ArticleJournal of molecular biology2019
DAMpred: Recognizing Disease-Associated nsSNPs through Bayes-Guided Neural-Network Model Built on Low-Resolution Structure Prediction of Proteins and Protein-Protein Interactions.
Article in Journal of molecular biology, 2019. 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, 26 citations in OpenAlex.
- Predicting high-risk clinical missense variants of SMARCB1 in rare neurogenetic disorder schwannomatosis (nerve tumor) through sequence, structure, and molecular dynamics analyses.Neurogenetics · 2025Article
- MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction.Journal of chemical information and modeling · 2023Article
- Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations.Briefings in bioinformatics · 2023Article
- Development and validation of a machine learning algorithm prediction for dense granule proteins in Apicomplexa.Parasites & vectors · 2023Article
- Article
- Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.Briefings in bioinformatics · 2022Article
- ADDRESS: A Database of Disease-associated Human Variants Incorporating Protein Structure and Folding Stabilities.Journal of molecular biology · 2021Article
- MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins.Computational and structural biotechnology journal · 2021Article
- Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.Current genomics · 2020Review
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Nearly one-third of non-synonymous single-nucleotide polymorphism (nsSNPs) are deleterious to human health, but recognition of the disease-associated mutations remains a significant unsolved problem. We proposed a new algorithm, DAMpred, to identify disease-causing nsSNPs through the coupling of evolutionary profiles with structure predictions of proteins and protein-protein interactions. The pipeline was trained by a novel Bayes-guided artificial neural network algorithm that incorporates posterior probabilities of distinct feature classifiers with the network training process. DAMpred was tested on a large-scale data set involving 10,635 nsSNPs from 2154 ORFs in the human genome and recognized disease-associated nsSNPs with an accuracy 0.80 and a Matthews correlation coefficient of 0.601, which is 9.1% higher than the best of other state-of-the-art methods. In the blind test on the TP53 gene, DAMpred correctly recognized the mutations causative of Li-Fraumeni-like syndrome with a Matthews correlation coefficient that is 27% higher than the control methods. The study demonstrates an efficient avenue to quantitatively model the association of nsSNPs with human diseases from low-resolution protein structure prediction, which should find important usefulness in diagnosis and treatment of genetic diseases.
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