Author

David C. Dalgarno

BrookeORCID 0009-0003-1081-1608Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:statistical model· 2 Research Focus:chromatin & epigenetic regulation· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Research Focus:protein folding & dynamics· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Brenda M. RubensteiniD· 2 shared Gabriel Monteiro da SilvaiD· 2 shared George P. LisiiD· 1 shared Jennifer Y. CuiiD· 1 shared Kyle Lam· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 8 mutual co-author links

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2 shared papersBiophysics

Brenda M. RubensteinORCID

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2 papers

Recent·Most cited

BiophysicsbioRxivPreprintMay 23, 2024

Compound Mutations in the Abl1 Kinase Cause Inhibitor Resistance by Shifting DFG Flip Mechanisms and Relative State Populations

Gabriel Monteiro da Silva, Kyle Lam, David C. Dalgarno, Brenda M. Rubenstein

This study uses Weighted Ensemble simulations to analyze DFG flip mechanisms in Abl1 kinase and shows how compound mutations increase free energy barriers, leading to inhibitor resistance.

DOI 10.1101/2024.05.23.595569

statistical modelchromatin & epigenetic regulation

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BioinformaticsbioRxivPreprintJul 25, 2023

Predicting Relative Populations of ProteinConformations without a Physics Engine UsingAlphaFold2

Gabriel Monteiro da Silva, Jennifer Y. Cui, David C. Dalgarno…·Brenda M. Rubenstein

This paper uses AlphaFold2 to predict protein conformational populations and mutation effects without a physics engine, achieving over 80% accuracy on two proteins.

10 citations

protein structuredeep learning

𝕏 PostBluesky