Author

J. Durairaj

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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Bioinformatics· 2 Biophysics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Assay:protein structure· 2 Research Focus:protein structure prediction· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Assay:DNA methylation· 1

Frequent Co-Authors

J. Pereira· 3 shared M. Akdel· 3 shared T. Schwede· 3 shared A. Andreeva· 1 shared A. Bateman· 1 shared A. Elofsson· 1 shared A. Frost· 1 shared A. M. Waterhouse· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBioinformatics

J. Pereira

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Structural implications of BK polyomavirus sequence variations in the major viral capsid protein Vp1 and large T-antigen: a computational study

J. Durairaj, O. Follonier, K. Leuzinger, L. T. Alexander, M. Wilhelm, J. Pereira, C. A. Hillenbrand, F. Weissbach, T. Schwede, H. H. Hirsch

This study analyzes BKPyV sequence variations in Vp1 and LTag using computational methods, finding high variability in Vp1 that may impact immune control and vaccine design, while LTag shows less variability but potential effects on T-cell epitopes.

bioRxiv · Dec 21, 2023Microbiology

What is hidden in the darkness? Deep-learning assisted large-scale protein family curation uncovers novel protein families and folds

J. Durairaj, A. M. Waterhouse, T. Mets, T. Brodiazhenko, M. Abdullah, G. Studer, M. Akdel, A. Andreeva, A. Bateman, T. Tenson, V. Hauryliuk, T. Schwede, J. Pereira

This work uses deep learning to uncover novel protein families and folds by analyzing sequence and structural data, adding new entries to Pfam and identifying a novel toxin-antitoxin system.

bioRxiv · Mar 14, 2023 · PreprintBioinformatics

What is hidden in the darkness? Characterization of AlphaFold structural space.

J. Durairaj, J. Pereira, M. Akdel, T. Schwede

The paper analyzes over 200 million AlphaFold predicted protein structures using a shape-mer approach to identify novel structural compositions and potential functional annotations.

bioRxiv · Oct 11, 2022 · PreprintBioinformatics

A structural biology community assessment of AlphaFold 2 applications

M. Akdel, D. E. Pires, E. Porta-Pardo, J. Janes, A. O. Zalevsky, B. Meszaros, P. Bryant, L. L. Good, R. A. Laskowski, G. Pozzati, A. Shenoy, W. Zhu, P. Kundrotas, V. Ruiz-Serra, C. H. Rodrigues, , P. Beltrao

This study assesses AlphaFold 2's applications in structural biology, finding it outperforms existing tools in predicting disorder, complexes, and structural features with high confidence.

bioRxiv · Sep 26, 2021 · PreprintBiophysics