Prediction, Analysis, and Comparison of Active Sites
Andrea Volkamer, Mathias M. von Behren, Stefan Bietz, Matthias Rarey · 2018
This chapter summarizes basic knowledge about active site/binding site detection techniques and target assessment approaches. It explains current active site prediction algorithms and introduces methods for target prioritization, that is, druggability annotation. To predict the target druggability, binding site descriptors are used in combination with machine learning techniques to extract patterns that separate (known) druggable from undruggable protein. Considering an ensemble of pocket conformations or sequentially homologous binding sites rather than a single structure is a popular way to incorporate protein variability. The search and selection of suitable alternative structures from a given structure database is an important preprocessing step for all computational approaches based on protein structure. The chapter describes approaches for the enrichment of structural knowledge by active site ensemble generation. It then introduces structure-based approaches for active site comparison.