Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal

Identification of structural and regulatory cell-shape determinants in Haloferax volcanii

Archaea play indispensable roles in global biogeochemical cycles, yet many crucial cellular processes, including cell-shape determination, are poorly understood. Haloferax volcanii, a model haloarchaeon, forms rods and disks, depending on growth conditions. Here, we used a combination of iterative proteomics, genetics, and live-cell imaging to identify mutants that only form rods or disks. We compared the proteomes of the mutants with wild-type cells across growth phases, thereby distinguishing between protein abundance changes specific to cell shape and those related to growth phases. The results identified a diverse set of proteins, including predicted transporters, transducers, signaling components, and transcriptional regulators, as important for cell-shape determination. Through phenotypic characterization of deletion strains, we established that rod-determining factor A (RdfA) and disk-determining factor A (DdfA) are required for the formation of rods and disks, respectively. We also identified structural proteins, including an actin homolog that plays a role in disk-shape morphogenesis, which we named volactin. Using live-cell imaging, we determined volactin's cellular localization and showed its dynamic polymerization and depolymerization. Our results provide insights into archaeal cell-shape determination, with possible implications for understanding the evolution of cell morphology regulation across domains.

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.

A peptidoform based proteomic strategy for studying functions of post-translational modifications

Protein post-translational modifications (PTMs) generate an enormous, but as yet undetermined, expansion of the produced proteoforms. In this Viewpoint, we firstly reviewed the concepts of proteoform and peptidoform. We show that many of the current PTM biological investigation and annotation studies largely follow a PTM site-specific rather than proteoform-specific approach. We further illustrate a potentially useful matching strategy in which a particular "modified peptidoform" is matched to the corresponding "unmodified peptidoform" as a reference for the quantitative analysis between samples and conditions. We suggest this strategy has the potential to provide more directly relevant information to learn the PTM site-specific biological functions. Accordingly, we advocate for the wider use of the nomenclature "peptidoform" in future bottom-up proteomic studies.

Proteomic Sample Preparation and Data Analysis in Line with the Archaeal Proteome Project

Despite the ecological, evolutionary and economical significance of archaea, key aspects of their cell biology, metabolic pathways, and adaptations to a wide spectrum of environmental conditions, remain to be elucidated. Proteomics allows for the system-wide analysis of proteins, their changes in abundance between different conditions, as well as their post-translational modifications, providing detailed insights into the function of proteins and archaeal cell biology. In this chapter, we describe a sample preparation and mass spectrometric analysis workflow that has been designed for Haloferax volcanii but can be applied to a broad range of archaeal species. Furthermore, proteomics experiments provide a wealth of data that is invaluable to various disciplines. Therefore, we previously initiated the Archaeal Proteome Project (ArcPP), a community project that combines the analysis of multiple datasets with expert knowledge in various fields of archaeal research. The corresponding bioinformatic analysis, allowing for the integration of new proteomics data into the ArcPP, as well as the interactive exploration of ArcPP results is also presented here. In combination, these protocols facilitate an optimized, detailed and collaborative approach to archaeal proteomics.

Comprehensive glycoproteomics shines new light on the complexity and extent of glycosylation in archaea

Glycosylation is one of the most complex posttranslational protein modifications. Its importance has been established not only for eukaryotes but also for a variety of prokaryotic cellular processes, such as biofilm formation, motility, and mating. However, comprehensive glycoproteomic analyses are largely missing in prokaryotes. Here, we extend the phenotypic characterization of N-glycosylation pathway mutants in Haloferax volcanii and provide a detailed glycoproteome for this model archaeon through the mass spectrometric analysis of intact glycopeptides. Using in-depth glycoproteomic datasets generated for the wild-type (WT) and mutant strains as well as a reanalysis of datasets within the Archaeal Proteome Project (ArcPP), we identify the largest archaeal glycoproteome described so far. We further show that different N-glycosylation pathways can modify the same glycosites under the same culture conditions. The extent and complexity of the Hfx. volcanii N-glycoproteome revealed here provide new insights into the roles of N-glycosylation in archaeal cell biology.

A comprehensive glycoproteomic analysis of Haloferax volcanii reveals the extent and complexity of glycosylation in archaea and provides new insights into the roles of this post-translational modification in various cellular processes, including cell shape determination.