Author

J. S. Chang

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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Microbiology· 2 Biochemistry· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 3 Organism:mouse· 2 Approach:deep learning· 1 Approach:experimental assay· 1 Approach:genetic perturbation / CRISPR· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:protein folding & dynamics· 1

Frequent Co-Authors

H. Wu· 2 shared S. Li· 2 shared A. A. Lee· 1 shared A. A. Vinogradov· 1 shared A. B. Vogel· 1 shared A. Balesano· 1 shared A. Behzadi· 1 shared A. Ephron· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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Curved chords = mutual co-authorship

2 shared papersMicrobiology

H. Wu

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

MicrobiologybioRxivPreprintJan 26, 2024

Discovery of SARS-CoV-2 papain-like protease (PLpro) inhibitors with efficacy in a murine infection model

M. R. Garnsey, M. C. Robinson, L. T. Nguyen…·A. A. Lee

This study identifies potent SARS-CoV-2 PLpro inhibitors using machine learning, showing efficacy in a mouse model of COVID-19.

DOI 10.1101/2024.01.26.577395

mousemachine learning (classical)

𝕏 PostBluesky

ImmunologybioRxivNov 17, 2023

Preclinical Characterization of the Omicron XBB.1.5-Adapted BNT162b2 COVID-19 Vaccine

K. Modjarrad, Y. Che, W. Chen…·K. Swanson

The monovalent Omicron XBB.1.5-adapted BNT162b2 mRNA vaccine elicits stronger neutralizing antibody responses against XBB.1.5 and related sublineages than the bivalent Wild Type + Omicron BA.4/5 vaccine.

DOI 10.1038/s41541-024-01013-9

mouseexperimental assay

𝕏 PostBluesky

BiochemistrybioRxivFeb 14, 2022

Accurate models of substrate preferences of post-translational modification enzymes from a combination of mRNA display and deep learning

A. A. Vinogradov, J. S. Chang, H. Onaka…·H. Suga

The paper develops a platform combining mRNA display and deep learning to accurately model substrate preferences of PTM enzymes, showing high accuracy and complexity in enzyme specificity.

DOI 10.1021/acscentsci.2c00223

proteomicsdeep learning

𝕏 PostBluesky

MicrobiologybioRxivPreprintNov 27, 2021

A chemical-genetic map of the pathways controlling drug potency in Mycobacterium tuberculosis

S. Li, N. C. Poulton, J. S. Chang…·J. M. Rock

This study uses a CRISPR interference platform to map drug resistance pathways in Mtb, identifying new intrinsic and acquired resistance mechanisms and potential drug targets.

DOI 10.1101/2021.11.27.469863

humanwhole-genome sequencing

𝕏 PostBluesky