太阳成集团tyc7111cc定量生物学中心

学术报告 

题    目: Mapping human interactome in human neurons with structural details for studying neurological disorders

报告人:  Professor Haiyuan Yu

Department of Computational Biology and Weill Institute for Cell and Molecular Biology, Cornell University

时  间: 7月23日（周二）13:00-14:00

地    点: 吕志和楼B101

主持人: 韩敬东 教授

摘要:

Almost all human proteins function through interacting with other proteins. In the past decade, a tremendous amount of resources and effort have been invested into building a comprehensive interactome network for human proteins, especially systematic interactome mapping efforts using yeast two-hybrid (Y2H) and using affinity-purification-followed-by-mass-spectrometry (AP-MS) (mostly in HEK293T and HCT116 cells). These interactome maps have been widely used in biomedical research and proven to be extremely informative for generating insights for hypothesis-driven research and help produce numerous mechanistic discoveries. However, one key limitation of these interactome mapping efforts is that the protein-protein interactions are mapped in cells (mostly yeast or HEK293T cells) that are not relevant to brain cell types in neurological disorders, especially autism spectrum disorders (ASD) and Alzheimer’s Disease (AD). Using the latest breakthrough in quantitive proteomics technologies, we have developed a high-throughput data-independent-acquisition (DIA) label-free-quantification (LFQ) AP-MS pipeline to map interactome networks for all currently-known ASD (121) and AD (127) risk genes.

Currently, only <10% of all known human interactions have any structural information. To solve this issue, we developed a unified deep learning framework, named PIONEER, to create a multiscale full-coverage structural interactome in human for all known protein interactions reported in the literature. We demonstrate that PIONEER outperforms existing state-of-the-art methods, and is >5500′ faster than AlphaFold-Multimer. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions enriched with PIONEER-predicted interface mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types.

报告人简介：

Yu is the Tisch University Professor in the Department of Computational Biology and Weill Institute for Cell and Molecular Biology, also the founding Director of Center for Innovative Proteomics (CIP) at Cornell University.

Haiyuan Yu performs research in the broad areas of Network Systems Biology. The Yu group uses integrated computational-experimental systems biology approaches to determine protein interactions and complex structures on the scale of the whole cell. In particular, his group focuses on protein-protein and gene regulatory networks and seeks to understand how such intricate systems evolve and how their perturbations lead to human disease, especially Autism Spectrum Disorder, Alzheimer’s Disease, and cancer. Towards these goals, Haiyuan led his group to develop the concept of “3D structurally-resolved interactome networks”, where they integrate multi-scale structural modeling, machine learning, and high-throughput genomics/proteomics experiments to determine protein interactions and their binding interfaces on the whole proteome scale. More recently, Haiyuan is leading his group to generate comprehensive brain cell-type-specific interactome maps using cutting-edge quantitative proteomics approaches in neurons, microglia and other cell types. Furthermore, in close collaboration with John Lis and his group, the Yu group demonstrate that enhancer RNAs (eRNAs) detected by the novel PRO-cap assay is a critical assay for active enhancers genome-wide. PRO-cap has great sensitivity and specificity, among all RNA-sequencing assays to detect eRNAs (thus active enhancers) across the whole genome with high resolution.