title: Reduction of false positives in structure-based virtual screening when receptor plasticity is considered.
abstract: Structure-based virtual screening for selecting potential drug candidates is usually challenged by how numerous false positives in a molecule library are excluded when receptor plasticity is considered. In this study, based on the binding energy landscape theory, a hypothesis that a true inhibitor can bind to different conformations of the binding site favorably was put forth, and related strategies to defeat this challenge were devised; reducing false positives when receptor plasticity is considered. The receptor in the study is the influenza A nucleoprotein, whose oligomerization is a requirement for RNA binding. The structural flexibility of influenza A nucleoprotein was explored by molecular dynamics simulations. The resultant distinctive structures and the crystal structure were used as receptor models in docking exercises in which two binding sites, the tail-loop binding pocket and the RNA binding site, were targeted with the Otava PrimScreen1 diversity-molecule library using the GOLD software. The intersection ligands that were listed in the top-ranked molecules from all receptor models were selected. Such selection strategy successfully distinguished high-affinity and low-affinity control molecules added to the molecule library. This work provides an applicable approach for reducing false positives and selecting true ligands from molecule libraries.
authors: School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore. yaw002@e.ntu.edu.sg.
authors: Yaw
authors: Y
authors: Awuni
authors: School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore. ygmu@ntu.edu.sg.
authors: Yuguang
authors: Y
authors: Mu
mesh_terms: D001665:Binding Sites / Q000187:drug effects; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D004353:Drug Evaluation, Preclinical; D005189:False Positive Reactions; D008956:Models, Chemical; D008958:Models, Molecular; D062105:Molecular Docking Simulation; D019590:Nucleocapsid Proteins; D011487:Protein Conformation; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism*; D054852:Small Molecule Libraries / Q000378:metabolism*; D012984:Software; D013329:Structure-Activity Relationship; D014758:Viral Core Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: C048842:NP protein, Influenza A virus; D019590:Nucleocapsid Proteins; D016601:RNA-Binding Proteins; D054852:Small Molecule Libraries; D014758:Viral Core Proteins