Jay

selected publications

journal articles

2023

1. Nature

   Uncovering new families and folds in the natural protein universe

   Janani Durairaj, Andrew M Waterhouse, Toomas Mets, Tetiana Brodiazhenko, Minhal Abdullah, Gabriel Studer, Gerardo Tauriello, Mehmet Akdel, Antonina Andreeva, Alex Bateman, Tanel Tensor, Vasili Hauryliuk, Torsten Schwede, and Joana Pereira

   In Nature

   Abstract Code BibTeX Website

   We are now entering a new era in protein sequence and structure annotation, with hundreds of millions of predicted protein structures made available through the AlphaFold database. These models cover nearly all proteins that are known, including those challenging to annotate for function or putative biological role using standard homology-based approaches. In this study, we examine the extent to which the AlphaFold database has structurally illuminated this "dark matter" of the natural protein universe at high predicted accuracy. We further describe the protein diversity that these models cover as an annotated interactive sequence similarity network. By searching for novelties from sequence, structure, and semantic perspectives, we uncovered the beta-flower fold, added multiple protein families to Pfam database, and experimentally demonstrate that one of these belongs to a new superfamily of translation-targeting toxin-antitoxin systems, TumE-TumA. This work underscores the value of large-scale efforts in identifying, annotating, and prioritising novel protein families. By leveraging the recent deep learning revolution in protein bioinformatics, we can now shed light into uncharted areas of the protein universe at an unprecedented scale, paving the way to innovations in life sciences and biotechnology.

   @article{durairaj2023uncovering,
     title = {Uncovering new families and folds in the natural protein universe},
     author = {Durairaj, Janani and Waterhouse, Andrew M and Mets, Toomas and Brodiazhenko, Tetiana and Abdullah, Minhal and Studer, Gabriel and Tauriello, Gerardo and Akdel, Mehmet and Andreeva, Antonina and Bateman, Alex and Tensor, Tanel and Hauryliuk, Vasili and Schwede, Torsten and Pereira, Joana},
     journal = {Nature},
     pages = {1--3},
     year = {2023},
     publisher = {Nature Publishing Group UK London},
     doi = {10.1038/s41586-023-06622-3},
   }

2. Proteins

   Automated benchmarking of combined protein structure and ligand conformation prediction

   Michèle Leemann, Ander Sagasta, Jerome Eberhardt, Torsten Schwede, Xavier Robin, and Janani Durairaj

   In Proteins: Structure, Function, and Bioinformatics

   Abstract BibTeX

   The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein–ligand interactions.

   @article{leemannAutomatedBenchmarkingCombined,
     title = {Automated benchmarking of combined protein structure and ligand conformation prediction},
     volume = {n/a},
     copyright = {© 2023 The Authors. Proteins: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.},
     issn = {1097-0134},
     url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.26605},
     doi = {10.1002/prot.26605},
     language = {en},
     number = {n/a},
     urldate = {2023-10-27},
     journal = {Proteins: Structure, Function, and Bioinformatics},
     author = {Leemann, Michèle and Sagasta, Ander and Eberhardt, Jerome and Schwede, Torsten and Robin, Xavier and Durairaj, Janani},
     note = {\_eprint: https://onlinelibrary.wiley.com/doi/  keywords = {3D structure prediction, CASP15, protein structure, protein-ligand complexes},
     year = {2023},
   }

2022

1. NSMB

   A structural biology community assessment of AlphaFold2 applications

   Mehmet Akdel*, Douglas E V Pires*, Eduard Porta Pardo*, Jürgen Jänes*, Arthur O Zalevsky*, Bálint Mészáros*, Patrick Bryant*, Lydia L. Good*, Roman A Laskowski*, Gabriele Pozzati, Aditi Shenoy, Wensi Zhu, Petras Kundrotas, Victoria Ruiz Serra, Carlos H M Rodrigues, Alistair S Dunham, David Burke, Neera Borkakoti, Sameer Velankar, Adam Frost, Kresten Lindorff-Larsen, Alfonso Valencia#, Sergey Ovchinnikov#, Janani Durairaj#, David B Ascher#, Janet M Thornton#, Norman E Davey#, Amelie Stein#, Arne Elofsson#, Tristan I Croll#, and Pedro Beltrao#

   In Nature Structural & Molecular Biology

   Abstract HTML BibTeX

   Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.

   @article{akdelStructuralBiologyCommunity2022b,
     title = {A structural biology community assessment of {AlphaFold2} applications},
     volume = {29},
     copyright = {2022 The Author(s)},
     issn = {1545-9985},
     doi = {10.1038/s41594-022-00849-w},
     language = {en},
     number = {11},
     urldate = {2023-10-10},
     journal = {Nature Structural \& Molecular Biology},
     author = {Akdel<nobr><em>*</em></nobr>, Mehmet and Pires<nobr><em>*</em></nobr>, Douglas E V and Porta Pardo<nobr><em>*</em></nobr>, Eduard and J{\"a}nes<nobr><em>*</em></nobr>, J{\"u}rgen and Zalevsky<nobr><em>*</em></nobr>, Arthur O and M{\'e}sz{\'a}ros<nobr><em>*</em></nobr>, B{\'a}lint and Bryant<nobr><em>*</em></nobr>, Patrick and Good<nobr><em>*</em></nobr>, Lydia L. and Laskowski<nobr><em>*</em></nobr>, Roman A and Pozzati, Gabriele and Shenoy, Aditi and Zhu, Wensi and Kundrotas, Petras and Ruiz Serra, Victoria and Rodrigues, Carlos H M and Dunham, Alistair S and Burke, David and Borkakoti, Neera and Velankar, Sameer and Frost, Adam and Lindorff-Larsen, Kresten and Valencia<nobr><em>#</em></nobr>, Alfonso and Ovchinnikov<nobr><em>#</em></nobr>, Sergey and Durairaj<nobr><em>#</em></nobr>, Janani and Ascher<nobr><em>#</em></nobr>, David B and Thornton<nobr><em>#</em></nobr>, Janet M and Davey<nobr><em>#</em></nobr>, Norman E and Stein<nobr><em>#</em></nobr>, Amelie and Elofsson<nobr><em>#</em></nobr>, Arne and Croll<nobr><em>#</em></nobr>, Tristan I and Beltrao<nobr><em>#</em></nobr>, Pedro},
     month = nov,
     year = {2022},
     note = {Number: 11
     Publisher: Nature Publishing Group},
     keywords = {Protein folding, Research data, Structural biology},
     pages = {1056--1067},
   }