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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]

The process of synthesizing radionuclide-coupled drugs, especially shutdown technology that links bipotent chelators with biomolecules, utilizes traditional coupling reactions, including emerging click chemistry; these reactions involve different drawbacks, such as complex and cumbersome reaction steps, long reaction times, and the use of catalysts at various pH values, which can negatively impact the effects of the chelating agent. To address the above problems in this study, This research designed a novel bipotent chelator coupled with peptides. In the present study, dichloromethane was used as a solvent, and the reaction was conducted at room temperature for 12 h. A one-step ring-opening method was employed to introduce the coupling functional group of tridentate amide acid. The coupling materials consisted of the amino active site of the peptide and diethylene glycol anhydride. In this paper, this study explored the reactions between different equivalents of acid anhydride coupled to the peptide (peptide sequence: HLRKLRKR) and determined that the maximum conversion of the peptide feedstock was 87%. To determine the selectivity of the reaction sites in this polypeptide, This study identified the peptide sequence at the reaction site using nuclear magnetic resonance (NMR) and liquid chromatography–mass spectrometry (LC-MS). For the selected peptide, the first reactive site was on the terminal amino group, followed by the amino group on the tetra- and hepta-lysine side chains. The tridentate amic acid framework functions as a chelating agent, capable of binding a range of lanthanide ions. This significantly reduces and optimizes the time and cost associated with synthesizing radionuclide-coupled drugs. [ABSTRACT FROM AUTHOR]

The average content of casein in yak milk is 40.2 g/L. Casein can be degraded by enzymatic digestion or food processing to produce abundant degradation peptides. International researchers have studied the degradation peptides of yak milk casein by using multiple techniques and methods, such as in vitro activity tests, cellular experiments, proteomics, bioinformatics, etc., and found that the degradation peptides have a wide range of functional activities that are beneficial to the human body, such as angiotensin-converting enzyme (ACE) inhibitory, antioxidant, anti-inflammatory, antidiabetic, antimicrobial, anticancer, and immunomodulatory activities, etc., and it has been proved that the types and strengths of functional activities are closely related to the structural characteristics of the peptides. This paper describes the characteristics of yak milk proteins, the functional activities, and mechanism of action of degraded peptides. Based on the types of functional activities of yak milk casein degradation peptides, we classified and elucidated the effects of structural factors, such as peptide molecular weight, peptide length, amino acid sequence, physicochemical properties, electrical charge, hydrophobicity, spatial conformation, chain length, and the type of enzyme on these activities. It reveals the great potential of yak milk casein degradation peptides as functional active peptide resources and as auxiliary treatments for diseases. It also provides important insights for analyzing yak casein degradation peptide activity and exploring high-value utilization. [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]

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]

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]

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]

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]

Antimicrobial peptides are promising alternatives to classical antibiotics. Their microbicidal activity can arise from different mechanisms, one of which is known as nutritional immunity and has metal micronutrients and metal-binding biomolecules as its main players. Calcitermin is an antimicrobial peptide and an effective metal chelator. Its properties as an antibacterial and anti-Candida agent have been recently studied both as a free peptide and in the presence of zinc and copper ions, with which it forms stable complexes. Calcitermin derivatives have also gained attention thanks to the possibility of improving their properties, like metal-binding affinity and/or stability in biological fluids, through ad hoc modifications of the native peptide sequence. In this work, the Ala-to-Ser substitutions close to the coordination site of calcitermin have been introduced to study the impact on the biological activity and metal-binding properties. Our results show that metal coordination has a clear impact on the bioactivity of the studied compounds, to the point that the truncated fragment of calcitermin, solely containing the main metal-binding residues, also shows antimicrobial activity. [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]

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]

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]

Almost 65% of the human protein supply in the world originates from plants, with legumes being one of the highest contributors, comprising between 20 and 40% of the protein supply. Bioactive peptides from various food sources including legumes have been reported to show efficacy in modulating starch digestion and glucose absorption. This paper will provide a comprehensive review on recent in vitro studies that have been performed on leguminous antidiabetic peptides, focusing on the α-amylase inhibitor, α-glucosidase inhibitor, and dipeptidyl peptidase-IV (DPP-IV) inhibitor. Variations in legume cultivars and methods affect the release of peptides. Different methods have been used, such as in sample preparation, including fermentation (t, T), germination (t), and pre-cooking; in protein extraction, alkaline extraction, isoelectric precipitation, phosphate buffer extraction, and water extraction; in protein hydrolysis enzyme types and combination, enzyme substrate ratio, pH, and time; and in enzyme inhibitory assays, positive control type and concentration, inhibitor or peptide concentration, and the unit of inhibitory activity. The categorization of the relative scale of inhibitory activities among legume samples becomes difficult because of these method differences. Peptide sequences in samples were identified by means of HPLC/MS. Software and online tools were used in bioactivity prediction and computational modelling. The identification of the types and locations of chemical interactions between the inhibitor peptides and enzymes and the type of enzyme inhibition were achieved through computational modelling and enzyme kinetic studies. [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]

Research on food-derived bioactive peptides is expanding and the need for a convenient online platform that combines a foolproof and intuitive user interface with a reliable database and tools for prediction and analysis is rising. In this regard, this paper presents an open-access web-based platform PepLab (Peptides Laboratory). The database contains 2764 peptide sequence entries, grouped into sixteen classes according to their biological activity and into seven classes according to the source from which they were derived. Moreover, it includes bioinformatic tools for their processing and analysis. The DMpep tool allows extracting information about a number of physicochemical characteristics of a peptide or a set of peptides, including those that are not recorded in the database. The main advantages of PepLab are a user-friendly interface, a responsive design, and optimized search engines for better visibility on the Internet. Prediction of non-reported activity is available based on amino acid sequence analysis. In addition, users can download data and results in a convenient format (text and/or graphic) that was limited in the existing platforms. In this way, the presented PepLab platform will be helpful for researchers from various fields—bioinformatics, pharmaceuticals, food sciences, dietetics, biotechnology, analytical chemistry, etc. [ABSTRACT FROM AUTHOR]

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Superoxide dismutase (SOD) enzymes are pivotal in regulating oxidative stress. In order to model Ni containing SOD enzymes, the results of the thermodynamic, spectroscopic and SOD activity studies on the complexes formed between nickel(II) and a NiSOD related peptide, CysCysAspLeuProCysGlyValTyr–NH2 (wtCC), are reported. Cysteine was introduced to replace the first histidine residue in the amino acid sequence of the active site of the NiSOD enzyme. The novel peptide exhibits 3 times higher metal binding affinity compared to the native NiSOD fragment. This is due to the presence of the first cysteine in the coordination sphere of nickel(II). At physiological pH, the (NH2,S–,S–,S–) coordinated complex is the major species. This coordination mode is altered when one thiolate group is replaced by an amide nitrogen of the peptide backbone above pH 7.5. The nickel complexes of wtCC exhibit similar SOD activity to that of the complex formed with the active site fragment of the native NiSOD. The reaction between the complexes and the superoxide anion was studied by the sequential stopped-flow method. These studies revealed that the nickel(II) complex is always in excess over the nickel(III) complex during the dismutation process. However, the nickel(III) species is also involved in a relatively fast degradation process. This unambiguously proves that a protective mechanism must be operative in the NiSOD enzyme which prevents the oxidation of the sulfur atom of cysteine in the presence of O2−. The results provide new possibilities for the use of NiSOD mimics in bio- and industrial catalytic processes. [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]

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]

The novel BIOPEP-UWM Virtual database is designed as a repository of peptide sequences whose bioactivity or taste information was the result of in silico predictions. It is a tool complementary to the existing BIOPEP-UWM database summarizing the results of experimental data on bioactive peptides. The layout and organization of the new database are identical to those of the existing BIOPEP-UWM database of bioactive peptides. The peptide data record includes the following information: name; sequence and function information (understood as information about the predicted target biomacromolecule); bibliographic data with the reference paper describing the peptide; additional information, including the peptide structure, annotated using chemical codes as well as the specification of the method used for bioactivity prediction; information about other activities discovered experimentally or predicted using computational methods; peptide taste (if available); and a database reference tab providing information about compound annotations in other databases (if available). [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]

The default choice of mobile phase acidifier for bottom-up LC-MS proteomic analyses is 0.10% formic acid because of its decent acidity, decent ion pairing ability, and low suppression of electrospray ionization. In recent years, state-of-the-art columns have been designed specifically to provide efficient separation even when using an MS-friendly mobile phase of low ionic strength. Despite this, no attempts have been made to improve the sensitivity of the MS-based analytical methods by reducing the amount of formic acid in the mobile phase. In this study, we evaluated the effect of reduced formic acid concentration in the mobile phase on the chromatographic behavior and MS response of peptides when separated using columns packed with a C18 stationary phase with a positively charged surface. Using 0.01% formic acid in the mobile phase maintained excellent chromatographic performance and increased MS signal response compared to the standard of 0.10%. The enhanced MS response translated to about 50% improved peptide identifications depending on the complexity and amount of sample injected. The increased retention of peptides at a reduced formic acid concentration was directly proportional to the number of acidic residues in the peptide sequence. The study was carried out by covering a spectrum of protein samples with varied complexity using analytical flow, micro-, and nanoflow regimes to expand the applicability in routine practice. [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]

The diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-integrated network algorithm for anti-cancer peptide sequence prediction called GRCI-Net. The main process is as follows: First, we implemented the fusion reduction of binary structure features and K-mer sparse matrix features through principal component analysis and generated a set of new features; second, we constructed a new bidirectional long- and short-term memory network. We used traditional convolution and dilated convolution to acquire features in the spatial dimension using the memory network's grouping sequence model, which is designed to better handle the diversification of anti-cancer peptide feature sequences and to fully learn the contextual information between features. Finally, we achieved the fusion of grouping sequence features and spatial dimensional integration features through two sets of dense network layers, achieved the prediction of anti-cancer peptides through the sigmoid function, and verified the approach with two public datasets, ACP740 (accuracy reached 0.8230) and ACP240 (accuracy reached 0.8750). The following is a link to the model code and datasets mentioned in this article: https://github.com/ YouHongfeng101/ACP-DL. [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]

Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. There are few relevant reports on the mapping relationship between the EC50 value of antihypertensive peptide activity (AHTPA-EC50) and its corresponding amino acid sequence (AAS) at present. In this paper, we have constructed two group series based on sorting natural logarithm of AHTPA-EC50 or sorting its corresponding AAS encoding number. One group possesses two series, and we find that there must be a random number series in any group series. The random number series manifests fractal characteristics, and the constructed series of sorting natural logarithm of AHTPA-EC50 shows good autocorrelation characteristics. Therefore, two non-linear autoregressive models with exogenous input (NARXs) were established to describe the two series. A prediction method is further designed for AHTPA-EC50 prediction based on the proposed model. Two dynamic neural networks for NARXs (NARXNNs) are designed to verify the two series characteristics. Dipeptides and tripeptides are used to verify the proposed prediction method. The results show that the mean square error (MSE) of prediction is about 0.5589 for AHTPA-EC50 prediction when the classification of AAS is correct. The proposed method provides a solution for AHTPA-EC50 prediction. [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]

The paper aimed to identify in silico bioactive peptides with antioxidant and antihypertensive effects from silver carp collagen. This approach involved the use of a wide range of specialized online databases and tools to identify bioactive peptides from various protein sources. In this case, the collagen type-I alpha-1 protein sequence was extracted from UniProtKB with the identification code A0A077B3P8. The digestion was simulated using the BIOPEP database with the following enzymes: subtilisin, papain and pepsin. ExPASy ProtParam, Peptide Ranker, PepCalc, and ToxinPred showed that silver carp collagen is a significant source of biologically active peptides, health promoters with potential antihypertensive and antioxidant effects. The computational approach used in this study offered useful initial insights for more extensive studies. [ABSTRACT FROM AUTHOR]

Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species. [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]

Aggregated gold nanoparticles (AuNPs) that respond to the acidic microenvironment of bacterial infection sites upon near-infrared (NIR) light irradiation provide a facile approach against bacterial infection. Generally, the aggregation of AuNPs with a larger size results in a higher photothermal conversion efficiency. However, it remains challenging to construct pH-sensitive large-sized AuNPs due to their poor dispersive stability and insensitive stimuli-response. To address this challenge, zwitterionic peptide-modified AuNPs (AuNPs@ZP) with three different sizes (15, 40 and 60 nm) were fabricated to achieve pH-induced aggregation and NIR photothermal antibacterial treatment. The self-complementary zwitterionic peptide (ZP) with the amino acid sequence of CVVVAAAEEEKKK was grafted on the surface of AuNPs, which underwent a fully reversible disassembly/aggregation switching process in response to pH changes. More importantly, AuNP40nm@ZP and AuNP60nm@ZP exhibited broad spectrum and efficient antibacterial activity upon 808 nm laser irradiation, due to their excellent photothermal conversion effect. This study provides a novel self-complementary zwitterionic peptide design to drive pH-induced large-sized AuNP aggregate formation for better NIR photothermal antibacterial treatment. [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]

Matrix metalloproteinase-9 (MMP-9) plays an important role in tumor progression. It is of great significance to establish a sensitive in situ assay strategy for MMP-9 activity in single living cells. Here a novel in situ single molecule spectroscopy method based on the fluorescence correlation spectroscopy (FCS) technique was proposed for measuring the MMP-9 activity at different locations within single living cells, using a fluorescent specific peptide and a reference dye as dual probes. The measurement principle is based on the decrease of the ratiometric translational diffusion time of dual probes in the detection volume due to the peptide cleavage caused by MMP-9. The peptide probe was designed to be composed of an MMP-9 cleavage and cell-penetrating peptide sequence that was labeled with a fluorophore and conjugated with a streptavidin (SAV) molecule. The ratiometric translational diffusion time was used as the measurement parameter to eliminate the effect of intracellular uncertain viscosity. The linear relationship between the ratiometric diffusion time and MMP-9 activity was established, and applied to the determination of enzymatic activity in cell lysates as well as the evaluation of the inhibitory effects of different inhibitors on MMP-9. More importantly, the method was successfully used to dynamically determine MMP-9 activity in single living cells or under the stimulation with phorbol 12-myristate 13-acetate (PMA) and inhibitors. [ABSTRACT FROM AUTHOR]

Copper(II) complexes of peptides with a histidine residue at the second position (His2 peptides) provide a unique profile of electrochemical behavior, offering signals of both Cu(II) reduction and Cu(II) oxidation. Furthermore, their structures with vacant positions in the equatorial coordination plane could facilitate interactions with other biomolecules. In this work, we designed a library of His2 peptides based on the sequence of Aβ5–9 (RHDSG), an amyloid beta peptide derivative. The changes in the Aβ5–9 sequence highly affect the Cu(II) oxidation signals, altered further by anionic species. As a result, Cu(II) complexes of Arg1 peptides without Asp residues were chosen as the most promising peptide-based molecular receptors for phosphates. The voltammetric data on Cu(II) oxidation for binary Cu(II)–His2 peptide complexes and ternary Cu(II)–His2 peptide/phosphate systems were also tested for His2 peptide recognition. We achieved a highly promising identification of subtle modifications in the peptide sequence. Thus, we introduce voltammetric measurement as a potential novel tool for peptide sequence recognition. [ABSTRACT FROM AUTHOR]

Nonspecific adsorption of non-target components (especially proteins) in food matrix on sensing surfaces deteriorates accuracy and sensitivity of electrochemical sensors, which is a key challenge for food safety sensing. Therefore, electrochemical sensors with antifouling capability of high-protein food matrices are highly desired. In this paper, an efficient and simple antifouling electrochemical aptasensor for tetracycline (TC) analysis was constructed based on a self-designed Y-shaped peptide [CPPPPEK-(RSERSERSE) 2 ] consisting of cysteine as the anchoring segment, polyproline as the supporting segment, and -EK-(RSERSERSE) 2 as antifouling branches. Hydrophilic and electrically neutral amino acid sequence and Y-shaped space structure of the peptide endow the sensing surface with good antifouling performance in protein solutions and diluted milk. Under optimized experiment conditions, the proposed aptasensor can detect TC with a wide linear range (0.01–100 ng mL−1) and a limit of detection of 5.3 pg mL−1 (S/N = 3). The spiked recovery experiments confirmed high accuracy of the aptasensor in diluted milk without other pretreatments (recoveries: 90.96%–95.86%). This strategy offers a valid way to reduce matrix interference, showing a promise to be extended to other sensing systems for different targets. [Display omitted] • An innovative electrochemical aptasensor was developed for TC determination. • It consists of CPPPPEK-(RSERSERSE) 2 and specific aptamer. • The Y-shaped peptide endowed the aptasensor with excellent antifouling ability. • The proposed sensor has satisfactory selectivity, repeatability and stability. [ABSTRACT FROM AUTHOR]

Dipeptidyl peptidase IV (DPP-IV) inhibitors are widely used in treating type 2 diabetes due to their ability to lower blood glucose levels. However, synthetic versions often lead to gastrointestinal side effects. This study explores DPP-IV inhibitory properties in peptides from bighead carp skin. Collagen was prepared, hydrolyzed into collagen peptides, and then fractionated for DPP-IV inhibitory activity examination. The most effective fractions were identified, and their peptide sequences were determined. Molecular docking analysis identified nine peptides with potential inhibitory activity, four of which (VYP, FVA, PPGF, PGLVG) were synthesized and tested in vitro. PPGF exhibited the highest potency with an IC50 of 4.63 nM, competitively binding to key DPP-IV sites, including ARG125, VAL711, TYR666, and TYR662. Other peptides showed varying effectiveness, with IC50 values of 398.87 nM (VYP), 402.02 nM (FVA), and 110.20 nM (PGLVG). These findings highlight bighead carp skin peptides as potent DPP-IV inhibitors with hypoglycemic potential, suggesting a novel avenue for diabetes management using natural peptides. Moreover, this research underscores the utilization of bighead carp by-products, contributing to environmental sustainability. [ABSTRACT FROM AUTHOR]

The skin of amphibians is a rich source of peptides with a wide range of biological activities. They are stored in secretory granules in an inactive form. Upon stimulation, they are secreted together with proteases into the skin. Once activated, they rapidly exert their biological effects, including fighting microorganisms and predators, while their excess is immediately destroyed by the released proteases. To keep bioactive peptides in their initial form, it is necessary to inhibit these enzymes. Several inhibitors for this purpose have previously been mentioned; however, there has not been any reliable comparison of their efficiency so far. Here, we studied the efficiency of methanol and hydrochloric and formic acids, as well as phenylmethylsulfonyl fluoride, in the inhibition of nine frog peptides with the known sequence, belonging to five families in the secretion of Pelophylax esculentus. The results demonstrated that methanol had the highest inhibitory efficiency, while phenylmethylsulfonyl fluoride was the least efficient, probably due to its instability in aqueous media. Possible cleavages between certain amino acid residues in the sequence were established for each of the inhibitors. These results may be helpful for future studies on the nature of proteases and on prediction of the possible cleavage sites in novel peptides. [ABSTRACT FROM AUTHOR]

Introduction: Before they can produce their own antibodies, newborns are protected from infections by transplacental transfer of maternal IgG antibodies and after birth through breast milk IgA antibodies. Rhinovirus (RV) infections are extremely common in early childhood, and while RV infections often result in only mild upper respiratory illnesses, they can also cause severe lower respiratory illnesses such as bronchiolitis and pneumonia. Methods: We used high-density peptide arrays to profile infant and maternal antibody reactivity to capsid and full proteome sequences of three human RVs - A16, B52, and C11. Results: Numerous plasma IgG and breast milk IgA RV epitopes were identified that localized to regions of the RV capsid surface and interior, and also to several non-structural proteins. While most epitopes were bound by both IgG and IgA, there were several instances where isotype-specific and RV-specific binding were observed. We also profiled 62 unique RV-C protein loop sequences characteristic of this species' capsid VP1 protein. Discussion: Many of the RV-C loop sequences were highly bound by IgG from one-year-old infants, indicating recent or ongoing active infections, or alternatively, a level of cross-reactivity among homologous RV-C sites. [ABSTRACT FROM AUTHOR]

Proteins play a key role in biological electron transport, but the structure-function relationships governing the electronic properties of peptides are not fully understood. Despite recent progress, understanding the link between peptide conformational flexibility, hierarchical structures, and electron transport pathways has been challenging. Here, we use single-molecule experiments, molecular dynamics (MD) simulations, nonequilibrium Green's function-density functional theory (NEGF-DFT), and unsupervised machine learning to understand the role of secondary structure on electron transport in peptides. Our results reveal a two-state molecular conductance behavior for peptides across several different amino acid sequences. MD simulations and Gaussian mixture modeling are used to show that this two-state molecular conductance behavior arises due to the conformational flexibility of peptide backbones, with a high-conductance state arising due to a more defined secondary structure (beta turn or 310 helices) and a low-conductance state occurring for extended peptide structures. These results highlight the importance of helical conformations on electron transport in peptides. Conformer selection for the peptide structures is rationalized using principal component analysis of intramolecular hydrogen bonding distances along peptide backbones. Molecular conformations from MD simulations are used to model charge transport in NEGF-DFT calculations, and the results are in reasonable qualitative agreement with experiments. Projected density of states calculations and molecular orbital visualizations are further used to understand the role of amino acid side chains on transport. Overall, our results show that secondary structure plays a key role in electron transport in peptides, which provides broad avenues for understanding the electronic properties of proteins. [ABSTRACT FROM AUTHOR]

The silicifying peptide R5 (H‐SSKKSGSYSGSKGSKRRIL‐OH) derived from diatoms is extensively investigated, but the mechanism underlying silica synthesis by R5 or R5‐alike peptides is still poorly understood, limiting the design of silicifying peptides Herein, machine learning techniques are used to design peptides with silicifying functionality. Utilizing a comprehensive dataset of peptides and their corresponding silicification outcomes, a deep learning model based on antimicrobial peptide migration learning is created. This model exhibits the remarkable capability to accurately predict peptide sequences with a high potential for facilitating silica formation. A selection of artificially designed peptides can catalyze the biomimetic synthesis of nanosilica, some of which demonstrate better catalytic activity with a wider pH range and faster reaction rate compared with the peptide R5. Additionally, the designed peptide is used to wrap the model diatom Phaeodactylum tricornutum with nanosilica coatings, resulting in a significant enhancement in the UV resistance of cells. The new silicified peptides are highly significant for advancing the understanding of the silica synthesis mechanism in diatoms, and the encapsulation of P. tricornutum has potential benefits in the development of new biosensors. [ABSTRACT FROM AUTHOR]

A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that generated each acquired tandem mass spectrum. Approaches that leverage known peptide sequence databases cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to tandem mass spectra without prior information—de novo peptide sequencing—is valuable for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this problem, it remains an outstanding challenge in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo's superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome. Identification of the peptide that generates each acquired tandem mass spectrum is a fundamental challenge in mass spectrometry-based proteomics. Here, the authors present Casanovo, a machine learning model that translates the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. [ABSTRACT FROM AUTHOR]

In this study, GO modified with sulfhydryl groups was prepared by thiolation on the surface of GO, which makes a relevant material. GO with sulfhydryl groups was combined with gold nanoparticles and then combined with peptide sequence using Au–S bonds. Using the change in absorbance before and after adding chymotrypsin, chymotrypsin was detected. This was label-free detection. The functional peptide EKEKEKEPPPPC was used to optimize the experiment by removing the non-specific adsorption in the system that was the reaction mixture. The detection limit of chymotrypsin was 0.25 pg mL−1. This can improve the sensitivity of detection. The method can be applied to real samples and has good development and application prospect. [ABSTRACT FROM AUTHOR]

The native chemical ligation (NCL) is a reaction between a pair of peptides, one with a cysteine unit at the N-terminal and the other with a thioester functional group at the C-terminal. The NCL reaction has been widely utilized for the synthesis of various peptides and proteins with long sequences. Here, we demonstrate that the intracellular NCL reaction can endow peptides with induced cytotoxicity in cancer cells. Based on the in situ reduction of protecting groups and spontaneous thioester formation in reducing environment, we hypothesize that the NCL reaction of peptide (NCL-Pep-1) with both N-terminal cysteine and C-terminal crypto-thioester with t-butyl mercaptan protecting groups can occur naturally in cancer cells and can be utilized for the preparation of peptide therapeutic agents. The NCL-Pep-1 peptide produces elongated cyclic peptides via sequential process with deprotection, spontaneous thioester formation, and NCL-induced peptide elongation. Owing to the remarkable enhancement of helicity after elongation, the NCL-Pep-1 peptide induces cancer cell death by intracellular NCL reaction. [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]

• 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]

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]

Introduction: The effector function of T cells is regulated via immune checkpoints, activating or inhibiting the immune response. The BTLA-HVEM complex, the inhibitory immune checkpoint, may act as one of the tumor immune escape mechanisms. Therefore, interfering with the binding of these proteins can prove beneficial in cancer treatment. Our study focused on peptides interacting with HVEM at the same place as BTLA, thus disrupting the BTLAHVEM interaction. These peptides’ structure and amino acid sequences are based on the gD protein, the ligand of HVEM. Here, we investigated their immunomodulatory potential in melanoma patients. Methods: Flow cytometry analyses of activation, proliferation, and apoptosis of T cells from patients were performed. Additionally, we evaluated changes within the T cell memory compartment. Results: The most promising compound – Pep(2), increased the percentages of activated T cells and promoted their proliferation. Additionally, this peptide affected the proliferation rate and apoptosis of melanoma cell line in coculture with T cells. Discussion: We conclude that the examined peptide may act as a booster for the immune system. Moreover, the adjuvant and activating properties of the gDderived peptide could be used in a combinatory therapy with currently used ICIbased treatment. Our studies also demonstrate that even slight differences in the amino acid sequence of peptides and any changes in the position of the disulfide bond can strongly affect the immunomodulatory properties of compounds. [ABSTRACT FROM AUTHOR]