Author

Karl F. Freed

University of ChicagoORCID 0000-0001-6240-2599Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biophysics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:protein folding & dynamics· 1 Assay:protein structure· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

Tobin R. SosnickiD· 2 shared Esmael J. HaddadianiD· 1 shared John M. JumperiD· 1 shared Joseph R. SachlebeniD· 1 shared Sashin Natesh· 1 shared Stephen C. MeredithiD· 1 shared

Activity by Year

2017-2018 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 11 mutual co-author links

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2 shared papersNeuroscience

Tobin R. SosnickORCID

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintSep 14, 2018

Aβ Fibrils Can Act as Aqueous Pores: a Molecular Dynamics Study

Sashin Natesh, Joseph R. Sachleben, Tobin R. Sosnick…·Esmael J. Haddadian

This study uses molecular dynamics simulations to show that Aβ fibrils can form aqueous pores, allowing water and ion flow through their structure.

1 citation

statistical model

𝕏 PostBluesky

BiophysicsbioRxivPreprintJul 27, 2017

Trajectory-Based Parameterization of a Coarse-Grained Forcefield for High-Throughput Protein Simulation

John M. Jumper, Karl F. Freed, Tobin R. Sosnick

The paper trains a coarse-grained forcefield using contrastive divergence on simulation trajectories to improve protein folding accuracy while maintaining computational efficiency.

DOI 10.1101/169326

protein structuredeep learning

𝕏 PostBluesky