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A single amino acid in a peptide sequence can play an important role to tune the self‐assembly and hydrogelation behaviour. Here, a C‐terminal cysteine‐containing ultrashort peptide hydrogelator forms hydrogel through non‐covalent and covalent interactions. Interestingly, the hydrogel is insoluble in water and buffer solutions at different pH values (1‐13) and is thixotropic and injectable. In recent years, removing dyes from contaminated water has become a significant concern because of the shortage of freshwater resources. Therefore, the adsorption of dyes through a reliable, straightforward, nontoxic, cheap, and environmentally friendly adsorbent has become a popular topic. Hence, the hydrogelator was exploited to remove organic dyes from wastewater, harnessing its applicability in the gel phase and solid supports (filter paper and cotton). [ABSTRACT FROM AUTHOR]

Bicyclic peptides have attracted the interest of pharmaceutical companies because of their remarkable properties, putting them on a new path in medicine. Their conformational rigidity improves proteolytic stability and leads to rapid penetration into tissues via any possible route of administration. Moreover, elimination of renal metabolism is of great importance, for example, for people with a history of liver diseases. In addition, each ring can function independently, making bicyclic peptides extremely versatile molecules for further optimization. In this paper, we compared the potentiometric and spectroscopic properties studied by UV–vis, MCD, and EPR of four synthetic analogues of the bi-cyclic peptide c(PKKHP-c(CFWKTC)-PKKH) (BCL). In particular, we correlated the structural and spectral properties of complexes with coordinating abilities toward Cu(II) ions of MCL1 (Ac-PKKHPc(CFWKTC)PKKH-NH2) that contains the unbinding cycle and N- and C-terminal linear parts with two histidine residues, one per part; two monocyclic ligands containing one histidine residue, both in the N-terminal position, i.e., MCL2 (Ac-PKKHPc(CFWKTC)PKKS-NH2) and in the C-terminal position, i.e., MCL3 (Ac-PKKSPc(CFWKTC)PKKH-NH2), respectively; and the linear structure LNL (Ac-PKKHPSFWKTSPKKH-NH2). Potentiometric results have shown that the bicyclic structure promotes the involvement of the side chain imidazole donors in Cu(II) binding. On the other hand, the results obtained for the mono-cyclic analogues lead to the conclusion that the coordination of the histidine moiety as an anchoring group is promoted by its location in the peptide sequence further from the nonbinding cycle, strongly influencing the involvement of the amide donors in Cu(II) coordination. [ABSTRACT FROM AUTHOR]

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]

Bioactive peptides derived from diverse food proteins have been part of diverse investigations. Whey is a rich source of proteins and components related to biological activity. It is known that proteins have effects that promote health benefits. Peptides derived from whey proteins are currently widely studied. These bioactive peptides are amino acid sequences that are encrypted within the first structure of proteins, which required hydrolysis for their release. The hydrolysis could be through in vitro or in vivo enzymatic digestion and using microorganisms in fermented systems. The biological activities associated with bio-peptides include immunomodulatory properties, antibacterial, antihypertensive, antioxidant and opioid, etc. These functions are related to general conditions of health or reduced risk of certain chronic illnesses. To determine the suitability of these peptides/ingredients for applications in food technology, clinical studies are required to evaluate their bioavailability, health claims, and safety of them. This review aimed to describe the biological importance of whey proteins according to the incidence in human health, their role as bioactive peptides source, describing methods, and obtaining technics. In addition, the paper exposes biochemical mechanisms during the activity exerted by biopeptides of whey, and their application trends. [ABSTRACT FROM AUTHOR]

Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide against an antibody, epitope mapping with peptides is the most extensively used method. However, this method is more time-consuming and inefficient than using present methods. The ability to retrieve data on protein sequences through laboratory procedures has led to the development of computational models that predict epitope binding based on machine learning and deep learning (DL). It has also evolved to become a crucial part of developing effective cancer immunotherapies. This paper proposes an architecture to generalize this case since various research strives to solve a low-performance classification problem. A proposed DL model is the fusion architecture, which combines two architectures: Transformer architecture and convolutional neural network (CNN), called MITNet and MITNet-Fusion. Combining these two architectures enriches feature space to correlate epitope labels with the binary classification method. The selected epitope–T-cell receptor (TCR) interactions are GILG, GLCT and NLVP, acquired from three databases: IEDB, VDJdb and McPAS-TCR. The previous input data was extracted using amino acid composition, dipeptide composition, spectrum descriptor and the combination of all those features called AADIP composition to encode the input data to DL architecture. For ensuring consistency, fivefold cross-validations were performed using the area under curve metric. Results showed that GILG, GLCT and NLVP received scores of 0.85, 0.87 and 0.86, respectively. Those results were compared to prior architecture and outperformed other similar deep learning models. [ABSTRACT FROM AUTHOR]

Spider venoms are composed, among other substances, of peptide toxins whose selectivity for certain physiological targets has made them powerful tools for applications such as bioinsecticides, analgesics, antiarrhythmics, antibacterials, antifungals and antimalarials, among others. Bioinsecticides are an environmentally friendly alternative to conventional agrochemicals. In this paper, the primary structure of an insecticidal peptide was obtained from the venom gland transcriptome of the ctenid spider Phoneutria depilata (Transcript ID PhdNtxNav24). The peptide contains 53 amino acids, including 10 Cys residues that form 5 disulfide bonds. Using the amino acid sequence of such peptide, a synthetic gene was constructed de novo by overlapping PCRs and cloned into an expression vector. A recombinant peptide, named delta-ctenitoxin (rCtx-4), was obtained. It was expressed, folded, purified and validated using mass spectrometry (7994.61 Da). The insecticidal activity of rCtx-4 was demonstrated through intrathoracic injection in crickets (LD50 1.2 μg/g insect) and it was not toxic to mice. rCtx-4 is a potential bioinsecticide that could have a broad spectrum of applications in agriculture. [ABSTRACT FROM AUTHOR]

Given that wet experimental methods were no longer adequate for the rapid identification of bitter peptides, this paper presented Bitter-Fus, a novel predictive deep learning method incorporating traditional manual features and pre-trained deep features. Firstly, the method automatically extracted deep learning features from peptide sequences using a pre-trained protein sequence language model, then fed the deep learning features into a long short-term memory (LSTM) network for dimensionality reduction to retain the most relevant features. Finally, the reduced-dimensional deep features were fused with the manual features composed of traditional amino acids composition (AAC) method and passed into the feedforward neural network to construct a prediction model. The validation experimental results showed that the prediction method Bitter-Fus obtained an accuracy precision value of 0. 902 and a Mathews correlation coefficient value of 0. 805 in a 10-fold cross-validation, and an accuracy precision value of 0. 930 and a Mathews correlation coefficient value of 0. 862 in the independent dataset test, which significantly outperformed the current state-of-the-art bitter peptide prediction methods BERT4Bitter and iBitter-SCM. [ABSTRACT FROM AUTHOR]

Simple Summary: This article is mainly about that molecular characterization and expression pattern of leptin in yellow cheek carp (Elopichthys bambusa) and its transcriptional changes in response to fasting and refeeding. In this study, the authors used PCR to clone the CDS of leptin in yellow cheek carp, and analyzed the sequence differences of the gene with other species, constructed the phylogenetic tree, used real-time PCR for analyzing the expression of leptin in different tissues, including the expression of leptin in the brain and liver after fasting–refeeding of yellow cheek carp. This paper found that the full-length cDNA sequence of Eblep was 1140 bp and the length of the open reading frame (ORF), which can encode a protein of 174 amino acids, was 525 bp. The Eblep mRNA transcript was detected in all tested tissues, with the highest expression in the liver and lowest expression in the spleen. It was found that the change in the mRNA expression of EbLep may be an adaptive strategy for different energy levels by studying the expression of EbLep mRNA in the brain and liver under fasting and refeeding. Leptin, a secretory protein encoded by obese genes, plays an important role in regulating feeding and energy metabolism in fish. To study the structure and function of the Leptin gene in yellow cheek carp (Elopichthys bambusa), the full-length cDNA sequence of leptin was cloned, named EbLep. The full-length cDNA of Eblep was 1140 bp, and the length of the open reading frame (ORF), which can encode a protein of 174 amino acids, was 525 bp. The signal peptide was predicted to contain 33 amino acids. Sequence alignment showed that the amino acid sequence of Leptin was conserved in cyprinid fish. Despite large differences between primary structures, the tertiary structure of the EbLep protein was similar to that of the human protein and had four α-helices. The EbLep mRNA transcript was detected in all tested tissues, with the highest expression in the liver and lowest expression in the spleen. In this study, short-term fasting significantly increased the mRNA expression of EbLep in the liver, which returned to a normal level after 6 days of refeeding and was significantly lower than the normal level after 28 days of refeeding. In the brain, the mRNA expression of EbLep significantly decreased during short-term fasting and significantly increased to a higher value than the control group after 1 h of refeeding. It then rapidly decreased to a lower value than the control group after 6 h of refeeding, returning to the normal level after 1 day of refeeding, and significantly decreasing to a lower value than the control group after 28 days of refeeding. To sum up, the change in the mRNA expression of EbLep in the brain and liver may be an adaptive strategy for different energy levels. [ABSTRACT FROM AUTHOR]

Food-derived antihypertensive peptides have attracted increasing attention in functional foods for health promotion, due to their high biological activity, low toxicity and easy metabolism in the human body. Angiotensin converting enzyme (ACE) is a key enzyme that causes the increase in blood pressure in mammals. However, few reviews have summarized the current understanding of ACE inhibitory peptides and their knowledge gaps. This paper focuses on the food origins and production methods of ACE inhibitory peptides. Compared with conventional methods, the advanced technologies and emerging bioinformatics approaches have recently been applied for efficient and targeted release of ACE inhibitory peptides from food proteins. Furthermore, the transport and underlying mechanisms of ACE inhibitory peptides are emphatically described. Molecular modeling and the Michaelis-Menten equation can provide information on how ACE inhibitors function. Finally, we discuss the structure-activity relationships and other bio-functional properties of ACE inhibitory peptides. Molecular weight, hydrophobic amino acid residues, charge, amino acid composition and sequence (especially at the C-terminal and N-terminal) have a significant influence on ACE inhibitory activity. Some studies are required to increase productivity, improve bioavailability of peptides, evaluate their bio-accessibility and efficiency on reducing blood pressure to provide a reference for the development and application of health products and auxiliary treatment drugs. [ABSTRACT FROM AUTHOR]

Amyloid aggregates play a major role in diseases as well as in normal physiological function. Receptor-interacting protein kinases 1 and 3 (RIP1/RIP3) aggregates complexes in cellular necroptosis is one example of protein aggregation in normal cellular function. Although recently there have been several studies on full kinase proteins aggregation, the aggregation potential of small peptide sequences of RIP1/RIP3, the physicochemical properties, and the potential in biomedical applications have not been explored. Hence, in this paper, we study the aggregation propensity of peptides consisting of four and twelve amino acid sequences in the RHIM region of RIP1/RIP3 proteins that are known to drive the beta-sheet formation and the subsequent aggregation. The aggregation kinetics, physicochemical characterization, mechanosensitive properties, cellular effects, and potential as a cancer drug depot have been investigated. The results show that the number and concentration of amino acids play a role in amyloid-like aggregates' properties. Further, the aggregates when formulated with cisplatin-induced significant lung cancer cell toxicity compared to an equal amount of cisplatin with and without ultrasound. The study would serve as a platform for further investigation on RIP1/RIP3 peptide and protein aggregates, their role in multiple cellular functions and diseases, and their potential as drug depots. [ABSTRACT FROM AUTHOR]

This paper first preseats a brief introduction to molecular biology, focusing on nucleotides and amino acids, the basic units of nucleic acid and protein sequence. With well-designed figures and tables, the name, classification, and property of four nucleotides and 20 amino acids are displayed. General concepts related to sequence alignment are described in the second section, including similarity and homology, global and local alignment, the dot plot method, dynamic and heuristic programming, scoring matrix and gap penalty, and alignment tools and platforms. The third part introduces the scoring matrices, which play a critical role in sequence alignment. Characteristics of DNAfull for DNA sequence, as well as BLOSUM62 and PAM250 for protein sequence are described in detail. By taking hemoglobin, peptide toxin, plant transcription factor, carcinoembryonic antigen, and cytosolic sialidase as examples, sections 4-8 illustrate how to choose alignment method and platform, how to select scoring matrix, how to change gap penalty, how to analyze alignment results and their biological significance. Finally, a simple summary is made. [ABSTRACT FROM AUTHOR]

Background: Anticancer peptide (ACP) inhibits and kills tumor cells. Research on ACP is of great significance for the development of new drugs, and the prediction of ACPs and non-ACPs is the new hotspot. Results: We propose a new machine learning-based method named GCNCPR-ACPs (a Graph Convolutional Neural Network Method based on collapse pooling and residual network to predict the ACPs), which automatically and accurately predicts ACPs using residual graph convolution networks, differentiable graph pooling, and features extracted using peptide sequence information extraction. The GCNCPR-ACPs method can effectively capture different levels of node attributes for amino acid node representation learning, GCNCPR-ACPs uses node2vec and one-hot embedding methods to extract initial amino acid features for ACP prediction. Conclusions: Experimental results of ten-fold cross-validation and independent validation based on different metrics showed that GCNCPR-ACPs significantly outperformed state-of-the-art methods. Specifically, the evaluation indicators of Matthews Correlation Coefficient (MCC) and AUC of our predicator were 69.5% and 90%, respectively, which were 4.3% and 2% higher than those of the other predictors, respectively, in ten-fold cross-validation. And in the independent test, the scores of MCC and SP were 69.6% and 93.9%, respectively, which were 37.6% and 5.5% higher than those of the other predictors, respectively. The overall results showed that the GCNCPR-ACPs method proposed in the current paper can effectively predict ACPs. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc [ABSTRACT FROM AUTHOR]

For mass spectrometry-based diagnostics of microorganisms, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used to identify urinary tract pathogens. However, it requires a lengthy culture step for accurate pathogen identification, and is limited by a relatively small number of available species in peptide spectral libraries (≤3329). Here, we propose a method for pathogen identification that overcomes the above limitations, and utilizes the MALDI-TOF/TOF MS instrument. Tandem mass spectra of the analyzed peptides were obtained by chemically activated fragmentation, which allowed mass spectrometry analysis in negative and positive ion modes. Peptide sequences were elucidated de novo, and aligned with the non-redundant National Center for Biotechnology Information Reference Sequence Database (NCBInr). For data analysis, we developed a custom program package that predicted peptide sequences from the negative and positive MS/MS spectra. The main advantage of this method over a conventional MALDI-TOF MS peptide analysis is identification in less than 24 h without a cultivation step. Compared to the limited identification with peptide spectra libraries, the NCBI database derived from genome sequencing currently contains 20,917 bacterial species, and is constantly expanding. This paper presents an accurate method that is used to identify pathogens grown on agar plates, and those isolated directly from urine samples, with high accuracy. [ABSTRACT FROM AUTHOR]

Selenium (Se)‐enriched peptides were isolated from Cardamine violifolia by enzymatic hydrolysis and ultrafiltration. S3 (molecular weight [MW] distribution of 3–5 kDa) exhibited the strongest inhibitory effect on HepG2 cells and was thus screened using the 3‐(4,5‐dimethylthiazole‐2‐yl)‐2,5‐diphenyltetrazolium bromide assay; it was found to have a high organic Se content. Its amino acid sequence was determined using HPLC‐MS/MS. We then examined its ability to inhibit tumor cell proliferation and found that it arrested tumor cells in the S phase; moreover, it could induce cancer cell apoptosis. Following S3 treatment, we observed a decrease in mitochondrial membrane potential and an increase in cell calcium content. Upon S3 treatment at 60 µg/ml, the relative activities of caspase‐3 and caspase‐9 increased by 1.48 times and 2.17 times, and the contents of PI3K and AKT decreased from 2.05 ng/L and 1.95 ng/L to 0.71 ng/L and 0.50 ng/L, respectively, when compared with the control group. Transcriptomic analysis revealed significant changes in the PI3K–AKT pathway following S3 treatment. This study thus established a foundation for additional development of Se‐enriched peptides from C. violifolia as a functional food. Practical Application: Cardamine violifolia is a Se‐tolerant cruciferous plant that can metabolically transform inorganic Se into organic Se that exists in the form of a selenoprotein. Se‐enriched peptide obtained by extraction and enzymolysis of selenoprotein, as an organic combination of organic Se and peptide, possess valuable biological activities. In this paper, the effect of Se‐enriched peptides of C. violifolia on tumor cells was studied via cell experiments, and its mechanism was preliminarily discussed, which should provide a theoretical basis for developing functional foods containing C. violifolia. [ABSTRACT FROM AUTHOR]

• Structure and properties of β-casein mutants in dairy products were summarized. • Impact of A1/A2 β-casein on gastrointestinal health in vivo studies was analyzed. • The detection method of β-casomorhin7 and A1/A2 β-casein was summarized. • Limitations and future directions in this promising field were presented. β-casein is the second most abundant form of casein in milk. Changes in amino acid sequence at specific positions in the primary structure of β-casein in milk will produce gene mutations that affect the physicochemical properties of dairy products and the hydrolysis site of digestive enzymes. The screening method of β-casein allele frequency detection in dairy products also has attracted the extensive attention of scientists and farmers. The A1 and A2 β-casein is the two usual mutation types, distinguished by histidine and proline at position 67 in the peptide chain. This paper summarizes the effects of A1 and A2 β-casein on the physicochemical properties of dairy products and evaluates the effects on human health, and the genotyping methods were also concluded. Impressively, this review presents possible future opportunities and challenges for the promising field of A2 β-casein, providing a valuable reference for the development of the functional dairy market. [ABSTRACT FROM AUTHOR]

As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs. A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models. The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities. [Display omitted] • Machine learning and virtual digestion were combined for the first time to enzymatic acquisition of naturally active peptides. • The SSA-LSTM machine learning model exhibited high accuracy performance in identifying neuroprotective peptides. • The neuroprotective efficacy of walnut protein hydrolysate obtained by SSA-LSTM-VD was verified by the mice model. [ABSTRACT FROM AUTHOR]

In recent years, there is a growing interest in the role of bioactive peptides obtained from natural food resources such as animal muscle proteins. Bioactive peptides/enzymatic protein hydrolysates are biomolecules broken down from proteins due to enzymatic hydrolysis, which usually contain 2–20 amino acids units with significant biological properties including antioxidant, anti-diabetic, and anti-cancer activities. Protein hydrolysates or peptides provide a natural therapeutics for controlling diabetes (type-II) by reducing amylase enzymatic activity and degrade the natural incretin hormone involved in blood glucose regulation. Such biological properties of peptides are highly correlated to sequence of amino acids and size. These bioactive molecules can be isolated, purified, and identified following different approaches such as gel filtration chromatography, Mass spectrometry approaches among others. In general, the animal muscles could be used as sustainable protein sources in the production of bioactive peptide-based functional foods and natural drugs for preventing chronic diseases. This review article aims to summarize the antioxidant, anti-diabetic, and anti-cancer activities of muscle proteins hydrolysates and purified bioactive peptides. [Display omitted] • This review paper discusses on functionality and bioactive characteristics of peptides. • Bioactive peptides can be produced through several enzymatic hydrolysis. • Specific bioactive peptides can be purified through different novel approaches. • Polar and non-polar peptides has anti-cancer and anti-diabetic properties. [ABSTRACT FROM AUTHOR]

One of the most desirable properties that biomaterials designed for tissue engineering or drug delivery applications should fulfill is biodegradation and resorption without toxicity. Therefore, there is an increasing interest in the development of biomaterials able to be enzymatically degraded once implanted at the injury site or once delivered to the target organ. In this paper, we demonstrate the protease sensitivity of self-assembling amphiphilic peptides, in particular, RAD16-I (AcN-RADARADARADARADA-CONH2), which contains four potential cleavage sites for trypsin. We detected that when subjected to thermal denaturation, the peptide secondary structure suffers a transition from β-sheet to random coil. We also used Matrix-Assisted Laser Desorption/Ionization-Time-Of-Flight (MALDI-TOF) to detect the proteolytic breakdown products of samples subjected to incubation with trypsin as well as atomic force microscopy (AFM) to visualize the effect of the degradation on the nanofiber scaffold. Interestingly, thermally treated samples had a higher extent of degradation than non-denatured samples, suggesting that the transition from β-sheet to random coil leaves the cleavage sites accessible and susceptible to protease degradation. These results indicate that the self-assembling peptide can be reduced to short peptide sequences and, subsequently, degraded to single amino acids, constituting a group of naturally biodegradable materials optimal for their application in tissue engineering and regenerative medicine. [ABSTRACT FROM AUTHOR]

Fungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot of interest as an alternative to currently available antifungal drugs. Although the AFPs are produced by diverse population of living organisms, identifying effective AFPs from natural sources is time-consuming and expensive. Therefore, there is a need to develop a robust in silico model capable of identifying novel AFPs in protein sequences. In this paper, we propose Deep-AFPpred, a deep learning classifier that can identify AFPs in protein sequences. We developed Deep-AFPpred using the concept of transfer learning with 1DCNN-BiLSTM deep learning algorithm. The findings reveal that Deep-AFPpred beats other state-of-the-art AFP classifiers by a wide margin and achieved approximately 96% and 94% precision on validation and test data, respectively. Based on the proposed approach, an online prediction server is created and made publicly available at https://afppred.anvil.app/. Using this server, one can identify novel AFPs in protein sequences and the results are provided as a report that includes predicted peptides, their physicochemical properties and motifs. By utilizing this model, we identified AFPs in different proteins, which can be chemically synthesized in lab and experimentally validated for their antifungal activity. [ABSTRACT FROM AUTHOR]

Milk proteins are recognized as the main source of biologically active peptides. Casein's primary structure contains several bioactive amino acid sequences on its latent inactive form. These potential active sequences can be released during cheese manufacture and ripening, giving rise to peptides with biological activity such as antihypertensive, antidiabetic, antioxidant, immunomodulatory, and mineral‐binding properties. However, the presence of biopeptides in cheese does not imply actual biological activity in vivo because these peptides can be further hydrolyzed during gastrointestinal transit. This paper reviews the recent advances in biopeptide formation in ripened cheeses production, focusing on the influence of technological parameters affecting proteolysis and the consequent release of peptides. The main discoveries in the field of cheese peptide digestion through recent in vivo and in vitro model studies are also reviewed. © 2021 Society of Chemical Industry [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]

Autophagy is a self-degradative process important for balancing the sources of energy and involved in the development of Alzheimer's disease (AD). To date, a number of papers have shown that elastin-derived peptides (EDPs) affect the expression and activation of peroxisome proliferator-activated receptor gamma (PPARγ), which is crucial for the development of AD and autophagy initiation. Therefore, the aim of the present study was to determine whether EDPs with a Val–Gly–Val–Ala–Pro–Gly (VGVAPG) amino acid sequence activate the autophagic process in undifferentiated SH-SY5Y human neuroblastoma cells. Our study is the first to show that EDPs with the VGVAPG sequence initiate the autophagy process in the undifferentiated SH-SY5Y cell line exhibiting a number of features of normal neuroblasts. In particular, we observed in our study that VGAVPG peptide increased ULK1, AKT, PPARγ, and LC3B protein expression. Moreover, our experiments with the agonist (rosiglitazone) and antagonist (GW9662) of PPARγ confirm that the studied EDP acts through the PPARγ pathway affecting mTOR and finally autophagy. Some studies have shown that autophagy disturbances are involved in the development of AD. Therefore, we believe that our study will provide new evidence of the possible involvement of EDPs (especially VGVAPG) in the development of AD. • The VGVAPG peptide induces autophagy in SH-SY5Y cells. • The VGVAPG peptide acts mainly through interacting with the PPARγ pathway. • The VGVAPG peptide does not initiate proliferation of undifferentiated SH-SY5Y cells. • The VGVAPG peptide increases ULK1, AKT, PPARγ, and LC3B protein expression. [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]

Motivation Machine learning plays a substantial role in bioscience owing to the explosive growth in sequence data and the challenging application of computational methods. Peptide-recognition domains (PRDs) are critical as they promote coupled-binding with short peptide-motifs of functional importance through transient interactions. It is challenging to build a reliable predictor of peptide-binding residue in proteins with diverse types of PRDs from protein sequence alone. On the other hand, it is vital to cope up with the sequencing speed and to broaden the scope of study. Results In this paper, we propose a machine-learning-based tool, named PBRpredict, to predict residues in peptide-binding domains from protein sequence alone. To develop a generic predictor, we train the models on peptide-binding residues of diverse types of domains. As inputs to the models, we use a high-dimensional feature set of chemical, structural and evolutionary information extracted from protein sequence. We carefully investigate six different state-of-the-art classification algorithms for this application. Finally, we use the stacked generalization approach to non-linearly combine a set of complementary base-level learners using a meta-level learner which outperformed the winner-takes-all approach. The proposed predictor is found competitive based on statistical evaluation. Availability and implementation PBRpredict-Suite software: http://cs.uno.edu/~tamjid/Software/PBRpredict/pbrpredict-suite.zip. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Over 20 years ago a remarkable paper was published in the Journal of American Society for Mass Spectrometry. This paper from Jimmy Eng, Ashley McCormack, and John Yates described the use of protein databases to drive the interpretation of tandem mass spectra of peptides. This paper now has over 3660 citations and continues to average more than 260 per year over the last decade. This is an amazing scientific achievement. The reason for this is the paper was a cutting edge development at the moment in time when genomes of organisms were being sequenced, protein and peptide mass spectrometry was growing into the field of proteomics, and the power of computing was growing quickly in accordance with Moore's law. This work by the Yates lab grew in importance as genomics, proteomics, and computation all advanced and eventually resulted in the widely used SEQUEST algorithm and platform for the analysis of tandem mass spectrometry data. This commentary provides an analysis of the impact of this paper by analyzing the citations it has generated and the impact of these citing papers. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Many useful properties are assigned to camel ( Camelus dromedarius ) milk, which is traditionally used for the treatment of tuberculosis, gastroenteritis, and allergy in many countries. Some amino acid sequences, which are encrypted in the camel proteins, may play a beneficial role in human health once they are released from milk either in vivo during normal digestion or by proteolysis with purified enzymes or during bacterial fermentation. Similar to the bovine milk counterparts, camel milk bioactive peptides may display a variety of potential activities that were almost always unveiled from in vitro analyses: anti-microbial, anti-oxidative, anti-hypertensive, anti-inflammatory, and immunomodulatory activities. Today, there is a growing interest for bioactive peptides generated from camel milk. This paper reviews available data on the potential biological activities of the camel milk proteins and their peptides liberated either during milk fermentation with proteolytic bacterial strains or by enzyme hydrolysis with specific proteases or simulated gastro-intestinal digestion. [ABSTRACT FROM AUTHOR]

Bioactive peptide (BP) is short sequence of amino acids, which can play different biological activities depending on its structural characteristics and amino acid sequence. As health promoters, bioactive peptides (BPs) have attracted more and more attention in recent years. Separation and purification methods of BPs such as pressure-driven and electrically-driven membrane processes were reviewed in this paper. Especially, cell configuration and application of electrodialysis with filtration membrane (EDFM), a new emerging electro-driven membrane technology, in separating anionic and cationic BPs were introduced in detail. It is difficult to separate and purify BPs due to their excellent water solubility as well as uncertain composition and structure. Thus, further development in efficient and economical technologies for the separation and purification of BPs is urgent to improve human health. • Multistage UF process can be used to obtain BPs with high purity and activity. • EDFM is a prospective technology due to its good flexibility and selectivity. • EDFM has potential to large-scale separation of BPs in the future. [ABSTRACT FROM AUTHOR]

Based on genome mining, a new antibacterial peptide named actinokineosin was isolated from a rare actinomycete Actinokineospora spheciospongiae. The amino acid sequence of the C-terminus of actinokineosin was established by TOF-MS/MS experiments. The amino acid sequence in the macrolactam ring was determined by TOF-MS/MS analyses after cleavage with BNPS-skatole and successive trypsin treatment. As a result of an antibacterial assay using a paper disk, actinokineosin showed antibacterial activity against Micrococcus luteus at a dosage of 50 μg per disk. From the genome sequence data of A. spheciospongiae, the biosynthetic gene cluster of actinokineosin was found and was indicated to consist of 10 genes. Among the genes, the gene aknA encoded the precursor of actinokineosin and the genes including aknC, aknB1 and aknB2 were proposed as modification enzymes to give mature actinokineosin. Significance and Impact of the Study Genome mining is a powerful tool to find new bioactive compounds from the genome database. In this report, we succeeded in isolation and structure determination of a new antibacterial peptide named actinokineosin based on genome mining. [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]

In this paper, the preparation, purification, and identification of the antioxidant peptides of millet gliadin after high hydrostatic pressure (HHP) treatment were studied to broaden the utilization range of millet and provide certain theoretical basis for its further processing and utilization. Millet gliadin treated with 300 MPa was enzymatically hydrolyzed by pepsin, and the optimal hydrolysis time was determined to be 50 min by in-vitro antioxidant activity evaluation method [1,1-diphenyl-2-trinitrophenylhydrazine (DPPH) free radical, hydroxyl radical, and superoxide anion scavenging activity]. The enzymatic hydrolysate was separated and purified by gel filtration and RP-HPLC, and the fraction with the strongest antioxidant activity (S4) was obtained. The scavenging ability of DPPH, •OH, and O 2 − were 53.96% ± 0.86%, 31.30% ± 0.90%, and 52.73% ± 0.99%, respectively. However, it still had lower antioxidant capacity compared with VC. Seven peptide sequences, namely, SLSHLTVQ, SLAHVTVQ, LHALTLQ, SGILAPSPVL, SHLTVQ, VSAAAA, and SPAAF, were identified from S4. The presence of certain amino acids, such as Glycine, Leucine, Phenylalanine, and Proline , is considered to induce high antioxidant activity against lipid peroxidation. Therefore, the antioxidant peptide extracted from millet gliadin after HHP treatment may be used as a natural antioxidant component in functional food. • HHP treatment significantly increased the enzymatic hydrolysis rate of millet gliadin. • The antioxidant peptide obtained had the strongest in-vitro activity after 300 MPa. • Seven novel antioxidant peptide sequences were identified and isolated. [ABSTRACT FROM AUTHOR]

Bioactive peptides are short amino acid sequences, that upon release from the parent protein may play different physiological roles, including antioxidant, antihypertensive, antimicrobial, and other bioactivities. They have been identified from a range of foods, including those of animal origin, e.g., milk and muscle sources (with pork, beef, or chicken and various species of fi sh and marine organism). Bioactive peptides are encrypted within the sequence of the parent protein molecule and latent until released and activated by enzymatic proteolysis, e.g. during gastrointestinal digestion or food processing. Bioactive peptides derived from food sources have the potential for incorporation into functional foods and nutraceuticals. The aim of this paper is to present an overview of the muscle-derived bioactive peptides, especially those of fermented meats and the potential benefits of these bioactive compounds to human health. [ABSTRACT FROM AUTHOR]

• ComProM -QSRR methodology has been developed for the prediction of retention time of peptides in liquid chromatography. • It identifies physicochemical properties at specific positions in the peptide sequences that modulate the retention time. • The prediction works well for all classes of peptides irrespective of size or sequence. • It is simple and straightforward approach to execute for retention time prediction. Peptide therapeutics plays a prominent role in medical practice. Both peptides and proteins have been used in several disease conditions like diabetes, cancer, bacterial infections etc. The optimization of a peptide library is a time consuming and expensive chore. The tools of computational chemistry offer a way to optimize the properties of peptides. Quantitative Structure Retention (Chromatographic) Relationships (QSRR) is a powerful tool which statistically derives relationships between chromatographic parameters and descriptors that characterize the molecular structure of analytes. In this paper, we show how Com parative Pro tein M odeling Q uantitative S tructure R etention R elationship (acronym ComProM -QSRR) can be used to predict the retention time of peptide sequences. This formalism is founded on our earlier published QSAR methodology Homo SAR. ComProM -QSRR can recognize and distinguish the contribution of amino acids at specific positions in the peptide sequences to the retention phenomena through their related physicochemical properties. This study firmly establishes the fact that this approach can be pragmatically used to predict the retention time to all classes of peptides regardless of size or sequence. [ABSTRACT FROM AUTHOR]

The histidine-rich peptides of the LAH4 family were designed using cationic antimicrobial peptides such as magainin and PGLa as templates. The LAH4 amphipathic helical sequences exhibit a multitude of interesting biological properties such as antimicrobial activity, cell penetration of a large variety of cargo and lentiviral transduction enhancement. The parent peptide associates with lipid bilayers where it changes from an orientation along the membrane interface into a transmembrane configuration in a pH-dependent manner. Here we show that LAH4 adopts a transmembrane configuration in fully saturated DMPC membranes already at pH 3.5, i.e. much below the pK a of the histidines whereas the transition pH in POPC correlates closely with histidine neutralization. In contrast in POPG membranes the in-planar configuration is stabilized by about one pH unit. The differences in pH can be converted into energetic contributions for the in-plane to transmembrane transition equilibrium, where the shift in the transition pH due to lipid saturation corresponds to energies which are otherwise obtained by the exchange of several cationic with hydrophobic residues. A similar dependence on lipid saturation has also been observed when the PGLa and magainin antimicrobial peptides interact within lipid bilayers suggesting that the quantitative evaluation presented in this paper also applies to other membrane polypeptides. [Display omitted] • Lipid saturation strongly drives the interface-to-transmembrane realignment. • The energetic contribution of lipid saturation corresponds to removing numerous charges from the amino acid sequence. • Anionic lipids stabilize the interfacial localization of cationic amphipathic peptides. [ABSTRACT FROM AUTHOR]

Soft tissue recovered from purportedly 68 million years old fossils of Tyrannosaurus rex Brachylophosauraus canadensis have been analyzed, and preservation mechanisms have been suggested by workers. A "preservation motif" based upon structure-function relationships at the molecular level is thought to explain the selective survival of soft tissue and particular peptide sequences. Preservation motifs include molecularly sheltered environments such as collagen fibril domains that offer tight molecular packing thereby conferring resistance to degradation. Sequences enriched with hydrophobic amino acid residues and depleted in acidic residues suggest another preservation pattern. More recent work by the same group adds that Fenton type reactions, arising from trace iron discovered with the bone tissue, is responsible for "fixing" the collagen for deep time survival. The present paper reviews these preservation motifs and supportive data in light of fast-degrading amino acids such as asparagine and glutamine, and oxidatively sensitive markers tyrosine, methionine, and histidine that survived the long burial age. [ABSTRACT FROM AUTHOR]

Repeat length disease thresholds vary among the 10 expanded polyglutamine (polyQ) repeat diseases, from about 20 to about 50 glutamine residues. The unique amino acid sequences flanking the polyQ segment are thought to contribute to these repeat length thresholds. The specific portions of the flanking sequences that modulate polyQ properties are not always clear, however. This ambiguity may be important in Huntington's disease (HD), for example, where in vitro studies of aggregation mechanisms have led to distinctly different mechanistic models. Most in vitro studies of the aggregation of the huntingtin (HTT) exon1 fragment implicated in the HD mechanism have been conducted on inexact molecules that are imprecise either on the N-terminus (recombinantly produced peptides) or on the C-terminus (chemically synthesized peptides). In this paper, we investigate the aggregation properties of chemically synthesized HTT exon1 peptides that are full-length and complete, containing both normal and expanded polyQ repeat lengths, and compare the results directly to previously investigated molecules containing truncated C-termini. The results on the full-length peptides are consistent with a two-step aggregation mechanism originally developed based on studies of the C-terminally truncated analogues. Thus, we observe relatively rapid formation of spherical oligomers containing from 100 to 600 HTT exon1 molecules and intermediate formation of short protofibril-like structures containing from 500 to 2600 molecules. In contrast to this relatively rapid assembly, mature HTT exon1 amyloid requires about one month to dissociate in vitro, which is similar to the time required for neuronal HTT exon1 aggregates to disappear in vivo after HTT production is discontinued. [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]

• Development of an efficient clean-up strategy for short peptides in plasma. • Comparison of 4 different clean-up protocols (advantages and disadvantages). • Untargeted peptidomic approach for short peptide identification in plasma. • Identification of 91 short peptides, the largest number never identified before. • Development of a rapid and simple method for clinical applications. Short peptides, namely di- tri- and tetra peptides, have been proven to play an important diagnostic role in several diseases. Therefore, the development of an analytical approach for their detection and identification is nowadays an important research goal. This paper describes an analytical procedure able to overcome the issues of short peptide isolation, clean-up and identification in plasma samples. Four different protocols were compared and tested to maximize both recovery and total number of identifications of short circulating plasma endogenous peptides. The purified peptides, coming from the four different tested protocols, were separated by zwitterionic hydrophilic liquid chromatography coupled to high-resolution mass spectrometry with the purpose of accomplishing an untargeted investigation based on suspect screening for short peptides in plasma. In particular, the use of Phree™ Phospholipid removal cartridge in combination with a purification step by solid phase extraction on a graphitized carbon black sorbent allowed the identification of the largest number of amino acid sequences (91 short peptides). The clean-up procedure allowed to tackle the issue of the low abundance of such peptides and their suppression during mass-spectrometric analysis. The results indicated that sample preparation is therefore fundamental for short peptide analysis in plasma samples. [ABSTRACT FROM AUTHOR]

World widely, cancer is considered a fatal disease and remains the major cause of death. Conventional medication approaches using therapies and anticancer drugs are deemed ineffective due to its high cost and harmful impacts on the normal cells. However, the innovation of anticancer peptides (ACPs) provides an effective way how to deals with cancer affected cells. Due to the rapid increases in peptide sequences, truly characterization of ACPs has become a challenging task for investigators. In this paper, an effort has been carried out to develop a reliable and intelligent computational method for the accurate discrimination of anticancer peptides. Three statistical feature representation schemes namely: Quasi-sequence order (QSO), conjoint triad feature, and Geary autocorrelation descriptor are applied to express motif of the target class. In order to eradicate irrelevant and noisy features, while select salient, profound and high variated features, principal component analysis is employed. Furthermore, the diverse nature of learning algorithms is utilized in order to select the best operational engine for the proposed model. After examining the empirical outcomes, support vector machine obtained quite encouraging results in combination with QSO feature space. It has achieved an accuracy of 96.91% and 89.54% using the main dataset and alternative dataset, respectively. It is observed that our proposed model shows an outstanding improvement compared to literature methods. It is expected that the developed model may be played a useful role in research academia as well as proteomics and drug development. • Computational model is developed for prediction of anticancer peptides. • Quasi-sequence order, conjoint triad feature, and Geary autocorrelation descriptor are used as feature extraction scheme. • PCA is applied as feature selection scheme. • Various classification algorithms are utilized. • Proposed model achieved quite promising results. [ABSTRACT FROM AUTHOR]

*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD). *: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively. *: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design. [ABSTRACT FROM AUTHOR]

2 ‐ Methoxy ‐ 4 ‐ methylsulfinylbenzyl (Mmsb) is a new backbone amide ‐ protecting group based on the safety ‐ catch concept. Mmsb is stable during the elongation of the peptide sequence and to trifluoroacetic acid cleavage from the resin. The presence of Mmsb reduces intra ‐ and inter ‐ chain interactions and thereby improves the synthetic process, as well as the properties of the quasi ‐ unprotected peptide. Mmsb offers the possibility to purify and characterize complex peptide sequences before the final cleavage that renders the target peptide. For more details, see the Full Paper by F. Albericio, J. Tulla ‐ Puche, and Paradís ‐ Bas on page 15031 ff. [ABSTRACT FROM AUTHOR]