This project addresses a broad spectrum of interrelated biological problems that exploit structure based approaches to biochemical function prediction: Proposed improvements in protein structure and function prediction methodologies will lead to better approaches for drug lead selection and identification of of-target protein interactions responsible for drug side effects. Similarly, since DNA-protein interactions are crucial to a variety of biological processes including transcription, methods that better predict the identity and structure of DNA binding proteins in complex with DNA will be developed. Towards elucidation of the qualitative behavior of subcellular processes, schematic molecular simulations will be undertaken. A key idea is the observation that across evolutionarily distant proteins, there are conserved structural and functional features that can be used for ligand and protein ranking and refinement;remarkably, this conservation holds even at the level of protein side chain and ligand functional groups (e.g. hydroxyls). To realize these objectives, five Specific Aims are proposed: 1. TASSER will be improved and extended to treat models at atomic detail. 2. Exploiting structural conservation across multiple-domain holo-proteins, multidomain protein structure prediction will be improved. 3. Improved approaches for ligand ranking/identification of off-target proteins in proteomes will be developed. 4. Prediction of the DNA sequence dependence and quaternary structure of protein-bound DNA will be improved. 5. To explore the roles of hydrodynamic interactions (shown to be important for intracellular molecular diffusion) and crowding in intracellular dynamics, idealized Brownian Dynamics simulations of subcellular processes such as metabolic pathways, the mechanism of actin assembly, the mechanism of myosin motion along actin filaments, and aspects of protein dynamics associated with transcription will be undertaken. At every stage, careful and comprehensive benchmarking will be done;often, this will be followed by application of the developed methodology to proteomes. All tools and databases will be provided as web services;all software will be downloadable for use on local servers.
Each Aim i s part of a more comprehensive objective to increase our understanding of subcellular processes and biochemical function that will help accelerate drug discovery as well as provide general biochemical insights.

Public Health Relevance

The development of more powerful computational methods for the prediction of protein structure, function and small molecule screening will improve the drug discovery process by providing better candidate drug leads as well as predicting possible off-target proteins that could give rise to drug side effects. The successful prediction of ligand cross-reactivity would significantly impact the development of safe and effective therapeutics. Finally, the simulations of subcellular processes will provide qualitative insights into how cells work and are the first tentative steps towards elucidating on a molecular level some of the intracellular changes giving rise to diseases such as cancer.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Macromolecular Structure and Function D Study Section (MSFD)
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Wehrle, Janna P
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Georgia Institute of Technology
Schools of Arts and Sciences
United States
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Srinivasan, Bharath; Zhou, Hongyi; Kubanek, Julia et al. (2014) Experimental validation of FINDSITE(comb) virtual ligand screening results for eight proteins yields novel nanomolar and micromolar binders. J Cheminform 6:16
Skolnick, Jeffrey; Gao, Mu; Zhou, Hongyi (2014) On the role of physics and evolution in dictating protein structure and function. Isr J Chem 54:1176-1188
Khoury, George A; Liwo, Adam; Khatib, Firas et al. (2014) WeFold: a coopetition for protein structure prediction. Proteins 82:1850-68
Skolnick, Jeffrey; Zhou, Hongyi; Gao, Mu (2013) Are predicted protein structures of any value for binding site prediction and virtual ligand screening? Curr Opin Struct Biol 23:191-7
Zhou, Hongyi; Skolnick, Jeffrey (2013) FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach. J Chem Inf Model 53:230-40
Jo, Sunhwan; Lee, Hui Sun; Skolnick, Jeffrey et al. (2013) Restricted N-glycan conformational space in the PDB and its implication in glycan structure modeling. PLoS Comput Biol 9:e1002946
Ando, Tadashi; Chow, Edmond; Skolnick, Jeffrey (2013) Dynamic simulation of concentrated macromolecular solutions with screened long-range hydrodynamic interactions: algorithm and limitations. J Chem Phys 139:121922
Zhang, Yang; Skolnick, Jeffrey (2013) Segment assembly, structure alignment and iterative simulation in protein structure prediction. BMC Biol 11:44
Ando, Tadashi; Skolnick, Jeffrey (2013) On the importance of hydrodynamic interactions in lipid membrane formation. Biophys J 104:96-105
Gao, Mu; Skolnick, Jeffrey (2012) The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation. Proc Natl Acad Sci U S A 109:3784-9

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