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Next-generation single-molecule protein sequencing technologies have the potential to significantly accelerate biomedical research. These technologies offer sensitivity and scalability for proteomic analysis. One auspicious method is fluorosequencing, which involves: cutting naturalized proteins into peptides, attaching fluorophores to specific amino acids, and observing variations in light intensity as one amino acid is removed at a time. The original peptide is classified from the sequence of light-intensity reads, and proteins can subsequently be recognized with this information. The amino acid step removal is achieved by attaching the peptides to a wall on the C-terminal and using a process called Edman Degradation to remove an amino acid from the N-Terminal. Even though a framework (Whatprot) has been proposed for the peptide classification task, processing times remain restrictive due to the massively parallel data acquisicion system. In this paper, we propose a new beam search decoder with a novel state formulation that obtains considerably lower processing times at the expense of only a slight accuracy drop compared to Whatprot. Furthermore, we explore how our novel state formulation may lead to even faster decoders in the future. Author summary: Proteomic analyses frequently rely on mass spectrometry, a method characterized by its limited dynamic range, potentially overlooking low-abundant proteins. To address this limitation, single-molecule protein sequencing methods offer a solution. Fluorosequencing is a cutting-edge single-molecule protein sequencing method, which can distinguish peptides or protein molecules massively parallelly. This method has attracted interest from investors, as evidenced by the recent funding of Erisyon, a company developing this technology. This technique contains a challenging classification task: determining the original peptide sequence from light-intensity observations obtained after several Edman cycles. A classifier based on a combination of k Nearest Neighbors (kNN) with Hidden Markov Models (HMM) had been shown to have close-to-optimal accuracy with tractable complexity. We propose in this paper a new algorithm that reduces computation time significantly at the expense of a slight reduction in accuracy compared to state-of-the-art method. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are short protein sequences; they perform functions like some hormones and enzymes inside the body. The role of any protein or peptide is related to its structure and the sequence of amino acids that make up it. There are 20 types of amino acids in humans, and each of them has a particular characteristic according to its chemical structure. Current machine and deep learning models have been used to classify ACPs problems. However, these models have neglected Amino Acid Repeats (AARs) that play an essential role in the function and structure of peptides. Therefore, in this paper, ACPs offer a promising route for novel anticancer peptides by extracting AARs based on N-Grams and k-mers using two peptides' datasets. These datasets pointed to breast and lung cancer cells assembled and curated manually from the Cancer Peptide and Protein Database (CancerPPD). Every dataset consists of a sequence of peptides and their synthesis and anticancer activity on breast and lung cancer cell lines. Five different feature selection methods were used in this paper to improve classification performance and reduce the experimental costs. After that, ACPs were classified using four classifiers, namely AdaBoost, Random Forest Tree (RFT), Multi-class Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). These classifiers were evaluated by applying five well-known evaluation metrics. Experimental results showed that the breast and lung ACPs classification process provided an accurate performance that reached 89.25% and 92.56%, respectively. In terms of AUC, it reached 95.35% and 96.92% for both breast and lung ACPs, respectively. The proposed classifiers performed competently somewhat equally in AUC, accuracy, precision, F-measures, and recall, except for Multi-class SVM-based feature selection, which showed superior performance. As a result, this paper significantly improved the predictive performance that can effectively distinguish ACPs as virtual inactive, experimental inactive, moderately active, and very active. [ABSTRACT FROM AUTHOR]

Fluorescence indicators capable of binding to human immunodeficiency virus-1 (HIV-1) trans-activation responsive (TAR) RNA are powerful tools for the exploratory studies of the identification of anti-HIV drug candidates. This work presents a new design strategy for fluorogenic indicators with a transactivator of transcription (Tat)-derived peptide based on the forced intercalation of thiazole orange (TO) dyes (FIT). The developed 9-mer FIT peptide (RKKRR-TO-RRR: named FiLuP) features the TO unit integrated onto a Dap (2,3-diaminopropionic acid) residue in the middle of the Tat peptide sequence; the Q (glutamic acid) residue in the Tat peptide (RKKRR-Q-RRR) is replaced with TO as if it were an amino acid surrogate. This facilitates a significant light-up response (450-fold at λem = 541 nm, Φfree = 0.0057, and Φbound = 0.61) upon binding to TAR RNA. The response of FiLuP is highly selective to TAR RNA over other non-cognate RNAs, and FiLuP maintains strong binding affinity (Kd = 1.0 ± 0.6 nM). Significantly, in contrast to previously developed Tat peptide-based FRET probes, FiLuP is able to discriminate between "competitive" and "noncompetitive" inhibitors when used in the fluorescence indicator displacement (FID) assay. The FID assay under stringent screening conditions is also possible, enabling super-strong competitive binders toward TAR RNA to be sieved out. [ABSTRACT FROM AUTHOR]

Insufficient research on the material basis and functional product development of horse bones has led to the waste of resources. This paper, as the by-product of horse meat bone marrow was the object, aimed to discover the flavor and antioxidant activity of novel collagen peptides. Two peptides, HBMP-1-1 and HBMP-2-1, were isolated and purified from horse bone marrow collagen protein (HBMP) hydrolysates. The combination of spectroscopic and chromatographic methods including SDS-PAGE, UV, FT-IR, CD, and SEM identified the structures of the peptides. The amino acid composition, flavor characteristics, and antioxidant activity were also evaluated. SDS-PAGE analysis showed that the molecular weight of the two peptides was concentrated under 4.1 kDa. The total amino acid contents of the HBMP-1-1 was up to 570.779 mg/g. Leucine, alanine, and glutamicacid were the predominant amino acids in HBMP-1-1 with high nutritional value, and it contained 10.07% β-folding, 46.39% β-turn, and 43.54% random coil structure. The peptide sequences IDDPTDSKPE, LNGKLTGM, ELDEGYVPK, AFQEDPDKF, and FVGKVVDPTQK with molecular weights of 1116.51, 833.45, 1049.51, 1096.49, and 1217.69 Da were detected using LC-MS/MS. The results of the antioxidant activity test showed that separation and purification significantly improved the bioactivity of the protein. The half-inhibition rate of HBMP-1-1 against DPPH, ABTS, and hydroxyl radicals were 0.054, 0.050, and 0.137 mg/mL, respectively. This article provides a scientific basis for clarifying the material basis of horse bone marrow collagen peptides. Also, it provides new approaches and ideas for exploring efficient and safe bone-derived lead compounds and products. [ABSTRACT FROM AUTHOR]

Self-assembling peptides are of huge interest for biological, medical and nanotechnological applications. The enormous chemical variety that is available from the 20 amino acids offers potentially unlimited peptide sequences, but it is currently an issue to predict their supramolecular behavior in a reliable and cheap way. Herein we report a computational method to screen and forecast the aqueous self-assembly propensity of amyloidogenic pentapeptides. This method was found also as an interesting tool to predict peptide crystallinity, which may be of interest for the development of peptide based drugs. [ABSTRACT FROM AUTHOR]

The alteration of amino acid register in peptides could affect their folding properties and thus functions. The effect of sequence in metal ion induced folding of peptide/protein is important in the design of new functional protein structures, such as biomimetics. In this paper, a set of hairpin peptides with identical composition but different histidine locations have been designed to exploit the metal ion‐mediated peptide intramolecular folding transformation through the adjustment of histidine and lysine residue locations. The results demonstrated that the histidine positions in peptide sequence could dramatically affect the coordination modes between peptides and Cu(II) ion. The Cu(II) coordination could affect the secondary structure transformation and self‐assembly along with electrostatic interaction and hydrogen bonding synergistically. Three molecules with VHVH sequence at each side of the turn segment (VDPPT) were apt to coordinate with Cu(II) ion with three or four histidines. The closer the VHVH motifs were to the turn segment, the more easier the molecules were to form β sheet structure with the promotion of Cu(II) ions. These results demonstrated that the purposeful location of key amino acids in peptides could achieve the controllable conformation transformation of hairpin peptides, which provides useful strategies for biomimetic design, such as metalloenzymes. [ABSTRACT FROM AUTHOR]

The construction of peptides to mimic heterogeneous proteins such as type I collagen plays a pivotal role in deciphering their function and pathogenesis. However, progress in the field has been severely hampered by the lack of capability to create stable heterotrimers with desired functional sequences and without the effect of homotrimers. We have herein developed a set of triblock peptides that can assemble into collagen mimetic heterotrimers with desired amino acids and are free from the interference of homotrimers. The triblock peptides comprise a central collagen-like block and two oppositely charged N-/C-terminal blocks, which display inherent incompetency of homotrimer formation. The favorable electrostatic attraction between two paired triblock peptides with complementary terminal charged sequences promptly leads to stable heterotrimers with controlled chain composition. The independence of the collagen-like block from the two terminal blocks endows this system with the adaptability to incorporate desired amino acid sequences while maintaining the heterotrimer structure. The triblock peptides provide a versatile and robust tool to mimic the composition and function of heterotrimer collagen and may have great potential in the design of innovative peptides mimicking heterogeneous proteins. [ABSTRACT FROM AUTHOR]

Coacervation, or liquid-liquid phase separation (LLPS) of biomacromolecules, is increasingly recognized to play an important role both intracellularly and in the extracellular space. Central questions that remain to be addressed are the links between the material properties of coacervates (condensates) and both the primary and the secondary structures of their constitutive building blocks. Short LLPS-prone peptides, such as GY23 variants explored in this study, are ideal model systems to investigate these links because simple sequence modifications and the chemical environment strongly affect the viscoelastic properties of coacervates. Herein, a systematic investigation of the structure/property relationships of peptide coacervates was conducted using GY23 variants, combining biophysical characterization (plate rheology and surface force apparatus, SFA) with secondary structure investigations by infrared (IR) and circular dichroism (CD) spectroscopy. Mutating specific residues into either more hydrophobic or more hydrophilic residues strongly regulates the viscoelastic properties of GY23 coacervates. Furthermore, the ionic strength and kosmotropic characteristics (Hofmeister series) of the buffer in which LLPS is induced also significantly impact the properties of formed coacervates. Structural investigations by CD and IR indicate a direct correlation between variations in properties induced by endogenous (peptide sequence) or exogenous (ionic strength, kosmotropic characteristics, aging) factors and the β-sheet content within coacervates. These findings provide valuable insights to rationally design short peptide coacervates with programmable materials properties that are increasingly used in biomedical applications. Peptide-based coacervates display interesting properties for biomedical applications, however, the link between peptide structure and coacervate material properties remains unclear. Here, the authors report a direct correlation between the primary and secondary structures of the peptides and the viscoelastic properties of the coacervates. [ABSTRACT FROM AUTHOR]

[Display omitted] • UMP789 database was collected in this paper. • The Umami-BERT model comprehensively outperformed other deep learning models in terms of umami peptides prediction. • The contribution of amino acids to umami peptide was ranked. • The motif pattern concerning A, D, E, and G were analyzed. Umami peptides have received extensive attention due to their ability to enhance flavors and provide nutritional benefits. The increasing demand for novel umami peptides and the vast number of peptides present in food call for more efficient methods to screen umami peptides, and further exploration is necessary. Therefore, the purpose of this study is to develop deep learning (DL) model to realize rapid screening of umami peptides. The Umami-BERT model was devised utilizing a novel two-stage training strategy with Bidirectional Encoder Representations from Transformers (BERT) and the inception network. In the pre-training stage, attention mechanisms were implemented on a large amount of bioactive peptides sequences to acquire high-dimensional generalized features. In the re-training stage, umami peptide prediction was carried out on UMP789 dataset, which is developed through the latest research. The model achieved the performance with an accuracy (ACC) of 93.23% and MCC of 0.78 on the balanced dataset, as well as an ACC of 95.00% and MCC of 0.85 on the unbalanced dataset. The results demonstrated that Umami-BERT could predict umami peptides directly from their amino acid sequences and exceeded the performance of other models. Furthermore, Umami-BERT enabled the analysis of attention pattern learned by Umami-BERT model. The amino acids Alanine (A), Cysteine (C), Aspartate (D), and Glutamicacid (E) were found to be the most significant contributors to umami peptides. Additionally, the patterns of summarized umami peptides involving A, C, D, and E were analyzed based on the learned attention weights. Consequently, Umami-BERT exhibited great potential in the large-scale screening of candidate peptides and offers novel insight for the further exploration of umami peptides. [ABSTRACT FROM AUTHOR]

The modification of nucleocytoplasmic proteins by O-linked N-acetylglucosamine (O-GlcNAc) is an important regulator of cell physiology. O-GlcNAc is installed on over a thousand proteins by just one enzyme, O-GlcNAc transferase (OGT). How OGT is regulated is therefore a topic of interest. To gain insight into these questions, we used OGT to perform phage display selection from an unbiased library of ~109 peptides of 15 amino acids in length. Following rounds of selection and deep mutational panning, we identified a high-fidelity peptide consensus sequence, [Y/F]-x-P-x-Y-x-[I/M/F], that drives peptide binding to OGT. Peptides containing this sequence bind to OGT in the high nanomolar to low micromolar range and inhibit OGT in a noncompetitive manner with low micromolar potencies. X-ray structural analyses of OGT in complex with a peptide containing this motif surprisingly revealed binding to an exosite proximal to the active site of OGT. This structure defines the detailed molecular basis driving peptide binding and explains the need for specific residues within the sequence motif. Analysis of the human proteome revealed this motif within 52 nuclear and cytoplasmic proteins. Collectively, these data suggest a mode of regulation of OGT by which polypeptides can bind to this exosite to cause allosteric inhibition of OGT through steric occlusion of its active site. We expect that these insights will drive improved understanding of the regulation of OGT within cells and enable the development of new chemical tools to exert fine control over OGT activity. [ABSTRACT FROM AUTHOR]

Mushroom bioactive peptides (MBPs) are bioactive peptides extracted directly or indirectly from edible mushrooms. MBPs are known to have antioxidant, anti-aging, antibacterial, anti-inflammatory and anti-hypertensive properties, and facilitate memory and cognitive improvement, antitumour and anti-diabetes activities, and cholesterol reduction. MBPs exert antioxidant and anti-inflammatory effects by regulating the MAPK, Keap1-Nrf2-ARE, NF-κB and TNF pathways. In addition, MBPs exert antibacterial, anti-tumour and anti-inflammatory effects by stimulating the proliferation of macrophages. The bioactivities of MBPs are closely related to their molecular weights, charge, amino acid compositions and amino acid sequences. Compared with animal-derived peptides, MBPs are ideal raw materials for healthy and functional products with the advantages of their abundance of resources, safety, low price, and easy-to-achieve large-scale production of valuable nutrients for health maintenance and disease prevention. In this review, the preparation, bioactivities, mechanisms and structure–activity relationships of MBPs were described. The main challenges and prospects of their application in functional products were also discussed. This review aimed to provide a comprehensive perspective of MBPs. [ABSTRACT FROM AUTHOR]

Soybeans (Glycine max) are an excellent source of dietary proteins and peptides with potential biological activities, such as antihypertensive, anti-cholesterol, and antioxidant activity; moreover, they could prevent cancer. Also, soy contains all the essential amino acids for nutrition; therefore, it represents an alternative to animal proteins. The goal of this paper was the comprehensive characterization of medium-sized and short peptides (two to four amino acids) obtained from simulated gastrointestinal digestion. Two different analytical approaches were employed for peptide characterization, namely a common peptidomic analysis for medium-sized peptides and a suspect screening analysis for short peptides, employing an inclusion list of exact m/z values of all possible amino acid combinations. Moreover, fractionation by preparative reversed-phase liquid chromatography was employed to simplify the starting protein hydrolysate. Six fractions were collected and tested for antioxidative activity by an innovative antioxidant assay on human gastric adenocarcinoma AGS cell lines. The two most active fractions (2 and 3) were then characterized by a peptidomic approach and database search, as well as by a suspect screening approach, in order to identify potential antioxidant amino acid sequences. Some of the peptides identified in these two fractions have been already reported in the literature for their antioxidant activity. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) represent a promising new therapeutic approach in cancer treatment. They can target cancer cells without affecting healthy tissues or altering normal physiological functions. Machine learning algorithms have increasingly been utilized for predicting peptide sequences with potential ACP effects. This study analyzed four benchmark datasets based on a well-established random forest (RF) algorithm. The peptide sequences were converted into 566 physicochemical features extracted from the amino acid index (AAindex) library, which were then subjected to feature selection using four methods: light gradient-boosting machine (LGBM), analysis of variance (ANOVA), chi-squared test (Chi2), and mutual information (MI). Presenting and merging the identified features using Venn diagrams, 19 key amino acid physicochemical properties were identified that can be used to predict the likelihood of a peptide sequence functioning as an ACP. The results were quantified by performance evaluation metrics to determine the accuracy of predictions. This study aims to enhance the efficiency of designing peptide sequences for cancer treatment. [ABSTRACT FROM AUTHOR]

Recent findings qualified aldehydes as potential biomarkers for disease diagnosis. One of the possibilities is to use electrochemical biosensors in point-of-care (PoC), but these need further development to overcome some limitations. Currently, the primary goal is to enhance their metrological parameters in terms of sensitivity and selectivity. Previous findings indicate that peptide OBPP4 (KLLFDSLTDLKKKMSEC-NH2) is a promising candidate for further development of aldehyde-sensitive biosensors. To increase the affinity of a receptor layer to long-chain aldehydes, a structure stabilization of the peptide active site via the incorporation of different linkers was studied. Indeed, the incorporation of linkers improved sensitivity to and binding of aldehydes in comparison to that of the original peptide-based biosensor. The tendency to adopt disordered structures was diminished owing to the implementation of suitable linkers. Therefore, to improve the metrological characteristics of peptide-based piezoelectric biosensors, linkers were added at the C-terminus of OBPP4 peptide (KLLFDSLTDLKKKMSE-linker-C-NH2). Those linkers consist of proteinogenic amino acids from group one: glycine, L-proline, L-serine, and non proteinogenic amino acids from group two: β-alanine, 4-aminobutyric acid, and 6-aminohexanoic acid. Linkers were evaluated with in silico studies, followed by experimental verification. All studied linkers enhanced the detection of aldehydes in the gas phase. The highest difference in frequency (60 Hz, nonanal) was observed between original peptide-based biosensors and ones based on peptides modified with the GSGSGS linker. It allowed evaluation of the limit of detection for nonanal at the level of 2 ppm, which is nine times lower than that of the original peptide. The highest sensitivity values were also obtained for the GSGSGS linker: 0.3312, 0.4281, and 0.4676 Hz/ppm for pentanal, octanal, and nonanal, respectively. An order of magnitude increase in sensitivity was observed for the six linkers used. Generally, the linker's rigidity and the number of amino acid residues are much more essential for biosensors' metrological characteristics than the amino acid sequence itself. It was found that the longer the linkers, the better the effect on docking efficiency. [ABSTRACT FROM AUTHOR]

Peptides are an important group of compounds contributing to the desired, as well as the undesired taste of a food product. Their taste impressions can include aspects of sweetness, bitterness, savoury, umami and many other impressions depending on the amino acids present as well as their sequence. Identification of short peptides in foods is challenging. We developed a method to assign identities to short peptides including homologous structures, i.e. peptides containing the same amino acids with a different sequence order, by accurate prediction of the retention times during reversed phase separation. To train the method, a large set of well-defined short peptides with systematic variations in the amino acid sequence was prepared by a novel synthesis strategy called 'swapped-sequence synthesis'. Additionally, several proteins were enzymatically digested to yield short peptides. Experimental retention times were determined after reversed phase separation and peptide MS2 data was acquired using a high-resolution mass spectrometer operated in data-dependent acquisition mode (DDA). A support vector regression model was trained using a combination of existing sequence-independent peptide descriptors and a newly derived set of selected amino acid index derived sequence-specific peptide (ASP) descriptors. The model was trained and validated using the experimental retention times of the 713 small food-relevant peptides prepared. Whilst selecting the most useful ASP descriptors for our model, special attention was given to predict the retention time differences between homologous peptide structures. Inclusion of ASP descriptors greatly improved the ability to accurately predict retention times, including retention time differences between 157 homologous peptide pairs. The final prediction model had a goodness-of-fit (Q2) of 0.94; moreover for 93% of the short peptides, the elution order was correctly predicted. [ABSTRACT FROM AUTHOR]

Staphylococcus aureus biofilms are composed of a population of bacteria that can conduct interdependent physiological processes that lend to their high resistance to antimicrobials. These biofilms are associated with several conditions including prosthetic limb device infections, osteomyelitis, endocarditis, and wound infections. Phage display aids in the identification of specific biofilm components for the development of new treatments against such biofilms. In this study, we utilized a peptide with sequence RVRSAPSSS specific for Staphylococcus aureus in the comparison of a general phage elution profile with an elution profile containing this peptide. In addition, both the general and specific elution profiles were tested separately on a strain of methicillin-sensitive Staphylococcus aureus (ATCC 12600) to assess their specificity to their biofilm components. The specific elution profile isolated two amino acid sequences obtained from phage DNA sequencing, SNYTYGP and SNMEQVA, previously unattested to in similar phage display research. The SNYTYGP and SNMEQVA sequences were analyzed by BLASTP to correlate their identities. The SNYTYGP sequence was associated with both a LysM peptidoglycan-binding-domain-containing protein and a secretory antigen precursor ssaA, each of which may act as a virulence factor in S. aureus biofilms. The SNMEQVA sequence was associated with a DDE-type integrase/transposase/recombinase implicated in transformation. [ABSTRACT FROM AUTHOR]

Cancer has become a major factor threatening human life and health. Under the circumstance that traditional treatment methods such as chemotherapy and radiotherapy are not highly specific and often cause severe side effects and toxicity, new treatment methods are urgently needed. Anticancer peptide drugs have low toxicity, stronger efficacy and specificity, and have emerged as a new type of cancer treatment drugs. However, experimental identification of anticancer peptides is time-consuming and expensive, and difficult to perform in a high-throughput manner. Computational identification of anticancer peptides can make up for the shortcomings of experimental identification. In this study, a deep learning-based predictor named ACPred-BMF is proposed for the prediction of anticancer peptides. This method uses the quantitative and qualitative properties of amino acids, binary profile feature to numerical representation for the peptide sequences. The Bidirectional LSTM network architecture is used in the model, and the attention mechanism is also considered. To alleviate the black-box problem of deep learning model prediction, we visualized the automatically extracted features and used the Shapley additive explanations algorithm to determine the importance of features to further understand the anticancer peptide mechanism. The results show that our method is one of the state-of-the-art anticancer peptide predictors. A web server as the implementation of ACPred-BMF that can be accessed via: http://mialab.ruc.edu.cn/ACPredBMFServer/. [ABSTRACT FROM AUTHOR]

Mouse monoclonal antibody M4M was recently designed to block human TRPM4 channel. The polypeptide for generating M4M is composed of peptide A1 between the transmembrane segment 5 (S5) and the pore, and a second peptide A2 between the pore and the transmembrane segment 6 (S6). Using peptide microarray, a 4-amino acid sequence EPGF within the A2 was identified to be the binding epitope for M4M. Substitution of EPGF with other amino acids greatly reduced binding affinity. Structural analysis of human TRPM4 structure indicates that EPGF is located externally to the channel pore. A1 is close to the EPGF binding epitope in space, albeit separated by a 37-amino acid peptide. Electrophysiological study reveals that M4M could block human TRPM4, but with no effect on rodent TRPM4 which shares a different amino acid sequence ERGS for the binding motif. Our results demonstrate that M4M is a specific inhibitor for human TRPM4. [ABSTRACT FROM AUTHOR]

Stability of eukaryotic mRNAs is associated with their codon, amino acid, and GC content. Yet, coding sequence motifs that predictably alter mRNA stability in human cells remain poorly defined. Here, we develop a massively parallel assay to measure mRNA effects of thousands of synthetic and endogenous coding sequence motifs in human cells. We identify several families of simple dipeptide repeats whose translation triggers mRNA destabilization. Rather than individual amino acids, specific combinations of bulky and positively charged amino acids are critical for the destabilizing effects of dipeptide repeats. Remarkably, dipeptide sequences that form extended β strands in silico and in vitro slowdown ribosomes and reduce mRNA levels in vivo. The resulting nascent peptide code underlies the mRNA effects of hundreds of endogenous peptide sequences in the human proteome. Our work suggests an intrinsic role for the ribosome as a selectivity filter against the synthesis of bulky and aggregation-prone peptides. Here, the authors measure the mRNA levels of thousands of coding sequence motifs in human cells to find that nascent peptides with a combination of β-strand structures and bulky and positively charged sequences reduce mRNA levels by slowing translation. [ABSTRACT FROM AUTHOR]

Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause the irregular growth and division of human cells. Several traditional approaches such as therapies and wet laboratory-based methods have been applied to treat cancer cells. However, these methods are considered less effective due to their high cost and diverse side effects. According to recent advancements, peptide-based therapies have attracted the attention of scientists because of their high selectivity. Peptide therapy can efficiently treat the targeted cells, without affecting the normal cells. Due to the rapid increase of peptide sequences, an accurate prediction model has become a challenging task. Keeping the significance of anticancer peptides (ACPs) in cancer treatment, an intelligent and reliable prediction model is highly indispensable. In this paper, a FastText-based word embedding strategy has been employed to represent each peptide sample via a skip-gram model. After extracting the peptide embedding descriptors, the deep neural network (DNN) model was applied to accurately discriminate the ACPs. The optimized parameters of DNN achieved an accuracy of 96.94 %, 93.41 %, and 94.02 % using training, alternate, and independent samples, respectively. It was observed that our proposed cACP-DeepGram model outperformed and reported ~10 % highest prediction accuracy than existing predictors. It is suggested that the cACP-DeepGram model will be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and the datasets are publicly available at https://github.com/shahidakbarcs/cACP-DeepGram. [ABSTRACT FROM AUTHOR]

Synthesis of long peptides is generally considered as a challenge to peptide chemists, in addition to producing significant amounts of toxic waste, such as DMF. Here we show that using solvent‐less methods, such as ball milling, enabled the production of the hexapeptide Boc‐(Ala‐Phe‐Gly)2‐OBn at the gram scale with high overall yield (77 %, 5 linear steps). This is the longest peptide chain synthesized in a ball mill to date, in which the amino acid sequence is precisely controlled. This study complements the current fundamental knowledge required to synthesize longer and more difficult peptide chains (or small proteins) by using peptide fragment couplings in a ball mill. [ABSTRACT FROM AUTHOR]

Motivation: Local protein structure is usually described via classifying each peptide to a unique class from a set of pre-defined structures. These classifications may differ in the number of structural classes, the length of peptides, or class attribution criteria. Most methods that predict the local structure of a protein from its sequence first rely on some classification and only then proceed to the 3D conformation assessment. However, most classification methods rely on homologous proteins' existence, unavoidably lose information by attributing a peptide to a single class or suffer from a suboptimal choice of the representative classes. Results: To alleviate the above challenges, we propose a method that constructs a peptide's structural representation from the sequence, reflecting its similarity to several basic representative structures. For 5-mer peptides and 16 representative structures, we achieved the Q16 classification accuracy of 67.9%, which is higher than what is currently reported in the literature. Our prediction method does not utilize information about protein homologues but relies only on the amino acids' physicochemical properties and the resolved structures' statistics. We also show that the 3D coordinates of a peptide can be uniquely recovered from its structural coordinates, and show the required conditions under various geometric constraints. [ABSTRACT FROM AUTHOR]

Short peptides are of extreme interest in clinical and food research fields, nevertheless they still represent a crucial analytical issue. The main aim of this paper was the development of an analytical platform for a considerable advancement in short peptides identification. For the first time, short sequences presenting both natural and post-translationally modified amino acids were comprehensively studied thanks to the generation of specific databases. Short peptide databases had a dual purpose. First, they were employed as inclusion lists for a suspect screening mass-spectrometric analysis, overcoming the limits of data dependent acquisition mode and allowing the fragmentation of such low-abundance substances. Moreover, the databases were implemented in Compound Discoverer 3.0, a software dedicated to the analysis of short molecules, for the creation of a data processing workflow specifically dedicated to short peptide tentative identification. For this purpose, a detailed study of short peptide fragmentation pathways was carried out for the first time. The proposed method was applied to the study of short peptide sequences in enriched urine samples and led to the tentative identification more than 200 short natural and modified short peptides, the highest number ever reported. Image 1 • 216 natural and modified short peptides (SPs) tentatively identified in urine sample. • Post translational modification on SPs were investigated for the first time. • Suspect screening is more suitable than untargeted analysis for short peptides. • Detailed study of SPs fragmentation pathways was provided for the first time. • The proposed method avoids highly time-consuming data analysis. [ABSTRACT FROM AUTHOR]

Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at the protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotations into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. However, they may neglect the possibility of SAV combinations, e.g., haplotypes, existing in bio-samples. Therefore, it is necessary to consider all SAV combinations of a protein when generating SAV-harboring protein sequences. In this paper, we propose MinProtMaxVP, a novel approach which selects a minimized number of SAV-harboring protein sequences generated from the exhaustive approach, while still accommodating all possible variant peptides, by solving a classic set covering problem. Our study on known haplotype variations of TAS2R38 justifies the necessity for MinProtMaxVP to consider all combinations of SAVs. The performance of MinProtMaxVP is demonstrated by an in silico study on OR2T27 with five SAVs and real experimental data of the HEK293 cell line. Furthermore, assuming simulated somatic and germline variants of OR2T27 in tumor and normal tissues demonstrates that when adopting the appropriate somatic and germline SAV integration strategy, MinProtMaxVP is adaptable to labeling and label-free mass spectrometry-based experiments. We present MinProtMaxVP, a novel approach to generate SAV-harboring protein sequences for constructing a customized protein sequence database, which is used in database searching for variant peptide identification. This approach outperforms the existing approaches in generating all possible variant peptides to be included in protein sequences and possibly leading to identification of more variant peptides in proteogenomic analysis. Unlabelled Image • MinProtMaxVP is a novel approach for converting single amino acid variations into protein sequences. • MinProtMaxVP considers the possibility of haplotype variations and heterogeneity of a bio-sample during the conversion. • MinProtMaxVP generates protein sequences containing all possible variant peptides by solving a set covering problem. • The efficacy of MinProtMaxVP were validated by in silico studies and an MS data set of HEK293 cell line. [ABSTRACT FROM AUTHOR]

Background and Purpose: Immunotherapeutic intervention is one of the most promising strategies for the prevention and treatment of Alzheimer's disease (AD). Although they showed great success in AD mouse models, the clinical trials of many immune approaches failed due to low efficacy and safety. Thus, an animal model which can show the potential side effects of vaccines or antibodies is urgently needed. In this study, we generated EAE/AD mice by crossing APP/PS1 mice with experimental autoimmune encephalomyelitis (EAE) mice. We then investigated the efficacy and safety of two vaccines: the immunogens of which were Aβ1-42 aggregates (Aβ42 vaccine) and an oligomer-specific conformational epitope (AOE1 vaccine), respectively.Experimental Approach: EAE/AD mice were immunized with the Aβ42 vaccine or AOE1 vaccine five times at biweekly intervals. After the final immunization, cognitive function was evaluated by the Morris water maze, Y maze, and object recognition tests. Neuropathological changes in the mouse brains were analysed by immunohistochemistry and ELISA.Key Results: In contrast to previous findings in conventional AD animal models, Aβ42 immunization promoted neuroinflammation, enhanced Aβ levels and plaque burden, and failed to restore cognitive deficits in EAE/AD mice. By contrast, AOE1 immunization dramatically attenuated neuroinflammation, reduced Aβ levels, and improved cognitive performance in EAE/AD mice.Conclusion and Implications: These results suggest that the EAE/AD mouse model can exhibit the potential side effects of AD immune approaches that conventional AD animal models fail to display. Furthermore, strategies specifically targeting Aβ oligomers may be safe and show clinical benefit for AD treatment. [ABSTRACT FROM AUTHOR]

There are still unmet needs in finding new technologies for biomedical diagnostic and industrial applications. A technology allowing the analysis of size and sequence of short peptide molecules of only few molecular copies is still challenging. The fast, low-cost and label-free single-molecule nanopore technology could be an alternative for addressing these critical issues. Here, we demonstrate that the wild-type aerolysin nanopore enables the sizediscrimination of several short uniformly charged homopeptides, mixed in solution, with a single amino acid resolution. Our system is very sensitive, allowing detecting and characterizing a few dozens of peptide impurities in a high purity commercial peptide sample, while conventional analysis techniques fail to do so. [ABSTRACT FROM AUTHOR]

Background: Given a peptide as a string of amino acids, the masses of all its prefixes and suffixes can be found by a trivial linear scan through the amino acid masses. The inverse problem is the ideal de novo peptide sequencing problem: Given all prefix and suffix masses, determine the string of amino acids. In biological reality, the given masses are measured in a lab experiment, and measurements by necessity are noisy. The (real, noisy) de novo peptide sequencing problem therefore has a noisy input: a few of the prefix and suffix masses of the peptide are missing and a few other masses are given in addition. For this setting, we ask for an amino acid string that explains the given masses as accurately as possible. Results: Past approaches interpreted accuracy by searching for a string that explains as many masses as possible. We feel, however, that it is not only bad to not explain a mass that appears, but also to explain a mass that does not appear. We propose to minimize the symmetric difference between the set of given masses and the set of masses that the string explains. For this new optimization problem, we propose an efficient algorithm that computes both the best and the k best solutions. Proof-of-concept experiments on measurements of synthesized peptides show that our approach leads to better results compared to finding a string that explains as many given masses as possible. Conclusions: We conclude that considering the symmetric difference as optimization goal can improve the identification rates for de novo peptide sequencing. A preliminary version of this work has been presented at WABI 2016. [ABSTRACT FROM AUTHOR]

RATIONALE Tandem mass (MS/MS) spectra generated by collision-induced dissociation (CID) typically lack redundant peptide sequence information in the form of e.g. b- and y-ion series due to frequent use of sequence-specific endopeptidases cleaving C- or N-terminal to Arg or Lys residues. METHODS Here we introduce arginyl-tRNA protein transferase (ATE, EC 2.3.2.8) for proteomics. ATE recognizes acidic amino acids or oxidized Cys at the N-terminus of a substrate peptide and conjugates an arginine from an aminoacylated tRNAArg onto the N-terminus of the substrate peptide. This enzymatic reaction is carried out under physiological conditions and, in combination with Lys-C/Asp-N double digest, results in arginylated peptides with basic amino acids on both termini. RESULTS We demonstrate that in vitro arginylation of peptides using yeast arginyl tRNA protein transferase 1 (yATE1) is a robust enzymatic reaction, specific to only modifying N-terminal acidic amino acids. Precursors originating from arginylated peptides generally have an increased protonation state compared with their non-arginylated forms. Furthermore, the product ion spectra of arginylated peptides show near complete 2× fragment ladders within the same MS/MS spectrum using commonly available electrospray ionization peptide fragmentation modes. Unexpectedly, arginylated peptides generate complete y- and c-ion series using electron transfer dissociation (ETD) despite having an internal proline residue. CONCLUSIONS We introduce a rapid enzymatic method to generate peptides flanked on either terminus by basic amino acids, resulting in a rich, redundant MS/MS fragment pattern. © 2014 The Authors. Rapid Communications in Mass Spectrometry published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

At present, most investigations involving the Maillard reaction models have focused on free amino acids (FAAs), whereas the effects of peptides on volatile products are poorly understood. In our study, the formation mechanism of pyrazines, which were detected as characteristic volatiles in sunflower seed oil, from the reaction system of glucose and lysine-containing dipeptides and tripeptides was studied. The effect of the amino acid sequences of the dipeptides and tripeptides on pyrazine formation was further highlighted. Four different dipeptides and six tripeptides were selected. The results showed that the production of pyrazines in the lysine-containing dipeptide models was higher than that in the tripeptide and control models. Compounds 2,5(6)-Dimethylpyrazine and 2,3,5-trimethylpyrazine were the main pyrazine compounds in the dipeptide models. Furthermore, the C- or N-terminal amino acids of lysine-containing dipeptides can exert an important effect on the formation of pyrazines. In dipeptide models with lysine at the C-terminus, the content of total pyrazines followed the order of Arg−Lys > His−Lys; the order of the total pyrazine content was Lys−His > Lys−Arg in dipeptide models with N-terminal lysine. Additionally, for the tripeptide models with different amino acid sequences, more pyrazines and a greater variety of pyrazines were detected in the tripeptide models with N-terminal lysine/arginine than in the tripeptide models with N-terminal histidine. However, the total pyrazine content and the percentage of pyrazines in the total volatiles were similar in the tripeptide models with the same amino acids at the N-terminus. This study clearly illustrates the ability of dipeptides and tripeptides containing lysine, arginine and histidine to form pyrazines, improving volatile formation during sunflower seed oil processing. [ABSTRACT FROM AUTHOR]

The BIOPEP-UWM™ database of bioactive peptides (formerly BIOPEP) has recently become a popular tool in the research on bioactive peptides, especially on these derived from foods and being constituents of diets that prevent development of chronic diseases. The database is continuously updated and modified. The addition of new peptides and the introduction of new information about the existing ones (e.g., chemical codes and references to other databases) is in progress. New opportunities include the possibility of annotating peptides containing D-enantiomers of amino acids, batch processing option, converting amino acid sequences into SMILES code, new quantitative parameters characterizing the presence of bioactive fragments in protein sequences, and finding proteinases that release particular peptides. [ABSTRACT FROM AUTHOR]