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

Nature-inspired protein ligation and its applications

The ability to manipulate the chemical composition of proteins and peptides has been central to the development of improved polypeptide-based therapeutics and has enabled researchers to address fundamental biological questions that would otherwise be out of reach. Protein ligation, in which two or more polypeptides are covalently linked, is a powerful strategy for generating semisynthetic products and for controlling polypeptide topology. However, specialized tools are required to efficiently forge a peptide bond in a chemoselective manner with fast kinetics and high yield. Fortunately, nature has addressed this challenge by evolving enzymatic mechanisms that can join polypeptides using a diverse set of chemical reactions. Here, we summarize how such nature-inspired protein ligation strategies have been repurposed as chemical biology tools that afford enhanced control over polypeptide composition.

DNA Hydrogel-Based Nanocomplexes with Cancer-Targeted Delivery and Light-Triggered Peptide Drug Release for Cancer-Specific Therapeutics

Cancer therapies based on chemotherapeutic drug delive ries have been the most facilitated studies. Recently, peptide drugs have emerged as anticancer drugs due to their less immunogenicity and lower production costs compared with other synthetics. However, still, the side effects of these chemotherapeutics on healthy tissues have been a great concern to deal with, and these side effects are usually caused by off-targeted delivery and unwanted leakage. In addition, peptides are easily degraded by enzyme attacks during delivery. To address these concerns, here, we developed a robust, cancer-specific peptide drug delivery system with negligible cytotoxicity in in vitro. A peptide drug delivery vehicle (Dgel-PD-AuNP-YNGRT) was constructed by stepwise functionalization on a nanoscale DNA hydrogel (Dgel). A cell-penetrating anticancer peptide drug, Buforin IIb, was loaded within the Dgel network via electrostatic attraction followed by AuNP assembly. The AuNPs were employed as photothermal reagents for light-triggered peptide drug release. An additional peptide, including a cancer-targeting YNGRT sequence, was also bound on the Dgel for cancer-cell-targeted delivery. According to the results obtained from the studies employing cancer cells as well as normal cells, Dgel-PD-AuNP-YNGRT nanocomplexes could be delivered specifically to cancer cells, activated by light illumination, and release anticancer peptide drugs to kill cancer cells with no cytotoxicity and negligible hazardous effect on normal cell lines. The obtained cell viability assay suggests that at a high intensity (15 W/cm²), photothermally triggered released peptide drug has shown up to 44% higher kill than only peptide drug treatments in cancer cells. Similarly, the Bradford assay demonstrated that up to 90% of peptide drugs were released with our engineered Dgel-PD-AuNP-YNGRT nanocomplex. The Dgel-PD-AuNP-YNGRT nanocomplex may serve as an ideal anticancer peptide drug delivery platform for safe, cancer-specific targeting and efficient peptide drug delivery in cancer therapy.

Predicting Biomolecular Binding Kinetics: A Review

Biomolecular binding kinetics including the association (k_on) and dissociation (k_off) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

Targeting WDxR motif reprograms immune microenvironment and inhibits hepatocellular carcinoma progression

The WD-repeat (WDR) family affects carcinogenesis, but its role in the immune microenvironment is poorly characterized. Although functional loss or gain of WDR6 does not markedly change in vitro proliferative and invasive capacity of HCC cells, its deficiency in hepa1-6 cells drastically inhibits the growth and lung metastasis of orthotopically implanted tumors in immune-competent C57BL/6J mice. Mechanistically, WDR6 targets tumor suppressor UVRAG to the CUL4A-DDB1-ROC1 E3 ubiquitin ligase complex through a unique WDxR motif and promotes its degradation. This upregulates chromatin accessibility at the TNFα locus by blocking autophagic degradation of p65, elevates intratumoral myeloid-derived suppressor cell (MDSC) number, and reduces CD8⁺ T cell infiltration, thereby promoting HCC progression. These immunosuppressive effects are reversed by TNFα blockade. TNFα recruits NF-κB to activate the transcription of WDR6, establishing a WDR6-TNFα loop. Clinically, the WDR6/UVRAG/NF-κB pathway is hyperactivated in HCC, predicting a poor prognosis. Importantly, a WDxR-like peptide disrupts the WDR6/UVRAG complex and enhances the efficiency of anti-PD-L1 against HCC with WDR6 dysregulation.

Structural diversity, biosynthesis, and biological functions of lipopeptides from Streptomyces

Covering: up to 2022Streptomyces are ubiquitous in terrestrial and marine environments, where they display a fascinating metabolic diversity. As a result, these bacteria are a prolific source of active natural products. One important class of these natural products is the nonribosomal lipopeptides, which have diverse biological activities and play important roles in the lifestyle of Streptomyces. The importance of this class is highlighted by the use of related antibiotics in the clinic, such as daptomycin (tradename Cubicin). By virtue of recent advances spanning chemistry and biology, significant progress has been made in biosynthetic studies on the lipopeptide antibiotics produced by Streptomyces. This review will serve as a comprehensive guide for researchers working in this multidisciplinary field, providing a summary of recent progress regarding the investigation of lipopeptides from Streptomyces. In particular, we highlight the structures, properties, biosynthetic mechanisms, chemical and chemoenzymatic synthesis, and biological functions of lipopeptides. In addition, the application of genome mining techniques to Streptomyces that have led to the discovery of many novel lipopeptides is discussed, further demonstrating the potential of lipopeptides from Streptomyces for future development in modern medicine.

Multicomponent synthesis of chromophores – The one-pot approach to functional π-systems

Multicomponent reactions, conducted in a domino, sequential or consecutive fashion, have not only considerably enhanced synthetic efficiency as one-pot methodology, but they have also become an enabling tool for interdisciplinary research. The highly diversity-oriented nature of the synthetic concept allows accessing huge structural and functional space. Already some decades ago this has been recognized for life sciences, in particular, lead finding and exploration in pharma and agricultural chemistry. The quest for novel functional materials has also opened the field for diversity-oriented syntheses of functional π-systems, i.e. dyes for photonic and electronic applications based on their electronic properties. This review summarizes recent developments in MCR syntheses of functional chromophores highlighting syntheses following either the framework forming scaffold approach by establishing connectivity between chromophores or the chromogenic chromophore approach by de novo formation of chromophore of interest. Both approaches warrant rapid access to molecular functional π-systems, i.e. chromophores, fluorophores, and electrophores for various applications.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Peptides Derived from a Plant Protease Inhibitor of the Coagulation Contact System Decrease Arterial Thrombus Formation in a Murine Model, without Impairing Hemostatic Parameters

Several plant protein inhibitors with anticoagulant properties have been studied and characterized, including the Delonix regia trypsin inhibitor (DrTI). This protein inhibits serine proteases (trypsin) and enzymes directly involved in coagulation, such as plasma kallikrein, factor XIIa, and factor XIa. In this study, we evaluated the effects of two new synthetic peptides derived from the primary sequence of DrTI in coagulation and thrombosis models to understand the mechanisms involved in the pathophysiology of thrombus formation as well as in the development of new antithrombotic therapies. Both peptides acted on in vitro hemostasis-related parameters, showing promising results, prolonging the Partially Activated Thromboplastin Time (aPTT) and inhibited platelet aggregation induced by adenosine diphosphate (ADP) and arachidonic acid. In murine models, for arterial thrombosis induced by photochemical injury, and platelet-endothelial interactions monitored by intravital microscopy, both peptides at doses of 0.5 mg/kg significantly extended the time of artery occlusion and modified the platelet adhesion and aggregation pattern with no changes in bleeding time, demonstrating the high biotechnological potential of both molecules.

Surrogate Based Genetic Algorithm Method for Efficient Identification of Low-Energy Peptide Structures

Identification of the most stable structure(s) of a system is a prerequisite for the calculation of any of its properties from first-principles. However, even for relatively small molecules, exhaustive explorations of the potential energy surface (PES) are severely hampered by the dimensionality bottleneck. In this work, we address the challenging task of efficiently sampling realistic low-lying peptide coordinates by resorting to a surrogate based genetic algorithm (GA)/density functional theory (DFT) approach (sGADFT) in which promising candidates provided by the GA are ultimately optimized with DFT. We provide a benchmark of several computational methods (GAFF, AMOEBApro13, PM6, PM7, DFTB3-D3(BJ)) as possible prescanning surrogates and apply sGADFT to two test case systems that are (i) two isomer families of the protonated Gly-Pro-Gly-Gly tetrapeptide (Masson, A.; J. Am. Soc. Mass Spectrom.2015, 26, 1444-1454) and (ii) the doubly protonated cyclic decapeptide gramicidin S (Nagornova, N. S.; J. Am. Chem. Soc.2010, 132, 4040-4041). We show that our GA procedure can correctly identify low-energy minima in as little as a few hours. Subsequent refinement of surrogate low-energy structures within a given energy threshold (≤10 kcal/mol (i), ≤5 kcal/mol (ii)) via DFT relaxation invariably led to the identification of the most stable structures as determined from high-resolution infrared (IR) spectroscopy at low temperature. The sGADFT method therefore constitutes a highly efficient route for the screening of realistic low-lying peptide structures in the gas phase as needed for instance for the interpretation and assignment of experimental IR spectra.

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

Combination of Experimental and Bioinformatic Approaches for Identification of Immunologically Relevant Protein-Peptide Interactions

Protein-peptide interactions are an essential player in cellular processes and, thus, of great interest as potential therapeutic agents. However, identifying the protein's interacting surface has been shown to be a challenging task. Here, we present a methodology for protein-peptide interaction identification, implementing phage panning, next-generation sequencing and bioinformatic analysis. One of the uses of this methodology is identification of allergen epitopes, especially suitable for globular inhaled and venom allergens, where their binding capability is determined by the allergen's conformation, meaning their interaction cannot be properly studied when denatured. A Ph.D. commercial system based on the M13 phage vector was used for the panning process. Utilization of various bioinformatic tools, such as PuLSE, SAROTUP, MEME, Hammock and Pepitope, allowed us to evaluate a large amount of obtained data. Using the described methodology, we identified three peptide clusters representing potential epitopes on the major wasp venom allergen Ves v 5.

A comparison of electrosprayed vs vortexed glycosaminoglycan-peptide nanoparticle platform for protection and improved delivery of therapeutic peptides

Therapeutic peptides capable of reducing inflammation via inhibition of the MAP kinase 2 pathway have the potential to reduce inflammation in atopic dermatitis by suppressing secretion of inflammatory cytokines by resident keratinocytes. One of the biggest hurdles to the use of therapeutic peptides, however, is their rapid degradation by intrinsic proteases and peptidases found in serum. Here we introduce a new nanoparticle technology that enhances and extends the bioactivity of a MAP KAP kinase 2 inhibitor peptide (MK2i) via electrostatic complexation with Dermatan sulfate (DS), a glycosaminoglycan, and explore their properties under various conditions. DS-MK2i nanoparticles can be made using electrospray ionization or sonication and vortexing with no stabilizing polymers or crosslinking. Average particle diameter, polydispersity index, and zeta potential were measured over a pH range of 2.5-11.5, in increments of 0.5, in water and at physiological ionic strength. Both particle types were shown to be shelf stable, robust, and behave differently in response to pH. They are also significantly more effective at suppressing cytokine secretion in inflamed, human keratinocytes than peptide alone in the presence of serum, providing a facile method of protecting peptides for therapeutic delivery in conditions such as atopic dermatitis, and abrogating the need for serum-starvation in in vitro testing.

Anti-Cancer Peptides: Status and Future Prospects

The dramatic rise in cancer incidence, alongside treatment deficiencies, has elevated cancer to the second-leading cause of death globally. The increasing morbidity and mortality of this disease can be traced back to a number of causes, including treatment-related side effects, drug resistance, inadequate curative treatment and tumor relapse. Recently, anti-cancer bioactive peptides (ACPs) have emerged as a potential therapeutic choice within the pharmaceutical arsenal due to their high penetration, specificity and fewer side effects. In this contribution, we present a general overview of the literature concerning the conformational structures, modes of action and membrane interaction mechanisms of ACPs, as well as provide recent examples of their successful employment as targeting ligands in cancer treatment. The use of ACPs as a diagnostic tool is summarized, and their advantages in these applications are highlighted. This review expounds on the main approaches for peptide synthesis along with their reconstruction and modification needed to enhance their therapeutic effect. Computational approaches that could predict therapeutic efficacy and suggest ACP candidates for experimental studies are discussed. Future research prospects in this rapidly expanding area are also offered.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Benchmarking AlphaFold2 on peptide structure prediction

Recent advancements in computational tools have allowed protein structure prediction with high accuracy. Computational prediction methods have been used for modeling many soluble and membrane proteins, but the performance of these methods in modeling peptide structures has not yet been systematically investigated. We benchmarked the accuracy of AlphaFold2 in predicting 588 peptide structures between 10 and 40 amino acids using experimentally determined NMR structures as reference. Our results showed AlphaFold2 predicts α-helical, β-hairpin, and disulfide-rich peptides with high accuracy. AlphaFold2 performed at least as well if not better than alternative methods developed specifically for peptide structure prediction. AlphaFold2 showed several shortcomings in predicting Φ/Ψ angles, disulfide bond patterns, and the lowest RMSD structures failed to correlate with lowest pLDDT ranked structures. In summary, computation can be a powerful tool to predict peptide structures, but additional steps may be necessary to analyze and validate the results.

Self-Assembled Peptide-Based Nanodrugs: Molecular Design, Synthesis, Functionalization, and Targeted Tumor Bioimaging and Biotherapy

Functional nanomaterials as nanodrugs based on the self-assembly of inorganics, polymers, and biomolecules have showed wide applications in biomedicine and tissue engineering. Ascribing to the unique biological, chemical, and physical properties of peptide molecules, peptide is used as an excellent precursor material for the synthesis of functional nanodrugs for highly effective cancer therapy. Herein, recent progress on the design, synthesis, functional regulation, and cancer bioimaging and biotherapy of peptide-based nanodrugs is summarized. For this aim, first molecular design and controllable synthesis of peptide nanodrugs with 0D to 3D structures are presented, and then the functional customization strategies for peptide nanodrugs are presented. Then, the applications of peptide-based nanodrugs in bioimaging, chemotherapy, photothermal therapy (PTT), and photodynamic therapy (PDT) are demonstrated and discussed in detail. Furthermore, peptide-based drugs in preclinical, clinical trials, and approved are briefly described. Finally, the challenges and potential solutions are pointed out on addressing the questions of this promising research topic. This comprehensive review can guide the motif design and functional regulation of peptide nanomaterials for facile synthesis of nanodrugs, and further promote their practical applications for diagnostics and therapy of diseases.

Effects of Conjugation of Ferrocene and Gallic Acid On desCys11/Lys12/Lys13-(p-BthTX-I)2K Peptide: Structure, Permeabilization and Antibacterial Activity

BACKGROUND

Antimicrobial resistance is an emerging global health challenge that has led researchers to study alternatives to conventional antibiotics. A promising alternative is antimicrobial peptides (AMPs), produced as the first line of defense by almost all living organisms. To improve its biological activity, the conjugation of AMPs is a promising approach.

OBJECTIVE

In this study, we evaluated the N-terminal conjugation of p-Bt (a peptide derived from Bothrops Jararacuçu`s venom) with ferrocene (Fc) and gallic acid (GA). Acetylated and linear versions of p-Bt were also synthesized to evaluate the importance of N-terminal charge and dimeric structure.

METHODS

The compounds were obtained using solid-phase peptide synthesis. Circular dichroism, vesicle permeabilization, antimicrobial activity, and cytotoxicity studies were conducted.

RESULTS

No increase in antibacterial activity against Escherichia coli was observed by adding either Fc or GA to p-Bt. However, Fc-p-Bt and GA-p-Bt exhibited improved activity against Staphylococcus aureus. No cytotoxicity upon fibroblast was observed for GA-p-Bt. On the other hand, conjugation with Fc increased cytotoxicity. This toxicity may be related to the membrane permeabilization capacity of this bioconjugate, which showed the highest carboxyfluorescein leakage in vesicle permeabilization experiments.

CONCLUSION

Considering these observations, our findings highlight the importance of adding bioactive organic compounds in the N-terminal position as a tool to modulate the activity of AMPs.

Opportunistic Challenges of Computer-aided Drug Discovery of Lipopeptides: New Insights for Large Molecule Therapeutics

Computer-aided drug designing is a promising approach to defeating the dry pipeline of drug discovery. It aims at reduced experimental efforts with cost-effectiveness. Naturally occurring large molecules with molecular weight higher than 500 Dalton such as cationic peptides, cyclic peptides, glycopeptides and lipopeptides are a few examples of large molecules which have successful applications as the broad spectrum antibacterial, anticancer, antiviral, antifungal and antithrombotic drugs. Utilization of microbial metabolites as potential drug candidates incur cost effectiveness through large scale production of such molecules rather than a synthetic approach. Computational studies on such compounds generate tremendous possibilities to develop novel leads with challenges to handle these complex molecules with available computational tools. The opportunities begin with the desired structural modifications in the parent drug molecule. Virtual modifications followed by molecular interaction studies at the target site through molecular modeling simulations and identification of structure-activity relationship models to develop more prominent and potential drug molecules. Lead optimization studies to develop novel compounds with increased specificity and reduced off targeting is a big challenge computationally for large molecules. Prediction of optimized pharmacokinetic properties facilitates development of a compound with lower toxicity as compared to the natural compounds. Generating the library of compounds and studies for target specificity and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) for large molecules are laborious and incur huge cost and chemical wastage through in-vitro methods. Hence, computational methods need to be explored to develop novel compounds from natural large molecules with higher specificity. This review article is focusing on possible challenges and opportunities in the pathway of computer-aided drug discovery of large molecule therapeutics.

[The use of neuropeptides of animal origin in the treatment of neurological diseases]

The issues of effective treatment of neurological diseases remain relevant to this day. Neuropeptide preparations have been used in domestic neurological practice for more than 20 years. The physiological activity of neuropeptides is many times greater than that of non-peptide compounds. Neuropeptides include preparations from the brain of animals and synthetically synthesized analogues. The drugs differ from each other not only in composition, but also in different mechanisms of action, while maintaining the commonality of a pronounced neurotrophic and neuroreparative action. Large peptides and amino acids work on the principle of «replacement therapy», minipeptides affect the signaling system of the nuclear erythroid factor and bind to molecular targets, being bioregulators. The specific action of bioregulators is the ability to prolong their action and change the prevailing mechanism by reducing or increasing the required dose when physiologically necessary. They are called SMART-peptides, have high selectivity and efficiency, safety can potentiate the actions of other drugs.

Identification and prioritization of potential therapeutic molecules against LpxA from Acinetobacter baumannii - A computational study

A. baumannii is a ubiquitously found gram-negative, multi-drug resistant bacterial species from the ESKAPE family of pathogens known to be the causative agent for hospital-acquired infections such as pneumonia, meningitis, endocarditis, septicaemia and urinary tract infections. A. baumannii is implicated as a contributor to bloodstream infections in approximately 2% of all worldwide infections. Hence, exploring novel therapeutic agents against the bacterium is essential. LpxA or UDP-N-acetylglucosamine acetyltransferase is an essential enzyme important in Lipid A biosynthesis which catalyses the reversible transfer of an acetyl group on the glucosamine 3-OH of the UDP-GlcNAc which is a crucial step in the biosynthesis of the protective Lipopolysaccharides (LPS) layer of the bacteria which upon disruption can lead to the elimination of the bacterium which delineates LpxA as an appreciable drug target from A. baumannii. The present study performs high throughput virtual screening of LpxA against the enamine-HTSC-large-molecule library and performs toxicity and ADME screening to identify the three promising lead molecules subjected to molecular dynamics simulations. Global and essential dynamics analysis of LpxA and its complexes along with FEL and MM/PBSA based binding free energy delineate Z367461724 and Z219244584 as potential inhibitors against LpxA from A. baumannii.

Peptide Triazole Inhibitors of HIV-1: Hijackers of Env Metastability

With 1.5 million new infections and 690,000 AIDS-related deaths globally each year, HIV- 1 remains a pathogen of significant public health concern. Although a wide array of effective antiretroviral drugs have been discovered, these largely target intracellular stages of the viral infectious cycle, and inhibitors that act at or before the point of viral entry still require further advancement. A unique class of HIV-1 entry inhibitors, called peptide triazoles (PTs), has been developed, which irreversibly inactivates Env trimers by exploiting the protein structure's innate metastable nature. PTs, and a related group of inhibitors called peptide triazole thiols (PTTs), are peptide compounds that dually engage the CD4 receptor and coreceptor binding sites of Env's gp120 subunit. This triggers dramatic conformational rearrangements of Env, including the shedding of gp120 (PTs and PTTs) and lytic transformation of the gp41 subunit to a post-fusion-like arrangement (PTTs). Due to the nature of their dual receptor site engagement, PT/PTT-induced conformational changes may elucidate mechanisms behind the native fusion program of Env trimers following receptor and coreceptor engagement, including the role of thiols in fusion. In addition to inactivating Env, PTT-induced structural transformation enhances the exposure of important and conserved neutralizable regions of gp41, such as the membrane proximal external region (MPER). PTT-transformed Env could present an intriguing potential vaccine immunogen prototype. In this review, we discuss the origins of the PT class of peptide inhibitors, our current understanding of PT/PTT-induced structural perturbations and viral inhibition, and prospects for using these antagonists for investigating Env structural mechanisms and for vaccine development.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Quantitative structure-activity relationships, molecular docking and molecular dynamics simulations reveal drug repurposing candidates as potent SARS-CoV-2 main protease inhibitors

The current outbreak of COVID-19 is leading an unprecedented scientific effort focusing on targeting SARS-CoV-2 proteins critical for its viral replication. Herein, we performed high-throughput virtual screening of more than eleven thousand FDA-approved drugs using backpropagation-based artificial neural networks (q²_LOO = 0.60, r² = 0.80 and r²_pred = 0.91), partial-least-square (PLS) regression (q²_LOO = 0.83, r² = 0.62 and r²_pred = 0.70) and sequential minimal optimization (SMO) regression (q²_LOO = 0.70, r² = 0.80 and r²_pred = 0.89). We simulated the stability of Acarbose-derived hexasaccharide, Naratriptan, Peramivir, Dihydrostreptomycin, Enviomycin, Rolitetracycline, Viomycin, Angiotensin II, Angiotensin 1-7, Angiotensinamide, Fenoterol, Zanamivir, Laninamivir and Laninamivir octanoate with 3CL_pro by 100 ns and calculated binding free energy using molecular mechanics combined with Poisson-Boltzmann surface area (MM-PBSA). Our QSAR models and molecular dynamics data suggest that seven repurposed-drug candidates such as Acarbose-derived Hexasaccharide, Angiotensinamide, Dihydrostreptomycin, Enviomycin, Fenoterol, Naratriptan and Viomycin are potential SARS-CoV-2 main protease inhibitors. In addition, our QSAR models and molecular dynamics simulations revealed that His41, Asn142, Cys145, Glu166 and Gln189 are potential pharmacophoric centers for 3CL_pro inhibitors. Glu166 is a potential pharmacophore for drug design and inhibitors that interact with this residue may be critical to avoid dimerization of 3CL_pro. Our results will contribute to future investigations of novel chemical scaffolds and the discovery of novel hits in high-throughput screening as potential anti-SARS-CoV-2 properties.Communicated by Ramaswamy H. Sarma.

PPARβ/δ-Interfering Peptide Enhanced Mesenchymal Stromal Cell Immunoregulatory Properties

Background

Mesenchymal stem/stromal cells (MSCs) have been widely used for their therapeutic properties in many clinical applications including osteoarthritis. Despite promising preclinical results showing the ability of MSC to reduce the clinical severity of osteoarthritis (OA) in experimental animal models, the benefits of intra-articular injection of MSC in OA patients are limited to the short term. In this regard, it is anticipated that improving the properties of MSC may collectively enhance their long-term beneficial effects on OA.

Methods and Results

Recently, we have shown that PPARβ/δ inhibition using a commercially available antagonist in murine MSC increases their immunoregulatory potential in vitro as well as their therapeutic potential in an experimental murine arthritis model. Here, we relied on an innovative strategy to inhibit PPARβ/δ:NF-κB TF65 subunit interaction in human MSC by designing and synthesizing an interfering peptide, referred to PP11. Through RT-qPCR experiments, we evidenced that the newly synthesized PP11 peptide reduced the expression level of PDK4, a PPARβ/δ target gene, but did not modify the expression levels of ACOX1 and CPT1A, PPARα target genes, and FABP4, a PPARγ target gene compared with untreated human MSC. Moreover, we showed that human MSCs pretreated with PP11 exhibit a significantly higher capacity to inhibit the proliferation of activated PBMC and to decrease the frequency of M1-like macrophages.

Conclusions

We designed and synthesized an interfering peptide that potently and specifically blocks PPARβ/δ activity with concomitant enhancement of MSC immunoregulatory properties.

Synergistic osteogenic and angiogenic effects of KP and QK peptides incorporated with an injectable and self-healing hydrogel for efficient bone regeneration

Irregular defects generated by trauma or surgery in orthopaedics practice were usually difficult to be fitted by the preformed traditional bone graft substitute. Therefore, the injectable hydrogels have attracted an increasing interest for bone repair because of their fittability and mini-invasivity. However, the uncontrollable spreading or mechanical failures during its manipulation remain a problem to be solved. Moreover, in order to achieve vascularized bone regeneration, alternatives of osteogenic and angiogenic growth factors should be adopted to avoid the problem of immunogenicity and high cost. In this study, a novel injectable self-healing hydrogel system (GMO hydrogel) loaded with KP and QK peptides had been developed for enhancing vascularized regeneration of small irregular bone defect. The dynamic imine bonds between gelatin methacryloyl and oxidized dextran provided the GMO hydrogel with self-healing and shear-thinning abilities, which led to an excellent injectability and fittability. By photopolymerization of the enclosed GelMA, GMO hydrogel was further strengthened and thus more suitable for bone regeneration. Besides, the osteogenic peptide KP and angiogenic peptide QK were tethered to GMO hydrogel by Schiff base reaction, leading to desired releasing profiles. In vitro, this composite hydrogel could significantly improve the osteogenic differentiation of BMSCs and angiogenesis ability of HUVECs. In vivo, KP and QK in the GMO hydrogel demonstrated a significant synergistic effect in promoting new bone formation in rat calvaria. Overall, the KP and QK loaded GMO hydrogel was injectable and self-healing, which can be served as an efficient approach for vascularized bone regeneration via a minimally invasive approach.

Compelling Cyclic Peptide Scaffolds for Antitubercular Action: An Account (2011-21) of the Natural Source

Natural cyclic peptide scaffolds are indispensable in medicinal chemistry, chemical biology, and drug discovery platforms due to their chemical diversity, structural integrity, proteolytic stability and biocompatibility. Historically, their isolation and profound understanding of target engagement have been identified as lead pharmacophore discovery. Natural cyclic peptides are the largest class of pharmacologically active scaffold, in which most show activity against drug-resistant Mycobacterium tuberculosis (Mtb). Nevertheless, eight recently discovered cyclic peptide scaffolds exhibit promising antitubercular activity among numerous naturally occurring antitubercular peptides, and they are amenable scaffolds to drug development. We examined their biological origin, scaffolds, isolations, chemical synthesis, and reasons for biological actions against Mtb. Understanding these peptide scaffold details will further allow synthetic and medicinal chemists to develop novel peptide therapeutics against tuberculosis-infected deadly diseases. This review emphasizes these cyclic peptides' in vitro and in vivo activity profiles, including their structural and chemical features.

Learn from antibody–drug conjugates: consideration in the future construction of peptide-drug conjugates for cancer therapy

Cancer is one of the leading causes of death worldwide due to high heterogeneity. Although chemotherapy remains the mainstay of cancer therapy, non-selective toxicity and drug resistance of mono-chemotherapy incur broad criticisms. Subsequently, various combination strategies have been developed to improve clinical efficacy, also known as cocktail therapy. However, conventional “cocktail administration” is just passable, due to the potential toxicities to normal tissues and unsatisfactory synergistic effects, especially for the combined drugs with different pharmacokinetic properties. The drug conjugates through coupling the conventional chemotherapeutics to a carrier (such as antibody and peptide) provide an alternative strategy to improve therapeutic efficacy and simultaneously reduce the unspecific toxicities, by virtue of the advantages of highly specific targeting ability and potent killing effect. Although 14 antibody–drug conjugates (ADCs) have been approved worldwide and more are being investigated in clinical trials so far, several limitations have been disclosed during clinical application. Compared with ADCs, peptide-drug conjugates (PDCs) possess several advantages, including easy industrial synthesis, low cost, high tissue penetration and fast clearance. So far, only a handful of PDCs have been approved, highlighting tremendous development potential. Herein, we discuss the progress and pitfalls in the development of ADCs and underline what can learn from ADCs for the better construction of PDCs in the future.

Translational aspect in peptide drug discovery and development: An emerging therapeutic candidate

In the last two decades, protein-protein interactions (PPIs) have been used as the main target for drug development. However, with larger or superficial binding sites, it has been extremely difficult to disrupt PPIs with small molecules. On the other hand, intracellular PPIs cannot be targeted by antibodies that cannot penetrate the cell membrane. Peptides that have a combination of conformational rigidity and flexibility can be used to target difficult binding interfaces with appropriate binding affinity and specificity. Since the introduction of insulin nearly a century ago, more than 80 peptide drugs have been approved to treat a variety of diseases. These include deadly diseases such as cancer and human immunodeficiency virus infection. It is also useful against diabetes, chronic pain, and osteoporosis. Today, more research is being done on these drugs as lessons learned from earlier approaches, which are still valid today, complement newer approaches such as peptide display libraries. At the same time, integrated genomics and peptide display libraries are new strategies that open new avenues for peptide drug discovery. The purpose of this review is to examine the problems in elucidating the peptide-protein recognition mechanism. This is important to develop peptide-based interventions that interfere with endogenous protein interactions. New approaches are being developed to improve the binding affinity and specificity of existing approaches and to develop peptide agents as potentially useful drugs. We also highlight the key challenges that must be overcome in peptide drug development to realize their potential and provide an overview of recent trends in peptide drug development. In addition, we take an in-depth look at early efforts in human hormone discovery, smart medicinal chemistry and design, natural peptide drugs, and breakthrough advances in molecular biology and peptide chemistry.

Nanomedicine based potentially transformative strategies for colon targeting of peptides: State-of-the-art

Recently, peptides have attracted tremendous attention among researchers attributed to their high target specificity and efficacy compared to conventional therapeutics. The ease of self-administration and non-invasiveness confers oral as the most desirable route. However, numerous challenges associated with peptide delivery through the oral route like harsh gastrointestinal environment, enzymatic degradation, and absorption barriers hinder its clinical translation. Protease activity is more pronounced in the proximal segments of the gastrointestinal tract (GIT). Distal segments like the colon possess lower proteolytic activity, enhanced retention time, etc. which could facilitate easy absorption. However, traversing of the upper segments to reach the colon requires the circumvention of the pitfalls of the GIT. The advent of nanomedicine strategies could help in overcoming the said challenges associated with oral delivery, colon-specific targeting, and improving stability and bioavailability at the active site. Furthermore, the classification of peptides and various nanomedicine strategies for oral delivery of peptides to the colon has been conveyed. Regulatory hurdles and ways to accomplish clinical translation have been addressed.

Trading places: Peptide and small molecule alternatives to oligonucleotide-based modulation of microRNA expression

It is well established that microRNA (miRNA) dysregulation is involved in the development and progression of various diseases, especially cancer. Emerging evidence suggests that small molecule and peptide agents can interfere with miRNA disease pathways. Despite this, very little is known about structural features that drive drug-miRNA interactions and subsequent inhibition. In this review, we highlight the advances made in the development of small molecule and peptide inhibitors of miRNA processing. Specifically, we attempt to draw attention to peptide features that may be critical for interaction with the miRNA secondary structure to regulate miRNA expression. We hope that this review will help to establish peptides as exciting miRNA expression modulators and will contribute towards the development of the first miRNA-targeting peptide therapy.

Advances in the stability challenges of bioactive peptides and improvement strategies

Bioactive peptides are widely used in functional foods due to their remarkable efficacy, selectivity, and low toxicity. However, commercially produced bioactive peptides lack quality stability between batches. Furthermore, the efficacies of bioactive peptides cannot be guaranteed in vivo due to gastrointestinal digestion and rapid plasma, liver, and kidney metabolism. The problem of poor stability has restricted the development of peptides. Bioactive peptide stability assessments use different stability assays, so the results of different studies are not always comparable. This review summarizes the quality stability challenges in the enzymatic hydrolysis production of bioactive peptides and the metabolism stability challenges after oral administration. Future directions on the strategies for improving their stability are provided. It was proposed that we use fingerprinting as a quality control measure using qualitative and quantitative characteristic functional peptide sequences. The chemical modification and encapsulation of bioactive peptides in microcapsules and liposomes are widely used to improve the digestive and metabolic stability of bioactive peptides. Additionally, the establishment of a universal stability test and a unified index would greatly improve uniformity and comparability in research into bioactive peptides. In summary, the reliable evaluation of stability is an essential component of peptide characterization, and these ideas may facilitate further development and utilization of bioactive peptides.

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Stability challenges encountered by bioactive peptides were summarized.

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Strategies to improve the stability of bioactive peptides were provided.

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A universal stability test and unified index would improve uniformity and comparability in research into bioactive peptides.

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It was proposed that we use a method of traditional Chinese medicine fingerprinting as a quality control measure.

An Overview of the Potentialities of Antimicrobial Peptides Derived from Natural Sources

Antimicrobial peptides (AMPs) are constituents of the innate immune system in every kind of living organism. They can act by disrupting the microbial membrane or without affecting membrane stability. Interest in these small peptides stems from the fear of antibiotics and the emergence of microorganisms resistant to antibiotics. Through membrane or metabolic disruption, they defend an organism against invading bacteria, viruses, protozoa, and fungi. High efficacy and specificity, low drug interaction and toxicity, thermostability, solubility in water, and biological diversity suggest their applications in food, medicine, agriculture, animal husbandry, and aquaculture. Nanocarriers can be used to protect, deliver, and improve their bioavailability effectiveness. High cost of production could limit their use. This review summarizes the natural sources, structures, modes of action, and applications of microbial peptides in the food and pharmaceutical industries. Any restrictions on AMPs' large-scale production are also taken into consideration.

Peptide-Drug Conjugates: A New Hope for Cancer Management

Cancer remains the leading cause of death worldwide despite advances in treatment options for patients. As such, safe and effective therapeutics are required. Short peptides provide advantages to be used in cancer management due to their unique properties, amazing versatility, and progress in biotechnology to overcome peptide limitations. Several appealing peptide-based therapeutic strategies have been developed. Here, we provide an overview of peptide conjugates, the better equivalents of antibody-drug conjugates, as the next generation of drugs for required precise targeting, enhanced cellular permeability, improved drug selectivity, and reduced toxicity for the efficient treatment of cancers. We discuss the basic components of drug conjugates and their release action, including the release of cytotoxins from the linker. We also present peptide-drug conjugates under different stages of clinical development as well as regulatory and other challenges.

Enzyme Prodrug Therapy with Photo-Cross-Linkable Anti-EGFR Affibodies Conjugated to Upconverting Nanoparticles

In this work, we demonstrate that a photo-cross-linkable conjugate of upconverting nanoparticles and cytosine deaminase can catalyze prodrug conversion specifically at tumor sites in vivo. Non-covalent association of proteins and peptides with cellular surfaces leads to receptor-mediated endocytosis and catabolic degradation. Recently, we showed that covalent attachment of proteins such as affibodies to cell receptors yields extended expression on cell surfaces with preservation of protein function. To adapt this technology for in vivo applications, conjugates were prepared from upconverting nanoparticles and fusion proteins of affibody and cytosine deaminase enzyme (UC-ACD). The affibody allows covalent photo-cross-linking to epidermal growth factor receptors (EGFRs) overexpressed on Caco-2 human colorectal cancer cells under near-infrared (NIR) light. Once bound, the cytosine deaminase portion of the fusion protein converts the prodrug 5-fluorocytosine (5-FC) to the anticancer drug 5-fluorouracil (5-FU). NIR covalent photoconjugation of UC-ACD to Caco-2 cells showed 4-fold higher retention than observed with cells that were not irradiated in vitro. Next, athymic mice expressing Caco-2 tumors showed 5-fold greater UC-ACD accumulation in the tumors than either conjugates without the CD enzyme or UC-ACDs in the absence of NIR excitation. With oral administration of 5-FC prodrug, tumors with photoconjugated UC-ACD yielded 2-fold slower growth than control groups, and median mouse survival increased from 28 days to 35 days. These experiments demonstrate that enzyme-decorated nanoparticles can remain viable after a single covalent photoconjugation in vivo, which can in turn localize prodrug conversion to tumor sites for multiple weeks.

Binary combinatorial scanning reveals potent poly-alanine-substituted inhibitors of protein-protein interactions

Establishing structure-activity relationships is crucial to understand and optimize the activity of peptide-based inhibitors of protein-protein interactions. Single alanine substitutions provide limited information on the residues that tolerate simultaneous modifications with retention of biological activity. To guide optimization of peptide binders, we use combinatorial peptide libraries of over 4,000 variants-in which each position is varied with either the wild-type residue or alanine-with a label-free affinity selection platform to study protein-ligand interactions. Applying this platform to a peptide binder to the oncogenic protein MDM2, several multi-alanine-substituted analogs with picomolar binding affinity were discovered. We reveal a non-additive substitution pattern in the selected sequences. The alanine substitution tolerances for peptide ligands of the 12ca5 antibody and 14-3-3 regulatory protein are also characterized, demonstrating the general applicability of this new platform. We envision that binary combinatorial alanine scanning will be a powerful tool for investigating structure-activity relationships.

In Silico Identification of Potential Insect Peptides against Biofilm-Producing Staphylococcus aureus

Biofilm-producing Staphylococcus aureus (SA) strains are frequently found in medical environments, from surgical/ wound sites, medical devices. These biofilms reduce the efficacy of applied antibiotics during the treatment of several infections, such as cystic fibrosis, endocarditis, or urinary tract infections. Thus, the development of potential therapeutic agents to destroy the extra protective biofilm layers or to inhibit the biofilm-producing enzymes is urgently needed. Advanced and cost-effective bioinformatics tools are advantageous in locating and speeding up the selection of antibiofilm candidates. Based on the potential drug characteristics, we have selected one-hundred thirty-three antibacterial peptides derived from insects to assess for their antibiofilm potency via molecular docking against five putative biofilm formation and regulated target enzymes: the staphylococcal accessory regulator A or SarA (PDB ID: 2FRH), 4,4'-diapophytoene synthase or CrtM (PDB ID: 2ZCQ), clumping factor A or ClfA (PDB ID: 1N67) and serine-aspartate repeat protein C or SdrC (PDB ID: 6LXH) and sortase A or SrtA (PDB ID: 1T2W) of SA bacterium. In this study, molecular docking was performed using HPEPDOCK and HDOCK servers, and molecular interactions were examined using BIOVIA Discovery Studio Visualizer-2019. The docking score (kcal/mol) range of five promising antibiofilm peptides against five targets was recorded as follows: diptericin A (-215.52 to -303.31), defensin (-201.11 to -301.92), imcroporin (-212.08 to -287.64), mucroporin (-228.72 to -286.76), apidaecin II (-203.90 to -280.20). Among these five, imcroporin and mucroporin were 13 % each, while defensin contained only 1 % of positive net charged residues (Arg+Lys) projected through ProtParam and NetWheels tools. Similarly, imcroporin, mucroporin and apidaecin II were 50 %, while defensin carried 21.05 % of hydrophobic residues predicted by the tool PEPTIDE. 2.0. Most of the peptides exhibited potential characteristics to inhibit S. aureus-biofilm formation via disrupting the cell membrane and cytoplasmic integrity. In summary, the proposed hypothesis can be considered a cost-effective platform for selecting the most promising bioactive drug candidates within a limited timeframe with a greater chance of success in experimental and clinical studies.

Serine O-acetyltransferase derived NV14 peptide reduces cytotoxicity in H2O2 induced MDCK cells and inhibits MCF-7 cell proliferation through caspase gene expression

BACKGROUND

Most of the bioactive peptides exhibit antioxidant effect and do elicit inhibitory effect on proliferation of cancer cells. This study investigates the in-vitro antioxidant and anti-cancer properties of NV14 peptide, derived from serine O-acetyltransferase (SAT) of spirulina, Arthrospira platensis.

METHODS

The anti-cancer effect of the peptide was evaluated using human adenocarcinoma epithelial cells (MCF-7), while the anti-oxidant potential, as in reduction in ROS concentration, has been established using the H2O2-exposed, Madin-Darby canine kidney (MDCK) cells. The outcome of the in vitro analyses has been evaluated by in silico molecular docking analyses.

RESULTS

The peptide, dose-dependently, reduced oxidative stress as well as cell proliferation. Besides, based on the binding scores between NV14 peptide and the important proteins associated with apoptosis and antioxidant defense, it is evident that the peptide has antioxidant and anti-cancer effect, in vitro.

CONCLUSIONS

Together, this study demonstrates that NV14 has a potent antioxidant and anti-cancer capability; however, further direction needs to be focused on clinical or pharmacodynamics aspects.

De Novo Design of an Androgen Receptor DNA Binding Domain-Targeted peptide PROTAC for Prostate Cancer Therapy

Androgen receptor splice variant-7 (AR-V7), one of the major driving factors, is the most attractive drug target in castration-resistant prostate cancer (CRPC). Currently, no available drugs efficiently target AR-V7 in clinical practice. The DNA binding domain (DBD) is indispensable for the transcriptional activity of AR full length and AR splice variants, including AR-V7. Based on the homodimerization structure of the AR DBD, a novel peptide-based proteolysis-targeting chimera (PROTAC) drug is designed to induce AR and AR-V7 degradation in a DBD and MDM2-dependent manner, without showing any activity on other hormone receptors. To overcome the short half-life and poor cell penetrability of peptide PROTAC drugs, an ultrasmall gold (Au)-peptide complex platform to deliver the AR DBD PROTAC in vivo is developed. The obtained Au-AR pep-PROTAC effectively degrades AR and AR-V7 in prostate cancer cell lines, particularly in CWR22Rv1 cells with DC50 values 48.8 and 79.2 nM, respectively. Au-AR pep-PROTAC results in suppression of AR levels and induces tumor regression in both enzalutamide sensitive and resistant prostate cancer animal models. Further optimization of the Au-AR pep-PROTAC can ultimately lead to a new therapy for AR-V7-positive CRPC.

Efficacy Evaluation of SDF-1α-Based Polypeptides in an Acute Myocardial Infarction Model Using Structure-Based Drug Design

Stromal cell-derived factor-1 alpha (SDF-1α, CXCL12) mediates the migration of circulating cells to desired sites for tissue development, homeostasis, and regeneration and can be used to promote cardiac regeneration by recruiting stem cells. However, the use of SDF-1α in the injured heart necessitates not only higher binding affinity to its receptor, CXCR4+, but also better robustness against enzymatic degradation than other SDF-1 isoforms. Here, we conduct a screening of SDF-1α analog peptides that were designed by structure-based drug design (SBDD), a type of computer-aided drug design (CADD). We have developed in vitro and in vivo methods that enable us to estimate the effect of peptides on the migration of human mesenchymal stem cells (hMSCs) and cardiac regeneration in acute myocardial infarction (AMI)-induced animals, respectively. We demonstrate that one type of SDF-1α analog peptide, SDP-4, among the four analog peptides preselected by SBDD, is more potent than native SDF-1α for cardiac regeneration in myocardial infarction. It is interesting to note that the migratory effects of SDP-4 determined by a wound healing assay, a Transwell assay, and a 2D migration assay are comparable to those of SDF-1α. These results suggest that in vivo, as well as in vitro, screening of peptides developed by SBDD is a quintessential process to the development of a novel therapeutic compound for cardiac regeneration. Our finding also has an implication that the SDP-4 peptide is an excellent candidate for use in the regeneration of an AMI heart.

Improving peptide-protein docking with AlphaFold-Multimer using forced sampling

Protein interactions are key in vital biological processes. In many cases, particularly in regulation, this interaction is between a protein and a shorter peptide fragment. Such peptides are often part of larger disordered regions in other proteins. The flexible nature of peptides enables the rapid yet specific regulation of important functions in cells, such as their life cycle. Consequently, knowledge of the molecular details of peptide-protein interactions is crucial for understanding and altering their function, and many specialized computational methods have been developed to study them. The recent release of AlphaFold and AlphaFold-Multimer has led to a leap in accuracy for the computational modeling of proteins. In this study, the ability of AlphaFold to predict which peptides and proteins interact, as well as its accuracy in modeling the resulting interaction complexes, are benchmarked against established methods. We find that AlphaFold-Multimer predicts the structure of peptide-protein complexes with acceptable or better quality (DockQ ≥0.23) for 66 of the 112 complexes investigated-25 of which were high quality (DockQ ≥0.8). This is a massive improvement on previous methods with 23 or 47 acceptable models and only four or eight high quality models, when using energy-based docking or interaction templates, respectively. In addition, AlphaFold-Multimer can be used to predict whether a peptide and a protein will interact. At 1% false positives, AlphaFold-Multimer found 26% of the possible interactions with a precision of 85%, the best among the methods benchmarked. However, the most interesting result is the possibility of improving AlphaFold by randomly perturbing the neural network weights to force the network to sample more of the conformational space. This increases the number of acceptable models from 66 to 75 and improves the median DockQ from 0.47 to 0.55 (17%) for first ranked models. The best possible DockQ improves from 0.58 to 0.72 (24%), indicating that selecting the best possible model is still a challenge. This scheme of generating more structures with AlphaFold should be generally useful for many applications involving multiple states, flexible regions, and disorder.

Peptide loaded polymeric nanoparticles by non-aqueous nanoprecipitation

It is always a challenge to encapsulate water-soluble peptides in polymer nanoparticle (NP) systems. We establish and validate our newly developed non-aqueous nanoprecipitation method to encapsulate neuro-peptides drugs such as oxytocin and Luteinizing hormone-releasing hormone (LHRH) in poly(sebacic anhydride) (PSA) NPs. NPs were prepared by a solvent-antisolvent process under a strict anhydrous environment to obtain high drug loading and to avoid premature PSA degradation and drug release. Dynamic light scattering (DLS) and Scanning Electron Microscopy (SEM) reveal the size for both drug loaded PSA NPs to ∼ 300 nm. The drug loaded NPs were dispersible and spherical in shape with uniform morphology. The in vitro release profile of oxytocin from PSA NPs occurs with the burst release of ∼ 50% within the first hour in the aqueous release medium, whereas LHRH release is comparatively slow. Thus, looking into the fast degrading properties of PSA and drug release behavior, the developed NPs can be used for direct delivery of the neuropeptides to the olfactory epithelium using a refillable nasal atomizer that deposits mist onto the olfactory neuro-epithelium. We also applied our developed method to prepare NPs of poly(lactic-co-glycolic acid) (PLGA), polylactic acid (PLA), and poly(ε-caprolactone) (PCL). A Thyrotropin releasing hormone (TRH) was used as the sample neuropeptide drug to validate our non-aqueous method. The results reveal the formation of TRH loaded PLGA, PLA and PCL NPs with 100% drug loading. TEM analysis shows the formation of spherical NPs, having similar release properties as those of PSA NPs. Overall, we report that our developed method is suitable for co-encapsulating hydrophilic drugs in polymer NPs with high drug loading and release properties.

Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

MOTIVATION

Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization.

RESULTS

Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark dataset of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 min on a single CPU core, compared with 496 min for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/hpepdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of antiviral peptide inhibitors for receptor binding domain of SARS-CoV-2 omicron and its sub-variants: an in-silico approach

Omicron, a variant of concern (VOC) of SARS-CoV-2, emerged in South Africa in November 2021. Omicron has been continuously acquiring a series of new mutations, especially in the spike (S) protein that led to high infectivity and transmissibility. Peptides targeting the receptor-binding domain (RBD) of the spike protein by which omicron and its variants attach to the host receptor, angiotensin-converting enzyme (ACE2) can block the viral infection at the first step. This study aims to identify antiviral peptides from the Antiviral peptide database (AVPdb) and HIV-inhibitory peptide database (HIPdb) against the RBD of omicron by using a molecular docking approach. The lead RBD binder peptides obtained through molecular docking were screened for allergenicity and physicochemical criteria (isoelectric point (pI) and net charge) required for peptide-based drugs. The binding affinity of the best five peptide inhibitors with the RBD of omicron was validated further by molecular dynamics (MD) simulation. Our result introduces five antiviral peptides, including AVP1056, AVP1059, AVP1225, AVP1801, and HIP755, that may effectively hinder omicron-host interactions. It is worth mentioning that all the three major sub-variants of omicron, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3), exhibits conserved ACE-2 interacting residues. Hence, the screened antiviral peptides with similar affinity can also interrupt the RBD-mediated invasion of different major sub-variants of omicron. Altogether, these peptides can be considered in the peptide-based therapeutics development for omicron treatment after further experimentation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03258-4.