权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Technology Research and Development Project 3: Spectral archives and spectral networks

技术研发项目3：光谱档案和光谱网络

基本信息

批准号：
9303407
负责人：
Nuno Bandeira
金额：
$ 12.25万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/9303407
关键词：
Address Adoption Algorithms Amino Acid Sequence Amino Acids Antibiotics Antibodies Antibody Repertoire Archives Biological Biological Markers Cancer Patient Cataract Chemicals Clinical Collaborations Communities Complex Computer software Consensus Cyclic Peptides Data Databases Development Dictionary Disease Drug toxicity Fingerprint Genomics Goals Histone Code Infection Institution Libraries Mainstreaming Malignant Neoplasms Mass Spectrum Analysis Modernization Modification Molecular Monoclonal Antibodies Mutation Natural Products Network-based Oncogenes Peptides Post-Translational Protein Processing Probability Proteins Proteomics Research Personnel Route Scientist Services Structure System Techniques Technology Therapeutic Time Tissues base biomedical scientist blind breast cancer vaccine combinatorial computerized tools human disease improved molecular sequence database next generation novel strategies novel therapeutics oral microbiome polyclonal antibody protein protein interaction proteogenomics repository research and development student training success tool

项目摘要

Project Summary Mass spectrometry is based on fragmenting biological molecules into smaller pieces, and using the fragment masses as a fingerprint for identifying and quantifying bio-molecules. It is the dominant technology for studying active molecules in healthy and diseased tissue, and identifying protein targets and natural products for novel therapeutics. When the initial proposal Center for Computational Mass Spectrometry (CCMS) was submitted in 2007, the lack of adequate computational tools for analyzing mass spectrometry data was the the key bottleneck. With great success in enabling applications of new experimental techniques such as FTMS, ETD, HCD, top-down mass spectrometry, and many others, the mandate of CCMS continues to be the development of next generation computational technologies and to apply them to open experimental. In this proposal, we will capitalize on our recent results in diverse subfields of computational proteomics and will further branch into previously unexplored MS applications. We will focus specifically on bridging proteomics and genomics technologies using 6 technology research and development platforms. Specifically, we will (a) apply proteogenomics approach for the discovery of abberant cancer genes and analyzing antibody repertoires; (b) sequence natural antibiotics; (c) collate spectral data through spectral archives and networks; (d) develop universal tools for peptide identification; (e) develop tools for top-down proteomics; and, (f) analyzing multiplexed spectra. The technology platforms are driven by a multitude of collaborative biomedical studies where the use of CCMS developed tools is essential for their success. These studies include (a) unraveling the combinatorial histone code in human diseases; (b) a proteogenomics approach to studies of oral microbiome and polybacterial infections; (c) detecting inter-species chemical interactions; (d) developing a systems approach towards the therapeutic modulation of the acetylome ; (e) developing tools for monoclonal and polyclonal antibody sequencing; (f) development of breast cancer vaccines; (g) clinical cancer proteogenomics; (h) discovery of lantibiotics; (i) discovering proteomic biomarkers for drug toxicity in cancer patients; and, (j) identifying protein-protein interactions and post-translational modifications in cataractous lens. These projects require three-way collaborative efforts on a wide range of topics involving biomedical scientists, mass spectrometrists, and computational scientists from various institutions. CCMS will also train students and practicing scientists from all over the world in computational proteomics, and educate the proteomics community about modern computational mass spectrometry to encourage its wide adoption.

项目总结