DIP-MS: ultra-deep interaction proteomics for the deconvolution of protein complexes.

Most proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing most cellular processes. Affinity purification of the protein of interest combined with liquid chromatography coupled to tandem mass spectrometry (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can, however, not be resolved from a single AP-MS experiment but requires computational inference from multiple time- and resource-intensive reciprocal AP-MS experiments. Here we introduce deep interactome profiling by mass spectrometry (DIP-MS), which combines AP with blue-native-PAGE separation, data-independent acquisition with mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organization of the human prefoldin family of complexes, resolving distinct prefoldin holo- and subcomplex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution.

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data.

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics.

Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

Rapid Profiling of Protein Complex Reorganization in Perturbed Systems.

Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition, or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information. Methods based on cofractionation paired with mass spectrometry have demonstrated the capability for deep biological insight, but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. There has been little uptake of this strategy into the broader life science community despite its rich biological information content. We present a rapid integrated experimental and computational workflow to assess the reorganization of protein complexes across multiple cellular states. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in a complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and subsequent stimulation of macrophage cells with lipopolysaccharide. We observed substantial proteome reorganization on differentiation and less pronounced changes in macrophage stimulation. We establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization.

The interactome of KRAB zinc finger proteins reveals the evolutionary history of their functional diversification.

Krüppel-associated box (KRAB)-containing zinc finger proteins (KZFPs) are encoded in the hundreds by the genomes of higher vertebrates, and many act with the heterochromatin-inducing KAP1 as repressors of transposable elements (TEs) during early embryogenesis. Yet, their widespread expression in adult tissues and enrichment at other genetic loci indicate additional roles. Here, we characterized the protein interactome of 101 of the ~350 human KZFPs. Consistent with their targeting of TEs, most KZFPs conserved up to placental mammals essentially recruit KAP1 and associated effectors. In contrast, a subset of more ancient KZFPs rather interacts with factors related to functions such as genome architecture or RNA processing. Nevertheless, KZFPs from coelacanth, our most distant KZFP-encoding relative, bind the cognate KAP1. These results support a hypothetical model whereby KZFPs first emerged as TE-controlling repressors, were continuously renewed by turnover of their hosts' TE loads, and occasionally produced derivatives that escaped this evolutionary flushing by development and exaptation of novel functions.

MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics.

Data-independent acquisition-mass spectrometry (DIA-MS) is the method of choice for deep, consistent, and accurate single-shot profiling in bottom-up proteomics. While classic workflows for targeted quantification from DIA-MS data require auxiliary data-dependent acquisition (DDA) MS analysis of subject samples to derive prior-knowledge spectral libraries, library-free approaches based on in silico prediction promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses are however limited, in part, by the large library size and persistent deviations from the experimental data. We present MSLibrarian, a new workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library and analysis approach. Predicted-vs-observed comparisons enabled optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups, and optimization of the library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded experiment of species-mixed proteins and quantitative ratio-validation confirmed gains of up to 13% on peptide and 8% on protein level at equivalent FDR control and validation criteria. MSLibrarian is made available as an open-source R software package, including step-by-step user instructions, at https://github.com/MarcIsak/MSLibrarian.

Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, as exemplified by the technique SWATH-MS, has emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale data sets. We demonstrate that statistical concepts developed for discovery proteomics based on spectrum-centric scoring can be adapted to large-scale DIA experiments that have been analyzed with peptide-centric scoring strategies, and we provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent the accumulation of false positives across large-scale data sets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for the detected peptide queries, peptides and inferred proteins.

Complex-centric proteome profiling by SEC-SWATH-MS.

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error-controlled, complex-centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub-complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.

Engineering of formate dehydrogenase: synergistic effect of mutations affecting cofactor specificity and chemical stability.

Formate dehydrogenases (FDHs) are frequently used for the regeneration of cofactors in biotransformations employing NAD(P)H-dependent oxidoreductases. Major drawbacks of most native FDHs are their strong preference for NAD(+) and their low operational stability in the presence of reactive organic compounds such as α-haloketones. In this study, the FDH from Mycobacterium vaccae N10 (MycFDH) was engineered in order to obtain an enzyme that is not only capable of regenerating NADPH but also stable toward the α-haloketone ethyl 4-chloroacetoacetate (ECAA). To change the cofactor specificity, amino acids in the conserved NAD(+) binding motif were mutated. Among these mutants, MycFDH A198G/D221Q had the highest catalytic efficiency (k cat/K m) with NADP(+). The additional replacement of two cysteines (C145S/C255V) not only conferred a high resistance to ECAA but also enhanced the catalytic efficiency 6-fold. The resulting quadruple mutant MycFDH C145S/A198G/D221Q/C255V had a specific activity of 4.00 ± 0.13 U mg(-1) and a K m, NADP(+) of 0.147 ± 0.020 mM at 30 °C, pH 7. The A198G replacement had a major impact on the kinetic constants of the enzyme. The corresponding triple mutant, MycFDH C145S/D221Q/C255V, showed the highest specific activity reported to date for a NADP(+)-accepting FDH (v max, 10.25 ± 1.63 U mg(-1)). However, the half-saturation constant for NADP(+) (K m, NADP(+) , 0.92 ± 0.10 mM) was about one order of magnitude higher than the one of the quadruple mutant. Depending on the reaction setup, both novel MycFDH variants could be useful for the production of the chiral synthon ethyl (S)-4-chloro-3-hydroxybutyrate [(S)-ECHB] by asymmetric reduction of ECAA with NADPH-dependent ketoreductases.

Structural basis of M3 muscarinic receptor dimer/oligomer formation.

Class A G protein-coupled receptors (GPCRs) are known to form dimers and/or oligomeric arrays in vitro and in vivo. These complexes are thought to play important roles in modulating class A GPCR function. Many studies suggest that residues located on the "outer" (lipid-facing) surface of the transmembrane (TM) receptor core are critically involved in the formation of class A receptor dimers (oligomers). However, no clear consensus has emerged regarding the identity of the TM helices or TM subsegments involved in this process. To shed light on this issue, we have used the M(3) muscarinic acetylcholine receptor (M3R), a prototypic class A GPCR, as a model system. Using a comprehensive and unbiased approach, we subjected all outward-facing residues (70 amino acids total) of the TM helical bundle (TM1-7) of the M3R to systematic alanine substitution mutagenesis. We then characterized the resulting mutant receptors in radioligand binding and functional studies and determined their ability to form dimers (oligomers) in bioluminescence resonance energy transfer saturation assays. We found that M3R/M3R interactions are not dependent on the presence of one specific structural motif but involve the outer surfaces of multiple TM subsegments (TM1-5 and -7) located within the central and endofacial portions of the TM receptor core. Moreover, we demonstrated that the outward-facing surfaces of most TM helices play critical roles in proper receptor folding and/or function. Guided by the bioluminescence resonance energy transfer data, molecular modeling studies suggested the existence of multiple dimeric/oligomeric M3R arrangements, which may exist in a dynamic equilibrium. Given the high structural homology found among all class A GPCRs, our results should be of considerable general relevance.