Predicting and Experimentally Validating Hot-Spot Residues at Protein–Protein Interfaces

Abstract

Protein–protein interactions (PPIs) are vital to all biological processes. These interactions are often dynamic, sometimes transient, typically occur over large topographically shallow protein surfaces, and can exhibit a broad range of affinities. Considerable progress has been made in determining PPI structures. However, given the above properties, understanding the key determinants of their thermodynamic stability remains a challenge in chemical biology. An improved ability to identify and engineer PPIs would advance understanding of biological mechanisms and mutant phenotypes and also provide a firmer foundation for inhibitor design. In silico prediction of PPI hot-spot amino acids using computational alanine scanning (CAS) offers a rapid approach for predicting key residues that drive protein–protein association. This can be applied to all known PPI structures; however there is a trade-off between throughput and accuracy. Here we describe a comparative analysis of multiple CAS methods, which highlights effective approaches to improve the accuracy of predicting hot-spot residues. Alongside this, we introduce a new method, BUDE Alanine Scanning, which can be applied to single structures from crystallography and to structural ensembles from NMR or molecular dynamics data. The comparative analyses facilitate accurate prediction of hot-spots that we validate experimentally with three diverse targets: NOXA-B/MCL-1 (an α-helix-mediated PPI), SIMS/SUMO, and GKAP/SHANK-PDZ (both β-strand-mediated interactions). Finally, the approach is applied to the accurate prediction of hot-spot residues at a topographically novel Affimer/BCL-xL protein–protein interface.

Identification of β-strand mediated protein–protein interaction inhibitors using ligand-directed fragment ligation

“…with the C-terminus hydrophobic residue and free carboxylate being the main determinants of affinity. 71,72 The plasticity of PDZ domains allows the accommodation of various hydrophobic side-chains at the C terminus of the peptide, 73 which we hypothesized to be an ideal target site for hydrophobic fragments; for SHANK1, Leu dominates for C-terminal carboxylates and Phe for non-C-terminal sequences. 74 This led to compounds 1 and 2 ( Fig.…”

SARS-CoV-2 nucleocapsid and Nsp3 binding: an in silico study

“…Inhibitors may be designed to block the ssRNA binding N-NTD site. Binding of drugs at protein interfaces is mostly controlled by some specific residues contributing disproportionately to the Gibbs free energy of binding and dynamics of proteins (Massova and Kollman 1999;Weiss et al 2000;Arkin and Wells 2004;Zhao and Chmielewski 2005;Moreira et al 2007;Wells and McClendon 2007;Boukharta et al 2014;Ibarra et al 2019;Khan et al 2020a).…”

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

“…Interestingly, significant improvements in ∆∆G prediction could be reached with a consensus of predictions from Flex ddG and alchemical calculations in both studies [111,112]. Another comparative study investigated the performance of five predictive tools when applied for alanine scanning to identify hotspot residues at protein-protein interfaces [113]. For a dataset of 748 single-point mutations to alanine from the SKEMPI database, Flex ddG ranked the best (PCC of 0.51) from the tools that were not trained using this database [113].…”

“…Another comparative study investigated the performance of five predictive tools when applied for alanine scanning to identify hotspot residues at protein-protein interfaces [113]. For a dataset of 748 single-point mutations to alanine from the SKEMPI database, Flex ddG ranked the best (PCC of 0.51) from the tools that were not trained using this database [113].…”