Gevorg Grigoryan
Affiliations: | 2011- | Computer Science | Dartmouth University, Hanover, NH, United States |
Area:
modeling of protein-peptide interactionsWebsite:
https://home.dartmouth.edu/faculty-directory/gevorg-grigoryanGoogle:
"Gevorg Grigoryan"Bio:
https://grigoryanlab.org/?sec=home
https://grigoryanlab.org/?sec=people
Grigoryan, Gevorg, Computational approaches for the design and prediction of protein-protein interactions. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2007
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Parents
Sign in to add mentor| Amy E. Keating | grad student | 2007 | MIT | |
| (Computational approaches for the design and prediction of protein-protein interactions.) | ||||
| William F. DeGrado | post-doc | 2008-2011 | Penn | |
| (postdoc) | ||||
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Publications
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| Ingraham JB, Baranov M, Costello Z, et al. (2023) Illuminating protein space with a programmable generative model. Nature |
| Li AJ, Lu M, Desta I, et al. (2022) Neural Network-Derived Potts Models for Structure-Based Protein Design using Backbone Atomic Coordinates and Tertiary Motifs. Protein Science : a Publication of the Protein Society. e4554 |
| Swanson S, Sivaraman V, Grigoryan G, et al. (2022) Tertiary motifs as building blocks for the design of protein-binding peptides. Protein Science : a Publication of the Protein Society. 31: e4322 |
| Singer JM, Novotney S, Strickland D, et al. (2022) Large-scale design and refinement of stable proteins using sequence-only models. Plos One. 17: e0265020 |
| Holland J, Grigoryan G. (2022) Structure-conditioned amino-acid couplings: How contact geometry affects pairwise sequence preferences. Protein Science : a Publication of the Protein Society. 31: 900-917 |
| Wang F, Gnewou O, Modlin C, et al. (2021) Structural analysis of cross α-helical nanotubes provides insight into the designability of filamentous peptide nanomaterials. Nature Communications. 12: 407 |
| Zhou J, Panaitiu AE, Grigoryan G. (2019) A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures. Proceedings of the National Academy of Sciences of the United States of America |
| Frappier V, Jenson JM, Zhou J, et al. (2019) Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1. Structure (London, England : 1993) |
| Verma D, Grigoryan G, Bailey-Kellogg C. (2018) Pareto optimization of combinatorial mutagenesis libraries. Ieee/Acm Transactions On Computational Biology and Bioinformatics |
| Holland J, Pan Q, Grigoryan G. (2018) Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials. Plos One. 13: e0199585 |