ArticleBioinformatics (Oxford, England)2009
Automated inference of molecular mechanisms of disease from amino acid substitutions.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 401 papers.
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401 citing papers in PubMed, 844 citations in OpenAlex.
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- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- Expanding the utility of variant effect predictions with phenotype-specific models.Nature communications · 2025Article
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- Prediction of pathogenic mutations in human transmembrane proteins and their associated diseases via utilizing pre-trained Bio-LLMs.Communications biology · 2025Article
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- Assessing the performance of 28 pathogenicity prediction methods on rare single nucleotide variants in coding regions.BMC genomics · 2025Article
- Integrating gene mutation spectra from tumors and the general population with gene expression topological networks to identify novel cancer driver genes.Human genetics · 2025Article
- PRP: pathogenic risk prediction for rare nonsynonymous single nucleotide variants.Human genetics · 2025Article
- Evaluating variant pathogenicity prediction tools to establish African inclusive guidelines for germline genetic testing.Communications medicine · 2025Article
- Accurate identification and mechanistic evaluation of pathogenic missense variants withProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Critical assessment of missense variant effect predictors on disease-relevant variant data.Human genetics · 2025Article
341 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
motivationAdvances in high-throughput genotyping and next generation sequencing have generated a vast amount of human genetic variation data. Single nucleotide substitutions within protein coding regions are of particular importance owing to their potential to give rise to amino acid substitutions that affect protein structure and function which may ultimately lead to a disease state. Over the last decade, a number of computational methods have been developed to predict whether such amino acid substitutions result in an altered phenotype. Although these methods are useful in practice, and accurate for their intended purpose, they are not well suited for providing probabilistic estimates of the underlying disease mechanism.
resultsWe have developed a new computational model, MutPred, that is based upon protein sequence, and which models changes of structural features and functional sites between wild-type and mutant sequences. These changes, expressed as probabilities of gain or loss of structure and function, can provide insight into the specific molecular mechanism responsible for the disease state. MutPred also builds on the established SIFT method but offers improved classification accuracy with respect to human disease mutations. Given conservative thresholds on the predicted disruption of molecular function, we propose that MutPred can generate accurate and reliable hypotheses on the molecular basis of disease for approximately 11% of known inherited disease-causing mutations. We also note that the proportion of changes of functionally relevant residues in the sets of cancer-associated somatic mutations is higher than for the inherited lesions in the Human Gene Mutation Database which are instead predicted to be characterized by disruptions of protein structure. AVAILABILITY: http://mutdb.org/mutpred CONTACT: predrag@indiana.edu; smooney@buckinstitute.org.
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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.