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.