Author

Govindarajan Sudha

Stockholm University

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· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:protein structure· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Assay:protein sequence· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

Arne ElofssoniD· 3 shared Claudio BassotiD· 3 shared John LambiD· 3 shared A. Keith DunkeriD· 1 shared Ágnes TantosiD· 1 shared Alexander Miguel MonzóniD· 1 shared Alok K. SharmaiD· 1 shared Anastasia ChasapiiD· 1 shared

Activity by Year

2020-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

Arne ElofssonORCID

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3 papers

Recent·Most cited

The evolutionary history of topological variations in the CPA/AT superfamily

Govindarajan Sudha, Claudio Bassot, John Lamb, Nanjiang Shu, Yan Hong Huang, Arne Elofsson

The paper identifies novel topologies in CPA/AT transporters using an integrated method and presents a database with structural classifications.

PLoS Computational Biology · Aug 17, 2021Peer-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

Identification of a novel fold type in CPA/AT transporters by ab-initio structure prediction

Claudio Bassot, Govindarajan Sudha, John Lamb, Arne Elofsson

This study identifies a novel reentrant-helix-reentrant fold in CPA/AT transporters using ab-initio structure prediction, revealing structural adaptations and the utility of deep-learning-based modeling.

bioRxiv · Nov 5, 2020 · PreprintBioinformatics