PRESnovo: Prescreening Prior to de novo Sequencing to Improve Accuracy and Sensitivity of Neuropeptide Identification

Decoding Neuropeptide Complexity: Advancing Neurobiological Insights from Invertebrates to Vertebrates through Evolutionary Perspectives

Neuropeptides are vital signaling molecules involved in neural communication, hormonal regulation, and stress response across diverse taxa. Despite their critical roles, neuropeptide research remains challenging due to their low abundance, complex post-translational modifications (PTMs), and dynamic expression patterns. Mass spectrometry (MS)-based neuropeptidomics has revolutionized peptide identification and quantification, enabling the high-throughput characterization of neuropeptides and their PTMs. However, the complexity of vertebrate neural networks poses significant challenges for functional studies. Invertebrate models, such as Cancer borealis, Drosophila melanogaster, and Caenorhabditis elegans, offer simplified neural circuits, well-characterized systems, and experimental tools for elucidating the functional roles of neuropeptides. These models have revealed conserved neuropeptide families, including allatostatins, RFamides, and tachykinin-related peptides, whose vertebrate homologues regulate analogous physiological functions. Recent advancements in MS techniques, including ion mobility spectrometry and MALDI MS imaging, have further enhanced the spatial and temporal resolution of neuropeptide analysis, allowing for insights into peptide signaling systems. Invertebrate neuropeptide research not only expands our understanding of conserved neuropeptide functions but also informs translational applications including the development of peptide-based therapeutics. This review highlights the utility of invertebrate models in neuropeptide discovery, emphasizing their contributions to uncovering fundamental biological principles and their relevance to vertebrate systems.

Composite Mapping for Peptide-Based Data Storage with Higher Coding Density and Fewer Synthesis Cycles

Peptides are natural information-bearing mediums and are promising for high-density data storage. However, conventional mapping of one amino acid (AA) to one binary code has limited the improvement of coding density by increasing the total number of different AAs. Here, a novel composite mapping strategy is developed, where each position in the peptide sequence is a composite letter consisting of several different AAs, and thousands of composite letters are available for mapping, thus breaking the limit of conventional mapping. When 20 different AAs are used, the coding density of six-AAs composite mapping achieves 15 bits/letter, while conventional mapping only reaches 4 bits/AA. The whole process of encoding data into composite letter sequences, synthesizing composite letter sequences via solid-phase peptide synthesis, sequencing composite letter sequences by mass spectrometry, and decoding data from composite letter sequences is successfully demonstrated for the first time. Composite mapping also demonstrates several distinct advantages, including high coding density, few synthesis cycles, high reliability against errors, low probability of homopolymers, and good compatibility with other encoding algorithms. The developed composite mapping strategy provides a novel way for peptide-based data storage to increase the coding density and reduce the synthesis cycles, showing great potential for large-scale data storage.

MotifQuest: An Automated Pipeline for Motif Database Creation to Improve Peptidomics Database Searching Programs

Endogenous peptides are an abundant and versatile class of biomolecules with vital roles pertinent to the functionality of the nervous, endocrine, and immune systems and others. Mass spectrometry stands as a premier technique for identifying endogenous peptides, yet the field still faces challenges due to the lack of optimized computational resources for reliable raw mass spectra analysis and interpretation. Current database searching programs can exhibit discrepancies due to the unique properties of endogenous peptides, which typically require specialized search considerations. Herein, we present a high throughput, novel scoring algorithm for the extraction and ranking of conserved amino acid sequence motifs within any endogenous peptide database. Motifs are conserved patterns across organisms, representing sequence moieties crucial for biological functions, including maintenance of homeostasis. MotifQuest, our novel motif database generation algorithm, is designed to work in partnership with EndoGenius, a program optimized for database searching of endogenous peptides and that is powered by a motif database to capitalize on biological context to produce identifications. MotifQuest aims to quickly develop motif databases without any prior knowledge, a laborious task not possible with traditional sequence alignment resources. In this work we illustrate the utility of MotifQuest to expand EndoGenius' identification utility to other endogenous peptides by showcasing its ability to identify antimicrobial peptides. Additionally, we discuss the potential utility of MotifQuest to parse out motifs from a FASTA database file that can be further validated as new peptide drug candidates.

EndoGenius: Optimized Neuropeptide Identification from Mass Spectrometry Datasets

Neuropeptides represent a unique class of signaling molecules that have garnered much attention but require special consideration when identifications are gleaned from mass spectra. With highly variable sequence lengths, neuropeptides must be analyzed in their endogenous state. Further, neuropeptides share great homology within families, differing by as little as a single amino acid residue, complicating even routine analyses and necessitating optimized computational strategies for confident and accurate identifications. We present EndoGenius, a database searching strategy designed specifically for elucidating neuropeptide identifications from mass spectra by leveraging optimized peptide-spectrum matching approaches, an expansive motif database, and a novel scoring algorithm to achieve broader representation of the neuropeptidome and minimize reidentification. This work describes an algorithm capable of reporting more neuropeptide identifications at 1% false-discovery rate than alternative software in five Callinectes sapidus neuronal tissue types.

An Updated Guide to the Identification, Quantitation, and Imaging of the Crustacean Neuropeptidome

Crustaceans serve as a useful, simplified model for studying peptides and neuromodulation, as they contain numerous neuropeptide homologs to mammals and enable electrophysiological studies at the single-cell and neural circuit levels. Crustaceans contain well-defined neural networks, including the stomatogastric ganglion, oesophageal ganglion, commissural ganglia, and several neuropeptide-rich organs such as the brain, pericardial organs, and sinus glands. As existing mass spectrometry (MS) methods are not readily amenable to neuropeptide studies, there is a great need for optimized sample preparation, data acquisition, and data analysis methods. Herein, we present a general workflow and detailed methods for MS-based neuropeptidomic analysis of crustacean tissue samples and circulating fluids. In conjunction with profiling, quantitation can also be performed with isotopic or isobaric labeling. Information regarding the localization patterns and changes of peptides can be studied via mass spectrometry imaging. Combining these sample preparation strategies and MS analytical techniques allows for a multi-faceted approach to obtaining deep knowledge of crustacean peptidergic signaling pathways.

Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles

Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.

Recent advances in mass spectrometry analysis of neuropeptides

Due to their involvement in numerous biochemical pathways, neuropeptides have been the focus of many recent research studies. Unfortunately, classic analytical methods, such as western blots and enzyme-linked immunosorbent assays, are extremely limited in terms of global investigations, leading researchers to search for more advanced techniques capable of probing the entire neuropeptidome of an organism. With recent technological advances, mass spectrometry (MS) has provided methodology to gain global knowledge of a neuropeptidome on a spatial, temporal, and quantitative level. This review will cover key considerations for the analysis of neuropeptides by MS, including sample preparation strategies, instrumental advances for identification, structural characterization, and imaging; insightful functional studies; and newly developed absolute and relative quantitation strategies. While many discoveries have been made with MS, the methodology is still in its infancy. Many of the current challenges and areas that need development will also be highlighted in this review.

Highly reliable and efficient encoding systems for hexadecimal polypeptide-based data storage

Polypeptides consisting of amino acid (AA) sequences are suitable for high-density information storage. However, the lack of suitable encoding systems, which accommodate the characteristics of polypeptide synthesis, storage and sequencing, impedes the application of polypeptides for large-scale digital data storage. To address this, two reliable and highly efficient encoding systems, i.e. RaptorQ-Arithmetic-Base64-Shuffle-RS (RABSR) and RaptorQ-Arithmetic-Huffman-Rotary-Shuffle-RS (RAHRSR) systems, are developed for polypeptide data storage. The two encoding systems realized the advantages of compressing data, correcting errors of AA chain loss, correcting errors within AA chains, eliminating homopolymers, and pseudo-randomized encrypting. The coding efficiency without arithmetic compression and error correction of audios, pictures and texts by the RABSR system was 3.20, 3.12 and 3.53 Bits/AA, respectively. While that using the RAHRSR system reached 4.89, 4.80 and 6.84 Bits/AA, respectively. When implemented with redundancy for error correction and arithmetic compression to reduce redundancy, the coding efficiency of audios, pictures and texts by the RABSR system was 4.43, 4.36 and 5.22 Bits/AA, respectively. This efficiency further increased to 7.24, 7.11 and 9.82 Bits/AA by the RAHRSR system, respectively. Therefore, the developed hexadecimal polypeptide-based systems may provide a new scenario for highly reliable and highly efficient data storage.

HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search

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.