ArticleInternational journal of molecular sciences2016
SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations.
Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 72 citations in OpenAlex.
- ProBASS-a language model with sequence and structural features for predicting the effect of mutations on binding affinity.Bioinformatics (Oxford, England) · 2025Article
- Pretrainable geometric graph neural network for antibody affinity maturation.Nature communications · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Rationally introducing non-canonical amino acids to enhance catalytic activity of LmrR for Henry reaction.Bioresources and bioprocessing · 2024Article
- Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations.International journal of molecular sciences · 2023Article
- Comprehensive screening strategy coupled with structure-guided engineering of l-threonine aldolase fromFrontiers in bioengineering and biotechnology · 2023Article
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
- Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants.Computers in biology and medicine · 2022Article
- Biomolecular Topology: Modelling and Analysis.Acta mathematica Sinica, English series · 2022Article
- PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.Communications biology · 2021Article
- Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.PLoS computational biology · 2021Article
- SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein-protein binding affinity.Bioinformatics (Oxford, England) · 2021Article
- Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions.Protein science : a publication of the Protein Society · 2020Article
- Mutations Strengthened SARS-CoV-2 Infectivity.Journal of molecular biology · 2020Article
- PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions.International journal of molecular sciences · 2020Article
- SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.International journal of molecular sciences · 2020Article
- Human IgG1 Fc pH-dependent optimization from a constant pH molecular dynamics simulation analysis.RSC advances · 2020Article
- MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions.iScience · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Predicting the effect of amino acid substitutions on protein-protein affinity (typically evaluated via the change of protein binding free energy) is important for both understanding the disease-causing mechanism of missense mutations and guiding protein engineering. In addition, researchers are also interested in understanding which energy components are mostly affected by the mutation and how the mutation affects the overall structure of the corresponding protein. Here we report a webserver, the Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) webserver, which addresses the demand for tools for predicting the change of protein binding free energy. SAAMBE is an easy to use webserver, which only requires that a coordinate file be inputted and the user is provided with various, but easy to navigate, options. The user specifies the mutation position, wild type residue and type of mutation to be made. The server predicts the binding free energy change, the changes of the corresponding energy components and provides the energy minimized 3D structure of the wild type and mutant proteins for download. The SAAMBE protocol performance was tested by benchmarking the predictions against over 1300 experimentally determined changes of binding free energy and a Pearson correlation coefficient of 0.62 was obtained. How the predictions can be used for discriminating disease-causing from harmless mutations is discussed. The webserver can be accessed via http://compbio.clemson.edu/saambe_webserver/.
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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.