DPAM: A Domain Parser for AlphaFold Models

Abstract

The recent breakthroughs in structure prediction, where methods such as AlphaFold demonstrated near atomic accuracy, herald a paradigm shift in structure biology. The 200 million high-accuracy models released in the AlphaFold Database are expected to guide protein science in the coming decades. Partitioning these AlphaFold models into domains and subsequently assigning them to our evolutionary hierarchy provides an efficient way to gain functional insights of proteins. However, classifying such a large number of predicted structures challenges the infrastructure of current structure classifications, including our Evolutionary Classification of protein Domains (ECOD). Better computational tools are urgently needed to automatically parse and classify domains from AlphaFold models. Here we present a Domain Parser for AlphaFold Models (DPAM) that can automatically recognize globular domains from these models based on predicted aligned errors, inter-residue distances in 3D structures, and ECOD domains found by sequence (HHsuite) and structural (DALI) similarity searches. Based on a benchmark of 18,759 AlphaFold models, we demonstrated that DPAM could recognize 99.5% domains and assign correct boundaries for 85.2% of them, significantly outperforming structure-based domain parsers and homology-based domain assignment using ECOD domains found by HHsuite or DALI. Application of DPAM to the massive set of AlphaFold models will allow for more efficient classification of domains, providing evolutionary contexts and facilitating functional studies.