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},
   }

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},
   }

conference articles & others

2024

1. ICML

   PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource

   Janani Durairaj*, Yusuf Adeshina*, Zhonglin Cao, Xuejin Zhang, Vladas Oleinikovas, Thomas Duignan, Zachary McClure, Xavier Robin, Gabriel Studer, Daniel Kovtun, Emanuele Rossi, Guoqing Zhou, Srimukh Veccham, Clemens Isert, Yuxing Peng, Prabindh Sundareson, Mehmet Akdel, Gabriele Corso, Hannes Stärk, Gerardo Tauriello, Zachary Carpenter, Michael Bronstein, Emine Kucukbenli, Torsten Schwede, and Luca Naef

   In ICML’24 Workshop ML for Life and Material Science: From Theory to Industry Applications

   Abstract Preprint Code Website

   Protein-ligand interactions (PLI) are foundational to small molecule drug design. With computational methods striving towards experimental accuracy, there is a critical demand for a well-curated and diverse PLI dataset. Existing datasets are often limited in size and diversity, and commonly used evaluation sets suffer from training information leakage, hindering the realistic assessment of method generalization capabilities. To address these shortcomings, we present PLINDER, the largest and most annotated dataset to date, comprising 449,383 PLI systems, each with over 500 annotations, similarity metrics at protein, pocket, interaction and ligand levels, and paired unbound (apo) and predicted structures. We propose an approach to generate training and evaluation splits that minimizes task-specific leakage and maximizes test set quality, and compare the resulting performance of DiffDock when retrained with different kinds of splits.