Modelling of potentially promising SARS protease inhibitors

Abstract

In many cases, at the beginning of a high throughput screening experiment some information about active molecules is already available. Active compounds (such as substrate analogues, natural products and inhibitors of related proteins) are often identified in low throughput validation studies on a biochemical target. Sometimes the additional structural information is also available from crystallographic studies on protein and ligand complexes. In addition, the structural or sequence similarity of various protein targets yields a novel possibility for drug discovery. Co-crystallized compounds from homologous proteins can be used to design leads for a new target without co-crystallized ligands. In this paper we evaluate how far such an approach can be used in a real drug campaign, with severe acute respiratory syndrome (SARS) coronavirus providing an example. Our method is able to construct small molecules as plausible inhibitors solely on the basis of the set of ligands from crystallized complexes of a protein target, and other proteins from its structurally homologous family. The accuracy and sensitivity of the method are estimated here by the subsequent use of an electronic high throughput screening flexible docking algorithm. The best performing ligands are then used for a very restrictive similarity search for potential inhibitors of the SARS protease within the million compounds from the Ligand.Info small molecule meta-database. The selected molecules can be passed on for further experimental validation.

The interactome: Predicting the protein-protein interactions in cells

“…Proper selection of the protein domain is necessary [102][103][104][105][106][107][108]. In addition to pure chemical data [109][110][111][112][113][114][115][116] in the context of the Drug Discovery [117][118][119][120][121][122][123][124][125][126][127], there is also a need for some knowledge on protein-protein interactions, the high quality structural prediction of proteins [2,[128][129][130][131][132][133][134][135][136] and their inhibitors, and a detailed understanding of how those inhibitors affect the molecular recognition between proteins. The development of theoretical methods for function annotation clearly shows that a detailed analysis of local characteristics of the protein chain can significantly improve accuracy.…”