Prediction of sub-cavity binding preferences using an adaptive physicochemical structure representation

Abstract

Motivation: The ability to predict binding profiles for an arbitrary protein can significantly improve the areas of drug discovery, lead optimization and protein function prediction. At present, there are no successful algorithms capable of predicting binding profiles for novel proteins. Existing methods typically rely on manually curated templates or entire active site comparison. Consequently, they perform best when analyzing proteins sharing significant structural similarity with known proteins (i.e. proteins resulting from divergent evolution). These methods fall short when used to characterize the binding profile of a novel active site or one for which a template is not available. In contrast to previous approaches, our method characterizes the binding preferences of sub-cavities within the active site by exploiting a large set of known protein–ligand complexes. The uniqueness of our approach lies not only in the consideration of sub-cavities, but also in the more complete structural representation of these sub-cavities, their parametrization and the method by which they are compared. By only requiring local structural similarity, we are able to leverage previously unused structural information and perform binding inference for proteins that do not share significant structural similarity with known systems.Results: Our algorithm demonstrates the ability to accurately cluster similar sub-cavities and to predict binding patterns across a diverse set of protein–ligand complexes. When applied to two high-profile drug targets, our algorithm successfully generates a binding profile that is consistent with known inhibitors. The results suggest that our algorithm should be useful in structure-based drug discovery and lead optimization.Contact: izharw@cs.toronto.edu; lilien@cs.toronto.edu

Alignment-Free Ultra-High-Throughput Comparison of Druggable Protein−Ligand Binding Sites

“…Importantly, only FuzCav and SiteEngine manage to find some similarity between 6cox and 1oq5 protein-ligand binding sites, thus suggesting that they may be used to detect subpocket similarities, a very important feature for annotating proteins with novel folds or no representative templates. 46 Additional benchmarks on common data sets are clearly needed to unambiguously compare site-matching programs. However, the current analysis shows that the FuzCav method is robust, relatively insensitive to the binding site definition, and fuzzy enough to be applied to ligand-bound as well as ligand-free protein structures.…”

Identification of Similar Binding Sites to Detect Distant Polypharmacology

“…[149,150] Wallach et al used this strategy to deconstruct binding cavities in a set of potentially overlapping sub-cavities according to chemical groups of co-crystallized ligands. [151] More recently, Jalencas and Mestres [43] used also chemical fragments to define protein environments as interacting binding-site surface regions of consistent pharmacophoric features.…”

Considerations of Protein Subpockets in Fragment‐Based Drug Design

“…KRIPO is able to use microenvironment pharmacophore fingerprints to identify similar binding sites between proteins, and potential bioisosteric replacements for queried molecular fragments. Combining a subcavity comparison search with pharmacophoric analysis, Wallach and Lilien developed a method to cluster similar binding site subcavities to predict patterns of binding between proteins that do not share any structural similarity with known systems.…”