A simple and fuzzy method to align and compare druggable ligand‐binding sites

Abstract

A novel method to measure distances between druggable protein cavities is presented. Starting from user-defined ligand binding sites, eight topological and physicochemical properties are projected from cavity-lining protein residues to an 80 triangle-discretised sphere placed at the centre of the binding site, thus defining a cavity fingerprint. Representing binding site properties onto a discretised sphere presents many advantages: (i) a normalised distance between binding sites of different sizes may be easily derived by summing up the normalised differences between the 8 computed descriptors; (ii) a structural alignment of two proteins is simply done by systematically rotating/translating one mobile sphere around one immobile reference; (iii) a certain degree of fuzziness in the comparison is reached by projecting global amino acid properties (e.g., charge, size, functional groups count, distance to the site centre) independently of local rotameric/tautomeric states of cavity-lining residues. The method was implemented in a new program (SiteAlign) and tested in a number of various scenarios: measuring the distance between 376 related active site pairs, computing the cross-similarity of members of a protein family, predicting the targets of ligands with various promiscuity levels. The proposed method is robust enough to detect local similarity among active sites of different sizes, to discriminate between protein subfamilies and to recover the known targets of promiscuous ligands by virtual screening.

Virtual Screening-Based Discovery and Mechanistic Characterization of the Acylthiourea MRT-10 Family as Smoothened Antagonists

“…Although the crystal structures for the ␤1/␤2-adrenergic receptors and rhodopsin, three members of the GPCR family, have been obtained (Lodowski et al, 2009;Rosenbaum et al, 2009), there are few data describing the identification of new compound leads for GPCRs based on homology modeling of the putative ligand binding site. This site is proposed to be located within the heptahelical bundle of these proteins, which displays some structural conservation (Schalon et al, 2008). Homology model-based virtual screening using ligand docking and target-based scoring have encountered limited success for the identification of mGluR5 or melanin-concentrating hormone receptor modulators (Cavasotto et al, 2008;Radestock et al, 2008).…”

Structure‐Based Approaches to Target Fishing and Ligand Profiling

“…Likewise, the NAD binding site of the Rossmann fold and the S-adenosyl-methionine (SAM)-binding site of SAM-methyltransferases were found similar and consequently permitted to predict the cross-reactivity of catechol-O-methyltransferase (COMT) inhibitors (entacapone, tolcapone) with the M.tuberculosis enoyl-acyl carrier protein reductase (InhA). [83] Systematic pair-wise comparison of the staurosporinebinding site of the proto-oncogene Pim-1 kinase with 6,412 druggable protein-ligand binding sites [33] using the SiteAlign algorithm, [84] suggested that the ATP-binding site of synapsin I (an ATP-binding protein regulating neurotransmitter release in the synapse) may recognize the pankinase inhibitor staurosporine ( Figure 5). [85] Biochemical validation of this hypothesis was realized by competition experiments of staurosporine with ATP-g 35 S for binding to synapsin I. Staurosporine, as well as other more specific protein kinase inhibitors (roscovitine, quercetagetin), effectively bound to synapsin I with nanomolar affinities and promoted synapsin-induced F-actin bundling.…”

Structure-guided selection of Specificity Determining Positions in the human kinome

“…First, a one-to-one correspondence needs to be established between relevant residues (e.g., binding site residues) among all structures. This correspondence can be computed using a multiple sequence alignment or using sequence independent methods [21][22][23][24]. Second, we consider the structural and physicochemical variation among all structures and all triplets of residues.…”

“…For each triplet, we compute a distance matrix of all pairwise distances between substructures. The distance measure used is a combination of structural distance and chemical dissimilarity introduced in [22]. In particular, the distance between any two substructures s 1 and s 2 is defined as:…”

“…The remaining terms account for differences in the amino acid properties between the substructures s 1 and s 2 as quantified by the pharmacophore feature dissimilarity matrix as defined in [22]. Each row in the distance matrix can be thought of as a "feature vector" that describes how a structure differs from all others with respect to a particular substructure.…”