Author

S. S. Roy Burman

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Biology Disciplines

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Biophysics· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Assay:protein structure· 3 Research Focus:protein structure prediction· 3 Approach:statistical model· 3

Frequent Co-Authors

J. J. Gray· 3 shared J. H. Lubin· 1 shared J. R. Jeliazkov· 1 shared J. W. Labonte· 1 shared M. L. Nance· 1 shared N. A. Marze· 1 shared N. Biswas· 1 shared R. A. Yovanno· 1 shared

Activity by Year

2017-2019 · peak 1/yr

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9 top collaborators · 19 mutual co-author links

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3 shared papersBiophysics

J. J. Gray

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3 papers

Recent·Most cited

BiophysicsbioRxivNov 3, 2019

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI Rounds 37-45

S. S. Roy Burman, M. L. Nance, J. R. Jeliazkov…·J. J. Gray

The paper improves Rosetta docking methods for complex targets in CAPRI Rounds 37-45, achieving successful predictions and refining models for symmetric and carbohydrate-containing complexes.

DOI 10.1002/prot.25855

protein structuredeep learning

𝕏 PostBluesky

BiophysicsbioRxivSep 6, 2018

Flexible backbone assembly and refinement of symmetrical homomeric complexes

S. S. Roy Burman, R. A. Yovanno, J. J. Gray

The paper introduces Rosetta SymDock2, a method for modeling symmetric protein complexes from homology-modeled monomers, achieving better accuracy than existing algorithms.

DOI 10.1016/j.str.2019.03.014

protein structuredeep learning

𝕏 PostBluesky

BiophysicsbioRxivNov 22, 2017

Efficient Flexible Backbone Protein-Protein Docking for Challenging Targets

N. A. Marze, S. S. Roy Burman, W. Sheffler, J. J. Gray

This paper introduces a new protein-protein docking method that efficiently samples flexible backbone conformations and achieves higher success rates for docking flexible complexes.

DOI 10.1093/bioinformatics/bty355

protein structuredeep learning

𝕏 PostBluesky