The development of highly accurate protein structure prediction algorithms has led to an explosion of structural data, transforming our understanding of protein structure-function relationships across diverse organisms. Domain classifications such as the Evolutionary Classification of Protein Domains (ECOD) have incorporated these computational predictions alongside experimental structures to create comprehensive resources for the research community. The AlphaFold Protein Structure Database (AFDB) plays a unique role, providing millions of predicted structures that ECOD has systematically classified for human proteins, small pathogens, and reference proteomes. Here, we extend this classification framework to the UniProtKB/Swiss-Prot dataset, applying the Domain Parser for AlphaFold Models (DPAM) pipeline to classify domains from over 542,000 Swiss-Prot protein structure predictions, resulting in more than 1,032,000 classified domains. These domains span 3,493 ECOD topologies and display high assignment confidence (mean DPAM probability: 0.992), with extensive taxonomic and functional diversity. Notably, over 100,000 domains lack existing Pfam mappings, reflecting the extended sensitivity of structure-based classification and identifying domain groups not yet captured by sequence-based profiles. These results significantly expand ECOD's coverage into a functionally and taxonomically diverse protein space, anchoring high-confidence structure predictions in an evolutionary framework. By integrating Swiss-Prot predictions, we enhance the utility and interpretability of AlphaFold models and establish a foundation for future large-scale, functionally informed domain classifications.

Recent advances in protein structure prediction, such as AlphaFold2, have enabled identification of vast numbers of putative novel protein domains across the sequence space, many of which adopt structures dissimilar to known folds. Based on structural segmentation and classification, the Encyclopedia of Domains (TED) project recently cataloged more than 7400 low-symmetry, structure-based domains as candidate novel-fold (CNF) domains. To place these domains in their broader evolutionary and structural context, we applied DPAM (Domain Parser for AlphaFold Models), a complementary method that combines AlphaFold-derived confidence metrics with sensitive sequence and structure similarity searches, to parse domains for the AlphaFold models of proteins containing TED CNF domains. We identified 8044 DPAM domains with significant overlap with TED CNF domains, among which 2490 were confidently assigned to entries in the ECOD (Evolutionary Classification of protein Domains) structural classification hierarchy. Our results suggest that a substantial subset of TED candidate novel-fold domains are distant homologs of existing ECOD domains. Comparison of domain boundaries between TED and DPAM showed varied patterns: more than one-third of cases featured TED CNF domains largely embedded within DPAM domains-often representing insertions or extensions into enzymatic or repeat folds. A smaller fraction (17%) exhibited consistent domain boundaries between TED and DPAM. These consistently defined domains are often characterized by significant structural diversity, including long insertions and duplications. An even smaller subset showed the reverse relationship, with DPAM domains largely embedded within TED CNF domains. In these cases, DPAM effectively separated multiple structural units that TED grouped as single domains. Together, these findings highlight the complementarity of structural and evolutionary approaches for domain annotation and demonstrate the power of integrative methods, such as DPAM, in refining the classification of challenging protein folds and uncovering distant evolutionary relationships.

Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets.

Rossmann folds are ancient, frequently diverged domains found in many biological reaction pathways where they have adapted for different functions. Consequently, discernment and classification of their homologous relations and function can be complicated. We define a minimal Rossmann-like structure motif (RLM) that corresponds for the common core of known Rossmann domains and use this motif to identify all RLM domains in the Protein Data Bank (PDB), thus finding they constitute about 20% of all known 3D structures. The Evolutionary Classification of protein structure Domains (ECOD) classifies RLM domains in a number of groups that lack evidence for homology (X-groups), which suggests that they could have evolved independently multiple times. Closely related, homologous RLM enzyme families can diverge to bind different ligands using similar binding sites and to catalyze different reactions. Conversely, non-homologous RLM domains can converge to catalyze the same reactions or to bind the same ligand with alternate binding modes. We discuss a special case of such convergent evolution that is relevant to the polypharmacology paradigm, wherein the same drug (methotrexate) binds to multiple non-homologous RLM drug targets with different topologies. Finally, assigning proteins with RLM domain to the Enzyme Commission classification suggest that RLM enzymes function mainly in metabolism (and comprise 38% of reference metabolic pathways) and are overrepresented in extant pathways that represent ancient biosynthetic routes such as nucleotide metabolism, energy metabolism, and metabolism of amino acids. In fact, RLM enzymes take part in five out of eight enzymatic reactions of the Wood-Ljungdahl metabolic pathway thought to be used by the last universal common ancestor (LUCA). The prevalence of RLM domains in this ancient metabolism might explain their wide distribution among enzymes.

Motivation: The ECOD database classifies protein domains based on their evolutionary relationships, considering both remote and close homology. The family group in ECOD provides classification of domains that are closely related to each other based on sequence similarity. Due to different perspectives on domain definition, direct application of existing sequence domain databases, such as Pfam, to ECOD struggles with several shortcomings. Results: We created multiple sequence alignments and profiles from ECOD domains with the help of structural information in alignment building and boundary delineation. We validated the alignment quality by scoring structure superposition to demonstrate that they are comparable to curated seed alignments in Pfam. Comparison to Pfam and CDD reveals that 27 and 16% of ECOD families are new, but they are also dominated by small families, likely because of the sampling bias from the PDB database. There are 35 and 48% of families whose boundaries are modified comparing to counterparts in Pfam and CDD, respectively. Availability and implementation: The new families are now integrated in the ECOD website. The aggregate HMMER profile library and alignment are available for download on ECOD website (http://prodata.swmed.edu/ecod). Supplementary information: Supplementary data are available at Bioinformatics online.

Understanding the evolution of a protein, including both close and distant relationships, often reveals insight into its structure and function. Fast and easy access to such up-to-date information facilitates research. We have developed a hierarchical evolutionary classification of all proteins with experimentally determined spatial structures, and presented it as an interactive and updatable online database. ECOD (Evolutionary Classification of protein Domains) is distinct from other structural classifications in that it groups domains primarily by evolutionary relationships (homology), rather than topology (or "fold"). This distinction highlights cases of homology between domains of differing topology to aid in understanding of protein structure evolution. ECOD uniquely emphasizes distantly related homologs that are difficult to detect, and thus catalogs the largest number of evolutionary links among structural domain classifications. Placing distant homologs together underscores the ancestral similarities of these proteins and draws attention to the most important regions of sequence and structure, as well as conserved functional sites. ECOD also recognizes closer sequence-based relationships between protein domains. Currently, approximately 100,000 protein structures are classified in ECOD into 9,000 sequence families clustered into close to 2,000 evolutionary groups. The classification is assisted by an automated pipeline that quickly and consistently classifies weekly releases of PDB structures and allows for continual updates. This synchronization with PDB uniquely distinguishes ECOD among all protein classifications. Finally, we present several case studies of homologous proteins not recorded in other classifications, illustrating the potential of how ECOD can be used to further biological and evolutionary studies.

MOTIVATION: The discovery of new protein folds is a relatively rare occurrence even as the rate of protein structure determination increases. This rarity reinforces the concept of folds as reusable units of structure and function shared by diverse proteins. If the folding mechanism of proteins is largely determined by their topology, then the folding pathways of members of existing folds could encompass the full set used by globular protein domains. RESULTS: We have used recent versions of three common protein domain dictionaries (SCOP, CATH and Dali) to generate a consensus domain dictionary (CDD). Surprisingly, 40% of the metafolds in the CDD are not composed of autonomous structural domains, i.e. they are not plausible independent folding units. This finding has serious ramifications for bioinformatics studies mining these domain dictionaries for globular protein properties. However, our main purpose in deriving this CDD was to generate an updated CDD to choose targets for MD simulation as part of our dynameomics effort, which aims to simulate the native and unfolding pathways of representatives of all globular protein consensus folds (metafolds). Consequently, we also compiled a list of representative protein targets of each metafold in the CDD. AVAILABILITY AND IMPLEMENTATION: This domain dictionary is available at www.dynameomics.org.