ArticleACS central science2018
Accurate Estimation of Ligand Binding Affinity Changes upon Protein Mutation.
Article in ACS central science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed.
- Evaluating molecular docking for binding affinity predictions: a systematic analysis of key parameters and the utility of AlphaFold2 structures for the Schrödinger dataset.Journal of computer-aided molecular design · 2026Article
- Discovery of potent and selective LRRK2 inhibitors with preserved activity against the G2019S mutant via multi-stage virtual screening.Journal of computer-aided molecular design · 2026Article
- Molecular determinants of arginine versus lysine cation-π interactions in biomolecular condensates.Communications chemistry · 2026Article
- Molecular Mechanism of Mitochondrial Complex I Disruption by m.14484T>C Underlying Leber Hereditary Optic Neuropathy.bioRxiv : the preprint server for biology · 2026Article
- BindFlow: A Free, User-Friendly Pipeline for Absolute Binding Free Energy Calculations Using Free Energy Perturbation or MM(PB/GB)SA.Journal of chemical theory and computation · 2026Article
- 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
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- Noncanonical amino acid-aided synthesis of anti-PD-L1 bispecific nanobody for colon cancer immunotherapy.Communications biology · 2025Article
- Benchmarking Free Energy Calculations: Analysis of Single and Double Mutations Across Two Simulation Software Platforms for Two Protein Systems.bioRxiv : the preprint server for biology · 2025Article
- Comprehensive engineering of novel glycosyltransferase for efficient, donor-promiscuous, and regioselective glycosylation of flavonoids.Science advances · 2025Article
- Advancing Binding Affinity Calculations: A Non-Equilibrium Simulations Approach for Calculation of Relative Binding Free Energies in Systems with Trapped Waters.Journal of chemical theory and computation · 2025Article
- Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology.PLoS computational biology · 2025Article
- Automated On-the-Fly Optimization of Resource Allocation for Efficient Free Energy Simulations.Journal of chemical information and modeling · 2025Article
- A Thermodynamic Cycle to Predict the Competitive Inhibition Outcomes of an Evolving Enzyme.Journal of chemical theory and computation · 2025Article
- Prospective Evaluation of Structure-Based Simulations Reveal Their Ability to Predict the Impact of Kinase Mutations on Inhibitor Binding.The journal of physical chemistry. B · 2025Article
- Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding.bioRxiv : the preprint server for biology · 2025Article
- Evaluating data partitioning strategies for accurate prediction of protein-ligand binding free energy changes in mutated proteins.Computational and structural biotechnology journal · 2025Article
- Alzheimer's Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aβ2-7 Binding Affinity.ACS chemical neuroscience · 2024Article
- Molecular basis for human aquaporin inhibition.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The design of proteins with novel ligand-binding functions holds great potential for application in biomedicine and biotechnology. However, our ability to engineer ligand-binding proteins is still limited, and current approaches rely primarily on experimentation. Computation could reduce the cost of the development process and would allow rigorous testing of our understanding of the principles governing molecular recognition. While computational methods have proven successful in the early stages of the discovery process, optimization approaches that can quantitatively predict ligand affinity changes upon protein mutation are still lacking. Here, we assess the ability of free energy calculations based on first-principles statistical mechanics, as well as the latest Rosetta protocols, to quantitatively predict such affinity changes on a challenging set of 134 mutations. After evaluating different protocols with computational efficiency in mind, we investigate the performance of different force fields. We show that both the free energy calculations and Rosetta are able to quantitatively predict changes in ligand binding affinity upon protein mutations, yet the best predictions are the result of combining the estimates of both methods. These closely match the experimentally determined ΔΔ
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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.