Author

Sujith Sritharan

Centre National de la Recherche ScientifiqueORCID 0000-0002-0580-1021Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Biophysics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:protein structure· 2 Organism:Arabidopsis· 1 Research Focus:benchmarking· 1 Research Focus:protein folding & dynamics· 1 Research Focus:protein structure prediction· 1 Research Focus:software / tool· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Antoine TalyiD· 2 shared Raphaëlle VersiniiD· 2 shared Alaa RegueiiD· 1 shared Beatrice CavigliaiD· 1 shared Carine TisnéiD· 1 shared Chantal PrévostiD· 1 shared Élise Duboué-DijoniD· 1 shared Emmanuelle BayeriD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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2 shared papersBiophysics

Antoine TalyORCID

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2 papers

Recent·Most cited

BiophysicsbioRxivPreprintJul 26, 2026

Deep-learning predictions of biomolecular structures : persistent limitations and new horizons extended by explicit ion addition

Jules Marien, Sujith Sritharan, Beatrice Caviglia…·Antoine Taly

This paper evaluates limitations of deep learning in biomolecular structure prediction, highlighting the benefits of explicit ion modeling for improving conformational accuracy.

DOI 10.64898/2026.07.24.740587

protein structuredeep learning

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BioinformaticsbioRxivPreprintAug 4, 2023

Prediction of A. thaliana's MCTP4 Structure using Deep Learning-Based tools and Exploration of Transmembrane domain Dynamics using Coarse-Grained Molecular Dynamics Simulations

Sujith Sritharan, Raphaëlle Versini, Jules D. Petit…·Antoine Taly

The paper predicts the structure of Arabidopsis MCTP4's transmembrane region using deep learning and explores its dynamics with molecular simulations, revealing conformational complexity not captured by deep learning alone.

4 citations

Arabidopsisprotein structure

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