© 2017 President and Fellows of Harvard University.

STRUCTURAL BIOINFORMATICS

Structural bioinformatics generates tools and computational technologies that are used by biologists to determine and study macromolecular systems. The focus of our bioinformatics program is to devise a global framework to support computational structural biology. The specific aims of our program include: a) developing a comprehensive software environment to support structural biology computations (SBGrid.org), b) establishing high-throughput structure determination pipelines that are integrated with Open Science Grid resources, c) integrating existing technologies to create a sustainable data management system for disseminating structural biology datasets (Structural Biology Data Grid), and d) analysis of software usage metrics and software policies. We are also interested in extending current SBGrid technologies to support other fields of computational biology. Our collaborators include structural biologists, software developers, and the folks from Dataverse, Open Science Grid, and Globus Toolkit projects. Our bioinformatics program is funded by the SBGrid Consortium (with contributions from 85 institutions in 18 countries), U.S. National Science Foundation, Harvard’s Tools and Technologies Fund, and Helmsley Charitable Trust.

 

Selected publications:

SBGrid Software Framework:

Structure Determination and Analysis Pipelines:

  • Stokes-Rees, I., and Sliz, P. (2010). Protein structure determination by exhaustive search of Protein Data Bank derived databases. Proc. Natl. Acad. Sci. U.S.A. 107, 21476–21481. 

  • O'Donovan, D.J., Stokes-Rees, I., Nam, Y., Blacklow, S.C., Schröder, G.F., Brunger, A.T., and Sliz, P. (2012). A grid-enabled web service for low-resolution crystal structure refinement. Acta Crystallogr. D Biol. Crystallogr. 68, 261–267. 

  • Lazarus, M.B., Nam, Y., Jiang, J., Sliz, P., and Walker, S. (2011). Structure of human O-GlcNAc transferase and its complex with a peptide substrate. Nature 469, 564–567. (Molecular Dynamics Simulation).

Software Policy:

  • Socias, S. M., Morin, A., Timony, M. A. & Sliz, P. AppCiter: A Web Application for Increasing Rates and Accuracy of Scientific Software Citation. Structure 23, 807–8 (2015).

  • Balch, C., Arias-Pulido, H., Banerjee, S., Lancaster, A.K., Clark, K.B., Perilstein, M., Hawkins, B., Rhodes, J., Sliz, P., Wilkins, J., et al. (2015). Science and technology consortia in U.S. biomedical research: a paradigm shift in response to unsustainable academic growth. Bioessays 37, 119–122

  • Sliz, P., and Morin, A. (2013). Optimizing peer review of software code. Science 341, 236–237.

  • Morin, A., Eisenbraun, B., Key, J., Sanschagrin, P.C., Timony, M.A., Ottaviano, M., and Sliz, P. Collaboration gets the most out of software. eLife e01456: (2013). PMC3771563.

  • Morin, A., Urban, J., and Sliz, P. (2012). A quick guide to software licensing for the scientist-programmer. PLoS Comput. Biol. 8, e1002598

  • Heo, I.I., Ha, M.M., Lim, J.J., Yoon, M.-J.M., Park, J.-E.J., Baker, D., and Sliz, P. (2012). Research priorities. Shining light into black boxes. Science 336, 159–160.

 

 

Sakthyda Corrado
Dmitry Filonov
Carol Herre
Jason Key
Pete Meyer
Justin O'Connor
Michelle Ottaviano
Shaun Rawson
Isaac Rosado
Jim Vincent
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