ArticleThe journal of physical chemistry. B2018
Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation.
Article in The journal of physical chemistry. B, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 171 papers.
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171 citing papers in PubMed, 308 citations in OpenAlex.
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- Predicting antifolate resistance in the unculturable fungal pathogen Pneumocystis jirovecii.PLoS genetics · 2026Article
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Mining of natural diversity enables efficient and expressible peptide asparaginyl ligases.Nature communications · 2026Article
- Cysteines are critical determinants of spontaneous and seeded tau aggregation in cells.Research square · 2026Article
111 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ΔΔ G values but also highlighted the necessity of future energy function improvements.
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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.