Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations.

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.

Robust Prediction of Relative Binding Energies for Protein-Protein Complex Mutations Using Free Energy Perturbation Calculations.

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.

Modular basis for potent SARS-CoV-2 neutralization by a prevalent VH1-2-derived antibody class.

Antibodies with heavy chains that derive from the VH1-2 gene constitute some of the most potent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-neutralizing antibodies yet identified. To provide insight into whether these genetic similarities inform common modes of recognition, we determine the structures of the SARS-CoV-2 spike in complex with three VH1-2-derived antibodies: 2-15, 2-43, and H4. All three use VH1-2-encoded motifs to recognize the receptor-binding domain (RBD), with heavy-chain N53I-enhancing binding and light-chain tyrosines recognizing F486RBD. Despite these similarities, class members bind both RBD-up and -down conformations of the spike, with a subset of antibodies using elongated CDRH3s to recognize glycan N343 on a neighboring RBD-a quaternary interaction accommodated by an increase in RBD separation of up to 12 Å. The VH1-2 antibody class, thus, uses modular recognition encoded by modular genetic elements to effect potent neutralization, with the VH-gene component specifying recognition of RBD and the CDRH3 component specifying quaternary interactions.