Comprehensive investigation of selectivity landscape of Glycogen Synthase Kinase-3 inhibitors
Creators: name: Pacureanu, Liliana
name: Avram, Sorin
name: Luminita Crisan
Publisher: name: Taylor & Francis
Year: 2020
Subjects: subject: Biochemistry
subject: Pharmacology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: 60506 Virology

description: Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today’s retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC–AUC) leave-one-out cross-validation (LOO CV). and additional 2D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2D similarity coefficients and distances, 3D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors. <b>List of Abbreviations:</b> AUC: Area under the curve; BMF: Bemis Murcko framework; CDK-2 : Cyclin dependent kinase-2; CDK-4 : Cyclin dependent kinase-4; FRED : Flexible Ligand - Rigid Protein Docking; GSK-3: Glycogen Synthase Kinase-3; HTS: High-throughput screening; HSS: Highly selective set; LB: Ligand Based; MSS: Moderately selective set; NSS: Nonselective set; VS: Virtual Screening; PKC: Protein kinase C; ROC: Receiver operating characteristic; ROCS: Rapid Overlay of Chemical Structures; PDB: Protein Data Bank; RMSD: Root Mean Squared Deviation; SB: Structure Based; RBN: rotatable bond.