Proteomics Update and Perspectives from the Proteomics in Cell Biology and Disease Mechanisms Conference

Article Biotechnology & Applied Microbiology

Deep Visual Proteomics defines single-cell identity and heterogeneity

Andreas Mund et al.

Summary: Deep Visual Proteomics combines machine learning, automated image analysis and single-cell proteomics to link protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. It successfully identifies distinct cell states with proteomic profiles and reveals spatial proteome changes in cancer progression.

NATURE BIOTECHNOLOGY (2022)

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Article Multidisciplinary Sciences

Restructured membrane contacts rewire organelles for human cytomegalovirus infection

Katelyn C. Cook et al.

Summary: This study designed a targeted proteomics platform to measure MCS proteins at all organelles simultaneously. Through studying the human cytomegalovirus (HCMV) infection, functional virus-driven alterations of MCS were discovered. Comparisons with other virus infections revealed the importance of contact regulation between organelles for virus replication.

NATURE COMMUNICATIONS (2022)

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Article Biochemical Research Methods

Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition

Trang Le et al.

Summary: Spatial proteomics by fluorescence imaging has become an important tool for researchers, but there is a lack of fast and scalable methods to classify and embed single-cell protein distributions in such images. A machine learning competition was held to develop models for labeling protein patterns of single cells in fluorescent images. The winning models provide subcellular omics tools that can annotate single-cell locations, extract single-cell features, and capture cellular dynamics.

NATURE METHODS (2022)

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Article Multidisciplinary Sciences

Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

Summary: Proteins are essential for life, and accurate prediction of their structures is a crucial research problem. Current experimental methods are time-consuming, highlighting the need for accurate computational approaches to address the gap in structural coverage. Despite recent progress, existing methods fall short of atomic accuracy in protein structure prediction.

NATURE (2021)

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Article Multidisciplinary Sciences