Author

C. Goolsby

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· 3

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Approach:deep learning· 2 Research Focus:protein folding & dynamics· 2 Assay:protein structure· 1 Assay:proteomics· 1

Frequent Co-Authors

M. Moradi· 3 shared A. Fakharzadeh· 1 shared A. Fakharzdeh· 1 shared D. Matteson· 1 shared J. Losey· 1 shared M. Getmansky Sherman· 1 shared M.-C. Düker· 1 shared Y. Xu· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 17 mutual co-author links

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3 shared papersBiophysics

M. Moradi

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3 papers

Recent·Most cited

Addressing the Embeddability Problem in Transition Rate Estimation

C. Goolsby, J. Losey, Y. Xu, M.-C. Düker, M. Getmansky Sherman, D. Matteson, M. Moradi

This paper addresses the embeddability problem in transition rate estimation by proposing eight methods, including a novel approach, tested on a one-dimensional toy model.

bioRxiv · Feb 26, 2021Biophysics

Thermodynamic and Kinetic Characterization of Protein Conformational Dynamics within a Riemannian Framework

C. Goolsby, A. Fakharzdeh, M. Moradi

This paper introduces a Riemannian framework for molecular dynamics simulations to improve sampling, path-finding, and transition rate estimation in biomolecular systems.

bioRxiv · Jan 19, 2021 · PreprintBiophysics

Thermodynamic and Kinetic Characterization of Protein Conformational Dynamics within a Riemannian Diffusion Formalism

C. Goolsby, A. Fakharzadeh, M. Moradi

The paper develops a Riemannian diffusion formalism to characterize protein conformational dynamics, enabling invariant calculations of thermodynamic and kinetic quantities in molecular simulations.

bioRxiv · Jul 26, 2019 · PreprintBiophysics