系统生物学

Chemical proteomics focuses on the drug–target–phenotype relationship for target deconvolution and elucidation of the mechanism of action—key and bottleneck in drug development and repurposing. Majorly due to the limits of using chemically modified ligands in affinity-based methods, new, unbiased, proteome-wide, and MS-based chemical proteomics approaches have been developed to perform drug target deconvolution, using full proteome profiling and no chemical modification of the studied ligand. Of note among them, thermal proteome profiling (TPP) aims to identify the target(s) by measuring the difference in melting temperatures between each identified protein in drug-treated versus vehicle-treated samples, with the thermodynamic interpretation of “protein melting” and curve fitting of all quantified proteins, at all temperatures, in each biological replicate. Including TPP, all the other chemical proteomics approaches often fail to provide target deconvolution with sufficient proteome depth, statistical power, throughput, and sustainability, which could hardly fulfill the final purpose of drug development. The proteome integral solubility alteration (PISA) assay provides no thermodynamic interpretation, but a throughput 10–100-fold compared to the other proteomics methods, high sustainability, much lower time of analysis and sample amount requirements, high confidence in results, maximal proteome coverage (~10,000 protein IDs), and up to five drugs / test molecules in one assay, with at least biological triplicates of each treatment. Each drug-treated or vehicle-treated sample is split into many fractions and exposed to a gradient of heat as solubility perturbing agent before being recomposed into one sample; each soluble fraction is isolated, then deep and quantitative proteomics is applied across all samples. The proteins interacting with the tested molecules (targets and off-targets), the activated mechanistic factors, or proteins modified during the treatment show reproducible changes in their soluble amount compared to vehicle-treated controls. As of today, the maximal multiplexing capability is 18 biological samples per PISA assay, which enables statistical robustness and flexible experimental design accommodation for fuller target deconvolution, including integration of orthogonal chemical proteomics methods in one PISA assay. Living cells for studying target engagement in vivo or, alternatively, protein extracts to identify in vitro ligand-interacting proteins can be studied, and the minimal need in sample amount unlocks target deconvolution using primary cells and their derived cultures.

Graphical abstract:

Cells sense and respond to mitogens by activating a cascade of signaling events, primarily mediated by tyrosine phosphorylation (pY). Because of its key roles in cellular homeostasis, deregulation of this signaling is often linked to oncogenesis. To understand the mechanisms underlying these signaling pathway aberrations, it is necessary to quantify tyrosine phosphorylation on a global scale in cancer cell models. However, the majority of the protein phosphorylation events occur on serine (86%) and threonine (12%) residues, whereas only 2% of phosphorylation events occur on tyrosine residues (Olsen et al., 2006). The low stoichiometry of tyrosine phosphorylation renders it difficult to quantify cellular pY events comprehensively with high mass accuracy and reproducibility. Here, we describe a detailed protocol for isolating and quantifying tyrosine phosphorylated peptides from drug-perturbed, growth factor-stimulated cancer cells, using immunoaffinity purification and tandem mass tags (TMT) coupled with mass spectrometry.

Protein N-glycosylation plays a vital role in diverse cellular processes, and dysregulated N-glycosylation is implicated in a variety of human diseases including neurodegenerative disorders and cancer. With recent advances in high-resolution mass spectrometry-based glycoproteomics technologies enabling large-scale N-glycoproteome profiling of disease and control samples, analysis of the large datasets has become a challenge. Here, we provide a protocol for the systems-level analysis of in vivo N-glycosylation sites on N-glycosylated proteins and their changes in human disease, such as Alzheimer's disease. The protocol includes quantitation and differential analysis of N-glycopeptide abundance, in addition to integrative N-glycoproteome and proteome data analyses, to determine disease-associated changes in N-glycosylation site occupancy and identify differentially N-glycosylated proteins in human disease versus control samples. This protocol can be modified and applied to study proteome-wide N-glycosylation alterations in response to different cellular stresses or pathophysiological states in other organisms or model systems.

Protein-ligand binding prediction is central to the drug-discovery process. This often follows an analysis of genomics data for protein targets and then protein structure discovery. However, the complexity of performing reproducible protein conformational analysis and ligand binding calculations, using vetted methods and protocols can be a challenge. Here we show how Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE), an open-source web-based compute and analytics platform for computational chemistry developed based on the Galaxy bioinformatics platform, makes protocol sharing seamless following genomics and proteomics. BRIDGE makes available tools and workflows to carry out protein molecular dynamics simulations and accurate free energy computations of protein-ligand binding. We illustrate the dynamics and simulation protocols for predicting protein-ligand binding affinities in silico on the T4 lysozyme system. This protocol is suitable for both novice and experienced practitioners. We show that with BRIDGE, protocols can be shared with collaborators or made publicly available, thus making simulation results and computations independently verifiable and reproducible.

The correct subcellular localization of proteins is vital for cellular function and the study of this process at the systems level will therefore enrich our understanding of the roles of proteins within the cell. Multiple methods are available for the study of protein subcellular localization, including fluorescence microscopy, organelle cataloging, proximity labeling methods, and whole-cell protein correlation profiling methods. We provide here a protocol for the systems-level study of the subcellular localization of the yeast proteome, using a version of hyperplexed Localization of Organelle Proteins by Isotope Tagging (hyperLOPIT) that has been optimized for use with Saccharomyces cerevisiae. The entire protocol encompasses cell culture, cell lysis by nitrogen cavitation, subcellular fractionation, monitoring of the fractionation using Western blotting, labeling of samples with TMT isobaric tags and mass spectrometric analysis. Also included is a brief explanation of downstream processing of the mass spectrometry data to produce a map of the spatial proteome. If required, the nitrogen cavitation lysis and Western blotting portions of the protocol may be performed independently of the mass spectrometry analysis. The protocol in its entirety, however, enables the unbiased, systems-level and high-resolution analysis of the localizations of thousands of proteins in parallel within a single experiment.

Advanced mass spectrometry technology has pushed proteomic analyses to the forefront of biological and biomedical research. Limitations of proteomic approaches now often remain with sample preparations rather than with the sensitivity of protein detection. However, deciphering proteomes and their context-dependent dynamics in subgroups of tissue-embedded cells still poses a challenge, which we meet with a detailed version of our recently established protocol for cell-selective and temporally controllable metabolic labeling of proteins in Drosophila. This method is based on targeted expression of a mutated variant of methionyl-tRNA-synthetase, MetRS^L262G, which allows for charging methionine tRNAs with the non-canonical amino acid azidonorleucine (ANL) and, thus, for detectable ANL incorporation into nascent polypeptide chains.

Protein-protein interactions are at the core of a plethora of developmental, physiological and biochemical processes. Consequently, insights into the origin and evolutionary dynamics of protein-protein interactions may provide information on the constraints and dynamics of specific biomolecular circuits and their impact on the organismal phenotype.

This protocol describes how ancestral protein-protein interaction patterns can be inferred using a set of known protein interactions from phylogenetically informative species. Although this protocol focuses on protein-protein interaction data, character-state reconstructions can in general be performed with other kinds of binary data in the same way.

Protein phosphorylation is one of the most common post-translational modifications in eukaryotic cells and plays a critical role in a vast array of cellular processes. Efficient methods of protein extraction and phosphopeptide purification are required to ensure the detection of high quality of proteins. In our hands, phenol extraction of proteins and TiO₂ chromatography enrich phosphorylated peptides more efficiently than other methods in the moss Physcomitrlla patens (P. patens).

This protocol describes how to extract nuclear protein from mouse lungs, including tissue preparation, stepwise lysis of cells and centrifugal isolation of nuclear protein fraction. This is an efficient method to get comparable nuclear protein extracts from lung tissues.

Leishmania is a genus of trypanosomatid protozoa and is the parasite responsible for the disease leishmaniasis. These protozoa, regulate their gene expression in an atypical way, compared to other higher eukaryotes. The regulation of gene expression is characterized by a predominance of post-transcriptional over pre-transcriptional regulatory mechanisms (Clayton, 2002). Thus proteomic analysis has proven an essential tool for understanding pathways implicated in Leishmania infectivity, host-parasite interactions, drug resistance and others. When employing a comparative proteomics analysis between different parasitic cell lines, it is essential that these lines are cultivated in exactly the same way, in the same cell density and growth phase. More importantly when cell-cycle defects are suspected, it is essential to synchronize cell-lines in the same cell-cycle phase so as to eliminate possible artifacts. This protocol describes the preparation of whole-protein samples for proteomic analysis in Leishmania donovani (L. donovani).