Several methods have been developed to address (i) protein docking;(ii) helical symmetry description and (iii) protein folding using docking techniques. (i) Symmetric protein complexes are abundant in the living cell. Predicting their atomic structure can shed light on the mechanism of many important biological processes. Symmetric docking methods aim to predict the structure of these complexes given the unbound structure of a single monomer, or its model. Symmetry constraints reduce the search-space of these methods and make the prediction easier compared to asymmetric protein-protein docking. However, the challenge of modeling the conformational changes that the monomer might undergo is a major obstacle. In this article, we present SymmRef, a novel method for refinement and reranking of symmetric docking solutions. The method models backbone and side-chain movements and optimizes the rigid-body orientations of the monomers. The backbone movements are modeled by normal modes minimization and the conformations of the side-chains are modeled by selecting optimal rotamers. Since solved structures of symmetric multimers show asymmetric side-chain conformations, we do not use symmetry constraints in the side-chain optimization procedure. The refined models are re-ranked according to an energy score. We tested the method on a benchmark of unbound docking challenges. The results show that the method significantly improves the accuracy and the ranking of symmetric rigid docking solutions. (ii) Assemblies with helical symmetry can be conveniently formulated in many distinct ways. Here, a new convention is presented which unifies the two most commonly used helical systems for generating helical assemblies from asymmetric units determined by X-ray fibre diffraction and EM imaging. A helical assembly is viewed as being composed of identical repetitive units in a one- or two-dimensional lattice, named 1-D and 2-D helical systems, respectively. The unification suggests that a new helical description with only four parameters [n(1), n(2), twist, rise], which is called the augmented 1-D helical system, can generate the complete set of helical arrangements, including coverage of helical discontinuities (seams). A unified four-parameter characterization implies similar parameters for similar assemblies, can eliminate errors in reproducing structures of helical assemblies and facilitates the generation of polymorphic ensembles from helical atomic models or EM density maps. Further, guidelines are provided for such a unique description that reflects the structural signature of an assembly, as well as rules for manipulating the helical symmetry presentation. (iii) The pathways by which proteins fold into their specific native structure are still an unsolved mystery. Currently, many methods for protein structure prediction are available, and most of them tackle the problem by relying on the vast amounts of data collected from known protein structures. These methods are often not concerned with the route the protein follows to reach its final fold. This work is based on the premise that proteins fold in a hierarchical manner. We present FOBIA, an automated method for predicting a protein structure. FOBIA consists of two main stages: the first finds matches between parts of the target sequence and independently folding structural units using profile-profile comparison. The second assembles these units into a 3D structure by searching and ranking their possible orientations toward each other using a docking-based approach. We have previously reported an application of an initial version of this strategy to homology based targets. Since then we have considerably enhanced our method's abilities to allow it to address the more difficult template-based target category. This allows us to now apply FOBIA to the template-based targets of CASP8 and to show that it is both very efficient and promising. Our method can provide an alternative for template-based structure prediction, and in particular, the docking-basedranking technique presented here can be incorporated into any profile-profile comparison based method.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
1ZIABC010442-10
Application #
8349006
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
10
Fiscal Year
2011
Total Cost
$128,512
Indirect Cost
Name
National Cancer Institute Division of Basic Sciences
Department
Type
DUNS #
City
State
Country
Zip Code
Ma, Buyong; Zhao, Jun; Nussinov, Ruth (2016) Conformational selection in amyloid-based immunotherapy: Survey of crystal structures of antibody-amyloid complexes. Biochim Biophys Acta 1860:2672-81
Nussinov, Ruth; Tsai, Chung-Jung; Chakrabarti, Mayukh et al. (2016) A New View of Ras Isoforms in Cancers. Cancer Res 76:18-23
Maximova, Tatiana; Moffatt, Ryan; Ma, Buyong et al. (2016) Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics. PLoS Comput Biol 12:e1004619
Nussinov, Ruth; Tsai, Chung-Jung (2015) Allostery without a conformational change? Revisiting the paradigm. Curr Opin Struct Biol 30:17-24
Nussinov, Ruth; Tsai, Chung-Jung (2015) The design of covalent allosteric drugs. Annu Rev Pharmacol Toxicol 55:249-67
Clausen, Rudy; Ma, Buyong; Nussinov, Ruth et al. (2015) Mapping the Conformation Space of Wildtype and Mutant H-Ras with a Memetic, Cellular, and Multiscale Evolutionary Algorithm. PLoS Comput Biol 11:e1004470
Nussinov, Ruth; Tsai, Chung-Jung (2015) 'Latent drivers' expand the cancer mutational landscape. Curr Opin Struct Biol 32:25-32
Nussinov, Ruth; Tsai, Chung-Jung (2014) Free energy diagrams for protein function. Chem Biol 21:311-8
Engin, H Billur; Gursoy, Attila; Nussinov, Ruth et al. (2014) Network-based strategies can help mono- and poly-pharmacology drug discovery: a systems biology view. Curr Pharm Des 20:1201-7
Acuner Ozbabacan, Saliha Ece; Gursoy, Attila; Nussinov, Ruth et al. (2014) The structural pathway of interleukin 1 (IL-1) initiated signaling reveals mechanisms of oncogenic mutations and SNPs in inflammation and cancer. PLoS Comput Biol 10:e1003470

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