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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Zhou, Hongyi; Gao, Mu; Skolnick, Jeffrey (2016) ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures. PLoS One 11:e0150965
Srinivasan, Bharath; Skolnick, Jeffrey (2015) Insights into the slow-onset tight-binding inhibition of Escherichia coli dihydrofolate reductase: detailed mechanistic characterization of pyrrolo [3,2-f] quinazoline-1,3-diamine and its derivatives as novel tight-binding inhibitors. FEBS J 282:1922-38
Zhou, Hongyi; Gao, Mu; Skolnick, Jeffrey (2015) Comprehensive prediction of drug-protein interactions and side effects for the human proteome. Sci Rep 5:11090
Gao, Mu; Zhou, Hongyi; Skolnick, Jeffrey (2015) Insights into Disease-Associated Mutations in the Human Proteome through Protein Structural Analysis. Structure 23:1362-9
Skolnick, Jeffrey; Gao, Mu; Roy, Ambrish et al. (2015) Implications of the small number of distinct ligand binding pockets in proteins for drug discovery, evolution and biochemical function. Bioorg Med Chem Lett 25:1163-70
Roy, Ambrish; Srinivasan, Bharath; Skolnick, Jeffrey (2015) PoLi: A Virtual Screening Pipeline Based on Template Pocket and Ligand Similarity. J Chem Inf Model 55:1757-70
Roy, Ambrish; Skolnick, Jeffrey (2015) LIGSIFT: an open-source tool for ligand structural alignment and virtual screening. Bioinformatics 31:539-44
Tonddast-Navaei, Sam; Skolnick, Jeffrey (2015) Are protein-protein interfaces special regions on a protein's surface? J Chem Phys 143:243149
Srinivasan, Bharath; Tonddast-Navaei, Sam; Skolnick, Jeffrey (2015) Ligand binding studies, preliminary structure-activity relationship and detailed mechanistic characterization of 1-phenyl-6,6-dimethyl-1,3,5-triazine-2,4-diamine derivatives as inhibitors of Escherichia coli dihydrofolate reductase. Eur J Med Chem 103:600-14
Boles, Richard G; Hornung, Holly A; Moody, Alastair E et al. (2015) Hurt, tired and queasy: Specific variants in the ATPase domain of the TRAP1 mitochondrial chaperone are associated with common, chronic "functional" symptomatology including pain, fatigue and gastrointestinal dysmotility. Mitochondrion 23:64-70

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