Autopilot: An Online Data Acquisition Control System for the Enhanced High-Throughput Characterization of Intact Proteins

The ability to study organisms by direct analysis of their proteomes without digestion via mass spectrometry has benefited greatly from recent advances in separation techniques, instrumentation, and bioinformatics. However, improvements to data acquisition logic have lagged in comparison. Past workflows for Top Down Proteomics (TDPs) have focused on high throughput at the expense of maximal protein coverage and characterization. This mode of data acquisition has led to enormous overlap in the identification of highly abundant proteins in subsequent LC-MS injections. Furthermore, a wealth of data is left underutilized by analyzing each newly targeted species as unique, rather than as part of a collection of fragmentation events on a distinct proteoform. Here, we present a major advance in software for acquisition of TDP data that incorporates a fully automated workflow able to detect intact masses, guide fragmentation to achieve maximal identification and characterization of intact protein species, and perform database search online to yield real-time protein identifications. On <i>Pseudomonas aeruginosa</i>, the software combines fragmentation events of the same precursor with previously obtained fragments to achieve improved characterization of the target form by an average of 42 orders of magnitude in confidence. When HCD fragmentation optimization was applied to intact proteins ions, there was an 18.5 order of magnitude gain in confidence. These improved metrics set the stage for increased proteome coverage and characterization of higher order organisms in the future for sharply improved control over MS instruments in a project- and lab-wide context

Autopilot: An Online Data Acquisition Control System for the Enhanced High-Throughput Characterization of Intact Proteins

The ability to study organisms by direct analysis of their proteomes without digestion via mass spectrometry has benefited greatly from recent advances in separation techniques, instrumentation, and bioinformatics. However, improvements to data acquisition logic have lagged in comparison. Past workflows for Top Down Proteomics (TDPs) have focused on high throughput at the expense of maximal protein coverage and characterization. This mode of data acquisition has led to enormous overlap in the identification of highly abundant proteins in subsequent LC-MS injections. Furthermore, a wealth of data is left underutilized by analyzing each newly targeted species as unique, rather than as part of a collection of fragmentation events on a distinct proteoform. Here, we present a major advance in software for acquisition of TDP data that incorporates a fully automated workflow able to detect intact masses, guide fragmentation to achieve maximal identification and characterization of intact protein species, and perform database search online to yield real-time protein identifications. On <i>Pseudomonas aeruginosa</i>, the software combines fragmentation events of the same precursor with previously obtained fragments to achieve improved characterization of the target form by an average of 42 orders of magnitude in confidence. When HCD fragmentation optimization was applied to intact proteins ions, there was an 18.5 order of magnitude gain in confidence. These improved metrics set the stage for increased proteome coverage and characterization of higher order organisms in the future for sharply improved control over MS instruments in a project- and lab-wide context

Cell-free Protein Synthesis from a Release Factor 1 Deficient <i>Escherichia coli</i> Activates Efficient and Multiple Site-specific Nonstandard Amino Acid Incorporation

Site-specific incorporation of nonstandard amino acids (NSAAs) into proteins enables the creation of biopolymers, proteins, and enzymes with new chemical properties, new structures, and new functions. To achieve this, amber (TAG codon) suppression has been widely applied. However, the suppression efficiency is limited due to the competition with translation termination by release factor 1 (RF1), which leads to truncated products. Recently, we constructed a genomically recoded <i>Escherichia coli</i> strain lacking RF1 where 13 occurrences of the amber stop codon have been reassigned to the synonymous TAA codon (<i>rEc.E13.ΔprfA</i>). Here, we assessed and characterized cell-free protein synthesis (CFPS) in crude S30 cell lysates derived from this strain. We observed the synthesis of 190 ± 20 μg/mL of modified soluble superfolder green fluorescent protein (sfGFP) containing a single <i>p</i>-propargyloxy-l-phenylalanine (pPaF) or <i>p</i>-acetyl-l-phenylalanine. As compared to the parent <i>rEc.E13</i> strain with RF1, this results in a modified sfGFP synthesis improvement of more than 250%. Beyond introducing a single NSAA, we further demonstrated benefits of CFPS from the RF1-deficient strains for incorporating pPaF at two- and five-sites per sfGFP protein. Finally, we compared our crude S30 extract system to the PURE translation system lacking RF1. We observed that our S30 extract based approach is more cost-effective and high yielding than the PURE translation system lacking RF1, ∼1000 times on a milligram protein produced/$ basis. Looking forward, using RF1-deficient strains for extract-based CFPS will aid in the synthesis of proteins and biopolymers with site-specifically incorporated NSAAs

Metabolic Perturbation of an Essential Pathway: Evaluation of a Glycine Precursor of Coenzyme A

Pantetheine and its corresponding disulfide pantethine play a key role in metabolism as building blocks of coenzyme A (CoA), an essential cofactor utilized in ∼4% of primary metabolism and central to fatty acid, polyketide, and nonribosomal peptide synthases. Using a combination of recombinant engineering and chemical synthesis, we show that the disulfide of <i>N</i>-pantoylglycyl-2-aminoethanethiol (GlyPan), with one fewer carbon than pantetheine, can rescue a mutant E. coli strain MG1655Δ<i>panC</i> lacking a functional pantothenate synthetase. Using mass spectrometry, we show that the GlyPan variant is accepted by the downstream CoA biosynthetic machinery, ultimately being incorporated into essential acyl carrier proteins. These findings point to further flexibility in CoA-dependent pathways and offer the opportunity to incorporate orthogonal analogues

Top Down Proteomics Reveals Mature Proteoforms Expressed in Subcellular Fractions of the <i>Echinococcus granulosus</i> Preadult Stage

<i>Echinococcus granulosus</i> is the causative agent of cystic hydatid disease, a neglected zoonosis responsible for high morbidity and mortality. Several molecular mechanisms underlying parasite biology remain poorly understood. Here, <i>E. granulosus</i> subcellular fractions were analyzed by top down and bottom up proteomics for protein identification and characterization of co-translational and post-translational modifications (CTMs and PTMs, respectively). Nuclear and cytosolic extracts of <i>E. granulosus</i> protoscoleces were fractionated by 10% GELFrEE and proteins under 30 kDa were analyzed by LC–MS/MS. By top down analysis, 186 proteins and 207 proteoforms were identified, of which 122 and 52 proteoforms were exclusively detected in nuclear and cytosolic fractions, respectively. CTMs were evident as 71% of the proteoforms had methionine excised and 47% were N-terminal acetylated. In addition, <i>in silico</i> internal acetylation prediction coupled with top down MS allowed the characterization of 9 proteins differentially acetylated, including histones. Bottom up analysis increased the overall number of identified proteins in nuclear and cytosolic fractions to 154 and 112, respectively. Overall, our results provided the first description of the low mass proteome of <i>E. granulosus</i> subcellular fractions and highlighted proteoforms with CTMs and PTMS whose characterization may lead to another level of understanding about molecular mechanisms controlling parasitic flatworm biology

A Proteomic Survey of Nonribosomal Peptide and Polyketide Biosynthesis in Actinobacteria

Actinobacteria such as streptomycetes are renowned for their ability to produce bioactive natural products including nonribosomal peptides (NRPs) and polyketides (PKs). The advent of genome sequencing has revealed an even larger genetic repertoire for secondary metabolism with most of the small molecule products of these gene clusters still unknown. Here, we employed a “protein-first” method called PrISM (Proteomic Investigation of Secondary Metabolism) to screen 26 unsequenced actinomycetes using mass spectrometry-based proteomics for the targeted detection of expressed nonribosomal peptide synthetases or polyketide synthases. Improvements to the original PrISM screening approach (Nat. Biotechnol. 2009, 27, 951−956), for example, improved <i>de novo</i> peptide sequencing, have enabled the discovery of 10 NRPS/PKS gene clusters from 6 strains. Taking advantage of the concurrence of biosynthetic enzymes and the secondary metabolites they generate, two natural products were associated with their previously “orphan” gene clusters. This work has demonstrated the feasibility of a proteomics-based strategy for use in screening for NRP/PK production in actinomycetes (often >8 Mbp, high GC genomes) versus the bacilli (2–4 Mbp genomes) used previously

Applying Label-Free Quantitation to Top Down Proteomics

With the prospect of resolving whole protein molecules into their myriad proteoforms on a proteomic scale, the question of their quantitative analysis in discovery mode comes to the fore. Here, we demonstrate a robust pipeline for the identification and stringent scoring of abundance changes of whole protein forms <30 kDa in a complex system. The input is ∼100–400 μg of total protein for each biological replicate, and the outputs are graphical displays depicting statistical confidence metrics for each proteoform (<i>i.e</i>., a volcano plot and representations of the technical and biological variation). A key part of the pipeline is the hierarchical linear model that is tailored to the original design of the study. Here, we apply this new pipeline to measure the proteoform-level effects of deleting a histone deacetylase (<i>rpd3</i>) in S. cerevisiae. Over 100 proteoform changes were detected above a 5% false positive threshold in WT vs the Δ<i>rpd3</i> mutant, including the validating observation of hyperacetylation of histone H4 and both H2B isoforms. Ultimately, this approach to label-free top down proteomics in discovery mode is a critical technical advance for testing the hypothesis that whole proteoforms can link more tightly to complex phenotypes in cell and disease biology than do peptides created in shotgun proteomics

Applying Label-Free Quantitation to Top Down Proteomics

With the prospect of resolving whole protein molecules into their myriad proteoforms on a proteomic scale, the question of their quantitative analysis in discovery mode comes to the fore. Here, we demonstrate a robust pipeline for the identification and stringent scoring of abundance changes of whole protein forms <30 kDa in a complex system. The input is ∼100–400 μg of total protein for each biological replicate, and the outputs are graphical displays depicting statistical confidence metrics for each proteoform (<i>i.e</i>., a volcano plot and representations of the technical and biological variation). A key part of the pipeline is the hierarchical linear model that is tailored to the original design of the study. Here, we apply this new pipeline to measure the proteoform-level effects of deleting a histone deacetylase (<i>rpd3</i>) in S. cerevisiae. Over 100 proteoform changes were detected above a 5% false positive threshold in WT vs the Δ<i>rpd3</i> mutant, including the validating observation of hyperacetylation of histone H4 and both H2B isoforms. Ultimately, this approach to label-free top down proteomics in discovery mode is a critical technical advance for testing the hypothesis that whole proteoforms can link more tightly to complex phenotypes in cell and disease biology than do peptides created in shotgun proteomics