Using machine‐learning‐driven approaches to boost hot‐spot's knowledge

Understanding protein–protein interactions (PPIs) is fundamental to describe and characterize the formation of biomolecular assemblies, establish energetic principles underlying biological networks. One key aspect these interfaces existence prevalence hot-spots (HS) residues that, upon mutation alanine, negatively impact such complexes. HS have been widely considered in research, both case studies a few large-scale predictive approaches. This review aims present current knowledge on PPIs, providing detailed understanding microspecifications involved those characteristics defined as through thorough assessment related field-specific methodologies. We explore recent accurate artificial intelligence-based techniques, which are progressively replacing well-established classical energy-based article categorized under: Data Science > Databases Expert Systems Structure Mechanism Computational Biochemistry Biophysics Molecular Statistical Mechanics Interactions