A Comprehensive Review on Current Advances in Peptide Drug Development and Design

Research Progress on Food-Derived Bioactive Peptides: An Overview of the 3rd International Symposium on Bioactive Peptides

Interest in food-derived bioactive peptides is on the rise. In 2023, the 3rd International Symposium on Bioactive Peptides (ISBP) was held in Niagara Falls, Canada, to provide a platform for knowledge exchange, networking, and collaboration among researchers in this field. This article aims to provide a high-level overview of the key progress and emerging trends in bioactive peptides based on the 3rd ISBP. This review highlights the production of bioactive peptides from sustainable sources through the integration of artificial intelligence and wet-lab research, the emerging roles of bioactive peptides in cognitive function, and the ability of peptides to act as taste modifiers. The emerging research trend in bioactive peptides focuses on utilizing novel processing technologies, understanding peptide-receptor interactions, applying omics in mechanistic studies, conducting clinical trials, and facilitating product development and commercialization.

PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model

The protein-peptide interaction plays a pivotal role in fields such as drug development, yet remains underexplored experimentally and challenging to model computationally. Herein, we introduce PepCA, a sequence-based approach for predicting peptide-binding sites on proteins. A primary obstacle in predicting peptide-protein interactions is the difficulty in acquiring precise protein structures, coupled with the uncertainty of polypeptide configurations. To address this, we first encode protein sequences using the Evolutionary Scale Modeling 2 (ESM-2) pre-trained model to extract latent structural information. Additionally, we have developed a multi-input coattention mechanism to concurrently update the encoding of both peptide and protein residues. PepCA integrates this module within an encoder-decoder structure. This model's high precision in identifying binding sites significantly advances the field of computational biology, offering vital insights for peptide drug development and protein science.

Peptide-Based Inhibitors of Protein-Protein Interactions (PPIs): A Case Study on the Interaction Between SARS-CoV-2 Spike Protein and Human Angiotensin-Converting Enzyme 2 (hACE2)

Protein-protein interactions (PPIs) are fundamental to many critical biological processes and are crucial in mediating essential cellular functions across diverse organisms, including bacteria, parasites, and viruses. A notable example is the interaction between the SARS-CoV-2 spike (S) protein and the human angiotensin-converting enzyme 2 (hACE2), which initiates a series of events leading to viral replication. Interrupting this interaction offers a promising strategy for blocking or significantly reducing infection, highlighting its potential as a target for anti-SARS-CoV-2 therapies. This review focuses on the hACE2 and SARS-CoV-2 spike protein interaction, exemplifying the latest advancements in peptide-based strategies for developing PPI inhibitors. We discuss various approaches for creating peptide-based inhibitors that target this critical interaction, aiming to provide potential treatments for COVID-19.

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

Deep Learning for Parkinson's Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein-Peptide Datasets

Background: An important neurological disorder of Parkinson's Disease (PD) is characterized by motor and non-motor activity of the patients. Empirical condition of the patient: PD assessment uses the Movement Disorder Society Unified Parkinson's Rating Scale part III (MDS-UPDRS-III) measures for identifying the prediction of PD. Due to the unstable value of the measurement, the PD prediction and tracking lead to a lower prediction rate. Methods: To overcome this limitation, this paper proposed the Graph Wavelet Transform (GWT) based weighted feature extraction along with the Graph Neutral Network (GNN) classification. The main contribution of this research is (i) The weighted correlation between the data is calculated by GWT for effective prediction of PD. (ii) Machine learning algorithms were trained to predict Parkinson's disease based on these patterns. In this research, we developed a new model called Graph Neural Network (GNN) to predict PD tremors' MDS-UPDRS-III score using input data. To strengthen PD research and enable the construction of individualized treatment plans, these linked networks work together to methodically examine the data and find significant discoveries. Results: The proposed approach for predicting PD severity (motor- and MDS_UPDRS) has a mean squared error of 0.1796 and a root mean squared error of 0.2845, according to the experimental data. The prediction accuracy is increased by 27.66%, 54.11%, and 0.71%, correspondingly, when compared with the most effective State-of-the-Art methods of DNN, ANFIS + SVR, and Mixed MLP models. Conclusion: In conclusion, this proves that the proposed strategy is more effective at making predictions.

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data

Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection. CSP_Rank is a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.

Peptide nucleic acids can form hairpins and bind RNA-binding proteins

RNA-binding proteins (RBPs) are a major class of proteins that interact with RNAs to change their fate or function. RBPs and the ribonucleoprotein complexes they constitute are involved in many essential cellular processes. In many cases, the molecular details of RBP:RNA interactions differ between viruses, prokaryotes and eukaryotes, making prokaryotic and viral RBPs good potential drug targets. However, targeting RBPs with small molecules has so far been met with limited success as RNA-binding sites tend to be extended, shallow and dynamic with a mixture of charged, polar and hydrophobic interactions. Here, we show that peptide nucleic acids (PNAs) with nucleic acid-like binding properties and a highly stable peptide-like backbone can be used to target some RBPs. We have designed PNAs to mimic the short RNA stem-loop sequence required for the initiation of prokaryotic signal recognition particle (SRP) assembly, a target for antibiotics development. Using a range of biophysical and biochemical assays, the designed PNAs were demonstrated to fold into a hairpin structure, bind the targeted protein and compete with the native RNA hairpin to inhibit SRP formation. To show the applicability of PNAs against other RBPs, a PNA was also shown to bind Nsp9 from SARS-CoV-2, a protein that exhibits non-sequence-specific RNA binding but preferentially binds hairpin structures. Taken together, our results support that PNAs can be a promising class of compounds for targeting RNA-binding activities in RBPs.

Molecular Integrative Study on Inhibitory Effects of Pentapeptides on Polymerization and Cell Toxicity of Amyloid-β Peptide (1-42)

Alzheimer's Disease (AD) is a multifaceted neurodegenerative disease predominantly defined by the extracellular accumulation of amyloid-β (Aβ) peptide. In light of this, in the past decade, several clinical approaches have been used aiming at developing peptides for therapeutic use in AD. The use of cationic arginine-rich peptides (CARPs) in targeting protein aggregations has been on the rise. Also, the process of peptide development employing computational approaches has attracted a lot of attention recently. Using a structure database containing pentapeptides made from 20 L-α amino acids, we employed molecular docking to sort pentapeptides that can bind to Aβ42, then performed molecular dynamics (MD) analyses, including analysis of the binding stability, interaction energy, and binding free energy to screen ligands. Transmission electron microscopy (TEM), circular dichroism (CD), thioflavin T (ThT) fluorescence detection of Aβ42 polymerization, MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay, and the flow cytometry of reactive oxygen species (ROS) were carried out to evaluate the influence of pentapeptides on the aggregation and cell toxicity of Aβ42. Two pentapeptides (TRRRR and ARRGR) were found to have strong effects on inhibiting the aggregation of Aβ42 and reducing the toxicity of Aβ42 secreted by SH-SY5Y cells, including cell death, reactive oxygen species (ROS) production, and apoptosis.

Alpha-1 antitrypsin inhibits Clostridium botulinum C2 toxin, Corynebacterium diphtheriae diphtheria toxin and B. anthracis fusion toxin

The bacterium Clostridium botulinum, well-known for producing botulinum neurotoxins, which cause the severe paralytic illness known as botulism, produces C2 toxin, a binary AB-toxin with ADP-ribosyltranferase activity. C2 toxin possesses two separate protein components, an enzymatically active A-component C2I and the binding and translocation B-component C2II. After proteolytic activation of C2II to C2IIa, the heptameric structure binds C2I and is taken up via receptor-mediated endocytosis into the target cells. Due to acidification of endosomes, the C2IIa/C2I complex undergoes conformational changes and consequently C2IIa forms a pore into the endosomal membrane and C2I can translocate into the cytoplasm, where it ADP-ribosylates G-actin, a key component of the cytoskeleton. This modification disrupts the actin cytoskeleton, resulting in the collapse of cytoskeleton and ultimately cell death. Here, we show that the serine-protease inhibitor α1-antitrypsin (α1AT) which we identified previously from a hemofiltrate library screen for PT from Bordetella pertussis is a multitoxin inhibitor. α1AT inhibits intoxication of cells with C2 toxin via inhibition of binding to cells and inhibition of enzyme activity of C2I. Moreover, diphtheria toxin and an anthrax fusion toxin are inhibited by α1AT. Since α1AT is commercially available as a drug for treatment of the α1AT deficiency, it could be repurposed for treatment of toxin-mediated diseases.

Discovery of potential antidiabetic peptides using deep learning

Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to limited data, the complex nature of peptide functions, and the expensive and time-consuming nature of traditional wet lab experiments. This study aims to address these challenges by exploring methods for the discovery and prediction of ADPs using advanced deep learning techniques. Specifically, we developed two models: a single-channel CNN and a three-channel neural network (CNN + RNN + Bi-LSTM). ADPs were primarily gathered from the BioDADPep database, alongside thousands of non-ADPs sourced from anticancer, antibacterial, and antiviral peptide datasets. Subsequently, data preprocessing was performed with the evolutionary scale model (ESM-2), followed by model training and evaluation through 10-fold cross-validation. Furthermore, this work collected a series of newly published ADPs as an independent test set through literature review, and found that the CNN model achieved the highest accuracy (90.48 %) in predicting the independent test set, surpassing existing ADP prediction tools. Finally, the application of the model was considered. SeqGAN was used to generate new candidate ADPs, followed by screening with the constructed CNN model. Selected peptides were then evaluated using physicochemical property prediction and structural forecasts for pharmaceutical potential. In summary, this study not only established robust ADP prediction models but also employed these models to screen a batch of potential ADPs, addressing a critical need in the field of peptide-based antidiabetic research.

Dual electro-/pH-responsive nanoparticle/hydrogel system for controlled delivery of anticancer peptide

An electro-chemo-responsive carrier has been engineered for the controlled release of a highly hydrophilic anticancer peptide, CR(NMe)EKA (Cys-Arg- N-methyl-Glu-Lys-Ala). Remotely controlled on demand release of CR(NMe)EKA, loaded in electro-responsive poly(3,4-ethylenedioxythiophene) (PEDOT) nanoparticles, has been achieved by applying electrical stimuli consisting of constant positive (+0.50 V) or negative voltages (-0.50 V) at pre-defined time intervals. In addition, after loading CR(NMe)EKA/PEDOT nanoparticles into an injectable pH responsive hydrogel formed by phenylboronic acid grafted to chitosan (PBA-CS), the efficiency of the controlled peptide release has increased approximately by a factor of 2.6. The hydration ratio of such hydrogel is significantly lower in acidic environments than in neutral and basic media, which has been attributed to the dissociation of the boronate bonds between polymer chains. Hence, the electro-controlled peptide release from PBA-CS/CR(NMe)EKA/PEDOT hydrogels, in the acidic environment of tumors, combines the effects of the oxidation and reduction of PEDOT chains on the interactions with the peptide and the carrier, with the peptide concentration gradient at the interface between the collapsed hydrogel and the release medium. Furthermore, the peptide released by electro-stimulation preserved its bioactivity assessed by promoting human prostate cancer cells death. Overall, this work is a promising attempt to develop a carrier platform for small hydrophilic anticancer peptides, which delivery rationale is synergistically regulated by the electrical and pH responsiveness of the carrier.

Changes of Transcriptomic Activity in Rat Brain Cells under the Influence of Synthetic Adrenocorticotropic Hormone-Like Peptides

Synthetic peptides have a wide range of clinical effects. Of particular interest are peptides based on adrenocorticotropic hormone (ACTH) both as already used and as potential drugs for preventing consequences of cerebral ischemia. However, it is necessary to study influence of the peptide on the brain cells under normal physiological conditions, including understanding the risks of their use. Here, we used high-throughput RNA sequencing (RNA-Seq) to identify differentially expressed genes (DEGs) in the brain frontal cortex of rat receiving intraperitoneal administration of ACTH-like peptides ACTH(4-7)PGP (Semax) and ACTH(6-9)PGP, or saline. We identified 258 and 228 DEGs, respectively, with the fold change > 1.5 and Padj < 0.05 at 22.5 h after the first administration of Semax and ACTH(6-9)PGP. Metabolic pathways, characterizing both common and specific effects of the peptides on the transcriptome were identified. Both peptides predominantly caused decrease in expression of the genes associated with the immune system. At the same time, when comparing the effects of ACTH(6-9)PGP relative to Semax, DEGs were identified that characterized the main differences in the effects of the peptides. These genes were mostly downregulated and associated with neurosignaling systems and regulation of ion channels, thus characterizing differences in the effects of the peptides. Our data show how differences in the structure of ACTH derivatives are associated with the changes in the brain cell transcriptome following exposure to these related peptides. Furthermore, our results demonstrate that when studying influence of regulatory peptides on transcriptome under pathological conditions, it is necessary to take into account their actions under normal physiological conditions.

Recent advances in Alzheimer's disease: Mechanisms, clinical trials and new drug development strategies

Alzheimer's disease (AD) stands as the predominant form of dementia, presenting significant and escalating global challenges. Its etiology is intricate and diverse, stemming from a combination of factors such as aging, genetics, and environment. Our current understanding of AD pathologies involves various hypotheses, such as the cholinergic, amyloid, tau protein, inflammatory, oxidative stress, metal ion, glutamate excitotoxicity, microbiota-gut-brain axis, and abnormal autophagy. Nonetheless, unraveling the interplay among these pathological aspects and pinpointing the primary initiators of AD require further elucidation and validation. In the past decades, most clinical drugs have been discontinued due to limited effectiveness or adverse effects. Presently, available drugs primarily offer symptomatic relief and often accompanied by undesirable side effects. However, recent approvals of aducanumab (1) and lecanemab (2) by the Food and Drug Administration (FDA) present the potential in disrease-modifying effects. Nevertheless, the long-term efficacy and safety of these drugs need further validation. Consequently, the quest for safer and more effective AD drugs persists as a formidable and pressing task. This review discusses the current understanding of AD pathogenesis, advances in diagnostic biomarkers, the latest updates of clinical trials, and emerging technologies for AD drug development. We highlight recent progress in the discovery of selective inhibitors, dual-target inhibitors, allosteric modulators, covalent inhibitors, proteolysis-targeting chimeras (PROTACs), and protein-protein interaction (PPI) modulators. Our goal is to provide insights into the prospective development and clinical application of novel AD drugs.

PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations

Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering. PepBinding has been demonstrated on seven distinct model peptides. In peptide docking using HPEPDOCK, the peptide backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures ranged from 3.8 to 16.0 Å, corresponding to the medium to inaccurate quality models according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. The Pep-GaMD simulations performed for only 200 ns significantly improved the docking models, resulting in five medium and two acceptable quality models. Therefore, PepBinding is an efficient workflow for predicting peptide binding structures and is publicly available at https://github.com/MiaoLab20/PepBinding.

An explainable stacking-based approach for accelerating the prediction of antidiabetic peptides

Diabetes is a chronic disease that is characterized by high blood sugar levels and can have several harmful outcomes. Hyperglycemia, which is defined by persistently elevated blood sugar, is one of the primary concerns. People can improve their overall well-being and get optimal health outcomes by prioritizing diabetes control. Although the use of experimental approaches in diabetes treatment is cost-effective, it necessitates the development of many strategies for evaluating the efficacy of therapies. Researchers can quickly create new strategies for managing diabetes and get vital insights by enabling virtual screening with computational tools and procedures. In this study, we suggest a predictor named STADIP (STacking-based predictor for AntiDiabetic Peptides), a new method to predict antidiabetic peptides (ADPs) utilizing a stacked-based ensemble approach. It uses 12 different feature encodings and seven machine-learning techniques to construct 84 baseline models. The impacts of various baseline models on ADP prediction were then thoroughly examined. A two-step feature selection method, eXtreme Gradient Boosting with Sequential Forward Selection (XGB-SFS), was employed to determine the optimal number, out of 84 PFs to enhance predictive performance. Subsequently, utilizing the meta-predictor approach, 45 selected PFs were integrated into an XGB classifier to formulate the final hybrid model. The proposed method demonstrated superior predictive capabilities compared to constituent baseline models, as evidenced by evaluations on both cross-validation and independent tests. During extensive independent testing, STADIP achieved promising performance with accuracy and mathew's correlation coefficient of 0.954 and 0.877, respectively. It is anticipated that it will be useful tool in helping the scientific community to identify new antidiabetic proteins.

Microscale measurements of protein complexes from single cells

Proteins execute numerous cell functions in concert with one another in protein-protein interactions (PPI). While essential in each cell, such interactions are not identical from cell to cell. Instead, PPI heterogeneity contributes to cellular phenotypic heterogeneity in health and diseases such as cancer. Understanding cellular phenotypic heterogeneity thus requires measurements of properties of PPIs such as abundance, stoichiometry, and kinetics at the single-cell level. Here, we review recent, exciting progress in single-cell PPI measurements. Novel technology in this area is enabled by microscale and microfluidic approaches that control analyte concentration in timescales needed to outpace PPI disassembly kinetics. We describe microscale innovations, needed technical capabilities, and methods poised to be adapted for single-cell analysis in the near future.

Does glucose-dependent insulinotropic polypeptide receptor blockade as well as agonism have a role to play in management of obesity and diabetes?

Recent approval of the dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonist, tirzepatide, for the management of type 2 diabetes mellitus (T2DM) has reinvigorated interest in exploitation of GIP receptor (GIPR) pathways as a means of metabolic disease management. However, debate has long surrounded the use of the GIPR as a therapeutic target and whether agonism or antagonism is of most benefit in management of obesity/diabetes. This controversy appears to be partly resolved by the success of tirzepatide. However, emerging studies indicate that prolonged GIPR agonism may desensitise the GIPR to essentially induce receptor antagonism, with this phenomenon suggested to be more pronounced in the human than rodent setting. Thus, deliberation continues to rage in relation to benefits of GIPR agonism vs antagonism. That said, as with GIPR agonism, it is clear that the metabolic advantages of sustained GIPR antagonism in obesity and obesity-driven forms of diabetes can be enhanced by concurrent GLP-1 receptor (GLP-1R) activation. This narrative review discusses various approaches of pharmacological GIPR antagonism including small molecule, peptide, monoclonal antibody and peptide-antibody conjugates, indicating stage of development and significance to the field. Taken together, there is little doubt that interesting times lie ahead for GIPR agonism and antagonism, either alone or when combined with GLP-1R agonists, as a therapeutic intervention for the management of obesity and associated metabolic disease.

Harnessing the Therapeutic Potential of Peptides for Synergistic Treatment of Alzheimer's Disease by Targeting Aβ Aggregation, Metal-Mediated Aβ Aggregation, Cholinesterase, Tau Degradation, and Oxidative Stress

Alzheimer's disease (AD) is a progressive multifaceted neurodegenerative disease and remains a formidable global health challenge. The current medication for AD gives symptomatic relief and, thus, urges us to look for alternative disease-modifying therapies based on a multitarget directed approach. Looking at the remarkable progress made in peptide drug development in the last decade and the benefits associated with peptides, they offer valuable chemotypes [multitarget directed ligands (MTDLs)] as AD therapeutics. This review recapitulates the current developments made in harnessing peptides as MTDLs in combating AD by targeting multiple key pathways involved in the disease's progression. The peptides hold immense potential and represent a convincing avenue in the pursuit of novel AD therapeutics. While hurdles remain, ongoing research offers hope that peptides may eventually provide a multifaceted approach to combat AD.

The Cell-Penetrating Peptide GV1001 Enhances Bone Formation via Pin1-Mediated Augmentation of Runx2 and Osterix Stability

Peptide-based drug development is a promising direction due to its excellent biological activity, minimal immunogenicity, high in vivo stability, and efficient tissue penetrability. GV1001, an amphiphilic peptide, has proven effective as an anti-cancer vaccine, but its effect on osteoblast differentiation is unknown. To identify proteins interacting with GV1001, biotin-conjugated GV1001 was constructed and confirmed by mass spectrometry. Proteomic analyses were performed to determine GV1001's interaction with osteogenic proteins. GV1001 was highly associated with peptidyl-prolyl isomerase A and co-immunoprecipitation assays revealed that GV1001 bound to peptidyl-prolyl cis-trans isomerase 1 (Pin1). GV1001 significantly increased alkaline phosphatase (ALP) activity, bone nodule formation, and the expression of osteogenic gene markers. GV1001-induced osteogenic activity was enhanced by Pin1 overexpression and abolished by Pin1 knockdown. GV1001 increased the protein stability and transcriptional activity of Runx2 and Osterix. Importantly, GV1001 administration enhanced bone mass density in the OVX mouse model, as verified by µCT analysis. GV1001 demonstrated protective effects against bone loss in OVX mice by upregulating osteogenic differentiation via the Pin1-mediated protein stabilization of Runx2 and Osterix. GV1001 could be a potential candidate with anabolic effects for the prevention and treatment of osteoporosis.

Peptide PDRPS6 attenuates myocardial ischemia injury by improving mitochondrial function

Mitochondrial dynamics play a crucial role in myocardial ischemia-reperfusion (I/R) injury, where an imbalance between fusion and fission processes occurs. However, effective measures to regulate mitochondrial dynamics in this context are currently lacking. Peptide derived from the 40 S ribosomal protein S6 (PDRPS6), a peptide identified via peptidomics, is associated with hypoxic stress. This study aimed to investigate the function and mechanism of action of PDRPS6 in I/R injury. In vivo, PDRPS6 ameliorated myocardial tissue injury and cardiomyocyte apoptosis and decreased cardiac function induced by I/R injury in rats. PDRPS6 supplementation significantly reduced apoptosis in vitro. Mechanistically, PDRPS6 improved mitochondrial function by decreasing reactive oxygen species (ROS) levels, maintaining mitochondrial membrane potential (MMP), and inhibiting mitochondrial fission. Pull-down assay analyses revealed that phosphoglycerate mutase 5 (PGAM5) may be the target of PDRPS6, which can lead to the dephosphorylation of dynamin-related protein1 (Drp1) at ser616 site. Overexpression of PGAM5 partially eliminated the effect of PDRPS6 on improving mitochondrial function. These findings suggest that PDRPS6 supplementation is a novel method for treating myocardial injuries caused by I/R.

Ultrashort-Peptide-Responsive Gene Switches for Regulation of Therapeutic Protein Expression in Mammalian Cells

Despite the array of mammalian transgene switches available for regulating therapeutic protein expression in response to small molecules or physical stimuli, issues remain, including cytotoxicity of chemical inducers and limited biocompatibility of physical cues. This study introduces gene switches driven by short peptides comprising eight or fewer amino acid residues. Utilizing a competence regulator (ComR) and sigma factor X-inducing peptide (XIP) from Streptococcus vestibularis as the receptor and inducer, respectively, this study develops two strategies for a peptide-activated transgene control system. The first strategy involves fusing ComR with a transactivation domain and utilizes ComR-dependent synthetic promoters to drive expression of the gene-of-interest, activated by XIP, thereby confirming its membrane penetrability and intracellular functionality. The second strategy features an orthogonal synthetic receptor exposing ComR extracellularly (ComREXTRA), greatly increasing sensitivity with exceptional responsiveness to short peptides. In a proof-of-concept study, peptides are administered to type-1 diabetic mice with microencapsulated engineered human cells expressing ComREXTRA for control of insulin expression, restoring normoglycemia. It is envisioned that this system will encourage the development of short peptide drugs and promote the introduction of non-toxic, orthogonal, and highly biocompatible personalized biopharmaceuticals for gene- and cell-based therapies.

Peptide-functionalized, -assembled and -loaded nanoparticles in cancer therapy

The combination of peptides and nanoparticles in cancer therapy has shown synergistic results. Nanoparticle functionalization with peptides can increase their targeting ability towards tumor cells. In some cases, the peptides can develop self-assembled nanoparticles, in combination with drugs, for targeted cancer therapy. The peptides can be loaded into nanoparticles and can be delivered by other drugs for synergistic cancer removal. Multifunctional types of peptide-based nanoparticles, including pH- and redox-sensitive classes, have been introduced in cancer therapy. The tumor microenvironment remolds, and the acceleration of immunotherapy and vaccines can be provided by peptide nanoparticles. Moreover, the bioimaging and labeling of cancers can be mediated by peptide nanoparticles. Therefore, peptides can functionalize nanoparticles in targeted cancer therapy.

ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model demonstrates remarkable performance, achieving an AUC of 0.856 and 0.823 on the PepBCL Set_1 and Set_2 datasets, respectively. Additionally, it attains a Precision of 0.564 in PepBCL Set 1 and 0.527 in PepBCL Set 2, surpassing the performance of previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells, and other fields. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development.

A Frog Skin-Derived Peptide Targeting SCD1 Exerts Radioprotective Effects Against Skin Injury by Inhibiting STING-Mediated Inflammation

The extensive application of nuclear technology has increased the potential of uncontrolled radiation exposure to the public. Since skin is the largest organ, radiation-induced skin injury remains a serious medical concern. Organisms evolutionally develop distinct strategies to protect against environment insults and the related research may bring novel insights into therapeutics development. Here, 26 increased peptides are identified in skin tissues of frogs (Pelophylax nigromaculatus) exposed to electron beams, among which four promoted the wound healing of irradiated skin in rats. Specifically, radiation-induced frog skin peptide-2 (RIFSP-2), from histone proteolysis exerted membrane permeability property, maintained cellular homeostasis, and reduced pyroptosis of irradiated cells with decreased TBK1 phosphorylation. Subsequently, stearyl-CoA desaturase 1 (SCD1) is identified, a critical enzyme in biogenesis of monounsaturated fatty acids (MUFAs) as a direct target of RIFSP-2 based on streptavidin-biotin system. The lipidomic analysis further assured the restrain of MUFAs biogenesis by RIFSP-2 following radiation. Moreover, the decreased MUFA limited radiation-induced and STING-mediated inflammation response. In addition, genetic depletion or pharmacological inhibition of STING counteracted the decreased pyroptosis by RIFSP-2 and retarded tissue repair process. Altogether, RIFSP-2 restrains radiation-induced activation of SCD1-MUFA-STING axis. Thus, the stress-induced amphibian peptides can be a bountiful source of novel radiation mitigators.

Comprehensive approaches to preclinical evaluation of monoclonal antibodies and their next-generation derivatives

Biotherapeutics hold great promise for the treatment of several diseases and offer innovative possibilities for new treatments that target previously unaddressed medical needs. Despite successful transitions from preclinical to clinical stages and regulatory approval, there are instances where adverse reactions arise, resulting in product withdrawals. As a result, it is essential to conduct thorough evaluations of safety and effectiveness on an individual basis. This article explores current practices, challenges, and future approaches in conducting comprehensive preclinical assessments to ensure the safety and efficacy of biotherapeutics including monoclonal antibodies, toxin-conjugates, bispecific antibodies, single-chain antibodies, Fc-engineered antibodies, antibody mimetics, and siRNA-antibody/peptide conjugates.

Involvement of Matricellular Proteins in Cellular Senescence: Potential Therapeutic Targets for Age-Related Diseases

Senescence is a physiological and pathological cellular program triggered by various types of cellular stress. Senescent cells exhibit multiple characteristic changes. Among them, the characteristic flattened and enlarged morphology exhibited in senescent cells is observed regardless of the stimuli causing the senescence. Several studies have provided important insights into pro-adhesive properties of cellular senescence, suggesting that cell adhesion to the extracellular matrix (ECM), which is involved in characteristic morphological changes, may play pivotal roles in cellular senescence. Matricellular proteins, a group of structurally unrelated ECM molecules that are secreted into the extracellular environment, have the unique ability to control cell adhesion to the ECM by binding to cell adhesion receptors, including integrins. Recent reports have certified that matricellular proteins are closely involved in cellular senescence. Through this biological function, matricellular proteins are thought to play important roles in the pathogenesis of age-related diseases, including fibrosis, osteoarthritis, intervertebral disc degeneration, atherosclerosis, and cancer. This review outlines recent studies on the role of matricellular proteins in inducing cellular senescence. We highlight the role of integrin-mediated signaling in inducing cellular senescence and provide new therapeutic options for age-related diseases targeting matricellular proteins and integrins.

In silico design of peptide inhibitors for Dengue virus to treat Dengue virus-associated infections

Dengue virus is a single positive-strand RNA virus that is composed of three structural proteins including capsid, envelope, and precursor membrane while seven non-structural proteins (NS1, NS2A, NS2B, NS3A, NS3B, NS4, and NS5). Dengue is a viral infection caused by the dengue virus (DENV). DENV infections are asymptomatic or produce only mild illness. However, DENV can occasionally cause more severe cases and even death. There is no specific treatment for dengue virus infections. Therapeutic peptides have several important advantages over proteins or antibodies: they are small in size, easy to synthesize, and have the ability to penetrate the cell membranes. They also have high activity, specificity, affinity, and less toxicity. Based on the known peptide inhibitor, the current study designs peptide inhibitors for dengue virus envelope protein using an alanine and residue scanning technique. By replacing I21 with Q21, L14 with H14, and V28 with K28, the binding affinity of the peptide inhibitors was increased. The newly designed peptide inhibitors with single residue mutation improved the binding affinity of the peptide inhibitors. The inhibitory capability of the new promising peptide inhibitors was further confirmed by the utilization of MD simulation and free binding energy calculations. The molecular dynamics simulation demonstrated that the newly engineered peptide inhibitors exhibited greater stability compared to the wild-type peptide inhibitors. According to the binding free energies MM(GB)SA of these developed peptides, the first peptide inhibitor was the most effective against the dengue virus envelope protein. All peptide derivatives had higher binding affinities for the envelope protein and have the potential to treat dengue virus-associated infections. In this study, new peptide inhibitors were developed for the dengue virus envelope protein based on the already reported peptide inhibitor.

The Rapid Evolution of De Novo Proteins in Structure and Complex

Recent studies in the rice genome-wide have established that de novo genes, evolving from noncoding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their evolution in protein structure over time remain unclear. Here, we addressed these issues within a surprisingly short evolutionary timescale (<1 million years for 97% of Oryza de novo genes) with comparative approaches to gene duplicates. We found that de novo genes evolve faster than gene duplicates in the intrinsically disordered regions (such as random coils), secondary structure elements (such as α helix and β strand), hydrophobicity, and molecular recognition features. In de novo proteins, specifically, we observed an 8% to 14% decay in random coils and intrinsically disordered region lengths and a 2.3% to 6.5% increase in structured elements, hydrophobicity, and molecular recognition features, per million years on average. These patterns of structural evolution align with changes in amino acid composition over time as well. We also revealed higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions showed that most de novo proteins, though not typically well folded on their own, readily form low-energy and compact complexes with other proteins facilitated by extensive residue contacts and conformational flexibility, suggesting a faster-binding scenario in de novo proteins to promote interaction. These analyses illuminate a rapid evolution of protein structure in de novo genes in rice genomes, originating from noncoding sequences, highlighting their quick transformation into active, protein complex-forming components within a remarkably short evolutionary timeframe.

Expanding the horizons of targeted protein degradation: A non-small molecule perspective

Targeted protein degradation (TPD) represented by proteolysis targeting chimeras (PROTACs) marks a significant stride in drug discovery. A plethora of innovative technologies inspired by PROTAC have not only revolutionized the landscape of TPD but have the potential to unlock functionalities beyond degradation. Non-small-molecule-based approaches play an irreplaceable role in this field. A wide variety of agents spanning a broad chemical spectrum, including peptides, nucleic acids, antibodies, and even vaccines, which not only prove instrumental in overcoming the constraints of conventional small molecule entities but also provided rapidly renewing paradigms. Herein we summarize the burgeoning non-small molecule technological platforms inspired by PROTACs, including three major trajectories, to provide insights for the design strategies based on novel paradigms.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity

Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 μM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.

Helical sulfonyl-γ-AApeptides for the inhibition of HIV-1 fusion and HIF-1α signaling

Synthetic helical peptidic foldamers show promising applications in chemical biology and biomedical sciences by mimicking protein helical segments. Sulfonyl-γ-AApeptide helices developed by our group exhibit good chemodiversity, predictable folding structures, proteolytic resistance, favorable cell permeability, and enhanced bioavailability. Herein, in this minireview, we highlight two recent examples of homogeneous left-handed sulfonyl-γ-AApeptide helices to modulate protein-protein interactions (PPIs). One is sulfonyl-γ-AApeptides as anti-HIV-1 fusion inhibitors mimicking the helical C-terminal heptad repeat (CHR), which show excellent anti-HIV-1 activities through tight binding with the N-terminal heptad repeat (NHR) and inhibiting the formation of the 6-helical bundle (HB) structure. Another example is helical sulfonyl-γ-AApeptides disrupting hypoxia-inducible factor 1α (HIF-1α) and p300 PPI, thus selectively inhibiting the relevant signaling cascade. We hope these findings could help to elucidate the principles of the structural design of sulfonyl-γ-AApeptides and inspire their future applications in PPI modulations.

Learning peptide properties with positive examples only

Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised learning frameworks. Notably, most peptide databases come with missing information and low number of observations on negative examples, as such sequences are hard to obtain using high-throughput screening methods. To address this challenge, we solely exploit the limited known positive examples in a semi-supervised setting, and discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU). In particular, we use the two learning strategies of adapting base classifier and reliable negative identification to build deep learning models for inferring solubility, hemolysis, binding against SHP-2, and non-fouling activity of peptides, given their sequence. We evaluate the predictive performance of our PU learning method and show that by only using the positive data, it can achieve competitive performance when compared with the classical positive-negative (PN) classification approach, where there is access to both positive and negative examples.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Stimuli-responsive peptide assemblies: Design, self-assembly, modulation, and biomedical applications

Peptide molecules have design flexibility, self-assembly ability, high biocompatibility, good biodegradability, and easy functionalization, which promote their applications as versatile biomaterials for tissue engineering and biomedicine. In addition, the functionalization of self-assembled peptide nanomaterials with other additive components enhances their stimuli-responsive functions, promoting function-specific applications that induced by both internal and external stimulations. In this review, we demonstrate recent advance in the peptide molecular design, self-assembly, functional tailoring, and biomedical applications of peptide-based nanomaterials. The strategies on the design and synthesis of single, dual, and multiple stimuli-responsive peptide-based nanomaterials with various dimensions are analyzed, and the functional regulation of peptide nanomaterials with active components such as metal/metal oxide, DNA/RNA, polysaccharides, photosensitizers, 2D materials, and others are discussed. In addition, the designed peptide-based nanomaterials with temperature-, pH-, ion-, light-, enzyme-, and ROS-responsive abilities for drug delivery, bioimaging, cancer therapy, gene therapy, antibacterial, as well as wound healing and dressing applications are presented and discussed. This comprehensive review provides detailed methodologies and advanced techniques on the synthesis of peptide nanomaterials from molecular biology, materials science, and nanotechnology, which will guide and inspire the molecular level design of peptides with specific and multiple functions for function-specific applications.

Enantioselective organocatalytic strategies to access noncanonical α-amino acids

Organocatalytic asymmetric synthesis has evolved over the years and continues to attract the interest of many researchers worldwide. Enantiopure noncanonical amino acids (ncAAs) are valuable building blocks in organic synthesis, medicinal chemistry, and chemical biology. They are employed in the elaboration of peptides and proteins with enhanced activities and/or improved properties compared to their natural counterparts, as chiral catalysts, in chiral ligand design, and as chiral building blocks for asymmetric syntheses of complex molecules, including natural products. The linkage of ncAA synthesis and enantioselective organocatalysis, the subject of this perspective, tries to imitate the natural biosynthetic process. Herein, we present contemporary and earlier developments in the field of organocatalytic activation of simple feedstock materials, providing potential ncAAs with diverse side chains, unique three-dimensional structures, and a high degree of functionality. These asymmetric organocatalytic strategies, useful for forging a wide range of C-C, C-H, and C-N bonds and/or combinations thereof, vary from classical name reactions, such as Ugi, Strecker, and Mannich reactions, to the most advanced concepts such as deracemisation, transamination, and carbene N-H insertion. Concurrently, we present some interesting mechanistic studies/models, providing information on the chirality transfer process. Finally, this perspective highlights, through the diversity of the amino acids (AAs) not selected by nature for protein incorporation, the most generic modes of activation, induction, and reactivity commonly used, such as chiral enamine, hydrogen bonding, Brønsted acids/bases, and phase-transfer organocatalysis, reflecting their increasingly important role in organic and applied chemistry.

Thermodynamic and molecular dynamic insights into how fusion influences peptide-tag recognition of an antibody

To understand the effect of protein fusion on the recognition of a peptide-tag by an antibody, we fused a CCR5-derived peptide-tag (pep1) to GFP and investigated its recognition by an anti-pep1 antibody, 4B08. First, to characterize the thermodynamic properties associated with the pep1-4B08 binding, isothermal titration calorimetry experiments were conducted. It was found that pep1 fused to the C-terminus of GFP (GFP-CT) enhanced the enthalpic gain by 2.1 kcal mol-1 and the entropic loss only by 0.9 kcal mol-1, resulting in an 8-fold increase in the binding affinity compared to the unfused pep1. On the other hand, pep1 fused to the N-terminus of GFP (GFP-NT) enhanced the enthalpic gain by 3.0 kcal mol-1 and the entropic loss by 3.2 kcal mol-1, leading to no significant enhancement of the binding affinity. To gain deeper insights, molecular dynamics simulations of GFP-NT, GFP-CT, and pep1 were performed. The results showed that the location of the fusion point sensitively affects the interaction energy, the solvent accessible surface area, and the fluctuation of pep1 in the unbound state, which explains the difference in the experimental thermodynamic properties.

Identification and functional characterization of putative ligand binding domain(s) of JlpA protein of Campylobacter jejuni

Among the major Surface Exposed Colonization Proteins (SECPs) of Campylobacter jejuni (C. jejuni), Jejuni lipoprotein A (JlpA) plays a crucial role in host cell adhesion specifically by binding to the N-terminal domain of the human heat shock protein 90α (Hsp90α-NTD). Although the JlpA binding to Hsp90α activates NF-κB and p38 MAP kinase pathways, the underlying mechanism of JlpA association with the cellular receptor remains unclear. To this end, we predicted two potential receptor binding sites within the C-terminal domain of JlpA: one spanning from amino acid residues Q332-A354 and the other from S258-T295; however, the latter exhibited weaker binding. To assess the functional attributes of these predicted sequences, we generated two JlpA mutants (JlpAΔ1: S258-T295; JlpAΔ2: Q332-A354) and assessed the Hsp90α-binding affinity-kinetics by in vitro and ex vivo experiments. Our findings confirmed that the residues Q332-A354 are of greater importance in host cell adhesion with a measurable impact on cellular responses. Further, thermal denaturation by circular dichroism (CD) confirmed that the reduced binding affinity of the JlpAΔ2 to Hsp90α is not associated with protein folding or stability. Together, this study provides a possible framework for determining the molecular function of designing rational inhibitors efficiently targeting JlpA.

Therapeutic proteins: developments, progress, challenges, and future perspectives

Proteins are considered magic molecules due to their enormous applications in the health sector. Over the past few decades, therapeutic proteins have emerged as a promising treatment option for various diseases, particularly cancer, cardiovascular disease, diabetes, and others. The formulation of protein-based therapies is a major area of research, however, a few factors still hinder the large-scale production of these therapeutic products, such as stability, heterogenicity, immunogenicity, high cost of production, etc. This review provides comprehensive information on various sources and production of therapeutic proteins. The review also summarizes the challenges currently faced by scientists while developing protein-based therapeutics, along with possible solutions. It can be concluded that these proteins can be used in combination with small molecular drugs to give synergistic benefits in the future.

Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization

Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability. In the algorithm, we mapped peptide amino acid sequences onto the latent space constructed using a variational autoencoder and searched for peptides with desired bioactivity and cell membrane permeability using Bayesian optimization. We used our proposed method to generate peptides with cell membrane permeability and bioactivity for each of the nine therapeutic targets, such as the estrogen receptor (ER). Our proposed method outperformed a previously developed peptide generator in terms of similarity to known active peptide sequences and the length of generated peptide sequences.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design

Peptide design, with the goal of identifying peptides possessing unique biological properties, stands as a crucial challenge in peptide-based drug discovery. While traditional and computational methods have made significant strides, they often encounter hurdles due to the complexities and costs of laboratory experiments. Recent advancements in deep learning and Bayesian Optimization have paved the way for innovative research in this domain. In this context, our study presents a novel approach that effectively combines protein structure prediction with Bayesian Optimization for peptide design. By applying carefully designed objective functions, we guide and enhance the optimization trajectory for new peptide sequences. Benchmarked against multiple native structures, our methodology is tailored to generate new peptides to their optimal potential biological properties.

Potential of targeting signal-transducing adaptor protein-2 in cancer therapeutic applications

Adaptor proteins play essential roles in various intracellular signaling pathways. Signal-transducing adaptor protein-2 (STAP-2) is an adaptor protein that possesses pleckstrin homology (PH) and Src homology 2 (SH2) domains, as well as a YXXQ signal transducer and activator of transcription 3 (STAT3)-binding motif in its C-terminal region. STAP-2 is also a substrate of breast tumor kinase (BRK). STAP-2/BRK expression is deregulated in breast cancers and enhances STAT3-dependent cell proliferation. In prostate cancer cells, STAP-2 interacts with and stabilizes epidermal growth factor receptor (EGFR) after stimulation, resulting in the upregulation of EGFR signaling, which contributes to cancer-cell proliferation and tumor progression. Therefore, inhibition of the interaction between STAP-2 and BRK/EGFR may be a possible therapeutic strategy for these cancers. For this purpose, peptides that interfere with STAP-2/BRK/EGFR binding may have great potential. Indeed, the identified peptide inhibitor successfully suppressed the STAP-2/EGFR protein interaction, EGFR stabilization, and cancer-cell growth. Furthermore, the peptide inhibitor suppressed tumor formation in human prostate- and lung-cancer cell lines in a murine xenograft model. This review focuses on the inhibitory peptide as a promising candidate for the treatment of prostate and lung cancers.

A General Approach to Optically Pure, α-Methyl Non-Natural Amino Acids: Enabling Unique Peptides as Drug Candidates

An α-methyl, non-natural amino acid (NNAA) building block equipped with an alkyl halide tail that could be readily transformed into an organozinc was prepared. This single organometallic was cross-coupled to an array of heterocyclic electrophiles using the Pd-PEPPSI-IHeptCl catalyst to produce a wide selection of optically pure α-methyl NNAAs. With these in hand, non-natural peptides are being produced for evaluation in a variety of therapeutic areas in drug discovery.

Development of an environmentally sensitive fluorescent peptide probe for MrgX2 and application in ligand screening of peptide antibiotics

Mast cells (MCs) are primary effector cells involved in immediate allergic reactions. Mas-related G protein-coupled receptor-X2 (MrgX2), which is highly expressed on MCs, is involved in receptor-mediated drug-induced pseudo-anaphylaxis. Many small-molecule drugs and peptides activate MrgX2, resulting in MC activation and allergic reactions. Although small-molecule drugs can be identified using existing MrgX2 ligand-screening systems, there is still a lack of effective means to screen peptide ligands. In this study, to screen for peptide drugs, the MrgX2 high-affinity endogenous peptide ligand substance P (SP) was used as a recognition group to design a fluorescent peptide probe. Spectroscopic properties and fluorescence imaging of the probe were assessed. The probe was then used to screen for MrgX2 agonists among peptide antibiotics. In addition, the effects of peptide antibiotics on MrgX2 activation were investigated in vivo and in vitro. The environment-sensitive property of the probe was revealed by the dramatic increase in fluorescence intensity after binding to the hydrophobic ligand-binding domain of MrgX2. Based on these characteristics, it can be used for in situ selective visualization of MrgX2 in live cells. The probe was used to screen ten types of peptide antibiotics, and we found that caspofungin and bacitracin could compete with the probe and are hence potential ligands of MrgX2. Pharmacological experiments confirmed this hypothesis; caspofungin and bacitracin activated MCs via MrgX2 in vitro and induced local anaphylaxis in mice. Our research can be expected to provide new ideas for screening MrgX2 peptide ligands and reveal the mechanisms of adverse reactions caused by peptide drugs, thereby laying the foundation for improving their clinical safety.

Mirror-Image Phage Display for the Selection of D-Amino Acid Peptide Ligands as Potential Therapeutics

In neurodegenerative diseases like Alzheimer's disease (AD), endogenous proteins or peptides aggregate with themselves. These proteins may lose their function or aggregates and/or oligomers can obtain toxicity, causing injury or death to cells. Aggregation of two major proteins characterizes AD. Amyloid-β peptide (Aβ) is deposited in amyloid plaques within the extracellular space of the brain and Tau in so-called neurofibrillary tangles in neurons. Finding peptide ligands to halt protein aggregation is a promising therapeutical approach. Using mirror-image phage display with a commercially available, randomized 12-mer peptide library, we have selected D-amino acid peptides, which bind to the Tau protein and modulate its aggregation in vitro. Peptides can bind specifically and selectively to a target molecule, but natural L-amino acid peptides may have crucial disadvantages for in vivo applications, as they are sensitive to protease degradation and may elicit immune responses. One strategy to circumvent these disadvantages is the use of non-naturally occurring D-amino acid peptides as they exhibit increased protease resistance and generally do not activate the immune system. To perform mirror-image phage display, the target protein needs to be synthesized as D-amino acid version. If the target protein sequence is too long to be synthesized properly, smaller peptides derived from the full length protein can be used for the selection process. This also offers the possibility to influence the binding region of the selected D-peptides in the full-length target protein. Here we provide the protocols for mirror-image phage display selection on the PHF6* peptide of Tau, based on the commercially available Ph.D.™-12 Phage Display Peptide Library Kit, leading to D-peptides that also bind the full length Tau protein (Tau441), next to PHF6*. In addition, we provide protocols and data for the first characterization of those D-peptides that inhibit Tau aggregation in vitro. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Mirror image phage display selection against D-PHF6* fibrils Support Protocol 1: Single phage ELISA Basic Protocol 2: Sequencing and D-peptide generation Basic Protocol 3: Thioflavin-T (ThT) test to control inhibition of Tau aggregation Support Protocol 2: Purification of full-length Tau protein Basic Protocol 4: ELISA to demonstrate the binding of the generated D-peptides to PHF6* and full-length Tau fibrils.

Mechanistic Modeling of Size Exclusion Chromatography-Assisted In Vitro Refolding of the Recombinant Biosimilar Teriparatide (PTH-34)

In vitro protein refolding is one of the critical unit operations in manufacturing recombinant peptides expressed using Escherichia coli as host cells. This study is focused on designing size exclusion chromatography-assisted in vitro refolding process for biosimilar recombinant parathyroid hormone. Inclusion bodies (IBs) of recombinant parathyroid hormone were solubilized at higher pH, and in vitro refolding was performed using size exclusion chromatography. In the first part of the investigation, DoE-based empirical optimization was performed to achieve a higher refolding yield for a biosimilar recombinant parathyroid hormone. The effect of solubilized inclusion body (IB) feed volume, concentration of IBs, and residence time on in vitro refolding of recombinant teriparatide was studied using the Box-Behnken design. Size exclusion chromatography (SEC)-assisted in vitro refolding was performed at 8 °C at pH 10.5 by using 20 mM Tris buffer. The maximum refolding yield of 98.12% was achieved at feed volume (12.5% of CV) and 20 mg/mL inclusion body (IB) concentration with a residence time of 50 min and a purity of 66.1% based on densitometric analysis using SDS-PAGE. In the latter part of the investigation, the general rate mechanistic model framework for size exclusion chromatography was developed and validated with the experimental results. The developed model helped in the accurate prediction of the elution volumes and product yield. The developed model also helps to predict the elution performance of a scalable column a priori. Post in vitro refolding, the formation of the native peptide structure was examined using various orthogonal analytical tools to study the protein's primary, secondary, and tertiary structures. The developed hybrid process development approach is a valuable tool toachieve high-yield, scalable refolding conditions for recombinant proteins without disulfide bonds.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.

The current landscape of the antimicrobial peptide melittin and its therapeutic potential

Melittin, a main component of bee venom, is a cationic amphiphilic peptide with a linear α-helix structure. It has been reported that melittin can exert pharmacological effects, such as antitumor, antiviral and anti-inflammatory effects in vitro and in vivo. In particular, melittin may be beneficial for the treatment of diseases for which no specific clinical therapeutic agents exist. Melittin can effectively enhance the therapeutic properties of some first-line drugs. Elucidating the mechanism underlying melittin-mediated biological function can provide valuable insights for the application of melittin in disease intervention. However, in melittin, the positively charged amino acids enables it to directly punching holes in cell membranes. The hemolysis in red cells and the cytotoxicity triggered by melittin limit its applications. Melittin-based nanomodification, immuno-conjugation, structural regulation and gene technology strategies have been demonstrated to enhance the specificity, reduce the cytotoxicity and limit the off-target cytolysis of melittin, which suggests the potential of melittin to be used clinically. This article summarizes research progress on antiviral, antitumor and anti-inflammatory properties of melittin, and discusses the strategies of melittin-modification for its future potential clinical applications in preventing drug resistance, enhancing the selectivity to target cells and alleviating cytotoxic effects to normal cells.