Biology subjects

Cong, Q.

Publications and source records attributed to Cong, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

AI-Based Comparison of UniProt annotations and Literature Across Human Kinases

Curated protein records and their supporting publications organize biological evidence at different levels of detail. Here, we used AI to independently answer a set of biological questions using either UniProt records or the full texts of the literature cited by those records, and then used AI-based evaluation to compare the resulting answers in terms of information coverage and semantic agreement. Fifty human proteins were selected from ten groups in a kinase sequence collection. Target-identity filtering retained 1,749 protein-publication records representing 1,655 distinct publications. For each protein, an AI language model generated one answer set from UniProt and another from the associated literature. A separate AI-based semantic evaluation compared all 2,500 pairwise combinations of the 50 UniProt-derived and 50 literature-derived answer sets, producing 87,500 question-level comparisons. Literature provided answers to 1,211 of 1,750 protein-question combinations, compared with 1,151 for UniProt. After correction for multiple testing across the 35 questions, literature coverage was significantly higher for autoinhibition and inhibitor type. Mean AI-assigned semantic agreement scores were higher for matched protein pairs (0.692) than for unmatched proteins within the same kinase group (0.384) and for proteins from different kinase groups (0.279). The matching literature-derived answer set ranked first for 49 of the 50 UniProt-derived queries, even after questions about gene symbol and kinase family were excluded. The sole exception was GUCY2C, whose UniProt-derived answer set showed slightly greater agreement with the NPR2 literature-derived answer set than with the GUCY2C literature-derived set. TYR proteins had the greatest overall answer coverage, whereas CK1 and RGC proteins had comparatively limited coverage in UniProt and the literature, respectively. Overall, AI analysis showed that UniProt records and their cited publications contain complementary yet strongly protein-specific biological information. Reporting gaps, question scope, and score aggregation influenced apparent agreement, underscoring that the framework evaluates consistency between AI-derived answers, not their biological accuracy.

bioinformatics↗

bioRxiv2026

Diaphragmatic EMG Uncovers Left-Right Diaphragmatic Imbalance After Ischemic Stroke with Implications for Motor Recovery

BackgroundStroke-associated respiratory dysfunction has been increasingly recognized, yet whether diaphragmatic impairment after stroke involves lateralized neuromuscular imbalance remains unclear. MethodsIn this controlled experimental study, transient left middle cerebral artery occlusion and reperfusion (tMCAO) was performed in mice, followed by multimodal assessment of treadmill-based peak oxygen uptake testing, diaphragm ultrasonography, bilateral diaphragmatic electromyography (dEMG) with simultaneous respiratory flow monitoring, behavioral assessment, and whole-mount immunofluorescence imaging of the diaphragm. ResultsIschemic stroke reduced exercise capacity and diaphragmatic excursion without detectable diaphragm thinning, indicating functional impairment rather than overt atrophy. Bilateral dEMG revealed a subacute left-right asymmetry in inspiratory activation, with the ipsilesional hemidiaphragm exhibiting reduced amplitude, decreased area under the curve, and altered burst duration, consistent with a relative contralateral-dominant pattern rather than frank hyperactivation. Whole-mount imaging demonstrated asymmetric nerve remodeling, characterized by a nadir in ipsilesional nerve fiber density at 1 week and partial recovery by 2 weeks after stroke. These structural changes closely paralleled the dEMG alterations, suggesting a neural substrate for lateralized dysfunction. Exploratory analyses further linked dEMG parameters with motor recovery. ConclusionsThese findings provide novel evidence that ischemic stroke induces a left-right diaphragmatic neuromuscular imbalance, with structural and functional correlates in diaphragmatic innervation. The study further supports the utility of bilateral dEMG as a functional marker for assessing diaphragmatic dysfunction and monitoring recovery after stroke. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/737872v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@10001a4org.highwire.dtl.DTLVardef@16bc3d5org.highwire.dtl.DTLVardef@5e40aorg.highwire.dtl.DTLVardef@be53b7_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Genomics Analysis Illuminates Morphology, Ecology, Phenology and Distribution of Two Cryptic Atrytonopsis Skippers (Hesperiidae: Hesperiinae)

The Hesperiine genus Atrytonopsis Godman, 1900, occurs broadly across the American Southwest. Atrytonopsis margarita (Skinner, 1913) and Atrytonopsis python (W. H. Edwards, 1882) have look-alike appearances, concurrent flights, and geographic distributions which converge in New Mexico. Their similar wing markings and intertwined taxonomic history has made it challenging to fully understand the identity and occurrence of each. Burns (2015) revealed differences in genitalia, clarifying that they are distinct species. Genomic DNA analysis of more than 100 specimens now illuminates their genetic uniqueness, phylogenetic relationship, field identification challenges and details of their geographic distributions.

evolutionary biology↗

bioRxiv2026

Cataloging cysteines in ECOD domains using a protein language model

Cysteine is among the most chemically versatile residues in the proteome, existing in three competing functional states: metal coordination, covalent disulfide bonding, and bioactive free thiols. Although these states can be readily assigned from experimentally determined protein structures using simple geometric criteria, accurately annotating them from predicted structures remains challenging. To bridge the gap between predicted structures and functional interpretation, we developed TriCyP (Tri-state Cysteine Predictor), an efficient two-layer neural network built on ESM-2 protein language model embeddings. On an independent benchmark set, TriCyP achieves near-perfect accuracy (AUROC = 0.99) and outperforms existing approaches for predicting both disulfide bonding and metal coordination. We applied TriCyP to classify 2.7 million cysteine residues across 0.9 million ECOD F70 representative domains. The resulting proteome-scale landscape recapitulates established biological patterns. Cysteines are enriched in eukaryotes: disulfide-bonded states are concentrated in extracellular proteins, and metal-coordinating cysteines peak in nuclear proteins owing to the abundance of zinc-finger transcription factors. We further demonstrate the utility of cysteine-state annotation through two pilot studies. First, predicted disulfide-forming cysteines lacking a corresponding structural partner in AlphaFold models may identify either regions of elevated structural uncertainty or latent inter-protein disulfide bonds that stabilize protein-protein interactions. Second, systematic analysis of known and predicted metal-coordinating cysteines across ECOD homologous groups uncovers previously unrecognized metal-binding protein families. This proteome-wide catalog of cysteine states is available as a community resource (http://prodata.swmed.edu/tricyp) and will be integrated into future ECOD releases.

bioinformatics↗

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

Modeling the structure-conditioned sequence landscape for large-scale protein design with TriFlow

Generative models have revolutionized computational protein design, and the design of high-quality sequences given backbone structure is a critical component for success. Current state-of-the-art design pipelines utilize sequence design methods with local structural context and autoregressive generation. To improve efficiency and quality of sequence design, we developed TriFlow, a model that combines a RoseTTAFold-like three-track architecture for global structural context with discrete flow-matching for efficient few-step sequence generation. We trained TriFlow on a large dataset of interacting protein chains from Protein Data Bank and interacting domains from AlphaFold protein structure Database to enrich its knowledge of natural protein and domain interfaces. TriFlow outperforms existing sequence design methods like ProteinMPNN across diverse benchmarks, including de novo binder design, where it boosts the in silico success rate of state-of-the-art design pipelines such as BindCraft. We demonstrated this by conducting a large-scale benchmark, generating and computationally validating binders for over 500 diverse protein targets. Experimental validation on a small set of targets also suggests that the performance of TriFlow is on par with BindCraft. By leveraging the model to explore the designed sequence landscape, we discovered that we can effectively highlight functional sites, by contrasting designed sequences that reflect structure constraints with natural evolutionary profiles. As a practical demonstration of its capabilities, we applied our pipeline to systematically design specific binders against human class I cytokines, computationally optimizing for on-target affinity while minimizing off-target interactions, demonstrating that specificity also scales with inference time and computational budget. TriFlow thus provides a robust framework both for large-scale protein engineering and for exploring the fundamental principles of the structure-conditioned sequence landscape. TriFlow software and generated resources are available at https://triflow.zhoulab.io/.

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↗

bioRxiv2025

Accelerating Biomolecular Modeling with AtomWorks andRF3

Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular foundation models spanning diverse tasks, including structure prediction, generative protein design, and fixed backbone sequence design. We use AtomWorks to train RosettaFold-3 (RF3), a structure prediction network capable of predicting arbitrary biomolecular complexes with an improved treatment of chirality that narrows the performance gap between closed-source AlphaFold3 (AF3) and existing open-source implementations. We expect that AtomWorks will accelerate the next generation of open-source biomolecular machine learning models and that RF3 will be broadly useful as a structure prediction tool. To this end, we release the AtomWorks framework (https://github.com/RosettaCommons/atomworks), together with curated training data, code and model weights for RF3 (https://github.com/RosettaCommons/modelforge) under a permissive BSD license.

biochemistry↗

bioRxiv2025

CASP16 protein monomer structure prediction assessment

The assessment of monomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) underscores that the problem of single-domain protein fold prediction is nearly solved--no target folds were incorrectly predicted across all Evaluation Units. However, challenges remain in accurately modeling truncated sequences, irregular secondary structures, and interaction-induced conformational changes. The release of AlphaFold3 (AF3) during CASP16, and its effective integration by many groups, demonstrated its superiority over AlphaFold2 (AF2), particularly in confidence estimation and model selection. Additional improvements in multiple sequence alignments (MSAs) and fragment-based prediction, i.e., selecting the optimal fragment of the full sequence for modeling, also contributed to enhanced prediction accuracy. The top three groups--all from the Yang lab--consistently outperformed others across CASP16 monomer targets, reflecting their robust modeling pipelines and successful adoption of AF3. CASP16 also introduced three new challenges: Phase 0, in which stoichiometry was withheld; Phase 2, which supplied [~]8,000 MassiveFold models per target to test model selection strategies; and Model 6, which limited predictors to using MSAs provided by the organizers. While we evaluated group performance in these additional challenges, the insights gained were limited due to low participation and caveats in the design of experiments. We suggest improvements for the organization of these challenges and encourage broader engagement from the prediction community. The progress in monomer modeling from CASP15 to CASP16 was subtle, but more groups in CASP16 were able to outperform ColabFold, reflecting the communitys improved ability in optimizing AF2 and the growing adoption of AF3. We anticipate that the recent release of the AF3 source code will stimulate future progress through user-driven optimization and innovations in model architecture. Finally, model ranking remains a persistent weakness across most groups, highlighting a critical area for future development.

biophysics↗

bioRxiv2025

Assessment of Protein Complex Predictions in CASP16: Are we making progress?

The assessment of oligomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) suggests that complex structure prediction remains an unsolved challenge. More than 30% of targets, particularly antibody-antigen targets, were highly challenging, with each group correctly predicting structures for only about a quarter of such targets. Most CASP16 groups relied on AlphaFold-Multimer (AFM) or AlphaFold3 (AF3) as their core modeling engines. By optimizing input MSAs, refining modeling constructs (using partial rather than full sequences), and employing massive model sampling and selection, top-performing groups were able to significantly outperform the default AFM/AF3 predictions. CASP16 also introduced two additional challenges: Phase 0, which required predictions without stoichiometry information, and Phase 2, which provided participants with thousands of models generated by MassiveFold (MF) to enable large-scale sampling for resource-limited groups. Across all phases, the MULTICOM series and Kiharalab emerged as top performers based on the quality of their best models per target. However, these groups did not have a strong advantage in model ranking, and thus their lead over other teams, such as Yang-Multimer and kozakovvajda, was less pronounced when evaluating only the first submitted models. Compared to CASP15, CASP16 showed moderate overall improvement, likely driven by the release of AF3 and the extensive model sampling employed by top groups. Several notable trends highlight key frontiers for future development. First, the kozakovvajda group significantly outperformed others on antibody-antigen targets, achieving over a 60% success rate without relying on AFM or AF3 as their primary modeling framework, suggesting that alternative approaches may offer promising solutions for these difficult targets. Second, model ranking and selection continue to be major bottlenecks. The PEZYFoldings group demonstrated a notable advantage in selecting their best models as first models, suggesting that their pipeline for model ranking may offer important insights for the field. Finally, the Phase 0 experiment indicated reasonable success in stoichiometry prediction; however, stoichiometry prediction remains challenging for high-order assemblies and targets that differ from available homologous templates. Overall, CASP16 demonstrated steady progress in multimer prediction while emphasizing the urgent need for more effective model ranking strategies, improved stoichiometry prediction, and the development of new modeling methods that extend beyond the current AF-based paradigm.

biophysics↗

bioRxiv2025

Assessment of nucleic acid structure prediction in CASP16

Consistently accurate 3D nucleic acid structure prediction would facilitate studies of the diverse RNA and DNA molecules underlying life. In CASP16, blind predictions for 42 targets canvassing a full array of nucleic acid functions, from dopamine binding by DNA to formation of elaborate RNA nanocages, were submitted by 65 groups from 46 different labs worldwide. In contrast to concurrent protein structure predictions, performance on nucleic acids was generally poor, with no predictions of previously unseen natural RNA structures achieving TM-scores above 0.8. Even though automated server performance has improved, all top-performing groups were human expert predictors: Vfold, GuangzhouRNA-human, and KiharaLab. Good performance on one template-free modeling target (OLE RNA) and accurate global secondary structure prediction suggested that structural information can be extracted from multiple sequence alignments. However, 3D accuracy generally appeared to depend on the availability of closely related 3D structure templates, and predictions still did not achieve consistent recovery of pseudoknots, singlet Watson-Crick-Franklin pairs, non-canonical pairs, or tertiary motifs like A-minor interactions. For the first time, blind predictions of nucleic acid interactions with small molecules, proteins, and other nucleic acids could be assessed in CASP16. As with nucleic acid monomers, prediction accuracy for nucleic acid complexes was generally poor unless 3D templates were available. Accounting for template availability, there has not been a notable increase in nucleic acid modeling accuracy between previous blind challenges and CASP16.

biophysics↗

bioRxiv2025

NUDT5 regulates purine metabolism and thiopurine sensitivity by interacting with PPAT

Cells generate purine nucleotides through both de novo purine biosynthesis (DNPB) and purine salvage. Purine accumulation represses energetically costly DNPB through feedback inhibition of the enzymatic steps that produce the precursor phosphoribosylamine. Excessive DNPB is associated with human diseases including neurological dysfunction and hyperuricemia. However, the mechanisms explaining how cells balance DNPB and purine salvage are incompletely understood. Data from a genome-wide CRISPR loss-of-function screen and extensive stable isotope tracing identified Nudix hydrolase 5 (NUDT5) as a suppressor of DNPB during purine salvage. NUDT5 ablation allows DNPB to persist in the presence of either native purines or thiopurine drugs; this renders NUDT5-deficient cells insensitive to thiopurine treatment. Surprisingly, this regulation occurs independently of NUDT5s known function in hydrolyzing ADP-ribose to AMP and ribose-5-phosphate. Rather, NUDT5 interacts with phosphoribosyl pyrophosphate amidotransferase (PPAT), the rate-limiting enzyme in DNPB that generates phosphoribosylamine. Upon induction of purine salvage, the PPAT-NUDT5 interaction is required to trigger disassembly of the purinosome, a cytosolic metabolon involved in efficient DNPB. Mutations that disrupt NUDT5s interaction with PPAT but leave its catalytic activity intact permit excessive DNPB during purine salvage, inducing thiopurine resistance. Collectively, our findings identify NUDT5 as a regulator governing the balance between DNPB and purine salvage, underscoring its impact on nucleotide metabolism and efficacy of thiopurine treatment.

biochemistry↗

bioRxiv2025

Ultradeep N-glycoproteome Atlas of Mouse Reveals Spatiotemporal Signatures of Brain Aging and Neurodegenerative Diseases

The current depth of site-specific N-glycoproteomics is insufficient to fully characterize glycosylation events in biological samples. Herein, we achieved an ultradeep and precision analysis of the N-glycoproteome of mouse tissues by integrating multiple workflows. The largest N-glycoproteomic dataset to date was established on mice, which contained 91,972 precursor glycopeptides, 62,216 glycoforms, 8,939 glycosites and 4,563 glycoproteins. The database consisted of 6.8 million glyco-spectra (containing oxonium ions), among which 160,928 were high-quality spectra with confident N-glycopeptide identifications. The large-scale and high-quality dataset enhanced the performance of current artificial intelligence models for glycopeptide tandem spectrum prediction. Using this ultradeep dataset, we observed tissue specific microheterogeneity and functional implications of protein glycosylation in mice. Furthermore, the region-resolved brain N-glycoproteomes for Alzheimers Diseases, Parkinson Disease and aging mice revealed the spatiotemporal signatures and distinct pathological functions of the N-glycoproteins. A comprehensive database resource of experimental N-glycoproteomic data from this study and previous literatures were further established. This N-glycoproteome atlas serves as a promising tool for revealing the role of protein glycosylation in biological systems.

neuroscience↗

bioRxiv2024

Computing the Human Interactome

Protein-protein interactions (PPI) are essential for biological function. Recent advances in coevolutionary analysis and Deep Learning (DL) based protein structure prediction have enabled comprehensive PPI identification in bacterial and yeast proteomes, but these approaches have limited success to date for the more complex human proteome. Here, we overcome this challenge by 1) enhancing the coevolutionary signals with 7-fold deeper multiple sequence alignments harvested from 30 petabytes of unassembled genomic data, and 2) developing a new DL network trained on augmented datasets of domain-domain interactions from 200 million predicted protein structures. These advancements allow us to systematically screen through 200 million human protein pairs and predict 18,316 PPIs with an expected precision of 90%, among which 5,578 are novel predictions. 3D models of these predicted PPIs nearly triple the number of human PPIs with accurate structural information, providing numerous insights into protein function and mechanisms of human diseases.

biochemistry↗

bioRxiv2024

Genetic and microbial analysis of invasiveness for Escherichia coli strains associated with inflammatory bowel disease

Background & AimsThe adherent-invasive Escherichia coli (AIEC) pathotype is implicated in inflammatory bowel disease (IBD) pathogenesis. AIEC strains are currently defined by phenotypic measurement of their pathogenicity, including invasion of epithelial cells. This broad definition, combined with the genetic diversity of AIEC across IBD patients, has complicated the identification of virulence determinants. We sought to quantify the invasion phenotype of clinical isolates from IBD patients and identify the genetic basis for their invasion into epithelial cells. MethodsA pangenome with core and accessory genes (genotype) was assembled using whole genome sequencing of 168 E. coli samples isolated from 13 IBD patients. A modified assay for invasion of epithelial cells (phenotype) was established with consideration of antibiotic resistance phenotypes. Isolate genotype was correlated to invasiveness phenotype to identify genetic factors that co-segregate with invasion. ResultsPangenome-wide comparisons of E. coli clinical isolates identified accessory genes that can co-segregate with invasion phenotype. These correlations found the acquisition of antibiotic resistance genes in clinical isolates compromised the traditional gentamicin protection assays used to quantify invasion. Therefore, an alternate assay, based on amikacin resistance, identified genes co-segregating with invasion. These genes encode an arylsulfatase, a glycoside hydrolase, and genetic islands carrying propanediol utilization and sulfoquinovose metabolism pathways. ConclusionsThis study highlights the importance of incorporating antibiotic resistance screening for invasion assays used in AIEC identification. Accurately screened invasion phenotypes identified accessory genome elements among E. coli IBD isolates that correlate with their ability to invade epithelial cells. These results help explain why single genetic markers for the AIEC phylotype are challenging to identify. SynopsisWe established a pangenome of Escherichia coli isolates from inflammatory bowel disease patients using whole genome sequencing. The genotypes were correlated with a newly developed measurement of invasion phenotype to identify co-segregating genes.

microbiology↗

bioRxiv2024

Essential and virulence-related protein interactions of pathogens revealed through deep learning

Identification of bacterial protein-protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep learning-based pipeline that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

biochemistry↗

bioRxiv2024

COA8 is a COX10-binding protein involved in the early biogenesis of cytochrome c oxidase

Human cytochrome c oxidase (COX) is composed of fourteen subunits, only two of which are catalytic. The functions of a myriad of accessory factors are needed to put together the structural subunits, as well as for the formation of the active centers. The molecular roles of most of these factors are still unknown, even if they are essential for COX function since their defects cause severe mitochondrial disease. COA8 is exclusively found in metazoa and loss-of-function genetic variants cause mitochondrial encephalopathy and COX deficiency in humans. In this work, we have resolved the function of COA8, as a factor that specifically binds to COX10, the heme O synthase. This interaction is necessary not only for the stabilization of human COX10, but also to guarantee its catalytic activity as the first step for the synthesis of the heme A cofactor, which is a crucial component of the COX catalytic center.

biochemistry↗

bioRxiv2024

Structure classification of the proteins from Salmonella enterica pangenome revealed novel potential pathogenicity islands

Salmonella enterica is a pathogenic bacterium known for causing severe typhoid fever in humans, making it important to study due to its potential health risks and significant impact on public health. This study provides evolutionary classification of proteins from Salmonella enterica pangenome. We classified 17,238 domains from 13,147 proteins from 79,758 Salmonella enterica strains and studied in detail domains of 272 proteins from fourteen characterized Salmonella pathogenicity islands (SPIs). Among SPIs-related proteins, 90 proteins function in the secretion machinery. 41% domains of SPI proteins have no previous sequence annotation. By comparing clinical and environmental isolates, we identified 3682 proteins that are overrepresented in clinical group that we consider as potentially pathogenic. 69 of them overlap with SPI proteins. Among domains of potentially pathogenic proteins only 50% domains were annotated by sequence methods previously. Moreover, 36% (1330 out of 3682) of potentially pathogenic proteins cannot be classified into Evolutionary Classification of Protein Domains database (ECOD). Among classified domains of potentially pathogenic proteins the most populated homology groups include helix-turn-helix (HTH), Immunoglobulin-related, and P-loop domains-related. Functional analysis revealed overrepresentation of these protein in biological processes related to viral entry into host cell, antibiotic biosynthesis, DNA metabolism and conformation change, and underrepresentation in translational processes. Analysis of the potentially pathogenic proteins indicates that they form 119 clusters (islands) within the Salmonella genome, suggesting their potential contribution to the bacteriums virulence. Overall, our analysis revealed that identified potentially pathogenic proteins are poorly studied. ECOD hierarchy of classified Salmonella enterica domains is available online: http://prodata.swmed.edu/ecod/index_salm.php Author SummarySalmonella enterica is a dangerous bacterium known for causing severe typhoid fever in humans, posing significant health risks. Our study focuses on understanding the proteins of this bacteriums genetic composition, unraveling their evolutionary classification. Analyzing a vast collection of strains, we identified and classified over 17,000 protein domains, with special focus on 272 proteins within Salmonella pathogenicity islands (SPIs). By comparing strains from clinical and environmental sources, we pinpointed 3,682 proteins overrepresented in clinical samples, signifying potential pathogenicity. Surprisingly, half of these proteins domains were not identified previously using sequence-based approaches. Our analysis identified these proteins forming 119 clusters within the Salmonella genome, suggesting their involvement in its virulence. Our study underscores the insufficient understanding of these potentially pathogenic proteins, highlighting the need for further investigation into their roles and implications in Salmonella-related illnesses.

bioinformatics↗