title: AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets.
abstract: BACKGROUND
  Sampling the conformational space is a fundamental step for both ligand- and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of methods such as Monte Carlo, genetic, and reconstruction algorithms. Although some software already implements clustering procedures, at present, a universally accepted protocol is still missing.
  
  
  RESULTS
  We integrated hierarchical agglomerative cluster analysis with a clusterability assessment method and a user independent cutting rule, to form a global protocol that we implemented in a MATLAB metalanguage program (AClAP). We tested it on the conformational space of a quite diverse set of drugs generated via Metropolis Monte Carlo simulation, and on the poses we obtained by reiterated docking runs performed by four widespread programs. In our tests, AClAP proved to remarkably reduce the dimensionality of the original datasets at a negligible computational cost. Moreover, when applied to the outcomes of many docking programs together, it was able to point to the crystallographic pose.
  
  
  BACKGROUND
  AClAP is available at the "AClAP" section of the website http://www.scfarm.unibo.it.
authors.affiliation: Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126, Bologna, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Bottegoni
authors.forename: Walter
authors.initials: W
authors.lastname: Rocchia
authors.forename: Maurizio
authors.initials: M
authors.lastname: Recanatini
authors.forename: Andrea
authors.initials: A
authors.lastname: Cavalli
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000677:Amsacrine; D001185:Artificial Intelligence; D001665:Binding Sites; D016000:Cluster Analysis; D003198:Computer Simulation; D030562:Databases, Protein; D016503:Drug Delivery Systems / Q000379:methods; D015195:Drug Design; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins; D000677:Amsacrine