Comprehensive Evaluation of Fourteen Docking Programs on Protein–Peptide Complexes

Disclose ATP-synthase as a protein target of the antimicrobial peptide RiLK1 in Escherichia coli: An alternative receptor-mediated bactericidal mechanism

In this study, we investigated the mechanism of action of the 10-aminoacid RiLK1 peptide against Escherichia coli (strain ATCC 25922), both in vitro and in contaminated meat matrices. Therefore, a mass spectrometry-based functional proteomics platform was employed to identify specific molecular targets of RiLK1 in a membrane protein-enriched E. coli lysate and to obtain information on their interaction mechanism. This target deconvolution approach combines MS-limited proteolysis techniques, like Drug Affinity Responsive Target Stability (DARTS) and targeted-limited Proteolysis coupled with Mass Spectrometry (t-LiP-MS). The b and δ subunits of the multimeric enzymatic complex ATP synthase, the smallest known biological nanomotor found in all cells, were identified as the relevant RiLK1 membrane protein targets. Extensive molecular docking and biochemical analyses validated and improved the suggested interaction profile. These unique findings could rationally explain the relevant RiLK1 bactericidal effects against E. coli strains, suggesting its potential application in food safety and preservation.

Competitive binding of actin and SH3 domains at proline-rich regions of Las17/WASP regulates actin polymerisation

Eukaryotic actin filaments bind factors that regulate their assembly and disassembly creating a self-organising system, the actin cytoskeleton. Despite extensive knowledge of signals that modulate actin organisation, significant gaps remain in our understanding of spatiotemporal regulation of de novo filament initiation. Yeast Las17/WASP is essential for actin polymerisation initiation supporting membrane invagination in Saccharomyces cerevisiae endocytosis and therefore its tight regulation is critical. The adaptor protein Sla1 inhibits Las17 but mechanisms underpinning Las17 activation remain elusive. Here we show that Las17 binding of tandem Sla1 SH3 domains is >100-fold stronger than single domains. Furthermore, SH3 domains directly compete with G-actin for binding in the Las17 polyproline region, thus rationalising how SH3 interactions can affect actin polymerisation despite their distance from C-terminal actin-binding and Arp2/3-interacting VCA domains. Our data and proposed model also highlight the likely importance of multiple weak interactions that together ensure spatial and temporal regulation of endocytosis.

A machine learning framework to identify complex physicochemical features of B cell epitopes

During infection with Plasmodium falciparum in pregnancy, parasites express a unique virulence factor, VAR2CSA, that mediates binding of infected red blood cells to the placenta. A major goal in designing vaccines to protect pregnant women from malaria is to elicit antibodies to VAR2CSA. The challenge is that VAR2CSA is highly polymorphic and identifying conserved epitopes is essential to elicit strain-transcending immunity. Unexpectedly, a mouse monoclonal antibody, 3D10, raised against the unrelated Duffy binding protein from P. vivax (DBPII) cross-reacts with diverse alleles of VAR2CSA in vitro. To identify these potentially conserved epitopes in VAR2CSA, we designed a machine learning framework to analyse 3D10 reactivity to peptides derived from two alleles of VAR2CSA, DBPII, and PvEBP2 (negative control). We used decision trees and a panel of 430 features to extract features correlated to 3D10 binding. We analysed patterns of these features in the dataset and designed mutant peptides to test complex sequence motifs. Features associated with 3D10 reactivity were mapped onto predicted 3D structures of Plasmodium proteins and validated based on 3D10 reactivity to the recombinant antigens. While the array data identified certain linear epitopes, the framework predicted other epitopes that are conformational. With this approach, peptide array data can be mined to extract physicochemical properties of epitopes recognized by polyreactive antibodies.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

Designing an optimized theta-defensin peptide for HIV therapy using in-silico approaches

The glycoproteins 41 (gp41) of human immunodeficiency virus (HIV), located on the virus's external surface, form six-helix bundles that facilitate viral entry into the host cell. Theta defensins, cyclic peptides, inhibit the formation of these bundles by binding to the GP41 CHR region. RC101, a synthetic analog of theta-defensin molecules, exhibits activity against various HIV subtypes. Molecular docking of the CHR and RC101 was done using MDockPeP and Hawdock server. The type of bonds and the essential amino acids in binding were identified using AlphaFold3, CHIMERA, RING, and CYTOSCAPE. Mutable amino acids within the peptide were determined using the CUPSAT and Duet. Thirty-two new peptides were designed, and their interaction with the CHR of the gp41 was analyzed. The physicochemical properties, toxicity, allergenicity, and antigenicity of peptides were also investigated. Most of the designed peptides exhibited higher binding affinities to the target compared to RC101; notably, peptides 1 and 4 had the highest binding affinity and demonstrated a greater percentage of interactions with critical amino acids of CHR. Peptides A and E displayed the best physiochemical properties among designed peptides. The designed peptides may present a new generation of anti-HIV drugs, which may reduce the likelihood of drug resistance.

TRPA1-Activated Peptides from Saiga Antelope Horn: Screening, Interaction Mechanism, and Bioactivity

Saiga antelope horn (SAH), a rare traditional Chinese medicine, exhibits activities of anti-feverish convulsions and anti-inflammation, whereas its underlying mechanism and specific pharmacological components are still unclear. In the present study, transient receptor potential ankyrin 1 (TRPA1), a major transient receptor potential cation channel was used as a target protein to identified TRPA1 high-affinity peptides (THPs) from SAH digests. Firstly, the SAH was digested under in vitro gastrointestinal conditions. With the method of affinity ultrafiltration and liquid chromatography-mass spectrometry (AUF-LC/MS), about 200 peptides that have a high-affinity interaction with the TRPA1 protein were screened from SAH digests. Subsequently, bioactivity databases and molecular docking were further exploited to identified three THPs, including RCWPDCR, FGFDGDF, and WFCEGSF. Furthermore, RIN-14B cells, characterized by the high expression of TRPA1 on cell surfaces, were used as the cell model to investigate the biological effect of THPs. Immunofluorescence and ELISA were conducted and showed that THPs can increase the intracellular Ca2+ concentration and serotonin (5-HT) secretion in RIN-14B cells by activating TRPA1, which is evidenced by impaired upregulation of intracellular Ca2+ levels and 5-HT secretion after pretreatment with the TRPA1 inhibitor (HC-030031). Moreover, an analysis of Western blots displayed that THPs up-regulated the expression levels of the 5-HT synthesis rate-limiting enzyme (TPH1) and 5-hydroxytryptophan decarboxylase (Ddc), while serotonin reuptake transporter (SERT) levels were down-regulated, suggesting that THPs enhance 5-HT secretion by regulating the 5-HT synthesis pathway. In summary, our findings demonstrate that THPs, which were identified from SAH digest via TRPA1-targeted affinity panning, exhibited the activation of the TRPA1 channel and enhanced 5-HT release in RIN-14B cells.

Design and Binding Affinity of Antisense Peptides for Snake Venom Neutralization

Snakebites are a significant public health problem in many tropical and subtropical regions, causing extensive morbidity and mortality. Traditional snake antivenoms face multiple challenges, including allergenicity, high production costs, and logistical difficulties, highlighting the urgent need for new therapeutic approaches. This pilot study explores the potential of oligopeptides as therapeutic inhibitors targeting the neurotoxic sites of ammodytoxin A (AtxA; PDB: 3G8G) from Vipera ammodytes. We selected two sense oligopeptides to represent critical neurotoxic regions of AtxA as targets for inhibition by complementary antisense peptides. Utilizing a heuristic antisense peptide design based on the molecular recognition theory, we modeled two antisense oligopeptides as complementary counterparts for each sense oligopeptide. The modeled sense and antisense peptides were commercially synthesized, and their binding affinities were evaluated using spectrofluorometric titrations. The determined dissociation constants (KD) were in the range of 1-10 μM for all sense-antisense pairs, revealing relatively strong binding affinities. Confirmation of sense-antisense peptide binding prompted further investigation into their potential binding to the native target protein through global docking simulations using the HPEPDOCK web server. The results highlight the applicability of molecular recognition theory in the development of antisense peptides that could change therapeutic strategies in various biomedical fields. Further studies are needed to investigate the therapeutic efficacy and broader applications of these peptides.

Unlocking the potential of in silico approach in designing antibodies against SARS-CoV-2

Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, in silico techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, in silico approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed in silico, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of in silico-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.

Comprehensive proteolytic profiling of Aedes aegypti mosquito midgut extracts: Unraveling the blood meal protein digestion system

To sustain the gonotrophic cycle, the Aedes aegypti mosquito must acquire a blood meal from a human or other vertebrate host. However, in the process of blood feeding, the mosquito may facilitate the transmission of several bloodborne viral pathogens (e.g., dengue, Zika, and chikungunya). The blood meal is essential as it contains proteins that are digested into polypeptides and amino acid nutrients that are eventually used for egg production. These proteins are digested by several midgut proteolytic enzymes. As such, the female mosquito's reliance on blood may serve as a potential target for vector and viral transmission control. However, this strategy may prove to be challenging since midgut proteolytic activity is a complex process dependent on several exo- and endo-proteases. Therefore, to understand the complexity of Ae. aegypti blood meal digestion, we used Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS) to generate global proteolytic profiles of sugar- and blood-fed midgut tissue extracts, along with substrate profiles of recombinantly expressed midgut proteases. Our results reveal a shift from high exoproteolytic activity in sugar-fed mosquitoes to an expressive increase in endoproteolytic activity in blood-fed mosquitoes. This approach allowed for the identification of 146 cleaved peptide bonds (by the combined 6 h and 24 h blood-fed samples) in the MSP-MS substrate library, and of these 146, 99 (68%) were cleaved by the five recombinant proteases evaluated. These reveal the individual contribution of each recombinant midgut protease to the overall blood meal digestion process of the Ae. aegypti mosquito. Further, our molecular docking simulations support the substrate specificity of each recombinant protease. Therefore, the present study provides key information of midgut proteases and the blood meal digestion process in mosquitoes, which may be exploited for the development of potential inhibitor targets for vector and viral transmission control strategies.

Artificial intelligence in peptide-based drug design

Protein-protein interactions (PPIs) are fundamental to a variety of biological processes, but targeting them with small molecules is challenging because of their large and complex interaction interfaces. However, peptides have emerged as highly promising modulators of PPIs, because they can bind to protein surfaces with high affinity and specificity. Nonetheless, computational peptide design remains difficult, hindered by the intrinsic flexibility of peptides and the substantial computational resources required. Recent advances in artificial intelligence (AI) are paving new paths for peptide-based drug design. In this review, we explore the advanced deep generative models for designing target-specific peptide binders, highlight key challenges, and offer insights into the future direction of this rapidly evolving field.

A claudin5-binding peptide enhances the permeability of the blood-brain barrier in vitro

The blood-brain barrier (BBB) maintains brain homeostasis but also prevents most drugs from entering the brain. No paracellular diffusion of solutes is allowed because of tight junctions that are made impermeable by the expression of claudin5 (CLDN5) by brain endothelial cells. The possibility of regulating the BBB permeability in a transient and reversible fashion is in strong demand for the pharmacological treatment of brain diseases. Here, we designed and tested short BBB-active peptides, derived from the CLDN5 extracellular domains and the CLDN5-binding domain of Clostridium perfringens enterotoxin, using a robust workflow of structural modeling and in vitro validation techniques. Computational analysis at the atom level based on solubility and affinity to CLDN5 identified a CLDN5-derived peptide not reported previously called f1-C5C2, which was soluble in biological media, displayed efficient binding to CLDN5, and transiently increased BBB permeability. The peptidomimetic strategy described here may have potential applications in the pharmacological treatment of brain diseases.

The identification of a SARs-CoV2 S2 protein derived peptide with super-antigen-like stimulatory properties on T-cells

Severe COVID-19 can trigger a cytokine storm, leading to acute respiratory distress syndrome (ARDS) with similarities to superantigen-induced toxic shock syndrome. An outstanding question is whether SARS-CoV-2 protein sequences can directly induce inflammatory responses. In this study, we identify a region in the SARS-CoV-2 S2 spike protein with sequence homology to bacterial super-antigens (termed P3). Computational modeling predicts P3 binding to sites on MHC class I/II and the TCR that partially overlap with sites for the binding of staphylococcal enterotoxins B and H. Like SEB and SEH derived peptides, P3 stimulated 25-40% of human CD4+ and CD8 + T-cells, increasing IFN-γ and granzyme B production. viSNE and SPADE profiling identified overlapping and distinct IFN-γ+ and GZMB+ subsets. The super-antigenic properties of P3 were further evident by its selective expansion of T-cells expressing specific TCR Vα and Vβ chain repertoires. In vivo experiments in mice revealed that the administration of P3 led to a significant upregulation of proinflammatory cytokines IL-1β, IL-6, and TNF-α. While the clinical significance of P3 in COVID-19 remains unclear, its homology to other mammalian proteins suggests a potential role for this peptide family in human inflammation and autoimmunity.

Insect Brain Proteomics: A Case Study of Periplaneta americana

Insects are known invertebrate species with economic, ecological, pathological, and medicinal value, as well as closely associated with human populations. Entomophagy and entomotherapy are future promising prospects largely attributable to the abundant availability, high protein content, and climatic sustainability of insects. In particular, the insect brain is an important system with a secluded, compact, and protective exoskeleton. It is immunologically privileged and capable of producing a robust immune response against pathogens. It is also a source of materials that initiate key activity throughout the body. Proteomic interrogation of Periplaneta americana enables understanding the role of this insect in the fields of food and pharmacology. Proximate analyses of P. americana highlights its richness in proteins. Here we perform a simple proteomic analysis to study the brain proteome of P. americana. The processes applied during the study include gel-based isolation and separation of proteins, followed by NanoLC-MS (Orbitrap) analyses and bioinformatic interrogation of the data. The results demonstrated that this insect proteome comprises antimicrobial proteins, allergens, and proteins required for metabolic processes.

Design of coiled-coil N-peptides against HIV-1 based on a CADD strategy

Human Immunodeficiency Virus (HIV) has continued to endanger human health for decades and has a substantial impact on global health defence. Peptide-based fusion inhibitors, as an integral part of Highly Active Anti-Retroviral Therapy (HAART), are effective in preventing and controlling the AIDS epidemic. Nevertheless, the current market leader, Enfuvirtide, is facing numerous challenges in clinical application. We herein devised a cutting-edge development strategy leveraging SWISS-MODEL and HDOCK, enabling the design of artificial N-peptides. The most active compound, IZNP02QE, surpassed the positive control by demonstrating remarkable nanomolar-level inhibitory activity against HIV-1. Mechanistic investigations unveiled IZNP02QE's ability to disrupt the crucial endogenous 6-helix bundle (6-HB) by forming heteropolymers, underscoring its potential as a novel anti-HIV-1 agent. This work not only pioneers a novel design methodology for N-peptides but also opens up the possibility of a CADD strategy for designing peptide-based fusion inhibitors.

Antibiotics daptomycin interacts with S protein of SARS-CoV-2 to promote cell invasion of Omicron (B1.1.529) pseudovirus

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has posed enormous challenges to global public health. The use of antibiotics has greatly increased during the SARS-CoV-2 epidemic owing to the presence of bacterial co-infection and secondary bacterial infections. The antibiotics daptomycin (DAP) is widely used in the treatment of infectious diseases caused by gram-positive bacteria owing to its highly efficient antibacterial activity. It is pivotal to study the antibiotics usage options for patients of coronavirus infectious disease (COVID-19) with pneumonia those need admission to receive antibiotics treatment for bacterial co-infection in managing COVID-19 disease. Herein, we have revealed the interactions of DAP with the S protein of SARS-CoV-2 and the variant Omicron (B1.1.529) using the molecular docking approach and Omicron (B1.1.529) pseudovirus (PsV) mimic invasion. Molecular docking analysis shows that DAP has a certain degree of binding ability to the S protein of SARS-CoV-2 and several derived virus variants, and co-incubation of 1-100 μM DAP with cells promotes the entry of the PsV into human angiotensin-converting enzyme 2 (hACE2)-expressing HEK-293T cells (HEK-293T-hACE2), and this effect is related to the concentration of extracellular calcium ions (Ca2+). The PsV invasion rate in the HEK-293T-hACE2 cells concurrently with DAP incubation was 1.7 times of PsV infection alone. In general, our findings demonstrate that DAP promotes the infection of PsV into cells, which provides certain reference of antibiotics selection and usage optimization for clinicians to treat bacterial coinfection or secondary infection during SARS-CoV-2 infection.

Nanobodies: From High-Throughput Identification to Therapeutic Development

The camelid single-domain antibody fragment, commonly referred to as a nanobody, achieves the targeting power of conventional monoclonal antibodies (mAbs) at only a fraction of their size. Isolated from camelid species (including llamas, alpacas, and camels), their small size at ∼15 kDa, low structural complexity, and high stability compared with conventional antibodies have propelled nanobody technology into the limelight of biologic development. Nanobodies are proving themselves to be a potent complement to traditional mAb therapies, showing success in the treatment of, for example, autoimmune diseases and cancer, and more recently as therapeutic options to treat infectious diseases caused by rapidly evolving biological targets such as the SARS-CoV-2 virus. This review highlights the benefits of applying a proteomic approach to identify diverse nanobody sequences against a single antigen. This proteomic approach coupled with conventional yeast/phage display methods enables the production of highly diverse repertoires of nanobodies able to bind the vast epitope landscape of an antigen, with epitope sampling surpassing that of mAbs. Additionally, we aim to highlight recent findings illuminating the structural attributes of nanobodies that make them particularly amenable to comprehensive antigen sampling and to synergistic activity-underscoring the powerful advantage of acquiring a large, diverse nanobody repertoire against a single antigen. Lastly, we highlight the efforts being made in the clinical development of nanobodies, which have great potential as powerful diagnostic reagents and treatment options, especially when targeting infectious disease agents.

Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization

Antimicrobial peptides (AMPs) emerge as a type of promising therapeutic compounds that exhibit broad spectrum antimicrobial activity with high specificity and good tolerability. Natural AMPs usually need further rational design for improving antimicrobial activity and decreasing toxicity to human cells. Although several algorithms have been developed to optimize AMPs with desired properties, they explored the variations of AMPs in a discrete amino acid sequence space, usually suffering from low efficiency, lack diversity, and local optimum. In this work, we propose a novel directed evolution method, named PepZOO, for optimizing multi-properties of AMPs in a continuous representation space guided by multi-objective zeroth-order optimization. PepZOO projects AMPs from a discrete amino acid sequence space into continuous latent representation space by a variational autoencoder. Subsequently, the latent embeddings of prototype AMPs are taken as start points and iteratively updated according to the guidance of multi-objective zeroth-order optimization. Experimental results demonstrate PepZOO outperforms state-of-the-art methods on improving the multi-properties in terms of antimicrobial function, activity, toxicity, and binding affinity to the targets. Molecular docking and molecular dynamics simulations are further employed to validate the effectiveness of our method. Moreover, PepZOO can reveal important motifs which are required to maintain a particular property during the evolution by aligning the evolutionary sequences. PepZOO provides a novel research paradigm that optimizes AMPs by exploring property change instead of exploring sequence mutations, accelerating the discovery of potential therapeutic peptides.

Identification, screening, and comprehensive evaluation of novel thrombin inhibitory peptides from the hirudo produced using pepsin

Purpose

The inhibition of thrombin has proven to be an efficacious therapeutic approach for managing cardiovascular disease (CVD), with widespread implementation in clinical settings. Oral ingestion of peptides and protein drugs is influenced by gastrointestinal digestive enzymes. We aimed to evaluate the thrombin inhibitory properties of hirudo hydrolysates (HHS) produced by pepsin and propose a comprehensive approach to screen and evaluate thrombin inhibitors.

Methods

We evaluated the in vitro inhibitory properties of the hirudo extract, both before and after hydrolysis with pepsin, toward thrombin. We screened for the most potent thrombin inhibitory peptide (TIP) using nano liquid chromatography-tandem mass spectrometry (Nano LC-MS/MS) coupled with in silico analysis. Next, we employed the thrombin inhibition activity IC50 to investigate the interaction between TIP and thrombin, and conducted in vitro evaluations of its anticoagulant effects (APTT, TT, PT), as well as its ability to inhibit platelet aggregation. Furthermore, we utilized UV-Vis spectroscopy to explore structural changes in thrombin upon binding with TIP and employed molecular dynamics simulations to delve deeper into the potential atomic-level interaction modes between thrombin and TIP.

Results

The retention rate of thrombin inhibition for HHS was found to be between 60% and 75%. A total of 90 peptides from the HHS were identified using LC-MS/MS combined with de novo sequencing. Asn-Asp-Leu-Trp-Asp-Gln-Gly-Leu-Val-Ser-Gln-Asp-Leu (NDLWDQGLVSQDL, P1) was identified as the most potent thrombin inhibitory peptide after in silico screening (molecular docking and ADMET). Then, the in vitro study revealed that P1 had a high inhibitory effect on thrombin (IC50: 2,425.5 ± 109.7 μM). P1 exhibited a dose-dependent prolongation of the thrombin time (TT) and a reduction in platelet aggregation rate. Both UV-Vis spectroscopy and molecular dynamics simulations demonstrated that P1 binds effectively to thrombin.

Conclusion

Overall, the results suggested that HHS provides new insights for searching and evaluating potential antithrombotic compounds. The obtained P1 can be structurally optimized for in-depth evaluation in animal and cellular experiments.

Assessing the therapeutic potential of KK14 peptide derived from Cyprinus Carpio in reducing intercellular ROS levels in oxidative Stress-Induced In vivo zebrafish larvae model: An integrated bioinformatics, antioxidant, and neuroprotective analysis

H2O2 is a significant reactive oxygen species (ROS) that hinders redox-mediated processes and contributes to oxidative stress and neurodegenerative disorders. Oxidative stress causes impairment of cell macromolecules, which results in cell dysfunction and neurodegeneration. Alzheimer's disease and other neurodegenerative diseases are serious conditions linked to oxidative stress. Antioxidant treatment approaches are a novel and successful strategy for decreasing neurodegeneration and reducing oxidative stress. This study explored the antioxidant and neuroprotective characteristics of KK14 peptide synthesized from LEAP 2B (liver-expressed antimicrobial peptide-2B) derived from Cyprinus carpio L. Molecular docking studies were used to assess the antioxidant properties of KK14. The peptide at concentrations 5-45 μM was examined by using in vitro and in vivo assessment. Analysis was done on the developmental and neuroprotective potential of KK14 peptide treatment in H2O2-exposed zebrafish larvae which showed Nonlethal deformities. KK14 improves antioxidant enzyme activity like catalase and superoxide dismutase. Furthermore, it reduces neuronal damage by lowering lipid peroxidation and nitric oxide generation while increasing acetylcholinesterase activity. It improved the changes in swimming behavior and the cognitive damage produced by exposure to H2O2. To further substantiate the neuroprotective potential of KK14, intracellular ROS levels in zebrafish larvae were assessed. This led to a reduction in ROS levels and diminished lipid peroxidation. The KK14 has upregulated the antioxidant genes against oxidative stress. Overall, this study proved the strong antioxidant activity of KK14, suggesting its potential as a strong therapeutic option for neurological disorders caused by oxidative stress.

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.

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.

Exploring the neuroprotective potential of KC14 peptide from Cyprinus carpio against oxidative stress-induced neurodegeneration by regulating antioxidant mechanism

BACKGROUND

Oxidative stress, a condition characterized by excessive production of reactive oxygen species (ROS), can cause significant damage to cellular macromolecules, leading to neurodegeneration. This underscores the need for effective antioxidant therapies that can mitigate oxidative stress and its associated neurodegenerative effects. KC14 peptide derived from liver-expressed antimicrobial peptide-2 A (LEAP 2 A) from Cyprinus carpio L. has been identified as a potential therapeutic agent. This study focuses on the antioxidant and neuroprotective properties of the KC14 peptide is to evaluate its effectiveness against oxidative stress and neurodegeneration.

METHODS

The antioxidant capabilities of KC14 were initially assessed through in silico docking studies, which predicted its potential to interact with oxidative stress-related targets. Subsequently, the peptide was tested at concentrations ranging from 5 to 45 µM in both in vitro and in vivo experiments. In vivo studies involved treating H2O2-induced zebrafish larvae with KC14 peptide to analyze its effects on oxidative stress and neuroprotection.

RESULTS

KC14 peptide showed a protective effect against the developmental malformations caused by H2O2 stress, restored antioxidant enzyme activity, reduced neuronal damage, and lowered lipid peroxidation and nitric oxide levels in H2O2-induced larvae. It enhanced acetylcholinesterase activity and significantly reduced intracellular ROS levels (p < 0.05) dose-dependently. Gene expression studies showed up-regulation of antioxidant genes with KC14 treatment under H2O2 stress.

CONCLUSIONS

This study highlights the potent antioxidant activity of KC14 and its ability to confer neuroprotection against oxidative stress can provide a novel therapeutic agent for combating neurodegenerative diseases induced by oxidative stress.

Perfect Partners: Biocatalytic Halogenation and Metal Catalysis for Protein Bioconjugation

Flavin-dependent halogenases (FDHs) are the most extensively researched halogenases and show great potential for biotransformation applications. These enzymes use chloride, bromide, or iodide ions as halogen donors to catalyze the oxygen-dependent halogenation of electron-rich aryl moieties, requiring stochiometric amounts of FADH2 in the process. This makes FDH-catalyzed aryl halogenation a highly selective and environmentally friendly tool for the synthesis of aryl halides. The latter in turn serve as valuable intermediates for transition metal catalyzed cross coupling reactions for C-C bond formation. Previous research made extensive use of this approach to halogenate small molecules as building blocks for late-stage functionalization by transition-metal catalyzed cross-coupling reactions. Based on these results, several groups have managed to expand this research to protein targets over the past two years. Their work indicates an emerging methodology for bioconjugation using late-stage biocatalytic halogenation in conjunction with biorthogonal Suzuki-Miyaura cross-coupling. This strategy could present an attractive alternative to existing approaches due to the stability of the C-C bond bridging the generated biaryl moiety and the ease of late-stage enzymatic modification while maintaining excellent selectivity under mild conditions.

Peptide inhibitors derived from the nsp7 and nsp8 cofactors of nsp12 targeting different substrate binding sites of nsp12 of the SARS-CoV-2

SARS-COV-2 is responsible for the COVID-19 pandemic, which has infected more than 767 million people worldwide including about 7 million deaths till 5 June 2023. Despite the emergency use of certain vaccines, deaths due to COVID-19 have not yet stopped completed. Therefore, it is imperative to design and develop drugs that can be used to treat patients suffering from COVID-19. Here, two peptide inhibitors derived from nsp7 and nsp8 cofactors of nsp12 have been shown to block different substrate binding sites of nsp12 that are mainly responsible for the replication of the viral genome of SARS-CoV-2. By using the docking, molecular dynamics (MD), and MM/GBSA techniques, it is shown that these inhibitors can bind to multiple binding sites of nsp12, such as the interface of nsp7 and nsp12, interface of nsp8 and nsp12, RNA primer entry site, and nucleoside triphosphate (NTP) entry site. The relative binding free energies of the most stable protein-peptide complexes are found to lie between ∼-34.20 ± 10.07 to -59.54 ± 9.96 kcal/mol. Hence, it is likely that these inhibitors may bind to different sites of nsp12 to block the access of its cofactors and the viral genome, thereby affecting the replication. It is thus proposed that these peptide inhibitors may be further developed as potential drug candidates to suppress the viral loads in COVID-19 patients.Communicated by Ramaswamy H. Sarma.

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Agonistic effect of peptides derived from a truncated HMGB1 acidic tail sequence in TLR5 from Salmo salar

Based on the structural knowledge of TLR5 surface and using blind docking platforms, peptides derived from a truncated HMGB1 acidic tail from Salmo salar was designed as TLR5 agonistic. Additionally, a template peptide with the native N-terminal of the acidic tail sequence as a reference was included (SsOri). Peptide binding poses complexed on TLR5 ectodomain model from each algorithm were filtrated based on docking scoring functions and predicted theoretical binding affinity of the complex. The best peptides, termed 6WK and 5LWK, were selected for chemical synthesis and experimental functional assay. The agonist activity by immunoblotting and immunocytochemistry was determined following the NF-κBp65 phosphorylation (p-NF-κBp65) and the nuclear translocation of the NF-κBp65 subunit from the cytosol, respectively. HeLa cells stably expressing a S. salar TLR5 chimeric form (TLR5c7) showed increased p-NF-κBp65 levels regarding extracts from flagellin-treated cells. No statistically significant differences (p > 0.05) were found in the detected p-NF-κBp65 levels between cellular extracts treated with peptides or flagellin by one-way ANOVA. The image analysis of NF-κBp65 immunolabeled cells obtained by confocal microscopy showed increased nuclear NF-κBp65 co-localization in cells both 5LWK and flagellin stimulated, while 6WK and SsOri showed less effect on p65 nuclear translocation (p < 0.05). Also, an increased transcript expression profile of proinflammatory cytokines such as TNFα, IL-1β, and IL-8 in HKL cells isolated from Salmo salar was evidenced in 5LWK - stimulated by RT-PCR analysis. Overall, the result indicates the usefulness of novel peptides as a potential immunostimulant in S. salar.

Molecular design and systematic optimization of a halogen-bonding system between the asthma interleukin-5 receptor and its cyclic peptide ligand

Human interleukin-5 (IL-5) functions as an important pro-inflammatory factor by binding to its specific receptor, IL-5Rα, which has been implicated in the pathogenesis of asthma. Previously, a disulfide-bonded cyclic peptide AF17121 obtained from random library screening and sequence variation was found to competitively disrupt the cognate IL-5Rα/IL-5 interaction with moderate potency. In this study, the crystal complex of IL-5Rα with AF17121 was investigated at structural and energetic levels. It is revealed that the side-chain indole moiety of the AF17121 Trp5 residue is a potential site for a stem putative halogen bond (X-bond) with IL-5Rα, which is just located within the key 3 EXXR6 motif region recognized specifically by IL-5Rα. We systematically examined four halogen substitution types at five positions of the indole moiety; QM/MM calculations theoretically unraveled that only halogenations at 5 and 6 positions can form effective X-bonds with the side-chain hydroxyl oxygen of the IL-5Rα Thr21 residue and the backbone carbonyl oxygen of Ala66 residue, respectively. Binding assays observed that I-substitution at the 5 position and Br-substitution at the 6 position can result in two potent halogenated peptides, [5I]AF17121 and [6Br]AF17121, which are improved by 1.6-fold and 3.5-fold relative to the native AF17121, respectively. 5I/6Br-double substitution, resulting in [5I/6Br]AF17121, can further enhance the peptide affinity by 7.5-fold. Structural analysis revealed that the X-bond stemming from 6Br-substitution is also involved in an orthogonal interaction system with a H-bond; they share a common backbone carbonyl oxygen acceptor of IL-5Rα Ala66 residue and exhibit a significant synergistic effect between them.

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination

Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds promise for drug development. However, experimental confirmation of the peculiarities of PPIs is challenging due to their dynamic and transient nature. As a complement to experimental technologies, multiple computational molecular docking (MD) methods have been developed to predict the structures of protein-protein complexes and their dynamics, still requiring further improvements in several issues. Here, we report an improved MD method, namely three-software docking (3SD), by employing three popular protein-peptide docking software (CABS-dock, HPEPDOCK, and HADDOCK) in combination to ensure constant quality for most targets. We validated our 3SD performance in known protein-peptide interactions (PpIs). We also enhanced MD performance in proteins having intrinsically disordered regions (IDRs) by applying the modified 3SD strategy, the three-software docking after removing random coiled IDR (3SD-RR), to the comparable crystal PpI structures. At the end, we applied 3SD-RR to the AlphaFold2-predicted receptors, yielding an efficient prediction of PpI pose with high relevance to the experimental data regardless of the presence of IDRs or the availability of receptor structures. Our study provides an improved solution to the challenges in studying PPIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery. SIGNIFICANCE STATEMENT: Protein-protein interactions (PPIs) are integral to life, and abnormal PPIs are associated with diseases such as cancer. Studying protein-peptide interactions (PpIs) is challenging due to their dynamic and transient nature. Here we developed improved docking methods (3SD and 3SD-RR) to predict the PpI poses, ensuring constant quality in most targets and also addressing issues like intrinsically disordered regions (IDRs) and artificial intelligence-predicted structures. Our study provides an improved solution to the challenges in studying PpIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery.

A flexible data-free framework for structure-based de novo drug design with reinforcement learning

Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.

Screening and optimization of phage display cyclic peptides against the WDR5 WBM site

Of the various WD40 family proteins, WDR5 is a particularly important multifunctional adaptor protein that can bind to several protein complexes to regulate gene activation, so it was considered as a promising epigenetic target in anti-cancer drug development. Despite many inhibitors having been discovered directing against the arginine-binding cavity in WDR5 called the WIN site, the side hydrophobic cavity called the WBM site receives rather scant attention. Herein, we aim to obtain novel WBM-targeted peptidic inhibitors with high potency and selectivity. We employed two improved biopanning approaches with a disulfide-constrained cyclic peptide phage library containing 7 randomized residues and identified several peptides with micromole binding activity by docking and binding assay. To further optimize the stability and activity, 9 thiol-reactive chemical linkers were utilized in the cyclization of the candidate peptide DH226027, which had good binding affinity. This study provides an effective method to discover potent peptides targeting protein-protein interactions and highlights a broader perspective of peptide-mimic drugs.

The use of peptides, aptamers, and variable domains of heavy chain only antibodies in tissue engineering and regenerative medicine

Over the years, much research has been focused on the use of small molecules such as peptides or aptamers or more recently on the use of variable antigen-binding domain of heavy chain only antibodies in the field of tissue engineering and regenerative medicine. The use of these molecules originated as an alternative for the larger conventional antibodies, of which most drawbacks are derived from their size and complex structure. In the field of tissue engineering and regenerative medicine, biological functionalities are often conjugated to biomaterials in order to (re-)create an in vivo like situation, especially when bioinert biomaterials are used. Those biomaterials are functionalized with these functionalities for instance for the purpose of cell attachment or cell targeting for targeted drug delivery but also for local enrichment or blocking of ligands such as growth factors or cytokines on the biomaterial surface. In this review, we further refer to peptides, aptamers, and variable antigen-binding domain of heavy chain only antibodies as biological functionalities. Here, we compare these biological functionalities within the field of tissue engineering and regenerative medicine and give an overview of recent work in which these biological functionalities have been explored. We focus on the previously mentioned purposes of the biological functionalities. We will compare structural differences, possible modifications and (chemical) conjugation strategies. In addition, we will provide an overview of biologicals that are, or have been, involved in clinical trials. Finally, we will highlight the challenges of each of these biologicals. STATEMENT OF SIGNIFICANCE: In the field of tissue engineering there is broad application of functionalized biomaterials for cell attachment, targeted drug delivery and local enrichment or blocking of growth factors. This was previously mostly done via conventional antibodies, but their large size and complex structure impose various challenges with respect of retaining biological functionality. Peptides, aptamers and VHHs may provide an alternative solution for the use of conventional antibodies. This review discusses the use of these molecules for biological functionalization of biomaterials. For each of the molecules, their characteristics, conjugation possibilities and current use in research and clinical trials is described. Furthermore, this review sets out the benefits and challenges of using these types of molecules for different fields of application.

Discovery and pharmacophoric characterization of chemokine network inhibitors using phage-display, saturation mutagenesis and computational modelling

CC and CXC-chemokines are the primary drivers of chemotaxis in inflammation, but chemokine network redundancy thwarts pharmacological intervention. Tick evasins promiscuously bind CC and CXC-chemokines, overcoming redundancy. Here we show that short peptides that promiscuously bind both chemokine classes can be identified from evasins by phage-display screening performed with multiple chemokines in parallel. We identify two conserved motifs within these peptides and show using saturation-mutagenesis phage-display and chemotaxis studies of an exemplar peptide that an anionic patch in the first motif and hydrophobic, aromatic and cysteine residues in the second are functionally necessary. AlphaFold2-Multimer modelling suggests that the peptide occludes distinct receptor-binding regions in CC and in CXC-chemokines, with the first and second motifs contributing ionic and hydrophobic interactions respectively. Our results indicate that peptides with broad-spectrum anti-chemokine activity and therapeutic potential may be identified from evasins, and the pharmacophore characterised by phage display, saturation mutagenesis and computational modelling.

SARS-CoV-2 inhibitory activity of a short peptide derived from internal fusion peptide of S2 subunit of spike glycoprotein

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has posed a great concern in human population. To fight coronavirus emergence, we have dissected the conserved amino acid region of the internal fusion peptide in the S2 subunit of Spike glycoprotein of SARS-CoV-2 to design new inhibitory peptides. Among the 11 overlapping peptides (9-23-mer), PN19, a 19-mer peptide, exhibited a powerful inhibitory activity against different SARS-CoV-2 clinical isolate variants in absence of cytotoxicity. The PN19 inhibitory activity was found to be dependent on conservation of the central Phe and C-terminal Tyr residues in the peptide sequence. Circular dichroism spectra of the active peptide exhibited an alpha-helix propensity, confirmed by secondary structure prediction analysis. The PN19 inhibitory activity, exerted in the first step of virus infection, was reduced after peptide adsorption treatment with virus-cell substrate during fusion interaction. Additionally, PN19 inhibitory activity was reduced by adding S2 membrane-proximal region derived peptides. PN19 showed binding ability to the S2 membrane proximal region derived peptides, confirmed by molecular modelling, playing a role in the mechanism of action. Collectively, these results confirm that the internal fusion peptide region is a good candidate on which develop peptidomimetic anti SARS-CoV-2 antivirals.

Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening

Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries' display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.

Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking

The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.

Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Peptide-based therapeutics are increasingly pushing to the forefront of biomedicine with their promise of high specificity and low toxicity. Although noncanonical residues can always be used, employing only the natural 20 residues restricts the chemical space to a finite dimension allowing for comprehensive in silico screening. Towards this goal, the dataset comprising all possible di-, tri-, and tetra-peptide combinations of the canonical residues has been previously reported. However, with increasing computational power, the comprehensive set of pentapeptides is now also feasible for screening as the comprehensive set of cyclic peptides comprising four or five residues. Here, we provide both the complete and prefiltered libraries of all di-, tri-, tetra-, and penta-peptide sequences from 20 canonical amino acids and their homodetic (N-to-C-terminal) cyclic homologues. The FASTA, simplified molecular-input line-entry system (SMILES), and structure-data file (SDF)-three dimension (3D) libraries can be readily used for screening against protein targets. We also provide a simple method and tool for conducting identity-based filtering. Access to this dataset will accelerate small peptide screening workflows and encourage their use in drug discovery campaigns. As a case study, the developed library was screened against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease to identify potential small peptide inhibitors.

Simultaneous control of infection and inflammation with keratin-derived antibacterial peptides targeting TLRs and co-receptors

Controlling infection-driven inflammation is a major clinical dilemma because of limited therapeutic options and possible adverse effects on microbial clearance. Compounding this difficulty is the continued emergence of drug-resistant bacteria, where experimental strategies aiming to augment inflammatory responses for enhanced microbial killing are not applicable treatment options for infections of vulnerable organs. As with corneal infections, severe or prolonged inflammation jeopardizes corneal transparency, leading to devastating vision loss. We hypothesized that keratin 6a-derived antimicrobial peptides (KAMPs) may be a two-pronged remedy capable of tackling bacterial infection and inflammation at once. We used murine peritoneal neutrophils and macrophages, together with an in vivo model of sterile corneal inflammation, to find that nontoxic and prohealing KAMPs with natural 10- and 18-amino acid sequences suppressed lipoteichoic acid (LTA)- and lipopolysaccharide (LPS)-induced NFκB and IRF3 activation, proinflammatory cytokine production, and phagocyte recruitment independently of their bactericidal function. Mechanistically, KAMPs not only competed with bacterial ligands for cell surface Toll-like receptor (TLR) and co-receptors (MD2, CD14, and TLR2) but also reduced cell surface availability of TLR2 and TLR4 through promotion of receptor endocytosis. Topical KAMP treatment effectively alleviated experimental bacterial keratitis, as evidenced by substantial reductions of corneal opacification, inflammatory cell infiltration, and bacterial burden. These findings reveal the TLR-targeting activities of KAMPs and demonstrate their therapeutic potential as a multifunctional drug for managing infectious inflammatory disease.

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.

Structural predictions of protein-DNA binding: MELD-DNA

Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein-DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein-DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein-DNA interactions).

Advancing our knowledge of antigen processing with computational modelling, structural biology, and immunology

Antigen processing is an immunological mechanism by which intracellular peptides are transported to the cell surface while bound to Major Histocompatibility Complex molecules, where they can be surveyed by circulating CD8+ or CD4+ T-cells, potentially triggering an immunological response. The antigen processing pathway is a complex multistage filter that refines a huge pool of potential peptide ligands derived from protein degradation into a smaller ensemble for surface presentation. Each stage presents unique challenges due to the number of ligands, the polymorphic nature of MHC and other protein constituents of the pathway and the nature of the interactions between them. Predicting the ensemble of displayed peptide antigens, as well as their immunogenicity, is critical for improving T cell vaccines against pathogens and cancer. Our predictive abilities have always been hindered by an incomplete empirical understanding of the antigen processing pathway. In this review, we highlight the role of computational and structural approaches in improving our understanding of antigen processing, including structural biology, computer simulation, and machine learning techniques, with a particular focus on the MHC-I pathway.

Computer-Aided Virtual Screening and In Vitro Validation of Biomimetic Tyrosinase Inhibitory Peptides from Abalone Peptidome

Hyperpigmentation is a medical and cosmetic problem caused by an excess accumulation of melanin or the overexpression of the enzyme tyrosinase, leading to several skin disorders, i.e., freckles, melasma, and skin cancer. Tyrosinase is a key enzyme in melanogenesis and thus a target for reducing melanin production. Although abalone is a good source of bioactive peptides that have been used for several properties including depigmentation, the available information on the anti-tyrosinase property of abalone peptides remains insufficient. This study investigated the anti-tyrosinase properties of Haliotis diversicolor tyrosinase inhibitory peptides (hdTIPs) based on mushroom tyrosinase, cellular tyrosinase, and melanin content assays. The binding conformation between peptides and tyrosinase was also examined by molecular docking and dynamics study. KNN1 showed a high potent inhibitory effect on mushroom tyrosinase with an IC₅₀ of 70.83 μM. Moreover, our selected hdTIPs could inhibit melanin production through the reductions in tyrosinase activity and reactive oxygen species (ROS) levels by enhancing the antioxidative enzymes. RF1 showed the highest activity on both cellular tyrosinase inhibition and ROS reduction. leading to the lower melanin content in B16F10 murine melanoma cells. Accordingly, it can be assumed that our selected peptides exhibited high potential in medical cosmetology applications.

A retro-inverso modified peptide alleviated ovalbumin-induced asthma model by affecting glycerophospholipid and purine metabolism of immune cells

Allergic asthma is a heterogeneous disease involving a variety of inflammatory cells. Immune imbalance or changes in the immune microenvironment are the essential causes that promote inflammation in allergic asthma. Tetraspanin CD81 can be used as a platform for receptor clustering and signal transmission owing to its special transmembrane structure and is known to participate in the physiological processes of cell proliferation, differentiation, adhesion, and migration. Previous studies have shown that CD81-targeting peptidomimetics exhibit anti-allergic lung inflammation. However, due to the low metabolic stability of peptide drugs, their druggability is limited. Here, we aimed to generate a metabolically stable anti-CD81 peptide, evaluate its anti-inflammatory action and establish its mechanism of action. Based on previous reports, we applied retro-inverse peptide modification to obtain a new compound, PD00 (NH2-D-Gly-D-Ser-D-Thr-D-Tyr-D-Thr-D-Gln-D-Gly-COOH), with high metabolic stability. Enhanced ultraperformance liquid chromatography-tandem mass spectrometry was used to investigate the in vitro and in vivo metabolic stabilities of PD00. The affinities of PD00 and CD81 were studied using molecular docking and surface plasmon resonance techniques. An ovalbumin (OVA)-induced asthma model was used to evaluate the effects of PD00 in vivo. Mice were treated with different concentrations of PD00 (175 and 350 μg/kg) for 10 days. Airway hyperresponsiveness (AHR) to acetyl-β-methacholine (Mch), inflammatory cell counts in the bronchoalveolar lavage fluid, and serum OVA-specific IgE levels were detected in the mice at the end of the experiment. Lung tissues were collected for haematoxylin and eosin staining, untargeted metabolomic analysis, and single-cell transcriptome sequencing. PD00 has a high affinity for CD81; therefore, administration of PD00 markedly ameliorated AHR and airway inflammation in mice after OVA sensitisation and exposure. Serum OVA-specific IgE levels decreased considerably. In addition, PD00 treatment increased glycerophospholipid and purine metabolism in immune cells. Collectively, PD00 may regulate the glycerophospholipid and purine metabolism pathways to ameliorate the pathophysiological features of asthma. These findings suggest that PD00 is a potential compound for the treatment of asthma.

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.