Author

Gregory Hannum

Synthetic Genomics (United States)ORCID 0000-0002-9291-7259Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 2 Synthetic Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 3 Research Focus:protein design· 2 Assay:protein sequence· 2 Organism:E. coli· 1 Assay:proteomics· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Abigail B. VenturaiD· 2 shared Anand V. SastryiD· 2 shared Christa Kohnert· 2 shared Hayley Carter· 2 shared Jahir M. GutierreziD· 2 shared Jennifer T. Stanton· 2 shared John M. SuttoniD· 2 shared Joshua MeieriD· 2 shared

Activity by Year

2018-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersSynthetic Biology

Abigail B. VenturaORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Deep learning-based codon optimization with large-scale synonymous variant datasets enables generalized tunable protein expression

David A. Constant, Jahir M. Gutierrez, Anand V. Sastry, Rebecca Viazzo, Nicholas R. Smith, Jubair Hossain, David A. Spencer, Hayley Carter, Abigail B. Ventura, Michael T. M. Louie, Christa Kohnert, Rebecca Consbruck, Joshua Bennett, Kenneth A. Crawford, John M. Sutton, , Miles W. Gander

The paper develops deep learning models trained on synonymous variant datasets to predict and optimize protein expression in E. coli.

bioRxiv · Feb 11, 2023 · PreprintSynthetic Biology

Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness

Sharrol T. Bachas, Goran Rakocevic, David Spencer, Anand V. Sastry, Robel Gemechu Haile, John M. Sutton, George W. Kasun, Andrew L. Stachyra, Jahir M. Gutierrez, Edriss Yassine, Borka Medjo, Vincent Blay, Christa Kohnert, Jennifer T. Stanton, Alexander Brown, , Roberto Spreafico

Language models trained on high-throughput affinity data predict KD of unseen antibody variants across three orders of magnitude, and a "naturalness" score can be optimised alongside affinity.

bioRxiv · Aug 16, 2022 · PreprintBioinformatics

Deep Semantic Protein Representation for Annotation, Discovery, and Engineering

Ariel S Schwartz, Gregory Hannum, Zach Dwiel, Michael E. Smoot, Ana R Grant, Jason M. Knight, Scott A. Becker, Jonathan R Eads, Matthew C. LaFave, Harini Eavani, Yinyin Liu, Arjun K. Bansal, Toby H. Richardson

D-SPACE provides a deep learning model for protein function annotation and discovery, enabling fast and accurate functional classification and mutation effect quantification.

bioRxiv · Jul 10, 2018 · PreprintBioinformatics