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

Abstract

AO_SCPLOWBSTRACTC_SCPLOWThe AlphaFold Protein Structure Database (AFDB), containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the information within the AFDB is precluded by an urgent need of tools that allow the efficient traversal, discovery and documentation of its contents. Identifying the regions that correspond to domains in the AFDB is a non-trivial endeavour and doing so will aid our understanding of protein structure and function, while facilitating drug discovery and comparative genomics. Here, we describe a deep learning method for accurate domain segmentation called Merizo, which learns to cluster residues into domains in a bottom-up manner. Merizo is trained on CATH domains and fine-tuned on a subset of AFDB models via self-distillation, enabling it to be applicable to both experimental and AFDB models. As proof of concept, we apply Merizo to models of the human proteome, and identify 40,818 putative domains that can be matched to CATH representative domains. Merizo is available at https://github.com/psipred/Merizo.