Dramatic advances in our understanding of molecular structure and function promise to accelerate the creation of new diagnostics and therapeutics. However the link between the structure of a biological macromolecule and its function is usually not obvious: fundamental to understanding how a molecule functions is an understanding of how its structure behaves over time. Recent advances in molecular dynamics simulations now allow the rapid collection of information about structural motion. These data sets are huge, and require statistical machine learning algorithms to characterize and recognize patterns relevant to function. The National Library of Medicine's new long-range plan calls for research in the use of advanced simulation and machine learning algorithms in support of biomedical research. This proposal focuses on annotating molecular structures with missing or incomplete functional information. We are particularly interested in identifying binding sites and active sites in proteins. We bring together simulation and machine learning, and hypothesize that the performance of structure- based function annotation methods will dramatically improve with the addition of information about dynamics. Thus, our specific aims are (1) to develop methods for recognizing function from structural dynamics and diversity, (2) to develop capabilities for large scale clustering and analysis tools for the discovery of novel functions, and (3) to apply our tools to challenging and important biological systems, while disseminating our software, data and capabilities to the biomedical research community. In particular, we will focus our new capabilities on three difficult function annotation challenges: ATP binding sites, phosphorylation sites, and metabolizing enzyme active sites.

Public Health Relevance

The explosion in data related to molecular biology has created great opportunities for new disease diagnostics and therapies. One source of data is the three-dimensional (3D) structure of biological molecules such as proteins, DNA and RNA. This work focuses on using computational technologies to understand how these structures perform their function, so we have a better understanding of both normal and disease processes.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
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Ye, Jane
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Stanford University
Schools of Medicine
United States
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Liu, T; Altman, R B (2014) Identifying druggable targets by protein microenvironments matching: application to transcription factors. CPT Pharmacometrics Syst Pharmacol 3:e93
Kohlhoff, Kai J; Shukla, Diwakar; Lawrenz, Morgan et al. (2014) Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem 6:15-21
Karczewski, Konrad J; Snyder, Michael; Altman, Russ B et al. (2014) Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association. PLoS Genet 10:e1004122
Gottlieb, A; Altman, R B (2014) Integrating systems biology sources illuminates drug action. Clin Pharmacol Ther 95:663-9
Percha, Bethany; Altman, Russ B (2013) Informatics confronts drug-drug interactions. Trends Pharmacol Sci 34:178-84
Lahti, Jennifer L; Tang, Grace W; Capriotti, Emidio et al. (2012) Bioinformatics and variability in drug response: a protein structural perspective. J R Soc Interface 9:1409-37
Kohlhoff, Kai J; Sosnick, Marc H; Hsu, William T et al. (2011) CAMPAIGN: an open-source library of GPU-accelerated data clustering algorithms. Bioinformatics 27:2322-3
Tang, Grace W; Altman, Russ B (2011) Remote thioredoxin recognition using evolutionary conservation and structural dynamics. Structure 19:461-70
Wu, Shirley; Liu, Tianyun; Altman, Russ B (2010) Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues. BMC Struct Biol 10:4
Glazer, Dariya S; Radmer, Randall J; Altman, Russ B (2009) Improving structure-based function prediction using molecular dynamics. Structure 17:919-29

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