Computational alanine scanning with GBSA and interaction entropy: a review of methodologies and applications in protein-protein and protein-ligand binding.

Computational alanine scanning (CAS) has emerged as a powerful in silico strategy for identifying binding hot spots in protein-protein and protein-ligand complexes, addressing the limitations of experimental alanine scanning mutagenesis. This review provides a comprehensive overview of CAS methodologies, with particular focus on the integration of GBSA (molecular mechanics/generalized born surface area) and the Interaction Entropy (IE) method. Traditional CAS approaches have been hampered by the systematic neglect of entropic contributions or computationally prohibitive entropy calculations via normal-mode analysis. The IE method overcomes this bottleneck by enabling efficient and rigorous entropy calculation directly from a single molecular dynamics trajectory, using only fluctuations of intermolecular interaction energies. We present the theoretical foundations of the ASGBIE (Alanine Scanning with Generalized Born and Interaction Entropy) framework, which combines GBSA with IE to accurately compute residue-specific binding free energies. The methodology is systematically described for both protein-protein and protein-ligand systems, including practical considerations such as residue type-specific dielectric constants and the single-trajectory approximation. We survey diverse applications across biologically and pharmacologically relevant systems, including the p53/MDM2 complex, PD-1/PD-L1 immune checkpoint, SARS-CoV-2 variant escape from monoclonal antibodies, Nipah virus antibody synergy, and drug resistance mechanisms in kinase inhibitors. These case studies demonstrate the practical utility of ASGBIE across diverse protein-binding systems of biological and pharmaceutical relevance.