Modeling sequence and function similarity between proteins for protein functional annotation

Searching for Protein Off-Targets of Prostate-Specific Membrane Antigen-Targeting Radioligands in the Salivary Glands

Background: Prostate specific membrane antigen (PSMA)-targeted radioligand therapies represent a highly effective treatment for metastatic prostate cancer. However, high and sustain uptake of PSMA-ligands in the salivary glands led to dose limiting dry mouth (xerostomia), especially with α-emitters. The expression of PSMA and histologic analysis couldn't directly explain the toxicity, suggesting a potential off-target mediator for uptake. In this study, we set out to search for possible off-target non-PSMA protein(s) in the salivary glands. Methods: A machine-learning based quantitative structure activity relationship (QSAR) model was built for seeking the possible off-target(s). The resulting target candidates from the model prediction were subjected to further analysis for salivary protein expression and structural homology at key regions required for PSMA-ligand binding. Furthermore, cellular binding assays were performed utilizing multiple cell lines with high expression of the candidate proteins and low expression of PSMA. Finally, PSMA knockout (PSMA-/-) mice were scanned by small animal PET/MR using [68Ga]Ga-PSMA-11 for in-vivo validation. Results: The screening of the trained QSAR model did not yield a solid off-target protein, which was corroborated in part by cellular binding assays. Imaging using PSMA-/- mice further demonstrated markedly reduced PSMA-radioligand uptake in the salivary glands. Conclusion: Uptake of the PSMA-targeted radioligands in the salivary glands remains primarily PSMA-mediated. Further investigations are needed to illustrate a seemingly different process of uptake and retention in the salivary glands than that in prostate cancer.

Comparative Bioinformatic Analysis of the Proteomes of Rabbit and Human Sex Chromosomes

Studying proteins associated with sex chromosomes can provide insights into sex-specific proteins. Membrane proteins accessible through the cell surface may serve as excellent targets for diagnostic, therapeutic, or even technological purposes, such as sperm sexing technologies. In this context, proteins encoded by sex chromosomes have the potential to become targets for X- or Y-chromosome-bearing spermatozoa. Due to the limited availability of proteomic studies on rabbit spermatozoa and poorly annotated databases for rabbits compared to humans, a bioinformatic analysis of the available rabbit X chromosome proteome (RX), as well as the human X (HX) and Y (HY) chromosomes proteome, was conducted to identify potential targets that could be accessible from the cell surface and predict which of the potential targets identified in humans might also exist in rabbits. We identified 100, 211, and 3 proteins associated with the plasma membrane or cell surface for RX, HX, and HY, respectively, of which 61, 132, and 3 proteins exhibit potential as targets as they were predicted to be accessible from the cell surface. Cross-referencing the potential HX targets with the rabbit proteome revealed an additional 60 proteins with the potential to be RX targets, resulting in a total of 121 potential RX targets. In addition, at least 53 possible common HX and RX targets have been previously identified in human spermatozoa, emphasizing their potential as targets of X-chromosome-bearing spermatozoa. Further proteomic studies on rabbit sperm will be essential to identify and validate the usefulness of these proteins for application in rabbit sperm sorting techniques as targets of X-chromosome-bearing spermatozoa.

Functional annotation of a divergent genome using sequence and structure-based similarity

BACKGROUND

Microsporidia are a large taxon of intracellular pathogens characterized by extraordinarily streamlined genomes with unusually high sequence divergence and many species-specific adaptations. These unique factors pose challenges for traditional genome annotation methods based on sequence similarity. As a result, many of the microsporidian genomes sequenced to date contain numerous genes of unknown function. Recent innovations in rapid and accurate structure prediction and comparison, together with the growing amount of data in structural databases, provide new opportunities to assist in the functional annotation of newly sequenced genomes.

RESULTS

In this study, we established a workflow that combines sequence and structure-based functional gene annotation approaches employing a ChimeraX plugin named ANNOTEX (Annotation Extension for ChimeraX), allowing for visual inspection and manual curation. We employed this workflow on a high-quality telomere-to-telomere sequenced tetraploid genome of Vairimorpha necatrix. First, the 3080 predicted protein-coding DNA sequences, of which 89% were confirmed with RNA sequencing data, were used as input. Next, ColabFold was used to create protein structure predictions, followed by a Foldseek search for structural matching to the PDB and AlphaFold databases. The subsequent manual curation, using sequence and structure-based hits, increased the accuracy and quality of the functional genome annotation compared to results using only traditional annotation tools. Our workflow resulted in a comprehensive description of the V. necatrix genome, along with a structural summary of the most prevalent protein groups, such as the ricin B lectin family. In addition, and to test our tool, we identified the functions of several previously uncharacterized Encephalitozoon cuniculi genes.

CONCLUSION

We provide a new functional annotation tool for divergent organisms and employ it on a newly sequenced, high-quality microsporidian genome to shed light on this uncharacterized intracellular pathogen of Lepidoptera. The addition of a structure-based annotation approach can serve as a valuable template for studying other microsporidian or similarly divergent species.

Predicting drug targets by homology modelling of Pseudomonas aeruginosa proteins of unknown function

The efficacy of antibiotics to treat bacterial infections declines rapidly due to antibiotic resistance. This problem has stimulated the development of novel antibiotics, but most attempts have failed. Consequently, the idea of mining uncharacterized genes of pathogens to identify potential targets for entirely new classes of antibiotics was proposed. Without knowing the biochemical function of a protein, it is difficult to validate its potential for drug targeting; therefore, the functional characterization of bacterial proteins of unknown function must be accelerated. Here, we present a paradigm for comprehensively predicting the biochemical functions of a large set of proteins encoded by hypothetical genes in human pathogens to identify candidate drug targets. A high-throughput approach based on homology modelling with ten templates per target protein was applied to the set of 2103 P. aeruginosa proteins encoded by hypothetical genes. The >21000 homology modelling results obtained and available biological and biochemical information about several thousand templates were scrutinized to predict the function of reliably modelled proteins of unknown function. This approach resulted in assigning one or often multiple putative functions to hundreds of enzymes, ligand-binding proteins and transporters. New biochemical functions were predicted for 41 proteins whose essential or virulence-related roles in P. aeruginosa were already experimentally demonstrated. Eleven of them were shortlisted as promising drug targets that participate in essential pathways (maintaining genome and cell wall integrity), virulence-related processes (adhesion, cell motility, host recognition) or antibiotic resistance, which are general drug targets. These proteins are conserved in other WHO priority pathogens but not in humans; therefore, they represent high-potential targets for preclinical studies. These and many more biochemical functions assigned to uncharacterized proteins of P. aeruginosa, made available as PaPUF database, may guide the design of experimental screening of inhibitors, which is a crucial step towards the validation of the highest-potential targets for the development of novel drugs against P. aeruginosa and other high-priority pathogens.

Whole-Genome Sequencing of Lactobacillus helveticus D75 and D76 Confirms Safety and Probiotic Potential

Whole-genome DNA sequencing of Lactobacillus D75 and D76 strains (Vitaflor, Russia) was determined using the PacBio RS II platform, which was followed by de novo assembly with SMRT Portal 2.3.0. The average nucleotide identity (ANI) test showed that both strains belong to the Lactobacillus helveticus, but not to the L. acidophilus, as previously assumed. In addition, 31 exopolysaccharide (EPS) production genes (nine of which form a single genetic cluster), 13 adhesion genes, 38 milk protein and 11 milk sugar utilization genes, 13 genes for and against specific antagonistic activity, eight antibiotic resistance genes, and also three CRISPR blocks and eight Cas I-B system genes were identified in the genomes of both strains. The expression of bacteriocin helveticin J genes was confirmed. In fact, the presence of identified genes suggests that L. helveticus D75 and D76 are able to form biofilms on the outer mucin layer, inhibit the growth of pathogens and pathobionts, utilize milk substrates with the formation of digestible milk sugars and bioactive peptides, resist bacteriophages, show some genome-determined resistance to antibiotics, and stimulate the host’s immune system. Pathogenicity genes have not been identified. The study results confirm the safety and high probiotic potential of the strains.