TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome.

LncRNAs are not only well-known as non-coding elements, but also serve as templates for peptide translation, playing important roles in fundamental cellular processes and diseases. Here, we describe a database, TransLnc (http://bio-bigdata.hrbmu.edu.cn/TransLnc/), which aims to provide comprehensive experimentally supported and predicted lncRNA peptides in multiple species. TransLnc currently documents approximate 583 840 peptides encoded by 33 094 lncRNAs. Six types of direct and indirect evidences supporting the coding potential of lncRNAs were integrated, and 65.28% peptides entries were with at least one type of evidence. Considering the strong tissue-specific expression of lncRNAs, TransLnc allows users to access lncRNA peptides in any of the 34 tissues involved in. In addition, both the unique characteristic and homology relationship were also predicted and provided. Importantly, TransLnc provides computationally predicted tumour neoantigens from peptides encoded by lncRNAs, which would provide novel insights into cancer immunotherapy. There were 220 791 and 237 915 candidate neoantigens binding by major histocompatibility complex (MHC) class I or II molecules, respectively. Several flexible tools were developed to aid retrieve and analyse, particularly lncRNAs tissue expression patterns, clinical relevance across cancer types. TransLnc will serve as a valuable resource for investigating the translation capacity of lncRNAs and greatly extends the cancer immunopeptidome.

A non-lethal method for studying scorpion venom gland transcriptomes, with a review of potentially suitable taxa to which it can be applied.

Scorpion venoms are mixtures of proteins, peptides and small molecular compounds with high specificity for ion channels and are therefore considered to be promising candidates in the venoms-to-drugs pipeline. Transcriptomes are important tools for studying the composition and expression of scorpion venom. Unfortunately, studying the venom gland transcriptome traditionally requires sacrificing the animal and therefore is always a single snapshot in time. This paper describes a new way of generating a scorpion venom gland transcriptome without sacrificing the animal, thereby allowing the study of the transcriptome at various time points within a single individual. By comparing these venom-derived transcriptomes to the traditional whole-telson transcriptomes we show that the relative expression levels of the major toxin classes are similar. We further performed a multi-day extraction using our proposed method to show the possibility of doing a multiple time point transcriptome analysis. This allows for the study of patterns of toxin gene activation over time a single individual, and allows assessment of the effects of diet, season and other factors that are known or likely to influence intraindividual venom composition. We discuss the gland characteristics that may allow this method to be successful in scorpions and provide a review of other venomous taxa to which this method may potentially be successfully applied.

Multiplexed single-cell proteomics using SCoPE2.

Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either label-free or multiplexed) have enabled quantifying >1,000 proteins per cell while simultaneously increasing the specificity of protein quantification. Here we describe the Single Cell ProtEomics (SCoPE2) protocol, which uses an isobaric carrier to enhance peptide sequence identification. Single cells are isolated by FACS or CellenONE into multiwell plates and lysed by Minimal ProteOmic sample Preparation (mPOP), and their peptides labeled by isobaric mass tags (TMT or TMTpro) for multiplexed analysis. SCoPE2 affords a cost-effective single-cell protein quantification that can be fully automated using widely available equipment and scaled to thousands of single cells. SCoPE2 uses inexpensive reagents and is applicable to any sample that can be processed to a single-cell suspension. The SCoPE2 workflow allows analyzing ~200 single cells per 24 h using only standard commercial equipment. We emphasize experimental steps and benchmarks required for achieving quantitative protein analysis.

PopCover-2.0. Improved Selection of Peptide Sets With Optimal HLA and Pathogen Diversity Coverage.

The use of minimal peptide sets offers an appealing alternative for design of vaccines and T cell diagnostics compared to conventional whole protein approaches. T cell immunogenicity towards peptides is contingent on binding to human leukocyte antigen (HLA) molecules of the given individual. HLA is highly polymorphic, and each variant typically presents a different repertoire of peptides. This polymorphism combined with pathogen diversity challenges the rational selection of peptide sets with broad immunogenic potential and population coverage. Here we propose PopCover-2.0, a simple yet highly effective method, for resolving this challenge. The method takes as input a set of (predicted) CD8 and/or CD4 T cell epitopes with associated HLA restriction and pathogen strain annotation together with information on HLA allele frequencies, and identifies peptide sets with optimal pathogen and HLA (class I and II) coverage. PopCover-2.0 was benchmarked on historic data in the context of HIV and SARS-CoV-2. Further, the immunogenicity of the selected SARS-CoV-2 peptides was confirmed by experimentally validating the peptide pools for T cell responses in a panel of SARS-CoV-2 infected individuals. In summary, PopCover-2.0 is an effective method for rational selection of peptide subsets with broad HLA and pathogen coverage. The tool is available at https://services.healthtech.dtu.dk/service.php?PopCover-2.0.

ChimST: An Efficient Spectral Library Search Tool for Peptide Identification from Chimeric Spectra in Data-Dependent Acquisition.

Accurate and sensitive identification of peptides from MS/MS spectra is a very challenging problem in computational shotgun proteomics. To tackle this problem, spectral library search has been one of the competitive solutions. However, most existing library search tools were developed on the basis of one peptide per spectrum, which prevents them from working properly on chimeric spectra where two or more peptides are co-fragmented. In this work, we present a new library search tool called ChimST, which is particularly capable of reliably identifying multiple peptides from a chimeric spectrum. It starts with associating each query MS/MS spectrum with MS precursor features. For each precursor feature, there is a list of peptide candidates extracted from an input spectral library. Then, it takes one peptide candidate from each associated feature and scores how well they could collectively interpret the query spectrum. The highest-scoring set of peptide candidates are finally reported as the identification of the query spectrum. Our experimental tests show that ChimST could significantly outperform the three state-of-the-art library search tools, SpectraST, reSpect, and MSPLIT, in terms of the numbers of both peptide-spectrum matches and unique peptides, especially when the acquisition isolation window is broad.

New developments in RiPP discovery, enzymology and engineering.

Covering: up to June 2020Ribosomally-synthesized and post-translationally modified peptides (RiPPs) are a large group of natural products. A community-driven review in 2013 described the emerging commonalities in the biosynthesis of RiPPs and the opportunities they offered for bioengineering and genome mining. Since then, the field has seen tremendous advances in understanding of the mechanisms by which nature assembles these compounds, in engineering their biosynthetic machinery for a wide range of applications, and in the discovery of entirely new RiPP families using bioinformatic tools developed specifically for this compound class. The First International Conference on RiPPs was held in 2019, and the meeting participants assembled the current review describing new developments since 2013. The review discusses the new classes of RiPPs that have been discovered, the advances in our understanding of the installation of both primary and secondary post-translational modifications, and the mechanisms by which the enzymes recognize the leader peptides in their substrates. In addition, genome mining tools used for RiPP discovery are discussed as well as various strategies for RiPP engineering. An outlook section presents directions for future research.

ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides.

The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.

Novel neuroprotective peptides in the venom of the solitary scoliid wasp Scolia decorata ventralis

Background Solitary wasp venoms may be a rich source of neuroactive substances, since their venoms are used for paralyzing preys. We have been exploring bioactive constituents of solitary wasp venoms and, in this study, the component profile of the venom from a solitary scoliid wasp, Scolia decorata ventralis, was investigated through a comprehensive analysis using LC-MS. Two peptides were synthesized, and their neuroprotective properties were evaluated. Methods A reverse-phase HPLC connected to ESI-MS was used for LC-MS analyses. Online mass fingerprinting was performed from TIC, and data-dependent tandem mass spectrometry gave the MS/MS spectra. The sequences of two major peptide components were determined by MALDI-TOF/TOF MS analysis, confirmed by solid phase synthesis. Using the synthetic peptides, biological activities were assessed. Cell integrity tests and neuroprotection analyzes using H2O2 as an oxidative stress inducer were performed for both peptides. Results Online mass fingerprinting revealed that the venom contains 123 components, and the MS/MS analysis resulted in 33 full sequences of peptide components. The two main peptides, α-scoliidine (DYVTVKGFSPLR) and β-scoliidine (DYVTVKGFSPLRKA), present homology with the bradykinin C-terminal. Despite this, both peptides did not behave as substrates or inhibitors of ACE, indicating that they do not interact with this metallopeptidase. In further studies, β-scoliidine, but not α -scoliidine, showed protective effects against oxidative stress-induced neurotoxicity in PC12 cells through integrity and metabolism cell assays. Interestingly, β-scoliidine has the extension of the KA dipeptide at the C-terminal in comparison with α-scoliidine. Conclusion Comprehensive LC-MS and MS/MS analyses from the Scolia decorata ventralis venom displayed the component profile of this venom. β-scoliidine showed an effective cytoprotective effect, probably due to the observed increase in the number of cells. This is the first report of solitary wasp venom peptides showing neuroprotective activity.(AU)

METATRYP v 2.0: Metaproteomic Least Common Ancestor Analysis for Taxonomic Inference Using Specialized Sequence Assemblies-Standalone Software and Web Servers for Marine Microorganisms and Coronaviruses.

We present METATRYP version 2 software that identifies shared peptides across the predicted proteomes of organisms within environmental metaproteomics studies to enable accurate taxonomic attribution of peptides during protein inference. Improvements include ingestion of complex sequence assembly data categories (metagenomic and metatranscriptomic assemblies, single cell amplified genomes, and metagenome assembled genomes), prediction of the least common ancestor (LCA) for a peptide shared across multiple organisms, increased performance through updates to the backend architecture, and development of a web portal (https://metatryp.whoi.edu). Major expansion of the marine METATRYP database with predicted proteomes from environmental sequencing confirms a low occurrence of shared tryptic peptides among disparate marine microorganisms, implying tractability for targeted metaproteomics. METATRYP was designed to facilitate ocean metaproteomics and has been integrated into the Ocean Protein Portal (https://oceanproteinportal.org); however, it can be readily applied to other domains. We describe the rapid deployment of a coronavirus-specific web portal (https://metatryp-coronavirus.whoi.edu/) to aid in use of proteomics on coronavirus research during the ongoing pandemic. A coronavirus-focused METATRYP database identified potential SARS-CoV-2 peptide biomarkers and indicated very few shared tryptic peptides between SARS-CoV-2 and other disparate taxa analyzed, sharing <1% peptides with taxa outside of the betacoronavirus group, establishing that taxonomic specificity is achievable using tryptic peptide-based proteomic diagnostic approaches.

Genome-wide analysis and transcript profiling of PSKR gene family members in Oryza sativa.

Peptide signalling is an integral part of cell-to-cell communication which helps to relay the information responsible for coordinating cell proliferation and differentiation. Phytosulfokine Receptor (PSKR) is a transmembrane LRR-RLK family protein with a binding site for small signalling peptide, phytosulfokine (PSK). PSK signalling through PSKR promotes normal growth and development and also plays a role in defense responses. Like other RLKs, these PSKRs might have a role in signal transduction pathways related to abiotic stress responses. Genome-wide analysis of phytosulfokine receptor gene family has led to the identification of fifteen putative members in the Oryza sativa genome. The expression analysis of OsPSKR genes done using RNA-seq data, showed that these genes were differentially expressed in different tissues and responded specifically to heat, salt, drought and cold stress. Furthermore, the real-time quantitative PCR for fifteen OsPSKR genes revealed temporally and spatially regulated gene expression corresponding to salinity and drought stress. Our results provide useful information for a better understanding of OsPSKR genes and provide the foundation for additional functional exploration of the rice PSKR gene family in development and stress response.

Anti-cancer peptides: classification, mechanism of action, reconstruction and modification.

Anti-cancer peptides (ACPs) are a series of short peptides composed of 10-60 amino acids that can inhibit tumour cell proliferation or migration, or suppress the formation of tumour blood vessels, and are less likely to cause drug resistance. The aforementioned merits make ACPs the most promising anti-cancer candidate. However, ACPs may be degraded by proteases, or result in cytotoxicity in many cases. To overcome these drawbacks, a plethora of research has focused on reconstruction or modification of ACPs to improve their anti-cancer activity, while reducing their cytotoxicity. The modification of ACPs mainly includes main chain reconstruction and side chain modification. After summarizing the classification and mechanism of action of ACPs, this paper focuses on recent development and progress about their reconstruction and modification. The information collected here may provide some ideas for further research on ACPs, in particular their modification.

Phylogenetic distribution of the bacteriocin repertoire of lactic acid bacteria species associated with artisanal cheese.

The microbiota contributes to artisanal cheese bioprotection and biopreservation through inter and intraspecific competition. This work aimed to investigate the phylogenetic distribution of the repertoire of bacteriocin structural genes of model lactic acid bacteria (LAB) in order to investigate its respective role in the artisanal cheeses microenvironment. A phylogenetic analysis of the rRNA 16S gene from 445 model strains of LAB was conducted using bayesian inference and the repertoire of bacteriocin genes was predicted from these strains by BAGEL software. Bacterial strains were clustered in five monophyletic clades (A, B, C, D and E) with high posterior probability values (PP > 0.99). One bacteriocin structural gene was predicted for 88.5% of the analyzed strains. The majority of the species encoded different classes of bacteriocins. Greater diversity of bacteriocin genes was found for strains included in clade A, comprising Lactococcus lactis, Streptococcus agalactiae, Streptococcus thermophilus, Streptococcus macedonicus, Enterococcus faecalis and Enterococcus faecium. In addition, Lactococcus lactis presented higher diversity of bacteriocin classes, encoding glycocins, lanthipeptides, sactipeptides, cyclic and linear azole-containing peptides, included in bacteriocins class I, besides class II and III. The results suggest that the distribution of bacteriocin structural genes is related to the phylogenetic clades of LAB species, with a higher frequency in some specific clades. Information comprised in this study contributes to comprehend the bacterial competition mechanisms in the artisanal cheese microenvironment.

Visualizing the agreement of peptide assignments between different search engines.

There is a trend in the analysis of shotgun proteomics data that aims to combine information from multiple search engines to increase the number of peptide annotations in an experiment. Typically, the degree of search engine complementarity and search engine agreement is visually illustrated by means of Venn diagrams that present the findings of a database search on the level of the nonredundant peptide annotations. We argue this practice to be not fit-for-purpose since the diagrams do not take into account and often conceal the information on complementarity and agreement at the level of the spectrum identification. We promote a new type of visualization that provides insight on the peptide sequence agreement at the level of the peptide-spectrum match (PSM) as a measure of consensus between two search engines with nominal outcomes. We applied the visualizations and percentage sequence agreement to an in-house data set of our benchmark organism, Caenorhabditis elegans, and illustrated that when assessing the agreement between search engine, one should disentangle the notion of PSM confidence and PSM identity. The visualizations presented in this manuscript provide a more informative assessment of pairs of search engines and are made available as an R function in the Supporting Information.

False Discovery Rate Estimation for Hybrid Mass Spectral Library Search Identifications in Bottom-up Proteomics.

We present a method for FDR estimation of mass spectral library search identifications made by a recently developed method for peptide identification, the hybrid search, based on an extension of the target-decoy approach. In addition to estimating confidence for a given identification, this allows users to compare and integrate identifications from the hybrid mass spectral library search method with other peptide identification methods, such as a sequence database-based method. In addition to a score, each hybrid score is associated with a "DeltaMass" value, which is the difference in mass of the search and library peptide, which can correspond to the mass of a modification. We explored the relation between FDR and DeltaMass using 100 concatenated random decoy libraries and discovered that a small number of DeltaMass values were especially likely to result from decoy searches. Using these values, FDR values could be adjusted for these specific values and a reliable FDR generated for any DeltaMass value. Finally, using this method, we find and examine common, reliable identifications made by the hybrid search for a range of proteomic studies.

Speeding Up Percolator.

The processing of peptide tandem mass spectrometry data involves matching observed spectra against a sequence database. The ranking and calibration of these peptide-spectrum matches can be improved substantially using a machine learning postprocessor. Here, we describe our efforts to speed up one widely used postprocessor, Percolator. The improved software is dramatically faster than the previous version of Percolator, even when using relatively few processors. We tested the new version of Percolator on a data set containing over 215 million spectra and recorded an overall reduction to 23% of the running time as compared to the unoptimized code. We also show that the memory footprint required by these speedups is modest relative to that of the original version of Percolator.

In silico design and optimization of selective membranolytic anticancer peptides.

Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.

Identification of amyloidogenic peptides via optimized integrated features space based on physicochemical properties and PSSM.

At present, the identification of amyloid becomes more and more essential and meaningful. Because its mis-aggregation may cause some diseases such as Alzheimer's and Parkinson's diseases. This paper focus on the classification of amyloidogenic peptides and a novel feature representation called PhyAve_PSSMDwt is proposed. It includes two parts. One is based on physicochemical properties involving hydrophilicity, hydrophobicity, aggregation tendency, packing density and H-bonding which extracts 15-dimensional features in total. And the other is 60-dimensional features through recursive feature elimination from PSSM by discrete wavelet transform. In this period, sliding window is introduced to reconstruct PSSM so that the evolutionary information of short sequences can still be extracted. At last, the support vector machine is adopted as a classifier. The experimental result on Pep424 dataset shows that PSSM's information makes a great contribution on performance. And compared with other existing methods, our results after cross-validation increase by 3.1%, 3.3%, 0.136 and 0.007 in accuracy, specificity, Matthew's correlation coefficient and AUC value, respectively. It indicates that our method is effective and competitive.

Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models.

Tandem mass spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. Peptide spectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments. Using millions of MS/MS spectra we show that there is high reproducibility across different fragmentation spectra given the precursor peptide and charge state, implying that there is a pattern to fragmentation. To capture this pattern we propose a novel prediction algorithm based on hidden Markov models with an efficient training process. We investigated the performance of our interpolated-HMM model, trained on millions of MS2 spectra, and found that our model picks up meaningful patterns in peptide fragmentation. Second, looking at the variability of the prediction performance by varying the train/test data split, we observed that our model performs well independent of the specific peptides that are present in the training data. Furthermore, we propose that the real value of this model is as a preprocessing step in the peptide identification process. The model can discern fragment ions that are unlikely to be intense for a given candidate peptide rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra will leverage better statistics.

The behavior of umami components in thermally treated yeast extract.

Umami proteolytics are natural food flavor alternatives to glutamate. In this study, key umami taste fractions were separated and purified from thermally treated yeast extract (YE) to yield fifteen umami peptides. Systematic approaches using sensory-guided fractionation on taste-active umami proteolytics separation and detection were utilized. A reaction temperature of 110 °C was optimum for umami peptide generation. Under this reaction temperature, the sensory score and E-tongue results of umami taste were the highest. The sensory evaluation-based taste dilution analysis and taste threshold determination supported the hypothesis that umami peptides have their physiological effect by binding to G-protein coupled receptors. The structural differences of umami peptides contribute to their taste profile and allow categorization into two group Types. Fifteen umami peptides were then categorized into Type I and Type II regarding the contractual-based taste mechanism: Type I peptides imparted complex tastes. The tastes of Type I peptides could split into two stages: bitterness and umami in pure water, whereas, Type II peptides presented strong umami taste at a high concentration in pure water, and the relationship between umami capacity and peptides concentration was linear. Finally, the guidance of the umami peptide usage in the flavor industry has been established according to broths dissolution test.

ProteomeGenerator: A Framework for Comprehensive Proteomics Based on de Novo Transcriptome Assembly and High-Accuracy Peptide Mass Spectral Matching.

Modern mass spectrometry now permits genome-scale and quantitative measurements of biological proteomes. However, analysis of specific specimens is currently hindered by the incomplete representation of biological variability of protein sequences in canonical reference proteomes and the technical demands for their construction. Here, we report ProteomeGenerator, a framework for de novo and reference-assisted proteogenomic database construction and analysis based on sample-specific transcriptome sequencing and high-accuracy mass spectrometry proteomics. This enables the assembly of proteomes encoded by actively transcribed genes, including sample-specific protein isoforms resulting from non-canonical mRNA transcription, splicing, or editing. To improve the accuracy of protein isoform identification in non-canonical proteomes, ProteomeGenerator relies on statistical target-decoy database matching calibrated using sample-specific controls. Its current implementation includes automatic integration with MaxQuant mass spectrometry proteomics algorithms. We applied this method for the proteogenomic analysis of splicing factor SRSF2 mutant leukemia cells, demonstrating high-confidence identification of non-canonical protein isoforms arising from alternative transcriptional start sites, intron retention, and cryptic exon splicing as well as improved accuracy of genome-scale proteome discovery. Additionally, we report proteogenomic performance metrics for current state-of-the-art implementations of SEQUEST HT, MaxQuant, Byonic, and PEAKS mass spectral analysis algorithms. Finally, ProteomeGenerator is implemented as a Snakemake workflow within a Singularity container for one-step installation in diverse computing environments, thereby enabling open, scalable, and facile discovery of sample-specific, non-canonical, and neomorphic biological proteomes.