ArticleJournal of molecular biology2024
Robust Prediction of Relative Binding Energies for Protein-Protein Complex Mutations Using Free Energy Perturbation Calculations.
Article in Journal of molecular biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- A Functional Investigation of Antibody Fc-FcRn Variant Binding Guided by In Silico Free Energy Perturbation Methods.Journal of molecular biology · 2026Article
- Discovery of 2H-Pyrrolo[3,4‑ACS medicinal chemistry letters · 2026Article
- MAVISp: A modular structure-based framework for protein variant effects.Protein science : a publication of the Protein Society · 2026Article
- A functional investigation of antibody Fc-FcRn variant binding guided bybioRxiv : the preprint server for biology · 2026Article
- Prediction of Protein-Ligand Binding Affinities Using Atomic Surface Site Interaction Points.Journal of chemical information and modeling · 2026Article
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Accurate Prediction of Protein Tertiary and Quaternary Stability Using Fine-Tuned Protein Language Models and Free Energy Perturbation.International journal of molecular sciences · 2025Article
- Improving pJournal of chemical theory and computation · 2025Article
- Konnektor: A Framework for Using Graph Theory to Plan Networks for Free Energy Calculations.Journal of chemical information and modeling · 2024Article
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23 authors.
Funding
Abstract
Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how Protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.
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