title: In silico fragment-based drug design using a PASS approach.
abstract: Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.
authors.affiliation: Department of Bioinformatics, Biomedical Chemistry Institute of the Russian Medical Sciences Academy, Moscow, Russia. fioland@yandex.ru
authors.forename: O A
authors.initials: OA
authors.lastname: Filz
authors.forename: A A
authors.initials: AA
authors.lastname: Lagunin
authors.forename: D A
authors.initials: DA
authors.lastname: Filimonov
authors.forename: V V
authors.initials: VV
authors.lastname: Poroikov
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016208:Databases, Factual; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D004798:Enzymes / Q000378:metabolism*; D008024:Ligands; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004798:Enzymes; D008024:Ligands