physicochemical and topological property conservation.BMC proceedings · 2014Article
- Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine.Genome medicine · 2014Review
- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
- Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?Briefings in bioinformatics · 2013Article
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- FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model.PloS one · 2012Article
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- Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data.Nature reviews. Genetics · 2011Review
- Computational prediction of heme-binding residues by exploiting residue interaction network.PloS one · 2011Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.
resultsWe found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.
conclusionsThe satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.
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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.