Haiyuan Yu, PhD, professor in the Department of Computational Biology and Weill Institute for Cell and Molecular Biology at Cornell University will deliver the talk titled, "Dissect Global Dynamics of Protein Interactome and Gene Regulation across Human Populations and in Disease".

Abstract: ECLAIR

Protein-protein interactions facilitate much of known cellular function. While simply knowing which proteins interact with each other provides valuable information to spur functional studies, far more specific hypotheses can be tested if the spatial contacts of interacting proteins are known. However, co-crystal structures and homology models cover only ~10% of all known human interactions. To solve this issue, we developed ECLAIR (Ensemble Classifier Learning Algorithm to predict Interface Residues), a unified machine learning framework that we used to create the first multi-scale whole-proteome structural interactome in human for all experimentally-determined binary interactions reported in major databases (Nature Methods, 2018). We demonstrate that our 3D interactome approach offers a generalizable interactome-based framework for prioritizing missense mutations that contribute risk to human disease and for understanding their mechanisms at the molecular level by analyzing 2,821 de novo missense mutations identified from whole-exome-sequencing of ~2,500 families from the Simons Simplex Collection (Nature Genetics, 2018), as well as 6,542 mutations from Autism Sequencing Consortium (Molecular Autism, 2021). Given the current global pandemic, we quickly applied our ECLAIR pipeline to generate a comprehensive multiscale 3D SARS-CoV-2-Human interactome for better understanding of COVID-19 etiology and comorbidities by examining the impact on these viral-host interactions by sequence divergences of SARS-CoV-1 and SARS-CoV-2, and human population variants. By comparing predicted binding sites on human proteins for binding with SARS-CoV-2 proteins and FDA approved drugs, we were able to prioritize a set of drugs as potential antiviral treatment (Nature Methods, in press).

Unlike any current interactome-mapping methods (Y2H or AP-MS), cross-linking mass spectrometry (XL-MS) has the unique capability to detect cell-type-specific/condition-specific protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a “MS2-centric” approach and suffers from a high rate of mis-identified crosslinks. We developed a novel “MS3-centric” search engine named MaXLinker, which outperforms the currently popular algorithms with a significantly lower mis-identification rate, and superior sensitivity and specificity (Molecular & Cellular Proteomics, 2020). Furthermore, we find that current XL-MS validation strategies are fundamentally flawed for proteome-scale studies, potentially leading to massive underestimation of error rates (Nature Methods, 2020). Finally, we performed human proteome-wide crosslinking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9,319 unique cross-links at 1% false discovery rate, comprising 8,051 intraprotein and 1,268 interprotein cross-links.

Distal enhancer elements remain one of the least understood genomic entities despite decades of research demonstrating their pivotal roles in development and disease. Recently, we developed the PROcap assay that is capable of detecting transcription start sites (TSSs) genome wide with at least an order of magnitude higher sensitivity than other assays. Since numerous chromatin features have been proposed to mark distal enhancer elements, we performed systematic and functional comparisons of enhancer predictions using our improved eSTARR-seq assay. Our results indicate gene-distal divergent TSSs detected by PRO-cap are a robust predictor of enhancer activity, with higher specificity than histone modifications. We propose a model of regulatory elements defined by divergent TSS boundaries, validate that these boundaries are necessary and sufficient to capture enhancer activities genome wide (Nature Genetics, 2020).