Yuval Inbar - home page


I am a PhD student of Prof. Haim Wolfson and Prof Ruth Nussinov at the Bioinfo3D lab (Tel Aviv university). My main field of interest is structural bioinformatics and drug design. I am especially interested in the development of algorithms that involve graph theory, combinatorics and computational geometry to practical problems in molecular biology.
 
Research:

  1. Drug Design
    • Pharmacophore Detection: A pharmacophore is a 3D arrangement of physico-chemical features in a ligand that is responsible for the interaction with a receptor. In the absence of a known 3D receptor structure, a pharmacophore can be identified from a multiple structural alignment of the ligand molecules. We have developed PharmaGist: a novel highly efficient method for the detection of a pharmacophore from a set of ligands/drugs which interact with a target receptor. The key advantages of the presented algorithm are: (a) the ability to multiply align flexible ligands, (b) its ability to focus on subsets of the input molecules which may share a large common substructure, resulting in the detection both of outliers molecules and of alternative binding modes, and (c) its computational efficiency which allows to handle in a deterministic manner detection of pharmacophore shared by a large number of molecules on a standard PC. The algorithm was extensively tested on a dataset of almost 80 ligands acting on 12 different receptors. The results, which were achieved using a standard default parameter set, were consistent with reference pharmacophores that were derived from the bound ligand-receptor complexes. The pharmacophores detected by the algorithm are expected to be a key component in the discovery of new leads by screening large drug-like molecule database [2]. The PharmaGist webserver is available at http://bioinfo3d.cs.tau.ac.il/PharmaGist/ .
    • Virtual Screening: We have further developed the PharmaGist method to perform a virtual screening of drug-like molecular databases. This application gets a small set of active ligands and a large (thousands) molecular database. The molecules in both sets are considered flexible. Based on the active ligands set we detect a pharmacophore, then we check each molecule in the database for its ability to present the detected pharmacophore upon conformational change. The molecules are then ranked according to this ability. The preliminary results are promising, showing that the method has a strong discrimination ability between active and non-active ligands.
  2. Protein Assembly
    • Prediction of Multi-Molecular Assemblies by Multiple Docking: The majority of proteins function when associated in multimolecular assemblies. Yet, prediction of the structures of multimolecular complexes has largely not been addressed, probably due to the magnitude of the combinatorial complexity of the problem. Docking applications have traditionally been used to predict pairwise interactions between molecules. We have developed an algorithm that extends the application of docking to multi-molecular assemblies. We apply it to predict both quaternary structures of oligomers and multi-protein complexes. The algorithm predicted well a near native arrangement of the input subunits for all cases in our data set, where the number of the subunits of the different target complexes varied from 3 to 10. In order to simulate a more realistic scenario, unbound cases were also tested. In these cases the input conformations of the subunits are either unbound conformations of the subunits or a model obtained by homology modelling technique. The successful predictions of the unbound cases, where the input conformations of the subunits are different from their conformations within the target complex, suggest that the algorithm is robust. We expect that this type of algorithm should be particularly useful to predict the structures of large macromolecular assemblies, difficult to solve by experimental structure determination.[4, 8]
    • Prediction of Symmetric Assemblies by Docking: The majority of proteins within the cell function as symmetrical oligomeric complexes. Symmetry gives several advantages to protein function, such as formation of large protein structures, stability against denaturation and folding efficiency. There are many types of symmetry in nature. We developed efficient docking methods for predicting symmetric assemblies starting from monomeric unit for different types of symmetry: cyclic, dihedral, cubic and helical. All the algorithms a priori restrict their transformational search space only to symmetric transformations, and thus gain both in efficiency and performance.[6] The SymmDock weserver for prediction of (cyclic) symmetric assemblies is available at http://bioinfo3d.cs.tau.ac.il/SymmDock/ .  
  3. Protein Folding
    • Protein Structure Prediction via Combinatorial Assembly of Sub-structural Units: Protein structure prediction and protein docking prediction are two related problems in molecular biology. We suggest to use multiple docking in the process of protein structure prediction. Once reliable structural models are predicted to disjoint fragments of the protein target sequence, a combinatorial assembly may be used to predict their native arrangement. CombDockFold, a combinatorial docking algorithm for the structural units assembly problem, is presented here. We have tested the algorithm on various examples using both domains and domain substructures as input. Inaccurate models of the structural units were also used, to test the robustness of the algorithm. The algorithm was able to predict a near-native arrangement of the input structural units in almost all of the cases, showing that the combinatorial approach succeeds to overcome the inexact shape complementarity caused by the inaccuracy of the models.[9, 10]
  4. Image Processing of Electron Microscopy Density Maps
    • 3D Segmentation of Low and Intermediate Resolution EM Density Maps: Electron Microscopy is becoming a powerful tool for structural studies of multimolecular complexes. Due to physical and technological barriers most solved structures are obtained at medium and low resolution. We present here a novel and efficient algorithm for 3D segmentation of low and intermediate resolution density maps. The algorithm was tested on different maps with structures varying in their shape, architecture, resolution, and voxel size. In most cases, the algorithm distinguished between different chains. In some cases, even different domains of the same chain were detected.


Publication list:   

  1. Nabil Miled, Ying Yan, Wai-Ching Hon, Olga Perisic, Marketa Zvelebil, Yuval Inbar, Dina Schneidman-Duhovny, Haim J. Wolfson, Jonathan M. Backer and Roger L. Williams (2007)  Phosphoinositide 3-kinase p110α/p85 heterodimer: Crystal structure and mechanism for gain-of-function of selected p110α cancer-associated mutations. Science (submitted)
  2. Yuval Inbar, Dina Schneidman-Duhovny, Oranit Dror, Ruth Nussinov and Haim J. Wolfson. (2007) Deterministic Pharmacophore Detection via Multiple Flexible Alignment of Drug-Like Molecules. In Proc. of RECOMB 2007. (In Press)
  3. Haspel N, Wainreb G, Inbar Y, Tsai HH, Tsai CJ, Wolfson HJ and Nussinov R. (2007) A hierarchical protein folding scheme based on the building block folding model. Methods Mol Biol. 350:189-204
  4. Inbar Y, Benyamini H, Nussinov R and Wolfson H. (2005) Combinatorial docking approach for structure prediction of large proteins and multi-molecular assemblies. Physical Biology, 3.
  5. Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., and Wolfson, H. (2005) Patch- Dock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33 (Web Server issue), 363-7.
  6. Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., and Wolfson, H. (2005) Geometry based flexible and symmetric protein docking. Proteins, 60(2):224-31.
  7. Inbar, Y., Schneidman-Duhovny, D., Halperin, I., Oron, A., Nussinov, R., and Wolfson, H. (2005) Approaching the CAPRI Challenge with Efficient Geometry Based Docking. Proteins, 60(2):217-23.
  8. Inbar, Y., Benyamini, H., Nussinov, R. and Wolfson, H. (2005) Prediction of multimolecular assemblies by multiple docking. (2005) J. Mol. Biol., 349, 435- 447.
  9. Inbar, Y., Nussinov, R. and Wolfson, H. (2005) Multiple docking for protein structure prediction. The Int. J. of Robotics Research, 24, 131-150.
  10. Yuval Inbar, Hadar Benyamini, Ruth Nussinov, Haim J. Wolfson. (2003) Protein structure prediction via combinatorial assembly of sub-structural units. Bioinformatics. (19 Suppl): i158-i168.
  11. Schneidman-Duhovny D, Inbar Y, Polak V, Shatsky M, Halperin I, Benyamini H, Barzilai A, Dror O, Haspel N, Nussinov R, Wolfson HJ. (2003) Taking geometry to its edge: fast unbound rigid (and hinge-bent) docking. Proteins. 52(1): 107-12.