Biology subjects

Kustatscher, G.

Publications and source records attributed to Kustatscher, G..

5 recordsLinked to original sources

bioRxiv2024

hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments

Macromolecular protein complexes carry out most functions in the cell including essential functions required for cell survival. Unfortunately, we lack the subunit composition for all human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify > 15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing [~]75% of human proteins into their physical contexts. We globally characterize our complexes using protein co-variation data (ProteomeHD.2) and identify co-varying complexes suggesting common functional associations. Our map also generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 511 mutually exclusive protein pairs in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBIs Complex Portal (https://www.ebi.ac.uk/complexportal/home) as well as provide complexes through our hu.MAP3.0 web interface (https://humap3.proteincomplexes.org/). We expect our resource to be highly impactful to the broader research community.

systems biology↗

bioRxiv2022

The Proteomic Landscape of Genome-Wide Genetic Perturbations

Functional genomic strategies help to address the genotype phenotype problem by annotating gene function and regulatory networks. Here, we demonstrate that combining functional genomics with proteomics uncovers general principles of protein expression, and provides new avenues to annotate protein function. We recorded precise proteomes for all non-essential gene knock-outs in Saccharomyces cerevisiae. We find that protein abundance is driven by a complex interplay of i) general biological properties, including translation rate, turnover, and copy number variations, and ii) their genetic, metabolic and physical interactions, including membership in protein complexes. We further show that combining genetic perturbation with proteomics provides complementary dimensions of functional annotation: proteomic profiling, reverse proteomic profiling, profile similarity and protein covariation analysis. Thus, our study generates a resource in which nine million protein quantities are linked to 79% of the yeast coding genome, and shows that functional proteomics reveals principles that govern protein expression. Highlights- Nine million protein quantities recorded in ~4,600 non-essential gene deletions in S. cerevisiae reveal principles of how the proteome responds to genetic perturbation - Genome-scale protein expression is determined by both functional relationships between proteins, as well as common biological responses - Broad protein expression profiles in slow-growing strains can be explained by chromosomal aneuploidies - Protein half-life and ribosome occupancy are predictable from protein abundance changes across knock-outs - Functional proteomics annotates missing gene function in four complementary dimensions

systems biology↗

bioRxiv2021

Mapping the invisible chromatin transactions of prophase chromosome remodelling

We have used a combination of chemical genetics, chromatin proteomics and imaging to map the earliest chromatin transactions during vertebrate cell entry into mitosis. Chicken DT40 CDK1as cells undergo synchronous mitotic entry within 15 minutes following release from a 1NM-PP1-induced arrest in late G2. In addition to changes in chromatin association with nuclear pores and the nuclear envelope, earliest prophase is dominated by changes in the association of ribonucleoproteins with chromatin, particularly in the nucleolus, where pre-rRNA processing factors leave chromatin significantly before RNA polymerase I. Nuclear envelope barrier function is lost early in prophase and cytoplasmic proteins begin to accumulate on the chromatin. As a result, outer kinetochore assembly appears complete by nuclear envelope breakdown (NEBD). Most interphase chromatin proteins remain associated with chromatin until NEBD, after which their levels drop sharply. An interactive proteomic map of chromatin transactions during mitotic entry is available as a resource at https://mitoChEP.bio.ed.ac.uk.

cell biology↗

bioRxiv2019

The human proteome co-regulation map reveals functional relationships between proteins

The annotation of protein function is a longstanding challenge of cell biology that suffers from the sheer magnitude of the task. Here we present ProteomeHD, which documents the response of 10,323 human proteins to 294 biological perturbations, measured by isotope-labelling mass spectrometry. Using this data matrix and robust machine learning we create a co-regulation map of the cell that reflects functional associations between human proteins. The map identifies a functional context for many uncharacterized proteins, including microproteins that are difficult to study with traditional methods. Co-regulation also captures relationships between proteins which do not physically interact or co-localize. For example, co-regulation of the peroxisomal membrane protein PEX11{beta} with mitochondrial respiration factors led us to discover a novel organelle interface between peroxisomes and mitochondria in mammalian cells. The co-regulation map can be explored at www.proteomeHD.net.

systems biology↗

bioRxiv2019

treeClust improves protein co-regulation analysis due to robust selectivity for close linear relationships

Gene co-expression analysis is a widespread method to identify the potential biological function of uncharacterised genes. Recent evidence suggests that proteome profiling may provide more accurate results than transcriptome profiling. However, it is unclear which statistical measure is best suited to detect proteins that are co-regulated. We have previously shown that expression similarities calculated using treeClust, an unsupervised machine-learning algorithm, outperformed correlation-based analysis of a large proteomics dataset. The reason for this improvement is unknown. Here we systematically explore the characteristics of treeClust similarities. Leveraging synthetic data, we find that tree-based similarities are exceptionally robust against outliers and detect only close-fitting, linear protein - protein associations. We then use proteomics data to demonstrate that both of these features contribute to the improved performance of treeClust relative to Pearson, Spearman and robust correlation. Our results suggest that, for large proteomics datasets, unsupervised machine-learning algorithms such as treeClust may significantly improve the detection of biologically relevant protein - protein associations relative to correlation metrics.

systems biology↗