Author

S. Sagawa

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· 2 Microbiology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Organism:bacteria (other)· 1 Approach:biophysical modeling· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Research Focus:protein folding & dynamics· 1 Assay:protein structure· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

D. E. Shaw· 2 shared A. C. Pan· 1 shared A. E. Leffler· 1 shared A. M. Deutschbauer· 1 shared A. P. Arkin· 1 shared E. T. Kim· 1 shared M. N. Price· 1 shared Q. Wang· 1 shared

Activity by Year

2016-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

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2 shared papersBiophysics

D. E. Shaw

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3 papers

Recent·Most cited

How does a small molecule bind at a cryptic binding site?

Y. Shan, V. P. Mysore, A. E. Leffler, E. T. Kim, S. Sagawa, D. E. Shaw

This study uses molecular dynamics simulations and free energy perturbation to identify and characterize small molecule binding at cryptic sites in protein-protein interactions.

bioRxiv · Mar 31, 2021Biophysics

Structural mechanism for Bruton’s tyrosine kinase activation at the cell membrane

Q. Wang, Y. Pechersky, S. Sagawa, A. C. Pan, D. E. Shaw

This study uses molecular dynamics simulations to show that PIP3 stabilizes Btk dimerization at the cell membrane, which is critical for Btk activation.

bioRxiv · Apr 18, 2018Biophysics

Validating Regulatory Predictions from Diverse Bacteria with Mutant Fitness Data

S. Sagawa, M. N. Price, A. M. Deutschbauer, A. P. Arkin

The paper validates regulatory predictions from bacterial transcription factors using mutant fitness data, identifying significant cofitness for 158 TFs across 24 bacteria.

bioRxiv · Dec 6, 2016Microbiology