Author

Jeremy Y. Leclercq

Centre National de la Recherche ScientifiqueORCID 0000-0002-0372-3277Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:protein sequence· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:multiple species· 1 Approach:statistical model· 1

Frequent Co-Authors

Andrey V. KajavaiD· 2 shared A. Keith DunkeriD· 1 shared Ágnes TantosiD· 1 shared Alexander Miguel MonzóniD· 1 shared Alok K. SharmaiD· 1 shared Anastasia ChasapiiD· 1 shared András HatosiD· 1 shared Andrew V. OleinikoviD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersBioinformatics

Andrey V. KajavaORCID

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2 papers

Recent·Most cited

Frameshifts in Tandem Repeats: Consequences on Protein Physicochemical Properties and Function

Zarifa Osmanli, Gudrun Aldrian, Jeremy Y. Leclercq, Théo Falgarone, Santiago M. Gómez Bergna, Denis N. Prada Gori, Andrew V. Oleinikov, Ilham Shahmuradov, Andrey V. Kajava

The paper analyzes frameshifts in tandem repeats across proteomes, showing they significantly alter protein physicochemical properties and function.

FEBS Journal · Sep 24, 2025Peer-reviewedBioinformatics

Critical Assessment of Protein Intrinsic Disorder Prediction

Marco Necci, Damiano Piovesan, CAID Predictors, Md Tamjidul Hoque, Ian Walsh, Sumaiya Iqbal, Michele Vendruscolo, Pietro Sormanni, Chen Wang, Daniele Raimondi, Ronesh Sharma, Yaoqi Zhou, Thomas Litfin, Oxana V. Galzitskaya, Michail Yu. Lobanov, , Silvio C. E. Tosatto

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

Nature Methods · Apr 19, 2021Peer-reviewedBioinformatics