Felipe da Veiga Leprevost

University of Michigan–Ann Arbor

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Alexey I. Nesvizhskii

University of Michigan–Ann Arbor

Bing Zhang

Baylor College of Medicine

Weiping Ma

Icahn School of Medicine at Mount Sinai

Francesca Petralia

Icahn School of Medicine at Mount Sinai

Henry Rodriguez

National Cancer Institute

Marcin Cieślik

Michigan Center for Translational Pathology

Mehdi Mesri

Cancer Clinic

Pei Wang

Anhui University of Science and Technology

Maciej Wiznerowicz

International Institute for Molecular Oncology

Amanda G. Paulovich

Fred Hutch Cancer Center

Cooperative Institutions

University of Michigan–Ann Arbor

139

Washington University in St. Louis

Fusion Academy

Johns Hopkins University

Icahn School of Medicine at Mount Sinai

Fusion (United States)

Clinical Research Consortium

Harvard University

James S. McDonnell Foundation

Broad Institute

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Proteogenomic Characterization of Endometrial Carcinoma

Cell (2020)

Yongchao Dou Emily Kawaler Daniel Cui Zhou Marina Gritsenko Chen Huang

We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/β-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets.

Druggability

10.1016/j.cell.2020.01.026

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Citations (367)

Autoinhibited kinesin-1 adopts a hierarchical folding pattern

Zhenyu Tan Yang Yue Felipe da Veiga Leprevost Sarah E. Haynes Venkatesha Basrur

Conventional kinesin-1 is the primary anterograde motor in cells for transporting cellular cargo. While there is a consensus that the C-terminal tail of kinesin-1 inhibits motility, the molecular architecture of a full-length autoinhibited kinesin-1 remains unknown. Here, we combine cross-linking mass spectrometry (XL-MS), electron microscopy (EM), and AlphaFold structure prediction to determine the architecture of the full-length autoinhibited kinesin-1 homodimer [kinesin-1 heavy chain (KHC)] and kinesin-1 heterotetramer [KHC bound to kinesin light chain 1 (KLC1)]. Our integrative analysis shows that kinesin-1 forms a compact, bent conformation through a break in coiled coil 3. Moreover, our XL-MS analysis demonstrates that kinesin light chains stabilize the folded inhibited state rather than inducing a new structural state. Using our structural model, we show that disruption of multiple interactions between the motor, stalk, and tail domains is required to activate the full-length kinesin-1. Our work offers a conceptual framework for understanding how cargo adaptors and microtubule-associated proteins relieve autoinhibition to promote activation.

Kinesin

Coiled coil

Folding (DSP implementation)

10.7554/elife.86776.2

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Citations (2)

Proteogenomic data and resources for pan-cancer analysis

Cancer Cell (2023)

Yize Li Yongchao Dou Felipe da Veiga Leprevost Yifat Geffen Anna Calinawan

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Proteogenomics

Harmonization

Proteome

10.1016/j.ccell.2023.06.009

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Citations (70)

HLA-Glyco: A large-scale interrogation of the glycosylated immunopeptidome

bioRxiv (Cold Spring Harbor Laboratory) (2022)

Georges Bedran Daniel A. Polasky Yi Hsiao Fengchao Yu Felipe da Veiga Leprevost

Abstract MHC-associated peptides (MAPs) bearing post-translational modifications (PTMs) have raised intriguing questions regarding their attractiveness for targeted therapies. Here, we developed a novel computational glyco-immunopeptidomics workflow that integrates the ultrafast glycopeptide search of MSFragger with a glycopeptide-focused false discovery rate (FDR) control. We performed a harmonized analysis of 8 large-scale publicly available studies and found that glycosylated MAPs are predominantly presented by the MHC class II. We created HLA-Glyco, a resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. Our comprehensive resource reveals high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between classical HLA class II allele groups. To support the nascent field of glyco-immunopeptidomics, we include the optimized workflow in the FragPipe suite and provide HLA-Glyco as a free web resource.

False Discovery Rate

10.1101/2022.12.05.519200

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Citations (1)

Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 Workshop report

Journal of Proteomics (2013)

Yasset Pérez‐Riverol Henning Hermjakob Oliver Kohlbacher Lennart Martens David M. Creasy

Cheminformatics

Identification

10.1016/j.jprot.2013.01.019

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Citations (19)

Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry

Analytical Chemistry (2020)

Kyung-Cho Cho David Clark Michael Schnaubelt Guo Ci Teo Felipe da Veiga Leprevost

Methodologies that facilitate high-throughput proteomic analysis are a key step toward moving proteome investigations into clinical translation. Data independent acquisition (DIA) has potential as a high-throughput analytical method due to the reduced time needed for sample analysis, as well as its highly quantitative accuracy. However, a limiting feature of DIA methods is the sensitivity of detection of low abundant proteins and depth of coverage, which other mass spectrometry approaches address by two-dimensional fractionation (2D) to reduce sample complexity during data acquisition. In this study, we developed a 2D-DIA method intended for rapid- and deeper-proteome analysis compared to conventional 1D-DIA analysis. First, we characterized 96 individual fractions obtained from the protein standard, NCI-7, using a data-dependent approach (DDA), identifying a total of 151,366 unique peptides from 11,273 protein groups. We observed that the majority of the proteins can be identified from just a few selected fractions. By performing an optimization analysis, we identified six fractions with high peptide number and uniqueness that can account for 80% of the proteins identified in the entire experiment. These selected fractions were combined into a single sample which was then subjected to DIA (referred to as 2D-DIA) quantitative analysis. Furthermore, improved DIA quantification was achieved using a hybrid spectral library, obtained by combining peptides identified from DDA data with peptides identified directly from the DIA runs with the help of DIA-Umpire. The optimized 2D-DIA method allowed for improved identification and quantification of low abundant proteins compared to conventional unfractionated DIA analysis (1D-DIA). We then applied the 2D-DIA method to profile the proteomes of two breast cancer patient-derived xenograft (PDX) models, quantifying 6,217 and 6,167 unique proteins in basal- and luminal- tumors, respectively. Overall, this study demonstrates the potential of high-throughput quantitative proteomics using a novel 2D-DIA method.

Proteome

Sample Preparation

Sample (material)

10.1021/acs.analchem.9b04418

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Citations (27)

Identification of LMAN1 and SURF4 dependent secretory cargoes

bioRxiv (Cold Spring Harbor Laboratory) (2023)

Vi T. Tang Prabhodh S. Abbineni Felipe da Veiga Leprevost Venkatesha Basrur Brian T. Emmer

Most proteins secreted into the extracellular space are first recruited from the endoplasmic reticulum into coat protein complex II (COPII)-coated vesicles or tubules that facilitate their transport to the Golgi apparatus. Although several secreted proteins have been shown to be actively recruited into COPII vesicles/tubules by the cargo receptors LMAN1 and SURF4, the full cargo repertoire of these receptors is unknown. We now report mass spectrometry analysis of conditioned media and cell lysates from HuH7 cells CRISPR targeted to inactivate the LMAN1 or SURF4 gene. We found that LMAN1 has limited clients in HuH7 cells whereas SURF4 traffics a broad range of cargoes. Analysis of putative SURF4 cargoes suggests that cargo recognition is governed by complex mechanisms rather than interaction with a universal binding motif.

COPII

COPI

Secretory protein

10.1101/2023.04.06.535922

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Citations (1)

Proteome Analysis of Formalin-Fixed Paraffin-Embedded Tissues from a Primary Gastric Melanoma and its Meningeal Metastasis: A Case Report

Current Topics in Medicinal Chemistry (2013)

Juliana de Saldanha da Gama Fischer Nathalie Henriques Silva Canêdo Katia Goncalves Leila Chimelli Monique França

Melanoma is the third most common brain metastasis cause in the United States as it has a relatively high susceptibility to metastasize to the central nervous system. Among the different origins for brain metastasis, those originating from primary gastric melanomas are extremely rare. Here, we compare protein profiles obtained from formalin-fixed paraffin- embedded (FFPE) tissues of a primary gastric melanoma with its meningeal metastasis. For this, the contents of a microscope slide were scraped and ultimately analyzed by nano-chromatography coupled online with tandem mass spectrometry using an Orbitrap XL. Our results disclose 184 proteins uniquely identified in the primary gastric melanoma, 304 in the meningeal metastasis, and 177 in common. Notably, we indentified several enzymes related to changes in the metabolism that are linked to producing energy by elevated rates of glycolysis in a process called the Warburg effect. Moreover, we show that our FFPE proteomic approach allowed identification of key biological markers such as the S100 protein that we further validated by immunohistochemistry for both, the primary and metastatic tumor samples. That said, we demonstrated a powerful strategy to retrospectively mine data for aiding in the understanding of metastasis, biomarker discovery, and ultimately, diseases. To our knowledge, these results disclose for the first time a comparison of the proteomic profiles of gastric melanoma and its corresponding meningeal metastasis. Keywords: Gastric melanoma, proteomic, meningeal metastasis.

Proteome

Primary (astronomy)

10.2174/1568026613666131204114218

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Citations (2)

Philosopher: a versatile toolkit for shotgun proteomics data analysis

Nature Methods (2020)

Felipe da Veiga Leprevost Sarah E. Haynes Dmitry M. Avtonomov Hui-Yin Chang Avinash Kumar Shanmugam

Shotgun proteomics

Shotgun

10.1038/s41592-020-0912-y

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