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

Editor’s summary

Only a small fraction of known proteomic space has been studied by experimental protein structure methods. The AlphaFold Protein Structure Database (AFDB) thus represents a truly massive expansion in the landscape of protein structural models. Such a large database represents a major challenge for categorization and analysis. Lau et al. developed an analytical pipeline based on deep learning for sectioning predicted protein structure models into discrete domains and classifying them. The resulting Encyclopedia of Domains expands and connects existing domain families, and there are also a large number of domains with folds that have not yet been observed in experimental structures. —Michael A. Funk

Structured Abstract

INTRODUCTION

The AlphaFold Protein Structure Database (AFDB) represents a major achievement in computational biology, containing high-quality predictions for more than 214 million protein structures. These structures are composed of domains, independently folding units that are critical to understanding protein function and evolution. The vast scale of the AFDB makes it difficult to accurately identify and classify these domains, thus limiting our ability to fully exploit its potential for biological discovery.

RATIONALE

To address this challenge, we developed The Encyclopedia of Domains (TED), a comprehensive resource for the systematic identification and classification of protein domains detectable within the AFDB. Traditional sequence-based methods, such as those used by Pfam and Gene3D, are unable to detect distant evolutionary relationships or entirely novel domains, but TED integrates multiple deep learning and advanced structural comparison techniques to uncover a much broader range of domain architectures, providing a more complete map of protein structure diversity.

RESULTS

TED identifies nearly 365 million domains across more than more than 1 million taxa, substantially expanding the scope of domain detection in the AFDB. This includes more than 100 million domains that were previously undetected by sequence-based methods, showcasing TED’s ability to leverage structure to increase domain coverage. We found that 77% of nonredundant domains were similar to existing CATH domains, validating the effectiveness of our approach.

Our study further highlights previously unobserved interactions between domains in the AFDB, uncovering more than 10,000 new structural interactions between superfamilies. We assessed the diversity of domain-packing geometries in interactions common to TED and CATH, and found that they were for the most part similar, with only around 5.4% of interactions showing a greater variety in TED. Overall, TED expands the set of known domain superfamily interactions to about three times the number seen in experimental structures, providing a means to greatly enrich our understanding of how multidomain proteins have evolved.

Moreover, TED has revealed thousands of putative new folds, substantially expanding the known fold space. This includes uncovering previously uncharacterized architectures, providing valuable new targets for structural and functional analysis. TED’s analysis of domain distribution across the Tree of Life has uncovered patterns of fold exclusivity and shared architectures among different lineages, suggesting important evolutionary trends. For example, certain folds were found to be highly conserved across all superkingdoms, indicating their essential roles in cellular functions, whereas others were lineage specific, pointing to unique evolutionary adaptations.

TED facilitates a deeper understanding of the structural diversity within the AFDB by identifying highly symmetric and repetitive domain architectures, which are often associated with specialized biological functions. Previously unknown domains, including unique beta-propeller and alpha-helical structures, represent new opportunities for studying protein evolution and function. Additionally, TED has demonstrated the ability to detect new variations in domain packing, further enriching the structural data available in the AFDB.

CONCLUSION

TED is a resource that not only fills critical gaps left by previous sequence-based methods, but also opens up new avenues for research in structural and evolutionary biology. It will be continually updated to keep in step with future releases of the AFDB. The comprehensive domain annotations provided by TED will facilitate a wide range of functional analyses, from drug discovery to the study of protein evolution. By revealing previously hidden relationships within the vast protein structure space, TED sets the stage for future breakthroughs in understanding the complex interplay among protein structure, function, and evolution.

TED workflow.
TED consists of 365 million domains identified from the AFDB by taking a consensus from three state-of-the-art domain segmentation methods. Nonredundant domains are assigned with CATH labels, and unassigned domains are subjected to a novel fold identification pipeline. Overall, we found several thousand novel folds, as well as novel high-symmetry fold clusters.

Abstract

The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein structures composed of domains, which are independently folding units found in multiple structural and functional contexts. Identifying domains can enable many functional and evolutionary analyses but has remained challenging because of the sheer scale of the data. Using deep learning methods, we have detected and classified every domain in the AFDB, producing The Encyclopedia of Domains. We detected nearly 365 million domains, over 100 million more than can be found by sequence methods, covering more than 1 million taxa. Reassuringly, 77% of the nonredundant domains are similar to known superfamilies, greatly expanding representation of their domain space. We uncovered more than 10,000 new structural interactions between superfamilies and thousands of new folds across the fold space continuum.

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