Abstract LB-242: Proteomic Data Commons: A resource for proteogenomic analysis

The objective of the National Cancer Institutes' Proteomic Data Commons (PDC) is to make cancer-related proteomic datasets accessible to the public. The PDC provides the cancer research community with a unified data repository that enables data sharing across cancer proteomic studies and also enables multi-omic integration in support of precision medicine. As a domain-specific repository within the Cancer Research Data Commons (CRDC), the vision for the PDC is to provide researchers the ability to find and analyze proteomic data across a wide variety of tumor types. Currently, the PDC houses data, supported by a large collection of metadata attributes, for nearly 40 datasets from over 12 cancer types produced by several large-scale cancer research programs, each with cohort sizes greater than 100 patients.

The PDC facilitates the analysis of proteomic, genomic, and imaging data derived from the same tumor. Most of the datasets in the PDC also have corresponding genomic and imaging data available in the Genomic Data Commons and The Cancer Imaging Archive respectively. Researchers can discover which genomic variants are detectable at the protein-level or better understand associations between gene expression, copy number variation, and protein abundance. The resource is currently available to the public in beta phase (https://pdc.esacinc.com) and will be officially launched on the cancer.gov domain in March 2020.

The PDC data portal is supported by a robust and extensible data model and provides user-friendly exploration, visualization and data analysis. This allows researchers to search for and visualize expression of proteins (through their mapped genes) across all studies, analyze protein abundance for all cases in a study through heatmaps, build and explore pan-cancer cohorts using highly curated, clinical metadata, and comprehensively view a study without needing to download the data.

The PDC provides quick access to mapping of peptide identities and quantities on the human genome as well as protein databases containing patient/tumor-specific variants and novel splicing events. It also enables fast, accurate, and convenient proteomic validation of novel genomic alterations through the PepQuery algorithm.

Through a highly versatile application programming interface (API), PDC allows users to interact with data programmatically and facilitates integration with data from other resources in their scripts for multi-omic analysis.

Big data interoperability is critical for progress in precision medicine. PDC is designed to interoperate with other resources including the CRDC nodes, allowing users to analyze PDC data with the tools and pipelines available on the NCI cloud resources. It further allows users to use their own tools to co-analyze genomic and proteomic data available from a common sample on Amazon Web Services (AWS) platform or on a local system.

The presentation will provide an overview of the PDC and it's available datasets, as well as a discussion of how it facilitates multi-omic data analyses.

Citation Format: Ratna Rajesh Thangudu, Paul A. Rudnick, Michael Holck, Deepak Singhal, Michael J. MacCoss, Nathan J. Edwards, Karen A. Ketchum, Christopher R. Kinsinger, Erika Kim, Anand Basu. Proteomic Data Commons: A resource for proteogenomic analysis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-242.

Crystal structure of a feruloyl esterase belonging to the tannase family: a disulfide bond near a catalytic triad

Feruloyl esterase (FAE) catalyzes the hydrolysis of the ferulic and diferulic acids present in plant cell wall polysaccharides, and tannase catalyzes the hydrolysis of tannins to release gallic acid. The fungal tannase family in the ESTHER database contains various enzymes, including FAEs and tannases. Despite the importance of FAEs and tannases in bioindustrial applications, three-dimensional structures of the fungal tannase family members have been unknown. Here, we determined the crystal structure of FAE B from Aspergillus oryzae (AoFaeB), which belongs to the fungal tannase family, at 1.5 Å resolution. AoFaeB consists of a catalytic α/β-hydrolase fold domain and a large lid domain, and the latter has a novel fold. To estimate probable binding models of substrates in AoFaeB, an automated docking analysis was performed. In the active site pocket of AoFaeB, residues responsible for the substrate specificity of the FAE activity were identified. The catalytic triad of AoFaeB comprises Ser203, Asp417, and His457, and the serine and histidine residues are directly connected by a disulfide bond of the neighboring cysteine residues, Cys202 and Cys458. This structural feature, the "CS-D-HC motif," is unprecedented in serine hydrolases. A mutational analysis indicated that the novel structural motif plays essential roles in the function of the active site.

Native and modeled disulfide bonds in proteins: knowledge-based approaches toward structure prediction of disulfide-rich polypeptides

Structure prediction and three-dimensional modeling of disulfide-rich systems are challenging due to the limited number of such folds in the structural databank. We exploit the stereochemical compatibility of substructures in known protein structures to accommodate disulfide bonds in predicting the structures of disulfide-rich polypeptides directly from disulfide connectivity pattern and amino acid sequence in the absence of structural homologs and any other structural information. This knowledge-based approach is illustrated using structure prediction of 40 nonredundant bioactive disulfide-rich polypeptides such as toxins, growth factors, and endothelins available in the structural databank. The polypeptide conformation could be predicted in 35 out of 40 nonredundant entries (87%). Nonhomologous templates could be identified and models could be obtained within 2 A deviation from the query in 29 peptides (72%). This procedure can be accessed from the World Wide Web (http://www.ncbs.res.in/ approximately faculty/mini/dsdbase/dsdbase.html).