Neuropeptides are a class of endogenous peptides that have key regulatory roles in biochemical, physiological, and behavioral processes. Mass spectrometry analyses of neuropeptides often rely on protein informatics tools for database searching and peptide identification. As neuropeptide databases are typically experimentally built and comprised of short sequences with high sequence similarity to each other, we developed a novel database searching tool, HyPep, which utilizes sequence homology searching for peptide identification. HyPep aligns de novo sequenced peptides, generated through PEAKS software, with neuropeptide database sequences and identifies neuropeptides based on the alignment score. HyPep performance was optimized using LC-MS/MS measurements of peptide extracts from various Callinectes sapidus neuronal tissue types and compared with a commercial database searching software, PEAKS DB. HyPep identified more neuropeptides from each tissue type than PEAKS DB at 1% false discovery rate, and the false match rate from both programs was 2%. In addition to identification, this report describes how HyPep can aid in the discovery of novel neuropeptides.

Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

Alzheimer's disease (AD) is the most common representation of dementia, with brain pathological hallmarks of protein abnormal aggregation, such as with amyloid beta and tau protein. It is well established that posttranslational modifications on tau protein, particularly phosphorylation, increase the likelihood of its aggregation and subsequent formation of neurofibrillary tangles, another hallmark of AD. As additional misfolded proteins presumably exist distinctly in AD disease states, which would serve as potential source of AD biomarkers, we used limited proteolysis-coupled with mass spectrometry (LiP-MS) to probe protein structural changes. After optimizing the LiP-MS conditions, we further applied this method to human cerebrospinal fluid specimens collected from healthy control, mild cognitive impairment (MCI), and AD subject groups to characterize proteome-wide misfolding tendencies as a result of disease progression. The fully tryptic peptides embedding LiP sites were compared with the half-tryptic peptides generated from internal cleavage of the same region to determine any structural unfolding or misfolding. We discovered hundreds of significantly up-and down-regulated peptides associated with MCI and AD indicating their potential structural changes in AD progression. Moreover, we detected 53 structurally changed regions in 12 proteins with high confidence between the healthy control and disease groups, illustrating the functional relevance of these proteins with AD progression. These newly discovered conformational biomarker candidates establish valuable future directions for exploring the molecular mechanism of designing therapeutic targets for AD.

Post‐translational modifications (PTM) of proteins increase the functional diversity of the proteome and have been implicated in the pathogenesis of numerous diseases. The most widely understood modifications include phosphorylation, methylation, acetylation, O‐linked/N‐linked glycosylation, and ubiquitination, all of which have been extensively studied and documented. Citrullination is a historically less explored, yet increasingly studied, protein PTM which has profound effects on protein conformation and protein‐protein interactions. Dysregulation of protein citrullination has been associated with disease development and progression. Identification and characterization of citrullinated proteins is highly challenging, complicated by the low cellular abundance of citrullinated proteins, making it difficult to identify and quantify the extent of citrullination in samples, coupled with challenges associated with development of mass spectrometry (MS)‐based methods, as the corresponding mass shift is relatively small, +0.984 Da, and identical to the mass shift of deamidation. The focus of this review is to discuss recent advancements of citrullination‐specific MS approaches and integration of the potential methodology for improved citrullination identification and characterization. In addition, the association of citrullination in disease networks is also highlighted.

Arginine methylation catalyzed by protein arginine methyltransferases (PRMTs) is a prevalent post-translational modification (PTM) that regulates diverse cellular processes. Aberrant expression of type I PRMTs that catalyze asymmetric arginine dimethylation (ADMA) is often found in cancer, though little is known about the ADMA status of substrate proteins in tumors. Using LC-MS/MS along with pan-specific ADMA antibodies, we performed global mapping of ADMA in five patient-derived xenograft (PDX) tumors representing different subtypes of human breast cancer. In total, 403 methylated sites from 213 proteins were identified, including 322 novel sites when compared to the PhosphositesPlus database. Moreover, using peptide arrays in vitro, approximately 70% of the putative substrates were validated to be methylated by PRMT1, PRMT4, and PRMT6. Notably, when compared with our previously identified ADMA sites from breast cancer cell lines, only 75 ADMA sites overlapped between cell lines and PDX tumors. Collectively, this study provides a useful resource for both PRMT and breast cancer communities for further exploitation of the functions of PRMT dysregulation during breast cancer progression.