ArticlePLoS computational biology2018
PremPDI estimates and interprets the effects of missense mutations on protein-DNA interactions.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 59 citations in OpenAlex.
- DeepPNI: a language- and graph-based model for mutation-driven protein-nucleic acid binding energetics.Nucleic acids research · 2026Article
- iDLDDG: predicting protein stability changes from missense mutations in DNA-binding proteins using integrated deep learning features.Briefings in bioinformatics · 2026Article
- Topological machine learning for protein-nucleic acid binding affinity changes upon mutation.Machine learning: science and technology · 2025Article
- Structural Modelling of Krüppel-Like Factor 15 Zinc Finger Binding Domain to DNA Using AlphaFold 3.0: Potential Therapeutic Target for Type 2 Diabetes.Journal of cellular and molecular medicine · 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
- Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength.Computational and structural biotechnology journal · 2024Article
- PRITrans: A Transformer-Based Approach for the Prediction of the Effects of Missense Mutation on Protein-RNA Interactions.International journal of molecular sciences · 2024Article
- PNBACE: an ensemble algorithm to predict the effects of mutations on protein-nucleic acid binding affinity.BMC biology · 2024Article
- Article
- Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning.Genes · 2024Article
- Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations.International journal of molecular sciences · 2023Article
- 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
- PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.Communications biology · 2021Article
- SAMPDI-3D: predicting the effects of protein and DNA mutations on protein-DNA interactions.Bioinformatics (Oxford, England) · 2021Article
- Nabe: an energetic database of amino acid mutations in protein-nucleic acid binding interfaces.Database : the journal of biological databases and curation · 2021Article
- Recent advances in user-friendly computational tools to engineer protein function.Briefings in bioinformatics · 2021Review
- An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties.BMC bioinformatics · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-DNA interactions play important roles in regulations of many vital cellular processes, including transcription, translation, DNA replication and recombination. Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function, potentially leading to diseases. Developing a mechanistic understanding of impacts of variants on protein-DNA interactions becomes a persistent need. To address this need we introduce a new computational method PremPDI that predicts the effect of single missense mutation in the protein on the protein-DNA interaction and calculates the quantitative binding affinity change. The PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms with parameters optimized on experimental sets of 219 mutations from 49 protein-DNA complexes. PremPDI yields a very good agreement between predicted and experimental values with Pearson correlation coefficient of 0.71 and root-mean-square error of 0.86 kcal mol-1. The PremPDI server could map mutations on a structural protein-DNA complex, calculate the associated changes in binding affinity, determine the deleterious effect of a mutation, and produce a mutant structural model for download. PremPDI can be applied to many tasks, such as determination of potential damaging mutations in cancer and other diseases. PremPDI is available at http://lilab.jysw.suda.edu.cn/research/PremPDI/.
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Registered trials
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.