David P. Nusinow

Current methods used for measuring amino acid side-chain reactivity lack the throughput needed to screen large chemical libraries for interactions across the proteome. Here we redesigned the workflow for activity-based protein profiling of reactive cysteine residues by using a smaller desthiobiotin-based probe, sample multiplexing, reduced protein starting amounts and software to boost data acquisition in real time on the mass spectrometer. Our method, streamlined cysteine activity-based protein profiling (SLC-ABPP), achieved a 42-fold improvement in sample throughput, corresponding to profiling library members at a depth of >8,000 reactive cysteine sites at 18 min per compound. We applied it to identify proteome-wide targets of covalent inhibitors to mutant Kirsten rat sarcoma (KRAS)^G12C and Bruton’s tyrosine kinase (BTK). In addition, we created a resource of cysteine reactivity to 285 electrophiles in three human cell lines, which includes >20,000 cysteines from >6,000 proteins per line. The goal of proteome-wide profiling of cysteine reactivity across thousand-member libraries under several cellular contexts is now within reach.

We recently reported the quantitative proteomics of 375 samples as part of the Cancer Cell Line Encyclopedia (Nusinow et al., 2020). Mass spectrometry-based proteomics data is broadly unfamiliar to most biologists in our experience, resulting in questions from analysts about how to use the data. From the proteomics community there was interest about how we normalized the data, as the scope of this project was so much larger than what has been commonly available. This paper serves as a guide to the data set to answer these questions and acts as a supplement to the main manuscript. The first part addresses users of the data, describing the experimental design, interpretation of the values, and dealing with standard issues in proteomics like multiple protein isoforms per gene and missing values. The second part of the manuscript details how we arrived at the normalization procedure reported in the paper, including the diagnostics used to assess multiple normalization schemes.

SUMMARY: Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins – half the proteome – in 293T cells and includes 118,162 interactions among 14,586 proteins; the second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome at unprecedented scale, encoding protein function, localization, and complex membership. Their comparison validates thousands of interactions and reveals extensive customization of each network. While shared interactions reside in core complexes and involve essential proteins, cell-specific interactions bridge conserved complexes, likely ‘rewiring’ each cell’s interactome. Interactions are gained and lost in tandem among proteins of shared function as the proteome remodels to produce each cell’s phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.

Quantitative proteomics employing isobaric reagents has been established as a powerful tool for biological discovery. Current workflows often utilize a dedicated quantitative spectrum to improve quantitative accuracy and precision. A consequence of this approach is a dramatic reduction in the spectral acquisition rate, which necessitates the use of additional instrument time to achieve comprehensive proteomic depth. This work assesses the performance and benefits of online and real-time spectral identification in quantitative multiplexed workflows. A Real-Time Search (RTS) algorithm was implemented to identify fragment spectra within milliseconds as they are acquired using a probabilistic score and to trigger quantitative spectra only upon confident peptide identification. The RTS-MS³ was benchmarked against standard workflows using a complex two-proteome model of interference and a targeted 10-plex comparison of kinase abundance profiles. Applying the RTS-MS³ method provided the comprehensive characterization of a 10-plex proteome in 50% less acquisition time. These data indicate that the RTS-MS³ approach provides dramatic performance improvements for quantitative multiplexed experiments.

Shamil Sunyaev, David Beier and colleagues report an analysis of the fitness effects of heterozygous protein-truncating variants from the Exome Aggregation Consortium. They find that high heterozygous selection coefficients are enriched in Mendelian disease-associated genes and essential mouse genes, suggesting that this coefficient can be used to prioritize candidate disease-associated genes from clinical exome-sequencing data. The evolutionary cost of gene loss is a central question in genetics and has been investigated in model organisms and human cell lines1,2,3. In humans, tolerance of the loss of one or both functional copies of a gene is related to the gene's causal role in disease. However, estimates of the selection and dominance coefficients in humans have been elusive. Here we analyze exome sequence data from 60,706 individuals4 to make genome-wide estimates of selection against heterozygous loss of gene function. Using this distribution of selection coefficients for heterozygous protein-truncating variants (PTVs), we provide corresponding Bayesian estimates for individual genes. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, Mendelian disease-associated genes, and regulators of transcription. Screening by essentiality, we find a large set of genes under strong selection that are likely to have crucial functions but have not yet been thoroughly characterized.

In shotgun proteomics experiments, modified peptides account for a large part of the unassigned spectra and can be identified using ultra-tolerant database searches. Fewer than half of all tandem mass spectrometry (MS/MS) spectra acquired in shotgun proteomics experiments are typically matched to a peptide with high confidence. Here we determine the identity of unassigned peptides using an ultra-tolerant Sequest database search that allows peptide matching even with modifications of unknown masses up to ± 500 Da. In a proteome-wide data set on HEK293 cells (9,513 proteins and 396,736 peptides), this approach matched an additional 184,000 modified peptides, which were linked to biological and chemical modifications representing 523 distinct mass bins, including phosphorylation, glycosylation and methylation. We localized all unknown modification masses to specific regions within a peptide. Known modifications were assigned to the correct amino acids with frequencies >90%. We conclude that at least one-third of unassigned spectra arise from peptides with substoichiometric modifications.

Multiplexed quantitation via isobaric chemical tags (e.g., tandem mass tags (TMT) and isobaric tags for relative and absolute quantitation (iTRAQ)) has the potential to revolutionize quantitative proteomics. However, until recently the utility of these tags was questionable due to reporter ion ratio distortion resulting from fragmentation of coisolated interfering species. These interfering signals can be negated through additional gas-phase manipulations (e.g., MS/MS/MS (MS3) and proton-transfer reactions (PTR)). These methods, however, have a significant sensitivity penalty. Using isolation waveforms with multiple frequency notches (i.e., synchronous precursor selection, SPS), we coisolated and cofragmented multiple MS2 fragment ions, thereby increasing the number of reporter ions in the MS3 spectrum 10-fold over the standard MS3 method (i.e., MultiNotch MS3). By increasing the reporter ion signals, this method improves the dynamic range of reporter ion quantitation, reduces reporter ion signal variance, and ultimately produces more high-quality quantitative measurements. To demonstrate utility, we analyzed biological triplicates of eight colon cancer cell lines using the MultiNotch MS3 method. Across all the replicates we quantified 8 378 proteins in union and 6 168 proteins in common. Taking into account that each of these quantified proteins contains eight distinct cell-line measurements, this data set encompasses 174 704 quantitative ratios each measured in triplicate across the biological replicates. Herein, we demonstrate that the MultiNotch MS3 method uniquely combines multiplexing capacity with quantitative sensitivity and accuracy, drastically increasing the informational value obtainable from proteomic experiments.