Identification of Hot Spots within Druggable Binding Regions by Computational Solvent Mapping of Proteins

Abstract

Here we apply the computational solvent mapping (CS-Map) algorithm toward the in silico identification of hot spots, that is, regions of protein binding sites that are major contributors to the binding energy and, hence, are prime targets in drug design. The CS-Map algorithm, developed for binding site characterization, moves small organic functional groups around the protein surface and determines their most energetically favorable binding positions. The utility of CS-Map algorithm toward the prediction of hot spot regions in druggable binding pockets is illustrated by three test systems: (1) renin aspartic protease, (2) a set of previously characterized druggable proteins, and (3) E. coli ketopantoate reductase. In each of the three studies, existing literature was used to verify our results. Based on our analyses, we conclude that the information provided by CS-Map can contribute substantially to the identification of hot spots, a necessary predecessor of fragment-based drug discovery efforts.

Perspectives on the Discovery of Small-Molecule Modulators for Epigenetic Processes

“…77 In addition, Hajduk et al 79 used the Druggability Index to prioritize targets by predicting their NMR screening hit rates using a model derived from polar and nonpolar surface area, surface, complexity and pocket dimensions. Another method, computational solvent mapping, 80 finds favorable interaction sites by moving small organic solvent groups around the protein surface, replicating experimental results. 81 Similarly, Seco et al 82 predicted druggable binding sites by performing molecular dynamics simulation of the protein within an isopropanol-water box and identifying clusters of isopropanol as surrogates for druglike molecules.…”

Identification and analysis of conserved pockets on protein surfaces

“…Vajda and coworkers [24] exploited another approach, computational solvent mapping, to identify hot spots for protein ligand interaction [24]. They chose ten important pharmaceutical targets and they found almost invariably aromatic residues, preferentially Tyr, among the residues important for ligand binding.…”

Computational Tools and Databases for the Study and Characterization of Protein Interactions

“…These methodologies range from scoring function derived from simple physical models (Guerois et al 2002;Kortemme and Baker 2002;Kruger and Gohlke 2010) to more complex, time consuming atomistic simulations to model effect of mutations in the binding energy (Almlof et al 2006;Lafont et al 2007;Moreira et al 2007;Benedix et al 2009;Diller et al 2010). Other methods exploit individual features (or combination of them) that are characteristic to hot spots such as solvent accessibility (Landon et al 2007;Tuncbag et al 2009;Xia et al 2010;, atomic contacts , structural conservation (Li et al 2004), restricted mobility (Yogurtcu et al 2008), relative location of residues in the interface (Keskin et al 2005), sequence conservation (Hu et al 2000;Ma and Nussinov 2007) and pattern mining (Hsu et al 2007). Other examples include a number of machine learning approaches (Darnell et al 2007;Cho et al 2009;Lise et al 2009;Assi et al 2010) such as PCRPi (see next) that integrate a range of structural-and sequence-based information and a docking-based approach (Grosdidier and Fernandez-Recio 2008).…”