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Palkowski, A.

Publications and source records attributed to Palkowski, A..

3 recordsLinked to original sources

Deep Learning-Based Structure Modeling of the Treponema pallidum Proteome: Insights into Pathogenesis and Syphilis Vaccine Development

Treponema pallidum ssp. pallidum, the causative agent of syphilis, has a small proteome and encompasses numerous strains. Knowledge gaps remain in understanding the molecular mechanisms of pathogenesis of this bacterium, as well as the structure and function of the full complement of proteins encoded by T. pallidum. Here, an AI-based structure-to-function modeling workflow was used to investigate the complement of proteins encoded by T. pallidum. High-confidence structure models were generated for 976 T. pallidum proteins, covering 99% of the proteome. Analysis of the generated models using the protein structure comparison server DALI enabled high-confidence, structure-based functional annotation of 877 T. pallidum proteins, including 240 of the 323 proteins of unknown function encoded by this pathogen. Additionally, 63 putative pathogenesis related proteins (PPRPs) and seven treponemal proteins with previously uncharacterized similarity to outer membrane proteins (OMPs) from Gram-negative bacteria were identified. A workflow for B cell epitope (BCE) prediction identified 1133 surface-exposed, host-facing potential epitopes in known and predicted T. pallidum OMPs, of which 92 were prioritized based on bioinformatic analyses, biophysical properties, amino acid sequence conservation, and previous protein expression data. This work provides insight into T. pallidum pathogenesis through structure modeling-based functional annotation, including characterization of proteins of unknown function. This study also informs syphilis vaccine design by identifying new potential T. pallidum OMPs, as well as host-facing regions of T. pallidum OMPs that have conserved amino acid sequences in globally circulating strains. Statement of importance/impactThis study presents the first AI-based global structure modeling-to-function analysis of the proteome of Treponema pallidum, the bacterium that causes syphilis. Structure-based functional predictions of previously uncharacterized proteins, including proteins potentially involved in virulence, provide novel insight into mechanisms of pathogenesis. The work also informs syphilis vaccine development by the identification and structural characterization of new candidate vaccine proteins in globally circulating strains of T. pallidum.

microbiology↗

Expanding the definition of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers with CARMEN

Promiscuity in T-cell antigen landscapes refers to the dual flexibility of peptides binding multiple MHC alleles and MHC alleles presenting diverse arrays of peptides. By understanding how neoantigens are shared across varied HLA backgrounds, promiscuity analysis can inform the selection of cancer-vaccine targets that reach a wider segment of the population and help refine patient stratification for diverse immunotherapies. We expand the concept of promiscuity to encompass peptides, MHC alleles, individuals, populations, and genomic regions. Our CARMEN database release harmonizes data from 72 publications (2,323 samples) across tissue types, with a focus on cancer. Using Gibbs clustering and dimensionality reduction (UMAP), we systematically map promiscuity and immunological versatility across these biological levels. Gene and mutation analysis reveals recurrent cancer mutations in highly promiscuous genomic regions, highly mutated cancer genes that avoid presented regions, and sheds light on genomic regions important to response to immunotherapy.

bioinformatics↗

A comprehensive library of canonical and non-canonical MHC class I antigens for cancer vaccine development.

A longstanding disconnect between the growing number of MHC Class I immunopeptidomic studies and genomic medicine hinders cancer vaccine design. We develop COD-dipp to genomically map the full spectrum of detected canonical and non-canonical (non-exonic) MHC Class I antigens from 26 cancer studies. We demonstrate that patient mutations in regions overlapping physically identified antigens better predict immunotherapy response when compared to neoantigen predictions. We suggest a vaccine design approach using 140,966 highly immune-visible regions of the genome annotated by their expression and haplotype frequency in the human population. These regions tend to be highly conserved, mutated in cancer and harbor 7.8 times more immunogenicity. Intersecting pan-cancer mutations with these immune surveilled regions revealed a potential to create off-the-shelf multi-epitope vaccines against public neoantigens. Here we release COD-dipp, a cancer vaccine toolkit as a web-application (https://www.proteogenomics.ca/COD-dipp) and open-source high-throughput resource.

bioinformatics↗