Structural motifs recurring in different folds recognize the same ligand fragments

<p>Abstract</p> <p>Background</p> <p>The structural analysis of protein ligand binding sites can provide information relevant for assigning functions to unknown proteins, to guide the drug discovery process and to infer relations among distant protein folds. Previous approaches to the comparative analysis of binding pockets have usually been focused either on the ligand or the protein component. Even though several useful observations have been made with these approaches they both have limitations. In the former case the analysis is restricted to binding pockets interacting with similar ligands, while in the latter it is difficult to systematically check whether the observed structural similarities have a functional significance.</p> <p>Results</p> <p>Here we propose a novel methodology that takes into account the structure of both the binding pocket and the ligand. We first look for local similarities in a set of binding pockets and then check whether the bound ligands, even if completely different, share a common fragment that can account for the presence of the structural motif. Thanks to this method we can identify structural motifs whose functional significance is explained by the presence of shared features in the interacting ligands.</p> <p>Conclusion</p> <p>The application of this method to a large dataset of binding pockets allows the identification of recurring protein motifs that bind specific ligand fragments, even in the context of molecules with a different overall structure. In addition some of these motifs are present in a high number of evolutionarily unrelated proteins.</p

Computational methods applied to the discovery of stem cell factor ligands

A computational study of the stem cell factor (SCF) and potential ligands was carried out starting with a crystallographic model deposited in the protein data bank. The inhibition of the SCF dimerization equilibrium was considered as the rationale for the lead identification of specific ligands. A preliminary molecular dynamics characterization of the SCF dimer allowed to verify the most flexible loop involved in the dimeric area. Then a virtual screening, coupled with energy minimization in GB/SA water, scored the compounds implemented in the NCI diversity molecular database. Ten top ranked ligands were analyzed considering both the SCF loop perturbation in the dimerization area and the network of intermolecular hydrogen bonds. Among these ten compounds two natural agents were identified. The computational work revealed useful new insights for rational design of novel SCF dimerization inhibitors

Computational methods applied to the discovery of stem cell factor ligands

A computational study of the stem cell factor (SCF) and potential ligands was carried out starting with a crystallographic model deposited in the protein data bank. The inhibition of the SCF dimerization equilibrium was considered as the rationale for the lead identification of specific ligands. A preliminary molecular dynamics characterization of the SCF dimer allowed to verify the most flexible loop involved in the dimeric area. Then a virtual screening, coupled with energy minimization in GB/SA water, scored the compounds implemented in the NCI diversity molecular database. Ten top ranked ligands were analyzed considering both the SCF loop perturbation in the dimerization area and the network of intermolecular hydrogen bonds. Among these ten compounds two natural agents were identified. The computational work revealed useful new insights for rational design of novel SCF dimerization inhibitors