Shaun M. Kandathil

TEDlm: domain-centric protein language models with optional structural pre-training

TEDlm is a domain-centric protein language model that improves remote-homology detection and Molecular Function prediction by pretraining on structurally-defined domain sequences.

Metagenomic-scale analysis of the predicted protein structure universe

The paper combines AlphaFold and ESMfold predictions to create a large structural dataset, identifies novel protein folds and domain combinations, and highlights the value of metagenomic data for structural diversity.

Exploring structural diversity across the protein universe with The Encyclopedia of Domains

The paper introduces TED, a resource that segments and classifies over 370 million domains from the AlphaFold Protein Structure Database, expanding known structural domains and uncovering new structural interactions.

Foldclass and Merizo-search: embedding-based deep learning tools for protein domain segmentation, fold recognition and comparison

Merizo: a rapid and accurate domain segmentation method using invariant point attention

Merizo is a deep learning method for domain segmentation in protein structures, trained on CATH domains and applied to human proteome models to identify 40,818 putative domains.

Design in the DARK: Learning Deep Generative Models for De Novo Protein Design

This paper introduces a deep generative model for de novo protein design that efficiently samples novel sequences with ordered structures, outperforming existing methods trained on natural sequences.

Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins

The paper introduces an ultrafast protein structure prediction method that directly outputs backbone coordinates from MSAs, enabling rapid modeling of uncharacterized proteins.

Prediction of inter-residue contacts with DeepMetaPSICOV in CASP13

The paper describes the development and application of DeepMetaPSICOV for contact prediction in CASP13, highlighting improvements in alignment generation and performance.