| Poster C01 Identification of Druggabable "Hot Spots" in Ligand Binding Pockets by Computational Solvent Mapping of Proteins Melissa R. Landon (1), Spencer C. Thiel (1), David R. Lancia, Jr. (2), Jessamin Yu (1), Sandor Vajda (2) (1) Bioinformatics Graduate Program, Boston University; (2) Department of Biomedical Engineering, Boston University |
| Abstract: Here we describe the application of the CS-Map algorithm to the identification of druggable subsites within a ligand binding pocket. Unlike other existing computational methods for binding site identification, the CS-Map algorithm is capable of resolving the affinities and binding modalities of lead-like fragments on a residue-level within a ligand binding pocket. The accuracy of CS-Map results is illustrated on a set of fifty proteins for which both high affinity drug-like compounds have been developed and structural information is available. Contact: mlandon@bu.edu Keywords: Druggability, Fragment-Based Drug Design |
| Poster C02 An Alternative Method for the Evaluation of Docking Performance: Real Space R Value (RSR) vs. RMSD Dilimulati Yusufujiangaili Institute for Theoretical Chemistry, University of Vienna |
| Abstract: The evaluation of the docking programs is usually based on the root mean square deviation (RMSD) between the nonhydrogen atoms positions of ligand's crystallographic model and those predicted by docking programs. Since the drawbacks of RMSD based evaluation causes much bias, an alternative evaluation method is presented in this paper. The new method is based on ligand's real space fit (RSR) to experimental density map. In most cases, RMSD and RSR do not have general agreement on quality of docked models. RSR based method gives a fairer evaluation on docked models. Contact: dilmurat@tbi.univie.ac.at Keywords: Docking Evalution, RMSD, RSR |
| Poster C03 A Probabilistic Approach to the Design of Structural Selectivity of Proteins Menachem Fromer, Chen Yanover, Julia Shifman, Yair Weiss The Hebrew University of Jerusalem |
| Abstract: The multiple-state protein design problem strives to predict the protein sequence(s) best suited to selectively fold to one and only one of a set of competing (related) protein structures. We have formulated the problem using probabilistic graphical models. Using this formulation, we performed computational multiple-state protein design on relevant biological examples. The results have been computationally assessed through comparison with competing state-of-the-art methods (and using exact energy computations, where feasible). Contact: fromer@cs.huji.ac.il Keywords: Protein Design, Probabilistic, Multi-state |
| Poster C04 Analysis of Antibiotic Binding Pockets on the Ribosome Large Subunit using Structure-Based Networks Hilda David, Yael Mandel-Gutfreund Technion-Israel Institute of Technology |
| Abstract: Clinically important antibiotics inhibit the activity of bacterial ribosome. Prominent among them are the MLSBK antibiotics which target the ribosome large subunit. We analyzed the 23S rRNA of bacteria and archea using mathematical graphs (networks) where nucleotides are represented as nodes and the intermolecular interactions as edges. For each individual node in the network we calculated different centrality measures. Our results show that the betweenness and the degree measures help to better understand the structural properties of the MLSBK antibiotics binding pockets. Contact: hildad@tx.technion.ac.il Keywords: Ribosome Structure Analysis, Network Analysis |
| Poster C05 Computational Design of RNA Structural Switches from Building Blocks Assaf Avihoo, Danny Barash Department of Computer Science, Ben-Gurion University |
| Abstract: We aim at devising a robust computational procedure for designing RNA structural switches from building blocks with favorable properties. To achieve maximal throughput for genetic control purposes, future designer RNA switches can be assembled based on a computerized buildup of the constituent domains, namely the aptamer and the expression platform in the case of a synthetic riboswitch. Initially, simulations can produce a list of short sequences that switch between two conformers when trigerred by point mutations. Consequently, one of the known aptamers is attached and screening is performed. Contact: dbarash@cs.bgu.ac.il Keywords: Energy Minimization Methods, RNA Switches |
| Poster C06 Recognition Properties of Catechols and beta-Ketoenols, Pharmacophores of HIV-1 Integrase Inhibitors Luba Tchertanov, Jean-Francois Mouscadet Laboratoire de Biotechnologies et Pharmacologie Genetique Appliquees, CNRS, Ecole Normale Superieure |
| Abstract: The recognition properties of catechol (I) and beta-ketoenol (II) moieties which are important pharmacophores of HIV-1 IN inhibitors, were studied using data retrieved from the Cambridge Structural Database (CSD). The established selectivity of I and II with respect to Mg2+/Mn2+ and to H-bond donors and acceptors may account for their respective affinity for the enzyme active site. The data were (i) compared with binding modes of IN inhibitors possessing I and II moieties docked into the active site of IN or IN-DNA complex and (ii) correlated with inhibiting properties of such molecules Contact: luba.tchertanov@lbpa.ens-cachan.fr Keywords: HIV-1 IN, Recognition, Inhibitors |
| Poster C07 Mapping Protein-Protein Interfaces using Combinatorial Libraries Itay Mayrose (1), Tomer Shlomi (2), Nimrod D. Rubinstein (1), Jonathan M. Gershoni (1), Eytan Ruppin (2), Roded Sharan (2), Tal Pupko (1) (1) Department of Cell Research and Immunology, Tel Aviv University; (2) School of Computer Science, Tel-Aviv University |
| Abstract: A phage-display library of random peptides is a combinatorial technique for obtaining random peptides that bind an antibody with high affinity. These peptides can be used to infer the antibody's corresponding epitope. Here we present 'PepSurf', an algorithm for mapping a set of affinity-selected peptides onto the 3D structure of the antigen. This is done by aligning each peptide to a graph representing the antigen's surface. The most significant paths are then clustered into predicted epitopes. We show that PepSurf accurately predicts the known epitopes in 3 antibody-antigen complexes. Contact: itaymay@post.tau.ac.il Keywords: Epitope Mapping, Combinatorial Libraries |