PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues

Characterization and preparation of food-derived peptides on improving osteoporosis: A review

Osteoporosis is a systemic bone disease characterized by reduced bone mass and deterioration of the microstructure of bone tissue, leading to an increased risk of fragility fractures and affecting human health worldwide. Food-derived peptides are widely used in functional foods due to their low toxicity, ease of digestion and absorption, and potential to improve osteoporosis. This review summarized and discussed methods of diagnosing osteoporosis, treatment approaches, specific peptides as alternatives to conventional drugs, and the laboratory preparation and identification methods of peptides. It was found that peptides interacting with RGD (arginine-glycine-aspartic acid)-binding active sites in integrin could alleviate osteoporosis, analyzed the interaction sites between these osteogenic peptides and integrin, and further discussed their effects on improving osteoporosis. These may provide new insights for rapid screening of osteogenic peptides, and provide a theoretical basis for their application in bone materials and functional foods.

THPdb2: compilation of FDA approved therapeutic peptides and proteins

During the past 20 years, there has been a significant increase in the number of protein-based drugs approved by the US Food and Drug Administration (FDA). This paper presents THPdb2, an updated version of the THPdb database, which holds information about all types of protein-based drugs, including peptides, antibodies, and biosimilar proteins. THPdb2 contains a total of 6,385 entries, providing comprehensive information about 894 FDA-approved therapeutic proteins, including 354 monoclonal antibodies and 85 peptides or polypeptides. Each entry includes the name of therapeutic molecule, the amino acid sequence, physical and chemical properties, and route of drug administration. The therapeutic molecules that are included in the database target a wide range of biological molecules, such as receptors, factors, and proteins, and have been approved for the treatment of various diseases, including cancers, infectious diseases, and immune disorders.

An Atomistic View on the Mechanism of Diatom Peptide-Guided Biomimetic Silica Formation

Deciphering nature's remarkable way of encoding functions in its biominerals holds the potential to enable the rational development of nature-inspired materials with tailored properties. However, the complex processes that convert solution-state precursors into solid biomaterials remain largely unknown. In this study, an unconventional approach is presented to characterize these precursors for the diatom-derived peptides R5 and synthetic Silaffin-1A1 (synSil-1A1). These molecules can form defined supramolecular assemblies in solution, which act as templates for solid silica structures. Using a tailored structural biology toolbox, the structure-function relationships of these self-assemblies are unveiled. NMR-derived constraints are employed to enable a recently developed fractal-cluster formalism and then reveal the architecture of the peptide assemblies in atomistic detail. Finally, by monitoring the self-assembly activities during silica formation at simultaneous high temporal and residue resolution using real-time spectroscopy, the mechanism is elucidated underlying template-driven silica formation. Thus, it is demonstrated how to exercise morphology control over bioinorganic solids by manipulating the template architectures. It is found that the morphology of the templates is translated into the shape of bioinorganic particles via a mechanism that includes silica nucleation on the solution-state complexes' surfaces followed by complete surface coating and particle precipitation.

Investigating molecular descriptors in cell-penetrating peptides prediction with deep learning: Employing N, O, and hydrophobicity according to the Eisenberg scale

Cell-penetrating peptides comprise a group of molecules that can naturally cross the lipid bilayer membrane that protects cells, sharing physicochemical and structural properties, and having several pharmaceutical applications, particularly in drug delivery. Investigations of molecular descriptors have provided not only an improvement in the performance of classifiers but also less computational complexity and an enhanced understanding of membrane permeability. Furthermore, the employment of new technologies, such as the construction of deep learning models using overfitting treatment, promotes advantages in tackling this problem. In this study, the descriptors nitrogen, oxygen, and hydrophobicity on the Eisenberg scale were investigated, using the proposed ConvBoost-CPP composed of an improved convolutional neural network with overfitting treatment and an XGBoost model with adjusted hyperparameters. The results revealed favorable to the use of ConvBoost-CPP, having as input nitrogen, oxygen, and hydrophobicity together with ten other descriptors previously investigated in this research line, showing an increase in accuracy from 88% to 91.2% in cross-validation and 82.6% to 91.3% in independent test.

Exploring peptidomes of by-products generated during chhurpi production using Lactobacillus delbrueckii WS4 for identification of novel bioactive peptides

The considerable value of whey is evident from its significant potential applications and contributions to the functional food and nutraceutical market. The by-products were individually obtained during functional chhurpi and novel soy chhurpi cheese production using defined lactic acid bacterial strains of Sikkim Himalaya's traditional chhurpi. Hydrolysis of substrate proteins by starter proteinases resulted in a comparable peptide content in whey and soy whey which was associated with antioxidant and ACE inhibition potential. Peptidome analysis of Lactobacillus delbrueckii WS4 whey and soy whey revealed the presence of several bioactive peptides including the multifunctional peptides PVVVPPFLQPE and YQEPVLGPVRGPFPIIV. In silico analyses predicted the antihypertensive potential of whey and soy whey peptides with strong binding affinity for ACE active sites. QSAR models predicted the highest ACE inhibition potential (IC50) for the β-casein-derived decapeptide PVRGPFPIIV (0.95 μM) and the Kunitz trypsin inhibitor protein-derived nonapeptide KNKPLVVQF (16.64 μM). Chhurpi whey and soy whey can be explored as a valuable source of diverse and novel bioactive peptides for applications in designer functional foods development.

AagingBase: a comprehensive database of anti-aging peptides

The process of aging is an intrinsic and inevitable aspect of life that impacts every living organism. As biotechnological advancements continue to shape our understanding of medicine, peptide therapeutics have emerged as a promising strategy for anti-aging interventions. This is primarily due to their favorable attributes, such as low immunogenicity and cost-effective production. Peptide-based treatments have garnered widespread acceptance and interest in aging research, particularly in the context of age-related therapies. To effectively develop anti-aging treatments, a comprehensive understanding of the physicochemical characteristics of anti-aging peptides is essential. Factors such as amino acid composition, instability index, hydrophobic areas and other relevant properties significantly determine their efficacy as potential therapeutic agents. Consequently, the creation of 'AagingBase', a comprehensive database for anti-aging peptides, aims to facilitate research on aging by leveraging the potential of peptide therapies. AagingBase houses experimentally validated 282 anti-aging peptides collected from 54 research articles and 236 patents. Employing state-of-the-art computational techniques, the acquired sequences have undergone rigorous physicochemical calculations. Furthermore, AagingBase presents users with various informative analyses highlighting atomic compositions, secondary structure fractions, tertiary structure, amino acid compositions and frequencies. The database also offers advanced search and filtering options and similarity search, thereby aiding researchers in understanding their biological functions. Hence, the database enables efficient identification and prioritization of potential peptide candidates in geriatric medicine and holds immense potential for advancing geriatric medicine research and innovations. AagingBase can be accessed without any restriction. Database URL: https://project.iith.ac.in/cgntlab/aagingbase/.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Insights into the Adsorption Mechanisms of the Antimicrobial Peptide CIDEM-501 on Membrane Models

CIDEM-501 is a hybrid antimicrobial peptide rationally designed based on the structure of panusin and panulirin template peptides. The new peptide exhibits significant antibacterial activity against multidrug-resistant pathogens (MIC = 2-4 μM) while conserving no toxicity in human cell lines. We conducted molecular dynamics (MD) simulations using the CHARMM-36 force field to explore the CIDEM-501 adsorption mechanism with different membrane compositions. Several parameters that characterize these interactions were analyzed to elucidate individual residues' structural and thermodynamic contributions. The membrane models were constructed using CHARMM-GUI, mimicking the bacterial and eukaryotic phospholipid compositions. Molecular dynamics simulations were conducted over 500 ns, showing rapid and highly stable peptide adsorption to bacterial lipids components rather than the zwitterionic eucaryotic model membrane. A predominant peptide orientation was observed in all models dominated by an electric dipole. The peptide remained parallel to the membrane surface with the center loop oriented to the lipids. Our findings shed light on the antibacterial activity of CIDEM-501 on bacterial membranes and yield insights valuable for designing potent antimicrobial peptides targeting multi- and extreme drug-resistant bacteria.

A PTP1B-Cdk3 Signaling Axis Promotes Cell Cycle Progression of Human Glioblastoma Cells through an Rb-E2F Dependent Pathway

PTP1B plays a key role in developing different types of cancer. However, the molecular mechanism underlying this effect is unclear. To identify molecular targets of PTP1B that mediate its role in tumorigenesis, we undertook a SILAC-based phosphoproteomic approach, which allowed us to identify Cdk3 as a novel PTP1B substrate. Substrate trapping experiments and docking studies revealed stable interactions between the PTP1B catalytic domain and Cdk3. In addition, we observed that PTP1B dephosphorylates Cdk3 at tyrosine residue 15 in vitro and interacts with it in human glioblastoma cells. Next, we found that pharmacological inhibition of PTP1B or its depletion with siRNA leads to cell cycle arrest with diminished activity of Cdk3, hypophosphorylation of Rb, and the downregulation of E2F target genes Cdk1, Cyclin A, and Cyclin E1. Finally, we observed that the expression of a constitutively active Cdk3 mutant bypasses the requirement of PTP1B for cell cycle progression and expression of E2F target genes. These data delineate a novel signaling pathway from PTP1B to Cdk3 required for efficient cell cycle progression in an Rb-E2F dependent manner in human GB cells.

Assessing the fast prediction of peptide conformers and the impact of non-natural modifications

We present an assessment of different approaches to predict peptide structures using modeling tools. Several small molecule, protein, and peptide-focused methodologies were used for the fast prediction of conformers for peptides shorter than 30 amino acids. We assessed the effect of including restraints based on annotated or predicted secondary structure motifs. A number of peptides in bound conformations and in solution were collected to compare the tools. In addition, we studied the impact of changing single amino acids to non-natural residues using molecular dynamics simulations. Deep learning methods such as AlphaFold2, or the combination of physics-based approaches with secondary structure information, produce the most accurate results for natural sequences. In the case of peptides with non-natural modifications, modeling the peptide containing natural amino acids first and then modifying and simulating the peptide using benchmarked force fields is a recommended pipeline. The results can guide the modeling of oligopeptides for drug discovery projects.

Xeno Amino Acids: A Look into Biochemistry as We Do Not Know It

Would another origin of life resemble Earth's biochemical use of amino acids? Here, we review current knowledge at three levels: (1) Could other classes of chemical structure serve as building blocks for biopolymer structure and catalysis? Amino acids now seem both readily available to, and a plausible chemical attractor for, life as we do not know it. Amino acids thus remain important and tractable targets for astrobiological research. (2) If amino acids are used, would we expect the same L-alpha-structural subclass used by life? Despite numerous ideas, it is not clear why life favors L-enantiomers. It seems clearer, however, why life on Earth uses the shortest possible (alpha-) amino acid backbone, and why each carries only one side chain. However, assertions that other backbones are physicochemically impossible have relaxed into arguments that they are disadvantageous. (3) Would we expect a similar set of side chains to those within the genetic code? Many plausible alternatives exist. Furthermore, evidence exists for both evolutionary advantage and physicochemical constraint as explanatory factors for those encoded by life. Overall, as focus shifts from amino acids as a chemical class to specific side chains used by post-LUCA biology, the probable role of physicochemical constraint diminishes relative to that of biological evolution. Exciting opportunities now present themselves for laboratory work and computing to explore how changing the amino acid alphabet alters the universe of protein folds. Near-term milestones include: (a) expanding evidence about amino acids as attractors within chemical evolution; (b) extending characterization of other backbones relative to biological proteins; and (c) merging computing and laboratory explorations of structures and functions unlocked by xeno peptides.

Molecular binding mechanism and novel antidiabetic and anti-hypertensive bioactive peptides from fermented camel milk with anti-inflammatory activity in raw macrophages cell lines

The investigation was to determine the effect of camel milk fermented with Limosilactobacillus fermentum KGL4 (MTCC 25515) on ACE-inhibiting, anti-inflammatory, and diabetes-preventing properties and also to release the novel peptides with antidiabetic and anti-hypertensive attributes with molecular interaction studies. Growth conditions were optimised on the basis of total peptide production by inoculating the culture in camel milk at different rates (1.5, 2.0, and 2.5%) along with different incubation periods (12, 24, 36, and 48 h). However, after 48 h of fermentation with a 2.5% rate of inoculum, the highest proteolytic activity was obtained. Reverse phase high-pressure liquid chromatography (RP-HPLC) was used to calculate the % Rpa from permeates of 3 kDa and 10 kDa fractions. Molecular weight distributions of fermented and unfermented camel milk protein fractions were compared using SDS-PAGE. Spots obtained from 2D gel electrophoresis were separated on the basis of pH and molecular weight. Spots obtained from 2D gel were digested with trypsin, and the digested samples were subjected to RP-LC/MS for the generation of peptide sequences. The inhibition of tumour necrosis factor alpha, interleukin-6, and interleukin-1 during fermentation was studied using RAW 264.7 macrophages. In the study, fermented camel milk with KGL4 (CMKGL4) inhibited LPS-induced nitric oxide (NO) production and pro-inflammatory cytokine production (TNF-α, IL-6, and IL-1β) by the murine macrophages. The results showed that the peptide structures (YLEELHRLNK and YLQELYPHSSLKVRPILK) exhibited considerable binding affinity against hPAM and hMGA during molecular interaction studies.

Exploring a Structural Data Mining Approach to Design Linkers for Head-to-Tail Peptide Cyclization

Peptides have recently regained interest as therapeutic candidates, but their development remains confronted with several limitations including low bioavailability. Backbone head-to-tail cyclization, i.e., setting a covalent peptide bond linking the last amino acid with the first one, is one effective strategy of peptide-based drug design to stabilize the conformation of bioactive peptides while preserving peptide properties in terms of low toxicity, binding affinity, target selectivity, and preventing enzymatic degradation. Starting from an active peptide, it usually requires the design of a linker of a few amino acids to make it possible to cyclize the peptide, possibly preserving the conformation of the initial peptide and not affecting its activity. However, very little is known about the sequence-structure relationship requirements of designing linkers for peptide cyclization in a rational manner. Recently, we have shown that large-scale data-mining of available protein structures can lead to the precise identification of protein loop conformations, even from remote structural classes. Here, we transpose this approach to linkers, allowing head-to-tail peptide cyclization. First we show that given a linker sequence and the conformation of the linear peptide, it is possible to accurately predict the cyclized peptide conformation. Second, and more importantly, we show that it seems possible to elaborate on the information inferred from protein structures to propose effective candidate linker sequences constrained by length and amino acid composition, providing the first framework for the rational design of head-to-tail cyclization linkers. Finally, we illustrate this for two peptides using a limited set of amino-acids likely not to interfere with peptide function. For a linear peptide derived from Nrf2, the peptide cyclized starting from the experimental structure showed a 26-fold increase in the binding affinity. For urotensin II, a peptide already cyclized by a disulfide bond that exerts a broad array of biological activities, we were able, starting from models of the structure, to design a head-to-tail cyclized peptide, the first synthesized bicyclic 14-residue long urotensin II analogue, showing a retention of in vitro activity. Although preliminary, our results strongly suggest that such an approach has strong potential for cyclic peptide-based drug design.

pyPept: a python library to generate atomistic 2D and 3D representations of peptides

We present pyPept, a set of executables and underlying python-language classes to easily create, manipulate, and analyze peptide molecules using the FASTA, HELM, or recently-developed BILN notations. The framework enables the analysis of both pure proteinogenic peptides as well as those with non-natural amino acids, including support to assemble a customizable monomer library, without requiring programming. From line notations, a peptide is transformed into a molecular graph for 2D depiction tasks, the calculation of physicochemical properties, and other systematic analyses or processing pipelines. The package includes a module to rapidly generate approximate peptide conformers by incorporating secondary structure restraints either given by the user or predicted via pyPept, and a wrapper tool is also provided to automate the generation and output of 2D and 3D representations of a peptide directly from the line notation. HELM and BILN notations that include circular, branched, or stapled peptides are fully supported, eliminating errors in structure creation that are prone during manual drawing and connecting. The framework and common workflows followed in pyPept are described together with illustrative examples. pyPept has been released at: https://github.com/Boehringer-Ingelheim/pyPept .

A novel ESKAPE-sensitive peptide with enhanced stability and its application in controlling multiple bacterial contaminations in chilled fresh pork

The co-existence of various pathogenic bacteria on the surface of pork products exacerbates difficulties in food safety control. Developing broad-spectrum and stable antibacterial agents that are not antibiotics is an unmet need. To address this issue, all l-arginine residues of a reported peptide (IIRR)₄-NH₂ (zp80) were substituted with the corresponding D enantiomers. This novel peptide (IIrr)₄-NH₂ (zp80r) was expected to maintain favourable bioactivity against ESKAPE strains and have enhanced proteolytic stability compared with zp80. In a series of experiments, zp80r maintained favourable bioactivities against starvation-induced persisters. Electron microscopy and fluorescent dye assays were used to verify the antibacterial mechanism of zp80r. Importantly, zp80r reduced bacterial colonies in chilled fresh pork contaminated with multiple bacterial species. This newly designed peptide is a potential antibacterial candidate to combat problematic foodborne pathogens during storage of pork.

Computer simulation approach to the identification of visfatin-derived angiogenic peptides

Angiogenesis plays an essential role in various normal physiological processes, such as embryogenesis, tissue repair, and skin regeneration. Visfatin is a 52 kDa adipokine secreted by various tissues including adipocytes. It stimulates the expression of vascular endothelial growth factor (VEGF) and promotes angiogenesis. However, there are several issues in developing full-length visfatin as a therapeutic drug due to its high molecular weight. Therefore, the purpose of this study was to develop peptides, based on the active site of visfatin, with similar or superior angiogenic activity using computer simulation techniques.Initially, the active site domain (residues 181∼390) of visfatin was first truncated into small peptides using the overlapping technique. Subsequently, the 114 truncated small peptides were then subjected to molecular docking analysis using two docking programs (HADDOCK and GalaxyPepDock) to generate small peptides with the highest affinity for visfatin. Furthermore, molecular dynamics simulations (MD) were conducted to investigate the stability of the protein-ligand complexes by computing root mean square deviation (RSMD) and root mean square fluctuation(RMSF) plots for the visfatin-peptide complexes. Finally, peptides with the highest affinity were examined for angiogenic activities, such as cell migration, invasion, and tubule formation in human umbilical vein endothelial cells (HUVECs). Through the docking analysis of the 114 truncated peptides, we screened nine peptides with a high affinity for visfatin. Of these, we discovered two peptides (peptide-1: LEYKLHDFGY and peptide-2: EYKLHDFGYRGV) with the highest affinity for visfatin. In an in vitrostudy, these two peptides showed superior angiogenic activity compared to visfatin itself and stimulated mRNA expressions of visfatin and VEGF-A. These results show that the peptides generated by the protein-peptide docking simulation have a more efficient angiogenic activity than the original visfatin.

Non-Canonical Amino Acids as Building Blocks for Peptidomimetics: Structure, Function, and Applications

This review provides a fresh overview of non-canonical amino acids and their applications in the design of peptidomimetics. Non-canonical amino acids appear widely distributed in nature and are known to enhance the stability of specific secondary structures and/or biological function. Contrary to the ubiquitous DNA-encoded amino acids, the structure and function of these residues are not fully understood. Here, results from experimental and molecular modelling approaches are gathered to classify several classes of non-canonical amino acids according to their ability to induce specific secondary structures yielding different biological functions and improved stability. Regarding side-chain modifications, symmetrical and asymmetrical α,α-dialkyl glycines, Cα to Cα cyclized amino acids, proline analogues, β-substituted amino acids, and α,β-dehydro amino acids are some of the non-canonical representatives addressed. Backbone modifications were also examined, especially those that result in retro-inverso peptidomimetics and depsipeptides. All this knowledge has an important application in the field of peptidomimetics, which is in continuous progress and promises to deliver new biologically active molecules and new materials in the near future.

Engineering an ACE2-Derived Fragment as a Decoy for Novel SARS-CoV-2 Virus

Entry inhibitors are an important resource in the response against emerging pathogens like the novel SARS-CoV-2, which enters human cells via interaction between the surface spike glycoprotein and the cellular membrane receptor angiotensin-converting enzyme 2 (ACE2). Using a combination of comparative structural analyses of the binding surface of the spike to ACE2, docking experiments, and molecular dynamics simulations, we identified a stable fragment of ACE2 that binds to the spike, is soluble, and is not predicted to bind to its physiological ligand angiotensin II. From this fragment we computationally designed and experimentally validated a smaller, stable peptide that disrupts ACE2-spike interaction at nanomolar concentrations, suggesting its potential use as a decoy that could interfere with viral binding by competition.

Strategic Approaches to Improvise Peptide Drugs as Next Generation Therapeutics

In recent years, the occurrence of a wide variety of drug-resistant diseases has led to an increase in interest in alternate therapies. Peptide-based drugs as an alternate therapy hold researchers' attention in various therapeutic fields such as neurology, dermatology, oncology, metabolic diseases, etc. Previously, they had been overlooked by pharmaceutical companies due to certain limitations such as proteolytic degradation, poor membrane permeability, low oral bioavailability, shorter half-life, and poor target specificity. Over the last two decades, these limitations have been countered by introducing various modification strategies such as backbone and side-chain modifications, amino acid substitution, etc. which improve their functionality. This has led to a substantial interest of researchers and pharmaceutical companies, moving the next generation of these therapeutics from fundamental research to the market. Various chemical and computational approaches are aiding the production of more stable and long-lasting peptides guiding the formulation of novel and advanced therapeutic agents. However, there is not a single article that talks about various peptide design approaches i.e., in-silico and in-vitro along with their applications and strategies to improve their efficacy. In this review, we try to bring different aspects of peptide-based therapeutics under one article with a clear focus to cover the missing links in the literature. This review draws emphasis on various in-silico approaches and modification-based peptide design strategies. It also highlights the recent progress made in peptide delivery methods important for their enhanced clinical efficacy. The article would provide a bird's-eye view to researchers aiming to develop peptides with therapeutic applications.

Graphical Abstract

Production and Characterization of ACE Inhibitory and Anti-Diabetic Peptides from Buffalo and Camel Milk Fermented with Lactobacillus and Yeast: A Comparative Analysis with In Vitro, In Silico, and Molecular Interaction Study

The investigation aimed at assessing a comparative study on the production and characterization of ACE inhibitory, anti-diabetic, and anti-inflammatory activities, along with the production of ACE inhibitory and anti-diabetic peptides through the fermentation of buffalo and camel milk by Limosilactobacillus fermentum (KGL4) and Saccharomyces cerevisiae (WBS2A). The angiotensin-converting enzyme (ACE) inhibitory and anti-diabetic properties were evaluated at particular time intervals (12, 24, 36, and 48 h) at 37 °C, and we discovered maximum activity at 37 °C after 48 h of incubation. The maximum ACE inhibitory, lipase inhibitory activities, alpha-glucosidase inhibitory, and alpha-amylase inhibitory activities were found in the fermented camel milk (77.96 ± 2.61, 73.85 ± 1.19, 85.37 ± 2.15, and 70.86 ± 1.02), as compared to the fermented buffalo milk (FBM) (75.25 ± 1.72, 61.79 ± 2.14, 80.09 ± 0.51, and 67.29 ± 1.75). Proteolytic activity was measured with different inoculation rates (1.5%, 2.0%, and 2.5%) and incubation times (12, 24, 36, and 48 h) to optimize the growth conditions. Maximum proteolysis was found at a 2.5% inoculation rate and at a 48 h incubation period in both fermented buffalo (9.14 ± 0.06) and camel milk (9.10 ± 0.17). SDS-PAGE and 2D gel electrophoresis were conducted for protein purification. The camel and buffalo milk that had not been fermented revealed protein bands ranging from 10 to 100 kDa and 10 to 75 kDa, respectively, whereas all the fermented samples showed bands ranging from 10 to 75 kDa. There were no visible protein bands in the permeates on SDS-PAGE. When fermented buffalo and camel milk were electrophoresed in 2D gel, 15 and 20 protein spots were detected, respectively. The protein spots in the 2D gel electrophoresis ranged in size from 20 to 75 kDa. To distinguish between different peptide fractions, water-soluble extract (WSE) fractions of ultrafiltration (3 and 10 kDa retentate and permeate) of fermented camel and buffalo milk were employed in RP-HPLC (reversed-phase high-performance liquid chromatography). The impact of fermented buffalo and camel milk on inflammation induced by LPS (lipopolysaccharide) was also investigated in the RAW 264.7 cell line. Novel peptide sequences with ACE inhibitory and anti-diabetic properties were also analyzed on the anti-hypertensive database (AHTDB) and bioactive peptide (BIOPEP) database. We found the sequences SCQAQPTTMTR, EMPFPK, TTMPLW, HPHPHLSFMAIPPK, FFNDKIAK, ALPMHIR, IPAVFK, LDQWLCEK, and AVPYPQR from the fermented buffalo milk and the sequences TDVMPQWW, EKTFLLYSCPHR, SSHPYLEQLY, IDSGLYLGSNYITAIR, and FDEFLSQSCAPGSDPR from the fermented camel milk.

Early Selection of the Amino Acid Alphabet Was Adaptively Shaped by Biophysical Constraints of Foldability

Whereas modern proteins rely on a quasi-universal repertoire of 20 canonical amino acids (AAs), numerous lines of evidence suggest that ancient proteins relied on a limited alphabet of 10 "early" AAs and that the 10 "late" AAs were products of biosynthetic pathways. However, many nonproteinogenic AAs were also prebiotically available, which begs two fundamental questions: Why do we have the current modern amino acid alphabet and would proteins be able to fold into globular structures as well if different amino acids comprised the genetic code? Here, we experimentally evaluate the solubility and secondary structure propensities of several prebiotically relevant amino acids in the context of synthetic combinatorial 25-mer peptide libraries. The most prebiotically abundant linear aliphatic and basic residues were incorporated along with or in place of other early amino acids to explore these alternative sequence spaces. The results show that foldability was likely a critical factor in the selection of the canonical alphabet. Unbranched aliphatic amino acids were purged from the proteinogenic alphabet despite their high prebiotic abundance because they generate polypeptides that are oversolubilized and have low packing efficiency. Surprisingly, we find that the inclusion of a short-chain basic amino acid also decreases polypeptides' secondary structure potential, for which we suggest a biophysical model. Our results support the view that, despite lacking basic residues, the early canonical alphabet was remarkably adaptive at supporting protein folding and explain why basic residues were only incorporated at a later stage of protein evolution.

Biological Activity of Cyclic Peptide Extracted from Sphaeranthus amaranthoides Using De Novo Sequencing Strategy by Mass Spectrometry for Cancer

Though there are several advancements and developments in cancer therapy, the treatment remains challenging. In recent years, the antimicrobial peptides (AMPs) from traditional herbs are focused for identifying and developing potential anticancer molecules. In this study, AMPs are identified from Sphaeranthus amaranthoides, a natural medicinal herb widely used as a crucial immune stimulant in Indian medicine. A total of 86 peptide traces were identified using liquid-chromatography–electrospray-ionisation mass spectrometry (LC-ESI-MS). Among them, three peptides were sequenced using the manual de novo sequencing technique. The in-silico prediction revealed that SA923 is a cyclic peptide with C-N terminal interaction of the carbon atom of ASP7 with the nitrogen atom of GLU1 (1ELVFYRD7). Thus, SA923 is presented under the orbitides class of peptides, which lack the disulfide bonds for cyclization. In addition, SA923, steered with the physicochemical properties and support vector machine (SVM) algorithm mentioned for the segment, has the highest in silico anticancer potential. Further, the in vitro cytotoxicity assay revealed the peptide has anti-proliferative activity, and toxicity studies were demonstrated in Danio rerio (zebrafish) embryos.

Understanding Self-Assembly of Silica-Precipitating Peptides to Control Silica Particle Morphology

The most advanced materials are those found in nature. These evolutionary optimized substances provide highest efficiencies, e.g., in harvesting solar energy or providing extreme stability, and are intrinsically biocompatible. However, the mimicry of biological materials is limited to a few successful applications since there is still a lack of the tools to recreate natural materials. Herein, such means are provided based on a peptide library derived from the silaffin protein R5 that enables rational biomimetic materials design. It is now evident that biomaterials do not form via mechanisms observed in vitro. Instead, the material's function and morphology are predetermined by precursors that self-assemble in solution, often from a combination of protein and salts. These assemblies act as templates for biomaterials. The RRIL peptides used here are a small part of the silica-precipitation machinery in diatoms. By connecting RRIL motifs via varying central bi- or trifunctional residues, a library of stereoisomers is generated, which allows characterization of different template structures in the presence of phosphate ions by combining residue-resolved real-time NMR spectroscopy and molecular dynamics (MD) simulations. Understanding these templates in atomistic detail, the morphology of silica particles is controlled via manipulation of the template precursors.

Effect of splenic transfer factor on the development of intestinal mucosal barrier in laying hens

BACKGROUND

The aim of this study was to investigate the effects of different doses of chicken spleen transfer factor (TF) on the structure of intestinal epithelial cells in different age groups. One-day-old White Leghorns laying hens were randomly divided into four groups: three groups were administered TF at different dosages (0.10, 0.25 or 1.00 mL) and a fourth group was set as control (administered saline, 1.00 mL). Using hematoxylin and eosin staining, high-throughput sequencing, microbiota analysis, quantitative polymerase reaction and western blotting.

RESULTS

We measured the effects of different doses of TF on the following: intestinal mucosal epithelial tissue morphology, intestinal mucosal epithelial barrier-related gene expression profiles, and intestinal epithelial tight junction gene protein levels. The collected data show that TF can improve the absorption of nutrients by increasing villus height and crypt depth, and regulate intestinal flora disorders. Furthermore, we verified that the expression of the claudin and occludin tight junctions between intestinal epithelial cells was increased with TF. this research is very important for focusing on the structure and gene expression of intestinal tissues.

CONCLUSION

The results provide a scientific rationale for feeding and nutrition programs for green and healthy farming, as well as technical support to improve the production efficiency of the livestock and poultry breeding industry. © 2022 Society of Chemical Industry.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

A refined pH-dependent coarse-grained model for peptide structure prediction in aqueous solution

Introduction: Peptides carry out diverse biological functions and the knowledge of the conformational ensemble of polypeptides in various experimental conditions is important for biological applications. All fast dedicated softwares perform well in aqueous solution at neutral pH. Methods: In this study, we go one step beyond by combining the Debye-Hückel formalism for charged-charged amino acid interactions and a coarse-grained potential of the amino acids to treat pH and salt variations. Results: Using the PEP-FOLD framework, we show that our approach performs as well as the machine-leaning AlphaFold2 and TrRosetta methods for 15 well-structured sequences, but shows significant improvement in structure prediction of six poly-charged amino acids and two sequences that have no homologous in the Protein Data Bank, expanding the range of possibilities for the understanding of peptide biological roles and the design of candidate therapeutic peptides.

Exploring the structural basis to develop efficient multi-epitope vaccines displaying interaction with HLA and TAP and TLR3 molecules to prevent NIPAH infection, a global threat to human health

Nipah virus (NiV) is an emerging zoonotic virus that caused several serious outbreaks in the south asian region with high mortality rates ranging from 40 to 90% since 2001. NiV infection causes lethal encephalitis and respiratory disease with the symptom of endothelial cell-cell fusion. No specific and effective vaccine has yet been reported against NiV. To address the urgent need for a specific and effective vaccine against NiV infection, in the present study, we have designed two Multi-Epitope Vaccines (MEVs) composed of 33 Cytotoxic T lymphocyte (CTL) epitopes and 38 Helper T lymphocyte (HTL) epitopes. Out of those CTL and HTL combined 71 epitopes, 61 novel epitopes targeting nine different NiV proteins were not used before for vaccine design. Codon optimization for the cDNA of both the designed MEVs might ensure high expression potential in the human cell line as stable proteins. Both MEVs carry potential B cell linear epitope overlapping regions, B cell discontinuous epitopes as well as IFN-γ inducing epitopes. Additional criteria such as sequence consensus amongst CTL, HTL and B Cell epitopes was implemented for the design of final constructs constituting MEVs. Hence, the designed MEVs carry the potential to elicit cell-mediated as well as humoral immune response. Selected overlapping CTL and HTL epitopes were validated for their stable molecular interactions with HLA class I and II alleles and in case of CTL epitopes with human Transporter Associated with antigen Processing (TAP) cavity. The structure based epitope cross validation for interaction with TAP cavity was used as another criteria choosing final epitopes for NiV MEVs. Finally, human Beta-defensin 2 and Beta-defensin 3 were used as adjuvants to enhance the immune response of both the MEVs. Molecular dynamics simulation studies of MEVs-TLR3 ectodomain (Human Toll-Like Receptor 3) complex indicated the stable molecular interaction. We conclude that the MEVs designed and in silico validated here could be highly potential vaccine candidates to combat NiV infections, with great effectiveness, high specificity and large human population coverage worldwide.

Immunoinformatic approach employing modeling and simulation to design a novel vaccine construct targeting MDR efflux pumps to confer wide protection against typhoidal Salmonella serovars

Overcoming multi drug resistance is one of the crucial challenges to control enteric typhoid fever caused by Salmonella typhi and Salmonella paratyphi. Overexpression of efflux pumps predominantly causes drug resistance in microorganisms. Therefore, immunotherapy targeting the various efflux pumps antigens could be a promising strategy to increase the success of vaccines. An immunoinformatic approach was employed to design a Salmonellosis multi-epitope subunit vaccine peptide consisting of linear B-cell and T-cell epitopes of multidrug resistance protein families including ATP Binding Cassette (ABC), major facilitator superfamily (MFS), resistance nodulation cell division (RND), small multidrug resistance (SMR), and multidrug and toxin extrusion (MATE). The selected epitopes exhibited conservation in both S. typhi and S. paratyphi and thus could be helpful for cross-protection. Further, the final vaccine construct encompassing the peptides, adjuvants and specific linker sequences showed high immunogenicity, solubility, non-allergenic, nontoxic, and wide population coverage due to strong binding affinity to maximum HLA alleles. The three-dimensional structure was predicted, and validated using various structure validation tools. Additionally, protein-protein docking of the chimeric vaccine construct with the TLR-2 protein and molecular dynamics demonstrated stable and efficient binding. Conclusively, the immunoinformatic study showed that the novel multi epitopic vaccine construct can simulate the both T-cell and B-cell immune responses in typhoidal Salmonella serovars and could potentially be used for prophylactic or therapeutic applications.Communicated by Ramaswamy H. Sarma.

In silico design of epitope-based peptide vaccine against non-typhoidal Salmonella through immunoinformatic approaches

Non-typhoidal Salmonella (NTS) is one of the leading bacterial causes of many invasive human infections with a high antibiotic resistance profile. With this concern, the current study aimed to design an effective epitope-based peptide vaccine against NTS species as a successive and substitutive protective measure of invasive NTS disease. To design rationally, the current study considered a comprehensive in silico workflow combination of both immunoinformatics and molecular modeling approaches, including molecular docking and molecular dynamics (MD) simulation. We identified the two most promising T cell epitopes KVLYGIFAI and YGIFAITAL, and three B cell epitopes AAPVQVGEAAGS, TGGGDGSNT, and TGGGDGSNTGTTTT, in the outer membrane of NTS. Using these epitopes, a multiepitope vaccine was subsequently constructed along with appropriate adjuvant and linkers, which showed a good binding affinity and stability with toll-like receptor 2 (TLR2) in both molecular docking and MD simulation. Furthermore, in silico immune simulation described a strong immune response with a high number of antibodies, interferon-γ, and activated B and T cells. This study collectively suggests that predicted vaccine constructs could be considered potential vaccine candidates against common NTS species.Communicated by Ramaswamy H. Sarma.

Purification and Characterization of Novel Antihypertensive and Antioxidative Peptides From Whey Protein Fermentate: In Vitro, In Silico, and Molecular Interactions Studies

OBJECTIVE

The goal of this research was to purify and characterize the novel angiotensin-converting enzyme (ACE)-inhibitory and antioxidant peptides from fermented whey protein concentrate produced by Lactobacillus paracasei and Saccharomyces cerevisiae in a co-fermentation system.

METHOD

Whey protein fermented with lactic acid bacteria and yeast culture was analyzed for antioxidative, ACE inhibition, as well as anti-inflammatory activity followed by SDS-PAGE, isoelectric focusing, and 2-dimensional (2D) analysis. Anti-inflammatory activity of whey protein fermentate was also studied on the RAW 264.7 cell line. The bioactive peptides were separated from the whey protein fermentate using reverse-phase high-performance liquid chromatography (RP-HPLC) and reverse-phase liquid chromatography mass spectrometry (RPLC/MS), and thus identification and characterization of purified bioactive peptide was performed.

RESULTS

Whey protein fermentate samples' bioactivity was analyzed at specific time intervals at 12, 24, 36, and 48 hours at 37 °C for M11 and at 25 °C for WBS2A. The development settings (incubation time [12, 24, 36, and 48 hours) and inoculation rates [1.5%, 2.0%, and 2.5%]) were optimized for peptide synthesis via the o-phthaldialdehyde (OPA) method (proteolytic activity). Maximum proteolytic activity was observed at 37 °C for M11 (6.50 mg/mL) and at 25 °C for WBS2A (8.59 mg/mL) for 48 hours of incubation. Protein profiling was carried out using SDS-PAGE and 2D gel electrophoresis, in which Sodium dodecyl-sulfate (SDS) exhibited protein bands in the 10- to 55-kDa range, while 2D showed protein bands varying from 10 to 70 kDa. Every spot from 2D was digested by trypsin and identified by RPLC/MS. Protein fractionations (3- and 10-kDa permeates) were carried out employing RP-HPLC. Whey protein fermentate has anti-inflammatory action in RAW 264.7 macrophages that have been exposed to lipopolysaccharide. A molecular docking system was also used to investigate the interactions of peptides (AFLDSRTR, ILGAFIQIITFR) with human myeloperoxidase enzyme.

CONCLUSIONS

The antihypertensive and antioxidative peptides discovered from whey protein fermentate may be helpful in the design of pharmacologically active healthy ingredients in the upcoming years.

Proteomics Characterization of Food-Derived Bioactive Peptides with Anti-Allergic and Anti-Inflammatory Properties

Bioactive peptides are found in foods and dietary supplements and are responsible for health benefits with applications in human and animal medicine. The health benefits include antihypertensive, antimicrobial, antithrombotic, immunomodulatory, opioid, antioxidant, anti-allergic and anti-inflammatory functions. Bioactive peptides can be obtained by microbial action, mainly by the gastrointestinal microbiota from proteins present in food, originating from either vegetable or animal matter or by the action of different gastrointestinal proteases. Proteomics can play an important role in the identification of bioactive peptides. High-resolution mass spectrometry is the principal technique used to detect and identify different types of analytes present in complex mixtures, even when available at low concentrations. Moreover, proteomics may provide the characterization of epitopes to develop new food allergy vaccines and the use of immunomodulating peptides to induce oral tolerance toward offending food allergens or even to prevent allergic sensitization. In addition, food-derived bioactive peptides have been investigated for their anti-inflammatory properties to provide safer alternatives to nonsteroidal anti-inflammatory drugs (NSAIDs). All these bioactive peptides can be a potential source of novel drugs and ingredients in food and pharmaceuticals. The following review is focused on food-derived bioactive peptides with antiallergic and anti-inflammatory properties and summarizes the new insights into the use of proteomics for their identification and quantification.

Production and characterization of bioactive peptides in novel functional soybean chhurpi produced using Lactobacillus delbrueckii WS4

A novel soy chhurpi product was developed by fermentation of soymilk using proteolytic Lactobacillus delbrueckii strains isolated from traditional chhurpi production of Sikkim Himalaya. Soymilk fermentation by L. delbrueckii WS4 was associated with the hydrolysis of globulin proteins, with observed antioxidant, and ACE-inhibitory activity which further increased upon simulated in vitro gastrointestinal digestion. Peptidomics analysis of soy chhurpi and its gastrointestinal digest resulted in the identification of bioactive peptides with ACE-inhibitory and antioxidant properties. In silico antihypertensive property prediction followed by molecular docking study demonstrated strong binding affinity of selected peptides with ACE. The glycinin-derived peptide, SVIKPPTDE escaped gastrointestinal digestion and demonstrated strong non-bond interactions with ACE catalytic residues. QSAR models predicted an ACE-inhibitory IC50 of 21.29 µM for SVIKPPTDE. This is the first report on the production of novel functional soy chhurpi cheese using defined starter strains and the identification of bioactive peptides in undigested and gastrointestinal digested soy chhurpi.

Novel Cyclic Peptides for Targeting EGFR and EGRvIII Mutation for Drug Delivery

The epidermal growth factor–epidermal growth factor receptor (EGF-EGFR) pathway has become the main focus of selective chemotherapeutic intervention. As a result, two classes of EGFR inhibitors have been clinically approved, namely monoclonal antibodies and small molecule kinase inhibitors. Despite an initial good response rate to these drugs, most patients develop drug resistance. Therefore, new treatment approaches are needed. In this work, we aimed to find a new EGFR-specific, short cyclic peptide, which could be used for targeted drug delivery. Phage display peptide technology and biopanning were applied to three EGFR expressing cells, including cells expressing the EGFRvIII mutation. DNA from the internalized phage was extracted and the peptide inserts were sequenced using next-generation sequencing (NGS). Eleven peptides were selected for further investigation using binding, internalization, and competition assays, and the results were confirmed by confocal microscopy and peptide docking. Among these eleven peptides, seven showed specific and selective binding and internalization into EGFR positive (EGFR+ve) cells, with two of them—P6 and P9—also demonstrating high specificity for non-small cell lung cancer (NSCLC) and glioblastoma cells, respectively. These peptides were chemically conjugated to camptothecin (CPT). The conjugates were more cytotoxic to EGFR+ve cells than free CPT. Our results describe a novel cyclic peptide, which can be used for targeted drug delivery to cells overexpressing the EGFR and EGFRvIII mutation.

Proteome-Wide and Protein-Specific Multi-Epitope Vaccine Constructs Against the Rift Valley Fever Virus Outbreak Using Integrated Omics Approaches

Rift Valley fever (RVF) is a viral disease caused by a member of the Bunyavirales family causing severe infections in humans. The RVF virus is an enveloped, negative-sense, single-stranded RNA virus that can infect both animals and humans. The symptoms associated with these infections span from minor (fever and headaches) to severe (meningoencephalitis and hemorrhagic fever syndrome) symptoms. Despite the outbreaks of the RVF virus being reported in different parts of the world, no effective therapy is available. Herein, the development of an efficient vaccine is critical for the control of infections associated with the RVF virus. Moreover, computational vaccine approaches are helpful in the design of specific, safe, and stable peptide-based designs when compared to the conventional methods of vaccine development. In this study, the whole proteome of the virus, comprising four proteins (NP, L, GP, and NSP), was screened to find putative vaccine epitope sequences (T cell, B cell, and HTL) specific for each protein. These shortlisted epitopes were then combined with flexible linkers to design protein-specific and proteome-wide immunogenic multi-epitope-based vaccine constructs. The results revealed that these multi-epitope vaccine constructs (MEVCs) are strongly antigenic and non-allergenic in nature. The efficacy of these constructs was further validated by docking with immune receptors, which revealed strong binding interactions with human TLR8. Using the MD simulation approach, the binding stability and residual flexibility of the best vaccine construct (proteome-wide) were confirmed, which revealed stable dynamic and favorable features. Furthermore, in-silico cloning and immune simulation analysis confirmed the expression and production of immune factors, that is, IgM, IgG, and IL-6, against the proposed vaccine designs. Additionally, 3D models of all the MEVC constructs have been developed and evaluated for potential immunization against the RVF virus. Finally, the proteome-wide vaccine candidate (MEVC-PW-RVFV) with the highest immune reinforcement potential provides new insights into the development of future vaccines against the emerging RVF virus.

Repositioning of experimentally validated anti-breast cancer peptides to target FAK-PAX complex to halt the breast cancer progression: a biomolecular simulation approach

FAK (focal adhesin kinase), a tyrosine kinase, plays an imperative role in cell-cell communication, particularly in cell signaling systems. It is a multi-functional signaling protein, which integrates and transduces signals into cancer cells through growth factor receptors or integrin and its interaction with Paxillin (PAX). The molecular processes by which FAK promotes the development and progression of cancer have progressively established the possible relationship between FAK-PAX complex in many types of cancer. The interaction of FAX and PAX is very important in breast cancer and thus acts as an essential biomarker for drugs, vaccines or peptide inhibitor designing. In this regard, computational approaches, particularly peptide designing to target the binding interface of the interacting partners, would greatly assist the design of peptide inhibitors against various cancer. Accordingly, in this present study, we screened 236 experimentally validated anti-breast cancer peptides using computational drugs repositioning approach to design peptides targeting the FAK-PAX complex. Using protein-peptide docking the binding site for the HP1 was confirmed and a total of 236 anti-breast cancer peptides were screened. Among the 236, only 12 peptides reported a docking score better than the control. From these 12, Magainin with the docking score - 103.8 ± 10.3 kcal/mol, NRC-07 with the docking score - 100.8 ± 16.5 kcal/mol, and Indolicidin with the docking score - 101.7 ± 3.9 kcal/mol, peptides potentially inhibit the FAX-PAX binding. Calculation of protein's motion and FEL revealed the binding and inhibitory behavior. Moreover, binding free energy (MM/GBSA) confirmed that Magainin exhibited the total binding energy - 53.28 kcal/mol, NRC-07 possessed the TBE - 44.16 kcal/mol, and Indolicidin reported the TBE of - 40.48 kcal/mol, thus explaining the inhibitory potential of these peptides. In conclusion, these peptides exhibit strong inhibitory potential and could abrogate the FAK-PAX complex in in vitro models and thus may relieve the burden of breast cancer.

Peptidomics as a useful tool in the follow-up of food bioactive peptides

There is an intense research activity on bioactive peptides derived from food proteins in view of their health benefits for consumers. However, their identification is quite challenging as a consequence of their small size and low abundance in complex matrices such as foods or hydrolyzates. Recent advances in peptidomics and bioinformatics are getting improved sensitivity and accuracy and therefore such tools are contributing to the development of sophisticated methodologies for the identification and quantification of peptides. These developments are very useful for the follow-up of peptides released through proteolysis either in the food itself through the action of endogenous peptidases during processing stages like fermentation, drying or ripening, or from food proteins hydrolyzed by commercial peptidases or microorganisms with proteolytic activity. This chapter is presenting the latest advances in peptidomics and its use for the identification and quantification of peptides, and as a useful tool for controlling the proteolysis phenomena in foods and protein hydrolyzates.

Cryptic inhibitory regions nearby activation domains

Previously, the Nine amino acid TransActivation Domain (9aaTAD) was identified in the Gal4 region 862-870 (DDVYNYLFD). Here, we identified 9aaTADs in the distal Gal4 orthologs by our prediction algorithm and found their conservation in the family. The 9aaTAD function as strong activators was demonstrated. We identified adjacent Gal4 region 871-811 (DEDTPPNPKKE) as a natural 9aaTAD inhibitory domain located at the extreme Gal4 terminus. Moreover, we identified conserved Gal4 region 172-185 (FDWSEEDDMSDGLP), which was capable to reverse the 9aaTAD inhibition. In conclusion, our results uncover the existence of the cryptic inhibitory domains, which need to be carefully implemented in all functional studies with transcription factors to avoid incorrect conclusions.

Efficient 3D conformer generation of cyclic peptides formed by a disulfide bond

Cyclic peptides formed by disulfide bonds have been one large group of common drug candidates in drug development. Structural information of a peptide is essential to understand its interaction with its target. However, due to the high flexibility of peptides, it is difficult to sample the near-native conformations of a peptide. Here, we have developed an extended version of our MODPEP approach, named MODPEP2.0, to fast generate the conformations of cyclic peptides formed by a disulfide bond. MODPEP2.0 builds the three-dimensional (3D) structures of a cyclic peptide from scratch by assembling amino acids one by one onto the cyclic fragment based on the constructed rotamer and cyclic backbone libraries. Being tested on a data set of 193 diverse cyclic peptides, MODPEP2.0 obtained a considerable advantage in both accuracy and computational efficiency, compared with other sampling algorithms including PEP-FOLD, ETKDG, and modified ETKDG (mETKDG). MODPEP2.0 achieved a high sampling accuracy with an average C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α RMSD of 2.20 Å and 1.66 Å when 10 and 100 conformations were considered, respectively, compared with 3.41 Å and 2.62 Å for PEP-FOLD, 3.44 Å and 3.16 Å for ETKDG, 3.09 Å and 2.72 Å for mETKDG. MODPEP2.0 also reproduced experimental peptide structures for 81.35% of the test cases when an ensemble of 100 conformations were considered, compared with 54.95%, 37.50% and 50.00% for PEP-FOLD, ETKDG, and mETKDG. MODPEP2.0 is computationally efficient and can generate 100 peptide conformations in one second. MODPEP2.0 will be useful in sampling cyclic peptide structures and modeling related protein-peptide interactions, facilitating the development of cyclic peptide drugs.

Production and characterization of bioactive peptides from rice beans using Bacillus subtilis

A bioprocess was developed for production of bioactive peptides on microbial fermentation of rice beans using proteolytic Bacillus subtilis strains. The peptides produced were identified by LC-MS/MS analysis, revealing the presence of many unique peptide sequences to individual hydrolysates. On functional properties prediction, antihypertensive peptides (3.90%) were found to be higher in comparison to other bioactive peptides. Among different strains, B. subtilis KN2B fermented hydrolysate exhibited highest angiotensin converting enzyme (ACE)-inhibitory activity (45.73%). Furthermore, 19 selected peptides, including the common and unique peptides were examined for their affinity towards the binding cavity of ACE using molecular docking. The results showed a common peptide PFPIPFPIPIPLP, and another IPFPPIPFLPPI unique to B. subtilis KN2B fermented hydrolysate exhibited promising binding at the ACE binding site with substantial free binding energy. The process developed can be used for the production of bioactive peptides from rice bean for application in nutraceutical industries.

A CHO-Based Cell-Free Dual Fluorescence Reporter System for the Straightforward Assessment of Amber Suppression and scFv Functionality

Incorporation of noncanonical amino acids (ncAAs) with bioorthogonal reactive groups by amber suppression allows the generation of synthetic proteins with desired novel properties. Such modified molecules are in high demand for basic research and therapeutic applications such as cancer treatment and in vivo imaging. The positioning of the ncAA-responsive codon within the protein’s coding sequence is critical in order to maintain protein function, achieve high yields of ncAA-containing protein, and allow effective conjugation. Cell-free ncAA incorporation is of particular interest due to the open nature of cell-free systems and their concurrent ease of manipulation. In this study, we report a straightforward workflow to inquire ncAA positions in regard to incorporation efficiency and protein functionality in a Chinese hamster ovary (CHO) cell-free system. As a model, the well-established orthogonal translation components Escherichia coli tyrosyl-tRNA synthetase (TyrRS) and tRNATyr_CUA were used to site-specifically incorporate the ncAA p-azido-l-phenylalanine (AzF) in response to UAG codons. A total of seven ncAA sites within an anti-epidermal growth factor receptor (EGFR) single-chain variable fragment (scFv) N-terminally fused to the red fluorescent protein mRFP1 and C-terminally fused to the green fluorescent protein sfGFP were investigated for ncAA incorporation efficiency and impact on antigen binding. The characterized cell-free dual fluorescence reporter system allows screening for ncAA incorporation sites with high incorporation efficiency that maintain protein activity. It is parallelizable, scalable, and easy to operate. We propose that the established CHO-based cell-free dual fluorescence reporter system can be of particular interest for the development of antibody-drug conjugates (ADCs).

Characterization of ACE inhibitory and antioxidant peptides in yak and cow milk hard chhurpi cheese of the Sikkim Himalayan region

•

Yak and cow hard chhurpi cheese of Sikkim Himalaya.

•

ACE-inhibitory activities of undigested and GI digested yak and cow hard chhurpi.

•

Bioactive peptides identified by LC-MS/MS peptidomics of yak and cow hard chhurpi.

•

In silico prediction and molecular docking of potential ACE-inhibitory peptides.

In this study, simulated in vitro GI digestion of the Himalayan hard chhurpi cheese resulted in the increase of hydrolyzed protein content, antioxidant and ACE-inhibitory activities. LC-MS/MS-based peptidomics revealed a total of 1473 peptides in the samples originating from different milk proteins, including α-S1-casein, α-S2-casein, β-casein, κ-casein, α-lactalbumin, and β-lactoglobulin, out of which 60 peptides have been reported for different functional properties. A total of 101 peptides were predicted to be antihypertensive using the bioactivity prediction web servers, AHTpin and mAHTPred. In silico molecular docking studies predicted 20 antihypertensive peptides, exhibiting non-bond interactions between hard chhurpi peptides and ACE catalytic residues. A peptide, SLVYPFPGPI, identified in GI digested cow hard chhurpi and undigested, and GI digested samples of yak hard chhurpi, showed a stronger binding affinity towards ACE. Identifying antioxidant and ACE inhibitory peptides in hard cheese products adds value to them as functional foods of the Himalayan region.

A Generalized Attraction-Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction

Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for non-bonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small β-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.

iANP-EC: Identifying Anticancer Natural Products Using Ensemble Learning Incorporated with Evolutionary Computation

Cancer is one of the most deadly diseases that annually kills millions of people worldwide. The investigation on anticancer medicines has never ceased to seek better and more adaptive agents with fewer side effects. Besides chemically synthetic anticancer compounds, natural products are scientifically proved as a highly potential alternative source for anticancer drug discovery. Along with experimental approaches being used to find anticancer drug candidates, computational approaches have been developed to virtually screen for potential anticancer compounds. In this study, we construct an ensemble computational framework, called iANP-EC, using machine learning approaches incorporated with evolutionary computation. Four learning algorithms (k-NN, SVM, RF, and XGB) and four molecular representation schemes are used to build a set of classifiers, among which the top-four best-performing classifiers are selected to form an ensemble classifier. Particle swarm optimization (PSO) is used to optimise the weights used to combined the four top classifiers. The models are developed by a set of curated 997 compounds which are collected from the NPACT and CancerHSP databases. The results show that iANP-EC is a stable, robust, and effective framework that achieves an AUC-ROC value of 0.9193 and an AUC-PR value of 0.8366. The comparative analysis of molecular substructures between natural anticarcinogens and nonanticarcinogens partially unveils several key substructures that drive anticancerous activities. We also deploy the proposed ensemble model as an online web server with a user-friendly interface to support the research community in identifying natural products with anticancer activities.