Author

L. R. Azouz

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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Biochemistry· 1 Biophysics· 1 Evolutionary Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:proteomics· 2 Research Focus:cell-cell communication· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Research Focus:host-pathogen & immune response· 1 Organism:human· 1 Research Focus:protein design· 1

Frequent Co-Authors

A. L. Bohanon· 1 shared A. M. DiVenere· 1 shared A. N. Qerqez· 1 shared A. W. Nguyen· 1 shared B. J. Yachnin· 1 shared C. A. S. A. Minetti· 1 shared C.-L. Hsieh· 1 shared D. Amengor· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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1 shared paperBiochemistry

A. L. Bohanon

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

BiochemistryeLifePeer-reviewedMar 21, 2023

Identification of a conserved neutralizing epitope present on spike proteins from all highly pathogenic coronaviruses

Y. Huang, A. W. Nguyen, C.-L. Hsieh…·J. A. Maynard

The paper identifies a conserved neutralizing epitope on the S2 domain of SARS-CoV, SARS-CoV-2, and MERS-CoV spike proteins that is targeted by antibody 3A3.

DOI 10.7554/elife.83710

humanproteomics

𝕏 PostBluesky

BiophysicsbioRxivMar 25, 2021

Massively parallel, computationally-guided design of a pro-enzyme

B. J. Yachnin, L. R. Azouz, R. E. White…·S. D. Khare

This paper designs pro-enzymes using computational methods and validates their activity regulation through protease cleavage.

DOI 10.1073/pnas.2116097119

protein sequencedeep learning

𝕏 PostBluesky

Evolutionary BiologybioRxivJun 4, 2019

Impact of in vivo protein folding probability on local fitness landscapes

M. S. Faber, E. E. Wrenbeck, L. R. Azouz…·T. A. Whitehead

This study investigates how in vivo protein folding probability affects local fitness landscapes using deep mutational scanning of amidase variants.

DOI 10.1093/molbev/msz184

proteomicsdeep learning

𝕏 PostBluesky