ArticleInternational journal of molecular sciences2020
SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 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
61 citing papers in PubMed, 124 citations in OpenAlex.
- Predicting interaction-specific protein-protein interaction perturbations by missense variants with MutPred-PPI.bioRxiv : the preprint server for biology · 2026Article
- Antibody Screening and Binding Prediction Analysis Targeting Stx2.Antibodies (Basel, Switzerland) · 2026Article
- Seq2Bind webserver for binding site prediction from sequences using fine-tuned protein language models.NAR genomics and bioinformatics · 2025Article
- AbAgym: a well-curated dataset for the mutational analysis of antibody-antigen complexes.mAbs · 2025Article
- Article
- Cancer histone mutations impact protein binding and DNA repair with possible links to genomic instability.Nucleic acids research · 2025Article
- CDKL5 Deficiency Disorder: Revealing the Molecular Mechanism of Pathogenic Variants.International journal of molecular sciences · 2025Article
- Classification of GBA1 variants and their impact on Parkinson's disease: an in silico score analysis.NPJ Parkinson's disease · 2025Article
- Mapping the interaction surface between CaNature communications · 2025Article
- ProBASS-a language model with sequence and structural features for predicting the effect of mutations on binding affinity.Bioinformatics (Oxford, England) · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Predicting Antibody Affinity Changes upon Mutation Based on Unbound Protein Structures.International journal of molecular sciences · 2025Article
- Functional and structural impacts of oncogenic missense variants on human polo-like kinase 1 protein.Frontiers in bioinformatics · 2025Article
- Article
- Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Communications biology · 2024Article
- Evaluation of an Affinity-Enhanced Anti-SARS-CoV2 Nanobody Design Workflow Using Machine Learning and Molecular Dynamics.Journal of chemical information and modeling · 2024Article
- MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes.Briefings in bioinformatics · 2024Article
- SAAMBE-MEM: a sequence-based method for predicting binding free energy change upon mutation in membrane protein-protein complexes.Bioinformatics (Oxford, England) · 2024Article
- MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.
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.