Jay

selected publications

1. Bioinformatics

   Geometricus represents protein structures as shape-mers derived from moment invariants

   Janani Durairaj, Mehmet Akdel, Dick de Ridder, and Aalt DJ van Dijk

   In Bioinformatics

   Abstract HTML Code BibTeX

   As the number of experimentally solved protein structures rises, it becomes increasingly appealing to use structural information for predictive tasks involving proteins. Due to the large variation in protein sizes, folds and topologies, an attractive approach is to embed protein structures into fixed-length vectors, which can be used in machine learning algorithms aimed at predicting and understanding functional and physical properties. Many existing embedding approaches are alignment based, which is both time-consuming and ineffective for distantly related proteins. On the other hand, library- or model-based approaches depend on a small library of fragments or require the use of a trained model, both of which may not generalize well. We present Geometricus, a novel and universally applicable approach to embedding proteins in a fixed-dimensional space. The approach is fast, accurate, and interpretable. Geometricus uses a set of 3D moment invariants to discretize fragments of protein structures into shape-mers, which are then counted to describe the full structure as a vector of counts. We demonstrate the applicability of this approach in various tasks, ranging from fast structure similarity search, unsupervised clustering and structure classification across proteins from different superfamilies as well as within the same family.

   @article{durairaj2020geometricus,
     title = {Geometricus represents protein structures as shape-mers derived from moment invariants},
     author = {Durairaj, Janani and Akdel, Mehmet and de Ridder, Dick and van Dijk, Aalt DJ},
     journal = {Bioinformatics},
     volume = {36},
     number = {Supplement\_2},
     pages = {i718--i725},
     year = {2020},
     publisher = {Oxford University Press},
   }

2. Preprint

   A structural biology community assessment of AlphaFold 2 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#

   Abstract Preprint Code 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 have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to 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.

   @unpublished{akdel2021structural,
     title = {A structural biology community assessment of AlphaFold 2 applications},
     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},
     journal = {bioRxiv},
     year = {2021},
     publisher = {Cold Spring Harbor Laboratory},
     eprint = {https://www.biorxiv.org/content/early/2021/09/26/2021.09.26.461876.full.  doi = {10.1101/2021.09.26.461876}
   }

3. Preprint

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

   Janani Durairaj, Joana Pereira, Mehmet Akdel, and Torsten Schwede

   Abstract Preprint Code BibTeX

   The recent public release of the latest version of the AlphaFold database has given us access to over 200 million predicted protein structures. We use a “shape-mer” approach, a structural fragmentation method analogous to sequence k-mers, to describe these structures and look for novelties - both in terms of proteins with rare or novel structural composition and possible functional annotation of under-studied proteins.

   @unpublished{durairaj2022what,
     title = {What is hidden in the darkness? Characterization of AlphaFold structural space},
     author = {Durairaj, Janani and Pereira, Joana and Akdel, Mehmet and Schwede, Torsten},
     journal = {bioRxiv},
     year = {2022},
     publisher = {Cold Spring Harbor Laboratory},
     eprint = {https://www.biorxiv.org/content/10.1101/2022.10.11.511548v1.full.  doi = {10.1101/2022.10.11.511548}
   }