title: Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands.
abstract: A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.
authors.affiliation: General Chemistry Department, Chemistry Faculty, University of Havana, Zapata y G, Havana, 10400, Cuba. german@fq.uh.cu.|Laboratory of Theoretic and Computational Chemistry, Chemistry Faculty, University of Havana, Havana, Cuba. german@fq.uh.cu.
authors.forename: Germán M
authors.identifier: http://orcid.org/0000-0002-2318-1700
authors.initials: GM
authors.lastname: Pérez
authors.affiliation: Applied Mathematics Department, Mathematics and Computer Science Faculty, University of Havana, Havana, Cuba.|Department of Mathematical Sciences, EAFIT University, Medellín, Colombia.
authors.forename: Luis A
authors.initials: LA
authors.lastname: Salomón
authors.affiliation: Laboratory of Theoretic and Computational Chemistry, Chemistry Faculty, University of Havana, Havana, Cuba.
authors.forename: Luis A
authors.initials: LA
authors.lastname: Montero-Cabrera
authors.affiliation: Department of applied chemical-physics, Autonomous University of Madrid, Teramo, Spain.
authors.forename: José M García
authors.initials: JM
authors.lastname: de la Vega
authors.affiliation: Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy.
authors.forename: Marcello
authors.initials: M
authors.lastname: Mascini
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008024:Ligands; D062105:Molecular Docking Simulation; D010455:Peptides / Q000378:metabolism*; D012372:ROC Curve; D062165:Receptors, Artificial / Q000378:metabolism*; D054852:Small Molecule Libraries / Q000378:metabolism* / Q000494:pharmacology*; D013269:Stochastic Processes; D013816:Thermodynamics; D014584:User-Computer Interface
chemical_list: D008024:Ligands; D010455:Peptides; D062165:Receptors, Artificial; D054852:Small Molecule Libraries
keywords: Inverse virtual screening; Molecular docking; Molecular modeling; Sampling techniques