Author

Shams Mehdi

University of Maryland, College ParkORCID 0000-0002-4078-7501Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biochemistry· 1 Biophysics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:structural biology· 2 Organism:bacteria (other)· 1 Approach:mechanistic / dynamical model· 1 Research Focus:protein folding & dynamics· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

John S. SchneeklothiD· 2 shared Pratyush TiwaryiD· 2 shared Adrian R. Ferré-D’AmaréiD· 1 shared Anjali R. VermaiD· 1 shared Christopher P. JonesiD· 1 shared Christopher R. FullenkampiD· 1 shared Disha Sanwal· 1 shared Logan Tenney· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersBiophysics

John S. SchneeklothORCID

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2 papers

Recent·Most cited

BiophysicsbioRxivPreprintFeb 1, 2026

Ab initio prediction of RNA structure ensembles with RNAnneal

Lukas Herron, Yunrui Qiu, Anjali R. Verma…·Pratyush Tiwary

RNAnneal predicts RNA structural ensembles using deep learning and physics-based modeling, outperforming existing methods in classifying experimentally resolved conformations.

5 citations

structural biologydeep learning

BiochemistryAngewandte Chemie International EditionPeer-reviewedMay 1, 2025

Machine learning-augmented molecular dynamics simulations (MD) reveal insights into the disconnect between affinity and activation of ZTPriboswitch ligands

Christopher R. Fullenkamp, Shams Mehdi, Christopher P. Jones…·John S. Schneekloth

The paper uses machine learning-augmented MD simulations to study ZTP riboswitch ligand interactions, finding that ligand on-rates, not binding affinity, determine activation potency.

13 citations

bacteria (other)structural biology