2024.10.19 活动预告 | 揭示隐藏的蛋白质组：利用moPepGen从蛋白质基因组学中全面鉴定非典型肽

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主讲人：诸承昊 UCLA博士后，研究方向为癌症蛋白基因组

主持人：张刘灯 Baylor College of Medicine博士生，研究方向为muli-omics

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活动大纲

1. Cancer molecular profiling: from genomics to proteogenomics

• Overview of cancer genomics: effectiveness and limitations.
• Transition to proteogenomics: Integrating proteomics with genomics for deeper insights.
• Technologies and platforms enabling proteogenomic profiling (e.g., mass spectrometry [MS], next-generation sequencing). Examples from CPTAC studies showcasing proteogenomics in clinical oncology studies.

2. Technology challenges in identifying non-canonical peptides from proteogenomic data

• Definition and importance of non-canonical peptides: unannotated sequences derived from variants.
• Bioinformatics challenges in detecting these peptides in MS data.
• Computational strategies for identifying proteomic consequences for tumor-specific variants.

3. moPepGen: algorithm for predicting comprehensive non-canonical peptides

• Detailed algorithmic approach of moPepGen for predicting non-canonical peptides from genomic data.
• Performance metrics: runtime efficiency and benchmark comparisons with existing tools.

4. moPepGen for detecting non-canonical peptides in proteomics data

• Application of moPepGen in MS-based proteomics for identifying non-canonical peptides.
• Case studies:
  Non-canonical peptide and neoantigen detection in cancer cell lines.
  Identification in kidney cancer DIA proteomics.
  Application to prostate tumor proteomics.

主要内容

Genomics is essential in cancer research, driving patient stratification, biomarker discovery, and treatment target identification. However, limitations such as the variability in treatment responses among patients with identical genomic variants remain. Combining genomics with proteomics opens new avenues to overcome these challenges, providing insights beyond genomic data alone.

By integrating protein abundance and post-translational modifications (PTMs) with genomic data, proteogenomics refines patient classification for better clinical relevance and facilitates the discovery of novel therapeutic targets. Proteogenomics enables identification of neoantigens, essential for immunotherapies, as non-canonical peptides harboring tumor-specific variants. However, identifying these peptides, part of the hidden proteome, remains a key challenge. In this talk, I will introduce moPepGen, a novel bioinformatics algorithmthat generates comprehensive non-canonical peptide databases from genomic data for subsequent identification in proteomics data. Coupled with our custom database search pipeline, moPepGen provides a robust solution for unveiling the hidden proteome, advancing the potential for more effective immunotherapies and precision oncology.

Unveiling the Hidden Proteome: Comprehensive Non-Canonical Peptide Identification from Proteogenomics with moPepGen

• 主讲人： 诸承昊，UCLA博士后，研究方向癌症蛋白基因组
• 时间： 2024年10月19日(周六)11:00-13:00
• 地点： 线上腾讯会议