ArticleNucleic acids research2024
DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Article
- Interpreting human genetic variation at atomic resolution.Nature genetics · 2026Review
- Article
- Computational discovery of HIF-1α/VHL protein-protein interaction inhibitors for hypoxic cell protection.Molecular diversity · 2026Article
- COSMIC-Linked Ras Mutations at the Interface Between H-Ras and PI3KγBiochemistry · 2026Article
- COSMIC-Linked Ras Mutations at the Interface Between H-Ras and PI3KγbioRxiv : the preprint server for biology · 2026Article
- Cofilin-1 is a redox-sensitive guard of the NLRP3 inflammasome.Nature immunology · 2026Article
- Isolated absence epilepsy associated with a de novo FBXW7 missense variant in the F-box domain.Epilepsia open · 2026Article
- Structural-functional analyses of the huntingtin/HAP40 complex inJournal of biomolecular structure & dynamics · 2026Article
- Regulated transformation system (RTS): sddi-mediated programmable shut-off and mode switching of base editors.Nucleic acids research · 2026Article
- MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Briefings in bioinformatics · 2026Article
- Integrative Computational Analysis ofGenes · 2026Article
- Targeting TCF21-TCF3 heterodimer in tumor-associated pericytes attenuates hematogenous metastasis by restoring pericyte gatekeeper function.Cell reports. Medicine · 2026Article
- Employing AlphaFold and TED to explore the structural impacts of stress-associated mutations in rice.Bioinformatics advances · 2026Article
- Multiscale Computational Dissection of CCRL2-Mediated Chemerin Presentation.Journal of chemical information and modeling · 2025Article
- LDLR variant classification through activity-normalized prime editing screening.bioRxiv : the preprint server for biology · 2025Article
- Seq2Bind webserver for binding site prediction from sequences using fine-tuned protein language models.NAR genomics and bioinformatics · 2025Article
- Article
- Protein-Protein Interactions as Promising Molecular Targets for Novel Antimicrobials Aimed at Gram-Negative Bacteria.International journal of molecular sciences · 2025Review
- Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes.Briefings in bioinformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting the effects of mutations on these complex interactions. To address this, we present DDMut-PPI, a deep learning model that efficiently and accurately predicts changes in PPI binding free energy upon single and multiple point mutations. Building on the robust Siamese network architecture with graph-based signatures from our prior work, DDMut, the DDMut-PPI model was enhanced with a graph convolutional network operated on the protein interaction interface. We used residue-specific embeddings from ProtT5 protein language model as node features, and a variety of molecular interactions as edge features. By integrating evolutionary context with spatial information, this framework enables DDMut-PPI to achieve a robust Pearson correlation of up to 0.75 (root mean squared error: 1.33 kcal/mol) in our evaluations, outperforming most existing methods. Importantly, the model demonstrated consistent performance across mutations that increase or decrease binding affinity. DDMut-PPI offers a significant advancement in the field and will serve as a valuable tool for researchers probing the complexities of protein interactions. DDMut-PPI is freely available as a web server and an application programming interface at https://biosig.lab.uq.edu.au/ddmut_ppi.
Indexed as
Identifiers
What OpenQuestion holds
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.