Author

G. Grigoryan

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 3 Bioengineering· 1

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Approach:deep learning· 4 Research Focus:protein design· 4 Assay:protein structure· 3 Assay:protein sequence· 2

Frequent Co-Authors

A. E. Keating· 2 shared J. Zhou· 2 shared A. E. Panaitiu· 1 shared A. Ford· 1 shared A. J. Li· 1 shared A. K. Bera· 1 shared A. Roy· 1 shared C. M. Chow· 1 shared

Activity by Year

2018-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersBiophysics

A. E. Keating

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4 papers

Recent·Most cited

Neural Network-Derived Potts Models for Structure-Based Protein Design using Backbone Atomic Coordinates and Tertiary Motifs

A. J. Li, M. Lu, I. T. Desta, V. Sundar, G. Grigoryan, A. E. Keating

This paper combines neural network models with tertiary motif data to improve protein design by generating Potts energy models that predict folding and energetics.

bioRxiv · Aug 2, 2022Biophysics

Large-scale design and refinement of stable proteins using sequence-only models

J. M. Singer, S. Novotney, D. Strickland, H. K. Haddox, N. Leiby, G. J. Rocklin, C. M. Chow, A. Roy, A. K. Bera, F. C. Motta, L. Cao, E.-M. Strauch, T. M. Chidyausiku, A. Ford, E. Ho, , E. Klavins

The paper presents two neural network models for predicting and generating stable proteins based on amino acid sequences, showing improved stability in both expert-designed and model-generated proteins.

bioRxiv · Mar 12, 2021Biophysics

Tertiary structural motif sequence statistics enable facile prediction and design of peptides that bind anti-apoptotic Bfl-1 and Mcl-1

V. Frappier, J. M. Jenson, J. Zhou, G. Grigoryan, A. E. Keating

This paper introduces a method using tertiary structural motifs to predict and design peptides that bind anti-apoptotic proteins Bfl-1 and Mcl-1 with high accuracy and efficiency.

bioRxiv · Oct 25, 2018Biophysics

A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures

J. Zhou, A. E. Panaitiu, G. Grigoryan

This paper introduces a protein design framework called dTERMen that uses sequence-structure compatibility patterns from known proteins to generate functional sequences, showing it can produce stable, foldable proteins with desired structures.

bioRxiv · Oct 1, 2018Bioengineering