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Identification of post-translationally or chemically modified peptides in mass spectrometry-based proteomics experiments is a crucial yet challenging task. We have recently introduced a fragment ion indexing method and the MSFragger search engine to empower an open search strategy for comprehensive analysis of modified peptides. However, this strategy does not consider fragment ions shifted by unknown modifications, preventing modification localization and limiting the sensitivity of the search. Here we present a localization-aware open search method, in which both modification-containing (shifted) and regular fragment ions are indexed and used in scoring. We also implement a fast mass calibration and optimization method, allowing optimization of the mass tolerances and other key search parameters. We demonstrate that MSFragger with mass calibration and localization-aware open search identifies modified peptides with significantly higher sensitivity and accuracy. Comparing MSFragger to other modification-focused tools (pFind3, MetaMorpheus, and TagGraph) shows that MSFragger remains an excellent option for fast, comprehensive, and sensitive searches for modified peptides in shotgun proteomics data. Mass spectrometry-based proteomics is the method of choice for the global mapping of post-translational modifications, but matching and scoring peaks with unknown masses remains challenging. Here, the authors present a refined open search strategy to score all peaks with higher sensitivity and accuracy.

Background A complete understanding of barnacle adhesion remains elusive as the process occurs within and beneath the confines of a rigid calcified shell. Barnacle cement is mainly proteinaceous and several individual proteins have been identified in the hardened cement at the barnacle-substrate interface. Little is known about the molt- and tissue-specific expression of cement protein genes but could offer valuable insight into the complex multi-step processes of barnacle growth and adhesion. Methods The main body and sub-mantle tissue of the barnacle Amphibalanus amphitrite (basionym Balanus amphitrite ) were collected in pre- and post-molt stages. RNA-seq technology was used to analyze the transcriptome for differential gene expression at these two stages and liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) was used to analyze the protein content of barnacle secretions. Results We report on the transcriptomic analysis of barnacle cement gland tissue in pre- and post-molt growth stages and proteomic investigation of barnacle secretions. While no significant difference was found in the expression of cement proteins genes at pre- and post-molting stages, expression levels were highly elevated in the sub-mantle tissue (where the cement glands are located) compared to the main barnacle body. We report the discovery of a novel 114kD cement protein, which is identified in material secreted onto various surfaces by adult barnacles and with the encoding gene highly expressed in the sub-mantle tissue. Further differential gene expression analysis of the sub-mantle tissue samples reveals a limited number of genes highly expressed in pre-molt samples with a range of functions including cuticular development, biominerialization, and proteolytic activity. Conclusions The expression of cement protein genes appears to remain constant through the molt cycle and is largely confined to the sub-mantle tissue. Our results reveal a novel and potentially prominent protein to the mix of cement-related components in A. amphitrite . Despite the lack of a complete genome, sample collection allowed for extended transcriptomic analysis of pre- and post-molt barnacle samples and identified a number of highly-expressed genes. Our results highlight the complexities of this sessile marine organism as it grows via molt cycles and increases the area over which it exhibits robust adhesion to its substrate.

Isobaric mass tags, such as iTRAQ and TMT, are widely utilized for peptide and protein quantification in multiplex quantitative proteomics. We present TMT-Integrator, a bioinformatics tool for processing quantitation results from TMT and iTRAQ experiments, offering integrative reports at the gene, protein, peptide, and post-translational modification site levels. We demonstrate the versatility of TMT-Integrator using five publicly available TMT datasets: an dataset with 13 spike-in proteins, the clear cell renal cell carcinoma (ccRCC) whole proteome and phosphopeptide-enriched datasets from the Clinical Proteomic Tumor Analysis Consortium, and two human cell lysate datasets showcasing the latest advances with the Astral instrument and TMT 35-plex reagents. Integrated into the widely used FragPipe computational platform (https://fragpipe.nesvilab.org/), TMT-Integrator is a core component of TMT and iTRAQ data analysis workflows. We evaluate the FragPipe/TMT-Integrator analysis pipeline's performance against MaxQuant and Proteome Discoverer with multiple benchmarks, facilitated by the bioinformatics tool OmicsEV. Our results show that FragPipe/TMT-Integrator quantifies more proteins in the E. coli and ccRCC whole proteome datasets, identifies more phosphorylated sites in the ccRCC phosphoproteome dataset, and delivers overall more robust quantification performance compared to other tools.

Conventional kinesin-1 is the primary anterograde motor in cells for transporting cellular cargo. While there is a consensus that the C-terminal tail of kinesin-1 inhibits motility, the molecular architecture of a full-length autoinhibited kinesin-1 remains unknown. Here, we combine cross-linking mass spectrometry (XL-MS), electron microscopy (EM), and AlphaFold structure prediction to determine the architecture of the full-length autoinhibited kinesin-1 homodimer [kinesin-1 heavy chain (KHC)] and kinesin-1 heterotetramer [KHC bound to kinesin light chain 1 (KLC1)]. Our integrative analysis shows that kinesin-1 forms a compact, bent conformation through a break in coiled coil 3. Moreover, our XL-MS analysis demonstrates that kinesin light chains stabilize the folded inhibited state rather than inducing a new structural state. Using our structural model, we show that disruption of multiple interactions between the motor, stalk, and tail domains is required to activate the full-length kinesin-1. Our work offers a conceptual framework for understanding how cargo adaptors and microtubule-associated proteins relieve autoinhibition to promote activation.Competing Interest StatementThe authors have declared no competing interest.

Large-scale analysis of proteins is a critical tool in the life sciences, guiding drug development and elucidating important cellular processes. These measurements are accomplished with liquid chromatography-mass spectrometry (LC-MS), where proteins are enzymatically digested into peptides, separated via liquid chromatography, and analyzed by mass spectrometry. Typically, the most abundant peptide ion at a given time is selected for sequence identification, but limited instrument scan speed often results in under-sampling, compromising data completeness and reproducibility. In contrast, all-ion acquisition methods bypass peptide ion selection, measuring peptides ions across broad mass ranges. Despite capturing data for all events, peptide annotation is limited by inadequate separation prior to fragmentation, which results in interfering peaks in fragment spectra. Ion mobility spectrometry (IMS), where peptide ions are separated based on cross-sectional size and charge in the gas phase, adds an orthogonal analytical dimension to LC-MS proteomics. In-line ion mobility spectrometry provides additional separation without increasing analysis times, reducing spectral interference to improve reproducibility, peak capacity, and peptide identifications. However, these ion mobility separations were not optimized for complex peptide mixtures, and the true peak capacity of the LC-IMS-MS platform was unknown. Additionally, fragment ion information acquired during protein quantification experiments using stable isotope labeling by amino acids in cell culture (SILAC) was underutilized. Rigidity and opacity of proprietary data analysis software also presents a barrier to improving LC-IMS-MS proteomics measurements. Chapter 1 presents the first quantitative characterization of traveling wave ion mobility separation in the context of real, complex proteomics samples. Taking into account the orthogonality of the LC, IMS, and MS separations, we found that IMS doubles the peak capacity of LC-MS alone under standard traveling wave settings. Seeking to improve the IMS separation, we discovered IMS settings that reproducibly increased peptide and protein identifications by over 40%. Chapter 2 describes a protein-centric statistical filtering method to leverage fragment ion quantification information. This filtering method reduces coefficients of variation by 4-fold, increasing confidence in differential protein measurements. Chapter 3 explores a new LC-IMS-MS software tool, focusing on 3D peak detection parameters, and reports the first database searches of LC-IMS-MS data performed entirely with free, open-source tools.

Abstract Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared to MaxQuant (19-38 times faster; 6-18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant, and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry (FAIMS) separation. Fully integrated in FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments. Competing Interest Statement The authors have declared no competing interest.

Ion mobility brings an additional dimension of separation to liquid chromatography-mass spectrometry, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to time-of-flight mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most commonly used to analyze these data. However, due to the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (~30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (~10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 minutes on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation prior to MS/MS analysis. Competing Interest Statement The authors have declared no competing interest.

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our educational material on GitHub (https://github.com/GWC-DCMB). Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/GWC-DCMB/curriculum-notebooks

A series of compounds (including CCG-1423 and CCG-203971) discovered through an MRTF/SRF dependent luciferase screen has shown remarkable efficacy in a variety of in vitro and in vivo models, including melanoma metastasis and bleomycin-induced fibrosis. Although these compounds are efficacious, the molecular target is unknown. Here, we describe affinity isolation-based target identification efforts which yielded pirin, an iron-dependent co-transcription factor, as a target of this series of compounds. Using biophysical techniques including isothermal titration calorimetry and X-ray crystallography, we verify that pirin binds these compounds in vitro. We also show with genetic approaches that pirin modulates MRTF-dependent SRE.L Luciferase activation. Finally, using both siRNA and a previously validated pirin inhibitor, we show a role for pirin in TGF- induced gene expression in primary dermal fibroblasts. A recently developed analog, CCG-257081, which co-crystallizes with pirin, is also effective in the prevention of bleomycin-induced dermal fibrosis.