ArticleBioinformatics (Oxford, England)2021
SAMPDI-3D: predicting the effects of protein and DNA mutations on protein-DNA interactions.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 25 citations in OpenAlex.
- DeepPNI: a language- and graph-based model for mutation-driven protein-nucleic acid binding energetics.Nucleic acids research · 2026Article
- C620Y Mutation-Induced Destabilization of the Androgen Receptor Disrupts DNA Recognition Specificity in Prostate Cancer: Insights from Extensive Parametric Normal Mode Simulations.Cell biochemistry and biophysics · 2026Article
- Topological machine learning for protein-nucleic acid binding affinity changes upon mutation.Machine learning: science and technology · 2025Article
- Cancer histone mutations impact protein binding and DNA repair with possible links to genomic instability.Nucleic acids research · 2025Article
- FLI1 and GATA1 governHaematologica · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Article
- Bioinformatics Approaches for Understanding the Binding Affinity of Protein-Nucleic Acid Complexes.Methods in molecular biology (Clifton, N.J.) · 2025Article
- PNBACE: an ensemble algorithm to predict the effects of mutations on protein-nucleic acid binding affinity.BMC biology · 2024Article
- Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning.Genes · 2024Article
- Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis.Briefings in bioinformatics · 2024Article
- Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations.International journal of molecular sciences · 2023Article
- On the linkage of thermodynamics and pathogenicity.Current opinion in structural biology · 2023Review
- Prediction of hot spots in protein-DNA binding interfaces based on discrete wavelet transform and wavelet packet transform.BMC bioinformatics · 2023Article
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
motivationMutations that alter protein-DNA interactions may be pathogenic and cause diseases. Therefore, it is extremely important to quantify the effect of mutations on protein-DNA binding free energy to reveal the molecular origin of diseases and to assist the development of treatments. Although several methods that predict the change of protein-DNA binding affinity upon mutations in the binding protein were developed, the effect of DNA mutations was not considered yet.
resultsHere, we report a new version of SAMPDI, the SAMPDI-3D, which is a gradient boosting decision tree machine learning method to predict the change of the protein-DNA binding free energy caused by mutations in both the binding protein and the bases of the corresponding DNA. The method is shown to achieve Pearson correlation coefficient of 0.76 and 0.80 in a benchmarking test against experimentally determined change of the binding free energy caused by mutations in the binding protein or DNA, respectively. Furthermore, three datasets collected from literature were used to do blind benchmark for SAMPDI-3D and it is shown that it outperforms all existing state-of-the-art methods. The method is very fast allowing for genome-scale investigations. AVAILABILITYAND IMPLEMENTATION: It is available as a web server and a stand-code at http://compbio.clemson.edu/SAMPDI-3D/. 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.