ArticleScientific reports2016
Modeling and fitting protein-protein complexes to predict change of binding energy.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Insights into Transient Dimerisation of Carnitine/Acylcarnitine Carrier (SLC25A20) from Sarkosyl/PAGE, Cross-Linking Reagents, and Comparative Modelling Analysis.Biomolecules · 2024Article
- Mining the Protein Data Bank to improve prediction of changes in protein-protein binding.PloS one · 2021Article
- SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function.Bioinformatics (Oxford, England) · 2020Article
- In Silico Analysis of Homologous Heterodimers of Cruzipain-Chagasin from Structural Models Built by Homology.International journal of molecular sciences · 2019Article
- Tools for the cryo-EM gold rush: going from the cryo-EM map to the atomistic model.Bioscience reports · 2017Review
- BindProfX: Assessing Mutation-Induced Binding Affinity Change by Protein Interface Profiles with Pseudo-Counts.Journal of molecular biology · 2017Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is possible to accurately and economically predict change in protein-protein interaction energy upon mutation (ΔΔG), when a high-resolution structure of the complex is available. This is of growing usefulness for design of high-affinity or otherwise modified binding proteins for therapeutic, diagnostic, industrial, and basic science applications. Recently the field has begun to pursue ΔΔG prediction for homology modeled complexes, but so far this has worked mostly for cases of high sequence identity. If the interacting proteins have been crystallized in free (uncomplexed) form, in a majority of cases it is possible to find a structurally similar complex which can be used as the basis for template-based modeling. We describe how to use MMB to create such models, and then use them to predict ΔΔG, using a dataset consisting of free target structures, co-crystallized template complexes with sequence identify with respect to the targets as low as 44%, and experimental ΔΔG measurements. We obtain similar results by fitting to a low-resolution Cryo-EM density map. Results suggest that other structural constraints may lead to a similar outcome, making the method even more broadly applicable.
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