title: Blind prediction of HIV integrase binding from the SAMPL4 challenge.
abstract: Here, we give an overview of the protein-ligand binding portion of the Statistical Assessment of Modeling of Proteins and Ligands 4 (SAMPL4) challenge, which focused on predicting binding of HIV integrase inhibitors in the catalytic core domain. The challenge encompassed three components--a small "virtual screening" challenge, a binding mode prediction component, and a small affinity prediction component. Here, we give summary results and statistics concerning the performance of all submissions at each of these challenges. Virtual screening was particularly challenging here in part because, in contrast to more typical virtual screening test sets, the inactive compounds were tested because they were thought to be likely binders, so only the very top predictions performed significantly better than random. Pose prediction was also quite challenging, in part because inhibitors in the set bind to three different sites, so even identifying the correct binding site was challenging. Still, the best methods managed low root mean squared deviation predictions in many cases. Here, we give an overview of results, highlight some features of methods which worked particularly well, and refer the interested reader to papers in this issue which describe specific submissions for additional details.
authors.affiliation: Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, 147 Bison Modular, Irvine, CA, 92697, USA, dmobley@mobleylab.org.
authors.forename: David L
authors.initials: DL
authors.lastname: Mobley
authors.forename: Shuai
authors.initials: S
authors.lastname: Liu
authors.forename: Nathan M
authors.initials: NM
authors.lastname: Lim
authors.forename: Karisa L
authors.initials: KL
authors.lastname: Wymer
authors.forename: Alexander L
authors.initials: AL
authors.lastname: Perryman
authors.forename: Stefano
authors.initials: S
authors.lastname: Forli
authors.forename: Nanjie
authors.initials: N
authors.lastname: Deng
authors.forename: Justin
authors.initials: J
authors.lastname: Su
authors.forename: Kim
authors.initials: K
authors.lastname: Branson
authors.forename: Arthur J
authors.initials: AJ
authors.lastname: Olson
mesh_terms: D020134:Catalytic Domain; D003198:Computer Simulation; D017076:Computer-Aided Design; D015195:Drug Design; D006678:HIV / Q000201:enzymology*; D015658:HIV Infections / Q000188:drug therapy / Q000201:enzymology / Q000821:virology; D019427:HIV Integrase / Q000737:chemistry / Q000378:metabolism*; D019428:HIV Integrase Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D006801:Humans; D008958:Models, Molecular; D015233:Models, Statistical; D011485:Protein Binding
chemical_list: D019428:HIV Integrase Inhibitors; D019427:HIV Integrase