Author

G. A. Babbitt

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· 5

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:protein folding & dynamics· 3 Assay:protein structure· 3 Approach:statistical model· 3 Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:chromatin & epigenetic regulation· 1 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

E. P. Fokoue· 3 shared M. L. Lynch· 3 shared A. O. Hudson· 2 shared P. Rynkiewicz· 2 shared C. J. Ryan· 1 shared F. Cui· 1 shared H. Srivastava· 1 shared J. R. Evans· 1 shared

Activity by Year

2019-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersBiophysics

E. P. Fokoue

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5 papers

Recent·Most cited

ATOMDANCE: kernel-based denoising and allosteric resonance analysis for functional and evolutionary comparisons of protein dynamics

G. A. Babbitt, M. Rajendran, M. L. Lynch, R. Asare-Bediako, L. T. Mouli, C. J. Ryan, H. Srivastava, P. Rynkiewicz, K. Phadke, M. L. Reed, N. Moore, M. C. Ferran, E. P. Fokoue

ATOMDANCE compares and denoises protein motion dynamics between functional or evolutionary states using kernel-based methods to identify key sites and functional shifts.

bioRxiv · Apr 20, 2023Biophysics

Functional binding dynamics relevant to the evolution of zoonotic spillovers in endemic and emergent Betacoronavirus strains

P. Rynkiewicz, G. A. Babbitt, F. Cui, A. O. Hudson, M. L. Lynch

The paper uses machine learning on molecular dynamics simulations to analyze conserved functional binding dynamics between Betacoronavirus strains and ACE2/CD26, identifying pre-adapted regions linked to zoonotic spillovers.

bioRxiv · Sep 11, 2020 · PreprintBiophysics

Information theoretics for the machine learning detection of functionally conserved and coordinated protein motions

G. A. Babbitt

This paper develops a method combining machine learning and information theory to detect functionally conserved protein motions by analyzing MD simulations of protein binding interactions.

bioRxiv · May 29, 2020 · PreprintBiophysics

Function and evolution of B-Raf loop dynamics relevant to cancer recurrence under drug inhibition

G. A. Babbitt, M. L. Lynch, M. McCoy, E. P. Fokoue, A. O. Hudson

This study uses molecular dynamics simulations and machine learning to analyze how B-Raf loop dynamics change under drug inhibition and genetic mutations, showing that ATP competitive inhibitors reduce hyperactivation risks.

bioRxiv · Jan 13, 2020Biophysics

Machine learning based detection of genetic and drug class variant impact on functionally conserved protein binding dynamics

G. A. Babbitt, E. P. Fokoue, J. R. Evans, K. I. Diller, L. E. Adams

This paper introduces DROIDS v3.0, a tool combining MD simulations and machine learning to analyze protein dynamics, identifying conserved regions and variant impacts on function.

bioRxiv · Sep 5, 2019 · PreprintBiophysics