Optimization of high throughput virtual screening by combining shape-matching and docking methods

Receptor flexibility is a critical issue in structure-based virtual screening methods. Although a multiplereceptor conformation docking is an efficient way to account for receptor flexibility, it is still too slow for large molecular libraries. It was reported that a fast ligand-centric, shape-based virtual screening was more consistent for hit enrichment than a typical single-receptor conformation docking. Thus, we designed a "distributed docking" method that improves virtual high throughput screening by combining a shape-matching method with a multiple-receptor conformation docking. Database compounds are classified in advance based on shape similarities to one of the crystal ligands complexed with the target protein. This classification enables us to pick the appropriate receptor conformation for a single-receptor conformation docking of a given compound, thereby avoiding time-consuming multiple docking. In particular, this approach utilizes cross-docking scores of known ligands to all available receptor structures in order to optimize the algorithm. The present virtual screening method was tested for reidentification of known PPARγ and p38 MAP kinase active compounds. We demonstrate that this method improves the enrichment while maintaining the computation speed of a typical single-receptor conformation docking

Development and use of databases for ligand-protein interaction studies

This project applies structure-activity relationship (SAR), structure-based and database mining approaches to study ligand-protein interactions. To support these studies, we have developed a relational database system called EDinburgh University Ligand Selection System (EDULISS 2.0) which stores the structure-data files of +5.5 million commercially available small molecules (+4.0 million are recognised as unique) and over 1,500 various calculated molecular properties (descriptors) for each compound. A user-friendly web-based interface for EDULISS 2.0 has been established and is available at http://eduliss.bch.ed.ac.uk/. We have utilised PubChem bioassay data from an NMR based screen assay for a human FKBP12 protein (PubChem AID: 608). A prediction model using a Logistic Regression approach was constructed to relate the assay result with a series of molecular descriptors. The model reveals 38 descriptors which are found to be good predictors. These are mainly 3D-based descriptors, however, the presence of some predictive functional groups is also found to give a positive contribution to the binding interaction. The application of a neural network technique called Self Organising Maps (SOMs) succeeded in visualising the similarity of the PubChem compounds based on the 38 descriptors and clustering the 36 % of active compounds (16 out of 44) in a cluster and discriminating them from 95 % of inactive compounds. We have developed a molecular descriptor called the Atomic Characteristic Distance (ACD) to profile the distribution of specified atom types in a compound. ACD has been implemented as a pharmacophore searching tool within EDULISS 2.0. A structure-based screen succeeded in finding inhibitors for pyruvate kinase and the ligand-protein complexes have been successfully crystallised. This study also discusses the interaction of metal-binding sites in metalloproteins. We developed a database system and web-based interface to store and apply geometrical information of these metal sites. The programme is called MEtal Sites in Proteins at Edinburgh UniverSity (MESPEUS; http://eduliss.bch.ed.ac.uk/MESPEUS/). MESPEUS is an exceptionally versatile tool for the collation and abstraction of data on a wide range of structural questions. As an example we carried out a survey using this database indicating that the most common protein types which contain Mg-OATP-phosphate site are transferases and the most common pattern is linkage through the β- and γ-phosphate groups