ArticleNAR genomics and bioinformatics2020
Mutation effect estimation on protein-protein interactions using deep contextualized representation learning.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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Who cites it
23 citing papers in PubMed, 66 citations in OpenAlex.
- Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures.Briefings in bioinformatics · 2026Article
- Evaluation of Physics-Based Protein Design Methods for Predicting Single Residue Effects on Peptide Binding Specificities.Journal of computational chemistry · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Decoding the functional impact of the cancer genome through protein-protein interactions.Nature reviews. Cancer · 2025Review
- Proteomic insights into molecular alterations associated with Kawasaki disease in children.Italian journal of pediatrics · 2025Article
- ProstaNet: A Novel Geometric Vector Perceptrons-Graph Neural Network Algorithm for Protein Stability Prediction in Single- and Multiple-Point Mutations with Experimental Validation.Research (Washington, D.C.) · 2025Article
- Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Communications biology · 2024Article
- SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.NAR genomics and bioinformatics · 2024Article
- Deep Local Analysis deconstructs protein-protein interfaces and accurately estimates binding affinity changes upon mutation.Bioinformatics (Oxford, England) · 2023Article
- LambdaPP: Fast and accessible protein-specific phenotype predictions.Protein science : a publication of the Protein Society · 2023Article
- Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations.Research (Washington, D.C.) · 2023Article
- D3AI-Spike: A deep learning platform for predicting binding affinity between SARS-CoV-2 spike receptor binding domain with multiple amino acid mutations and human angiotensin-converting enzyme 2.Computers in biology and medicine · 2022Article
- Embeddings from protein language models predict conservation and variant effects.Human genetics · 2022Article
- Comparative molecular dynamics study of the receptor-binding domains in SARS-CoV-2 and SARS-CoV and the effects of mutations on the binding affinity.Journal of biomolecular structure & dynamics · 2022Article
- Implications of disease-related mutations at protein-protein interfaces.Current opinion in structural biology · 2022Review
- Biomolecular Topology: Modelling and Analysis.Acta mathematica Sinica, English series · 2022Article
- EpitopeVec: linear epitope prediction using deep protein sequence embeddings.Bioinformatics (Oxford, England) · 2021Article
- Mutations of SARS-CoV-2 RBD May Alter Its Molecular Structure to Improve Its Infection Efficiency.Biomolecules · 2021Article
- Learning the protein language: Evolution, structure, and function.Cell systems · 2021Article
- Mutation Edgotype Drives Fitness Effect in Human.Frontiers in bioinformatics · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein-protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein-protein Interaction PRediction Using Contextualized Representations), to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wild-type protein pair and its mutation pair. Multi-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that, with only sequence information, MuPIPR outperforms various state-of-the-art systems on estimating the changes of binding affinity for SKEMPI v1, and offers comparable performance on SKEMPI v2. Meanwhile, MuPIPR also demonstrates state-of-the-art performance on estimating the changes of buried surface areas. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR.
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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.