Biology subjects

Grishin, N.

Publications and source records attributed to Grishin, N..

4 recordsLinked to original sources

bioRxiv2026

Domain classification of archaeal proteomes reveals conserved fold repertoire

Archaea represent one of the three domains of cellular life and yet account for fewer than 1% of experimentally determined protein structures, leaving the extent of their structural novelty unknown. Here we present a systematic domain-level classification of 124,075 proteins from 65 archaeal classes spanning 21 phyla and all major lineages, using both AFDB and newly predicted AlphaFold3 structures classified against the Evolutionary Classification of protein Domains (ECOD). We assigned 204,758 domains, of which 76.8% received high-confidence classifications, spanning 987 ECOD X-groups; 40% of known structural diversity within a single domain of life. Clustering by Foldseek recovered structural relationships for 63% of domains that are singletons by sequence comparison. To characterize the 21% of proteins lacking high-confidence classification, we applied successive filters for structure prediction confidence, protein length, and structural cluster context, reducing 8,452 domain-free proteins to a small number of well-folded structural orphans (less than 0.1% of the dataset). The unclassified fraction is dominated by sub-threshold matches to known folds (14% of all proteins) and low-confidence structure predictions (5%), not by novel structures. These results demonstrate that the protein fold repertoire at the single-domain level is broadly conserved across the deepest phylogenetic distances in cellular life, and that the gap between archaeal and well-characterized proteomes reflects classification sensitivity for divergent sequences rather than unexplored structural diversity.

bioinformatics↗

bioRxiv2025

ECOD: Classification of domains in AFDB Swiss-Prot structure predictions

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 Classificatioxn of Protein Domains (ECOD) have incorporated these computational predictions alongside experimental structures to create comprehensive resources for the research community. The AlphaFold 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 Swiss-Prot/UniProtKB 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, indicating novel evolutionary groups. These results significantly expand ECODs 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. Author SummaryLarge-scale protein structure prediction using deep learning has revolutionized our ability to study protein families and infer biological function. However, connecting these predicted structures to well-understood evolutionary classifications remains challenging. In this work, we apply a domain parsing pipeline to classify over half a million AlphaFold-predicted Swiss-Prot proteins into evolutionary groups using ECOD, a structure-based domain classification system. This enables the systematic integration of structural predictions with functional annotation across a broad range of species. Our analysis reveals taxonomic and functional diversity, highlights domain clusters with no prior annotation, and expands ECOD coverage with high-confidence, evolutionarily meaningful predictions.

bioinformatics↗

bioRxiv2021

TMEM120 is a coenzyme A-binding membrane protein with structural similarities to ELOVL fatty acid elongase

TMEM120A, also named as TACAN, is a novel membrane protein highly conserved in vertebrates and was recently proposed to be a mechanosensitive channel involved in sensing mechanical pain. Here we present the single particle cryo-EM structure of human TMEM120A which forms a tightly packed dimer with extensive interactions mediate by the N-terminal coiled coil domain (CCD), the C-terminal transmembrane domain (TMD), and the re-entrant loop between the two domains. The TMD of each TMEM120A subunit contains six transmembrane helices (TMs) and has no clear structural feature of a channel protein. Instead, the six TMs form an -barrel with a deep pocket where a coenzyme A (CoA) molecule is bound. Intriguingly, some structural features of TMEM120A resemble those of elongase for very long-chain fatty acid (ELOVL) despite low sequence homology between them, pointing to the possibility that TEME120A may function as an enzyme for fatty acid metabolism, rather than a mechanosensitive channel.

biophysics↗

bioRxiv2019

Genomic determinants of speciation

Studies of life rely on classifying organisms into species. However, since Darwin, there is no agreement about how to separate species from varieties. Contrary to a frequent belief, quantitative standards for species delineation are lacking and overdue, and debates about species delimitation create obstacles for conservation biology, agriculture, legislation, and education. To tackle this key biological question, we have chosen butterflies as model organisms. We sequenced and analyzed transcriptomes of 188 specimens representing pairs of close but clearly distinct species, populations, and taxa that are debated among experts. We find that species are robustly separated from populations by the combination of two measures computed on Z-linked genes: fixation index that detects hiatus between species, and the extent of gene flow that quantifies reproductive isolation. These criteria suggest that all 9 butterfly pairs that caused experts disagreement are distinct species, not populations. When applied to Homo, our criteria agree that all modern humans are the same species distinct from Neanderthals, suggesting relevance of this study beyond butterflies. Furthermore, we found that proteins involved in interactions with DNA (including proteins encoded by trans-regulatory elements), circadian clock, pheromone sensing, development, and immune response recurrently correlate with speciation. A significant fraction of these proteins is encoded by the Z-chromosome, which appears to be resistant to introgression. Taken together, we find common speciation mechanisms in butterflies, reveal the central role of Z-chromosome in speciation, and suggest quantitative criteria for species delimitation using genomic data, which is vital for the exploration of biodiversity.

evolutionary biology↗