Protein-peptide Docking Research Articles

In silico investigation of potential interleukin-8 (IL-8) and Cathelicidin (LL-37) inhibitors for rosacea treatment

Emerging clinical observations underscore the correlation between interleukin-8 (IL-8) and rosacea. Increased IL-8 expression has been detected in rosacea samples, particularly in moderate to severe manifestations. This phenomenon has prompted the exploration of IL-8 as a prospective therapeutic target for rosacea treatment. To this end, a selection of compounds sourced from the ZINC database, encompassing six small molecules, was made with the intent of identifying promising lead candidates that exhibit drug-like characteristics against IL-8. Through an integrated in silico approach involving structure-guided drug design, encompassing molecular docking, molecular dynamics (MD) simulation, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis, protein-peptide docking, and scrutiny of toxicity profiles, it was ascertained that the small molecule ZINC000022339916 effectively inhibits IL-8 activity. These findings present a novel lead compound that warrants further validation through in vitro, in vivo, and ongoing clinical investigations to confirm its potential for therapeutic management of rosacea.

Current Computational Methods for Protein-peptide Complex Structure Prediction.

Peptide-mediated protein-protein interactions (PPIs) play an important role in various biological processes. The development of peptide-based drugs to modulate PPIs has attracted increasing attention due to the advantages of high specificity and low toxicity. In the development of peptide-based drugs, one of the most important steps is to determine the interaction details between the peptide and the target protein. In addition to experimental methods, recently developed computational methods provide a cost-effective way for studying protein-peptide interactions. In this article, we carefully reviewed recently developed protein-peptide docking methods, which were classified into three groups: template-based docking, template-free docking, and hybrid method. Then, we presented available benchmarking sets and evaluation metrics for assessing protein-peptide docking performance. Furthermore, we discussed the use of molecular dynamics simulations, as well as deep learning approaches in protein-peptide complex prediction.

A Structure-based Data Set of Protein-peptide Affinities and its Nonredundant Benchmark: Potential Applications in Computational Peptidology.

Peptides play crucial roles in diverse cellular functions and participate in many biological processes by interacting with a variety of proteins, which have also been exploited as a promising class of therapeutic agents to target druggable proteins over the past decades. Understanding the intrinsic association between the structure and affinity of protein-peptide interactions (PpIs) should be considerably valuable for the computational peptidology area, such as guiding protein-peptide docking calculations, developing protein-peptide affinity scoring functions, and designing peptide ligands for specific protein receptors. We attempted to create a data source for relating PpI structure to affinity. By exhaustively surveying the whole protein data bank (PDB) database as well as the ontologically enriched literature information, we manually curated a structure- based data set of protein-peptide affinities, PpI[S/A]DS, which assembled over 350 PpI complex samples with both the experimentally measured structure and affinity data. The data set was further reduced to a nonredundant benchmark consisting of 102 culled samples, PpI[S/A]BM, which only selected those of structurally reliable, functionally diverse and evolutionarily nonhomologous. The collected structures were resolved at a high-resolution level with either Xray crystallography or solution NMR, while the deposited affinities were characterized by dissociation constant, i.e. Kd value, which is a direct biophysical measure of the intermolecular interaction strength between protein and peptide, ranging from subnanomolar to millimolar levels. The PpI samples in the set/benchmark were arbitrarily classified into α-helix, partial α-helix, β-sheet formed through binding, β-strand formed through selffolding, mixed, and other irregular ones, totally resulting in six classes according to the secondary structure of their peptide ligands. In addition, we also categorized these PpIs in terms of their biological function and binding behavior. The PpI[S/A]DS set and PpI[S/A]BM benchmark can be considered a valuable data source in the computational peptidology community, aiming to relate the affinity to structure for PpIs.

In Silico Design of CT26 Polytope and its Surface Display by ClearColi™-Derived Outer Membrane Vesicles as a Cancer Vaccine Candidate Against Colon Carcinoma.

Simultaneous targeting of several mutations can be useful in colorectal cancer (CRC) due to its heterogeneity and presence of somatic mutations. As CT26 mutations and expression profiles resemble those of human CRC, we focused on designing a polyepitope vaccine based on CT26 neoepitopes. Due to its low immunogenicity, outer membrane vesicles (rOMV) as an antigen delivery system and adjuvant was applied. Herein, based on previous experimental and our in silico studies four CT26 neoepitopes with the ability to bind MHC-I and MHC-II, TCR, and induce IFN-α production were selected. To increase their immunogenicity, the gp70 and PADRE epitopes were added. The order of the neoepitopes was determined through 3D structure analysis using ProSA, Verify 3D, ERRAT, and Ramachandran servers. The stable peptide-protein docking between the selected epitopes and MHC alleles strengthen our prediction. The CT26 polytope vaccine sequence was fused to the C-terminal of cytolysin A (ClyA) anchor protein and rOMVs were isolated from endotoxin-free ClearColi™ strain. The results of the C-ImmSim server showed that the ClyA-CT26 polytope vaccine could induce T and B cells immunity.The ClyA-CT26 polytope was characterized as a soluble, stable, immunogen, and non-allergen vaccine and optimized for expression in ClearColi™ 24h after induction with 1mM IPTG at 25°C. Western blot analysis confirmed the expression of ClyA-CT26 polytope by ClearColi™ and also on ClearColi™-derived rOMVs. In conclusion, we found that ClearColi™-derived rOMVs with CT26 polytope can deliver CRC neoantigens and induce antitumor immunity, but in vivo immunological studies are needed to confirm vaccine efficacy.

LC-MS/MS profiling and analysis of Bacillus licheniformis extracellular proteins for antifungal potential against Candida albicans

Candida albicans, a significant human pathogenic fungus, employs hydrolytic proteases for host invasion. Conventional antifungal agents are reported with resistance issues from around the world. This study investigates the role of Bacillus licheniformis extracellular proteins (ECP) as effective antifungal peptides (AFPs). The aim was to identify and characterize the ECP of B. licheniformis through LC-MS/MS and bioinformatics analysis. LC-MS/MS analysis identified 326 proteins with 69 putative ECP, further analyzed in silico. Of these, 21 peptides exhibited antifungal properties revealed by classAMP tool and are predominantly anionic. Peptide-protein docking revealed interactions between AFPs like Peptide chain release factor 1 (Q65DV1_Seq1: SASEQLSDAK) and Putative carboxy peptidase (Q65IF0_Seq7: SDSSLEDQDFILESK) with C. albicans virulent SAP5 proteins (PDB ID 2QZX), forming hydrogen bonds and significant Pi-Pi interactions. The identification of B. licheniformis ECP is the novelty of the study that sheds light on their antifungal potential. The identified AFPs, particularly those interacting with bonafide pharmaceutical targets SAP5 of C. albicans represent promising avenues for the development of antifungal treatments with AFPs that could be the pursuit of a novel therapeutic strategy against C. albicans. Significance of studyThe purpose of this work was to carry out proteomic profiling of the secretome of B. licheniformis. Previously, the efficacy of Bacillus licheniformis extracellular proteins against Candida albicans was investigated and documented in a recently communicated manuscript, showcasing the antifungal activity of these proteins. In order to achieve high-throughput identification of ES (Excretory-secretory) proteins, the utilization of liquid chromatography tandem mass spectrometry (LC-MS) was utilized. There was a lack of comprehensive research on AFPs in B. licheniformis, nevertheless. The proteins secreted by B. licheniformis in liquid medium were initially discovered using liquid chromatography-tandem mass spectrometry (LC-MS) analysis and identification in order to immediately characterize the unidentified active metabolites in fermentation broth.

Covalent Flexible Peptide Docking in Drug Discovery: Current Challenges and Potential Interventions

Recently, there has been a significant increase in the search for new covalent inhibitors through drug discovery platforms, which has led to the development and implementation of new computational tools, including covalent docking methods to predict the binding mode and affinity of covalent ligands. Since the discovery of insulin nearly a century ago, more than 80 peptide medications have hit the market to treat a wide range of diseases, including diabetes, cancer, osteoporosis, multiple sclerosis, HIV infection, and chronic pain. Electrophilic peptides that form covalent bonds with target proteins have great potential for binding targets that have been previously considered undruggable. Despite the recent advancements in computational performance and docking algorithms, covalent docking still poses a number of challenges. Peptides covalent docking presents additional challenges, the main ones being the choice of the optimal peptide sequence, incorporation of electrophilic warheads, the inherent flexibility of peptide structures, and the fact that, unlike small molecules, peptides do fold. In this review, we present a brief overview of the current state of peptide therapeutics in drug discovery, covalent docking in general, covalent peptide binders, and-with particular emphasis-the difficulties that covalent peptide-protein docking is currently facing and some potential solutions.

Identification and Evaluation of Olive Phenolics in the Context of Amine Oxidase Enzyme Inhibition and Depression: In Silico Modelling and In Vitro Validation.

The Mediterranean diet well known for its beneficial health effects, including mood enhancement, is characterised by the relatively high consumption of extra virgin olive oil (EVOO), which is rich in bioactive phenolic compounds. Over 200 phenolic compounds have been associated with Olea europaea, and of these, only a relatively small fraction have been characterised. Utilising the OliveNetTM library, phenolic compounds were investigated as potential inhibitors of the epigenetic modifier lysine-specific demethylase 1 (LSD1). Furthermore, the compounds were screened for inhibition of the structurally similar monoamine oxidases (MAOs) which are directly implicated in the pathophysiology of depression. Molecular docking highlighted that olive phenolics interact with the active site of LSD1 and MAOs. Protein-peptide docking was also performed to evaluate the interaction of the histone H3 peptide with LSD1, in the presence of ligands bound to the substrate-binding cavity. To validate the in silico studies, the inhibitory activity of phenolic compounds was compared to the clinically approved inhibitor tranylcypromine. Our findings indicate that olive phenolics inhibit LSD1 and the MAOs in vitro. Using a cell culture model system with corticosteroid-stimulated human BJ fibroblast cells, the results demonstrate the attenuation of dexamethasone- and hydrocortisone-induced MAO activity by phenolic compounds. The findings were further corroborated using human embryonic stem cell (hESC)-derived neurons stimulated with all-trans retinoic acid. Overall, the results indicate the inhibition of flavin adenine dinucleotide (FAD)-dependent amine oxidases by olive phenolics. More generally, our findings further support at least a partial mechanism accounting for the antidepressant effects associated with EVOO and the Mediterranean diet.

Isolation and Characterization of the Vascular Endothelial Growth Factor Receptor Targeting ScFv Antibody Fragments Derived from Phage Display Technology.

Angiogenesis, as a tumor hallmark, plays an important role in the growth and development of the tumor vasculature system. There is a huge amount of evidence suggesting that the vascular endothelial growth factor receptor (VEGFR-2)/VEGF-A axis is one of the main contributors to tumor angiogenesis and metastasis. Thus, inhibition of the VEGFR-2 signaling pathway by anti-VEGFR-2 mAb can retard tumor growth. In this study, we employ phage display technology and solution-phase biopanning (SPB) to isolate specific single-chain variable fragments (scFvs) against VEGFR-2 and report on the receptor binding characteristics of the candidate scFvs A semisynthetic phage antibody library to isolate anti-VEGFR-2 scFvs through an SPB performed with decreasing concentrations of the VEGFR-2-His tag and VEGFR-2-biotin. After successful expression and purification, the specificity of the selected scFv clones was further analyzed by enzyme-linked immunosorbent assay (ELISA), flow cytometry, and immunoblotting. The competition assay was undertaken to identify the VEGFR-2 receptor-blocking properties of the scFvs. Furthermore, the molecular binding characteristics of candidate scFvs were extensively studied by peptide-protein docking. Polyclonal ELISA analysis subsequent to four rounds of biopanning showed a significant enrichment of VEGFR-2-specific phage clones by increasing positive signals from the first round toward the fourth round of selection. The individual VEGFR-2-reactive scFv phage clones were identified by monoclonal phage ELISA. The sequence analysis and complementarity-determining region alignment identified the four unique anti-VEGFR-2-scFv clones. The soluble and purified scFvs displayed binding activity against soluble and cell-associated forms of VEGFR-2 protein in the ELISA and flow cytometry assays. Based on the inference from the molecular docking results, scFvs D3, E1, H1, and E9 recognized domains 2 and 3 on the VEGFR-2 protein and displayed competition with VEGF-A for binding to VEGFR-2. The competition assay confirmed that scFvs H1 and D3 can block the VEGFR-2/VEGF-A interaction. In conclusion, we identified novel VEGFR-2-blocking scFvs that perhaps exhibit the potential for angiogenesis inhibition in VEGFR-2-overexpressed tumor cells.

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.

Computational Modeling and Characterization of Peptides Derived from Nanobody Complementary-Determining Region 2 (CDR2) Targeting Active-State Conformation of the β2-Adrenergic Receptor (β2AR).

This study assessed the suitability of the complementarity-determining region 2 (CDR2) of the nanobody (Nb) as a template for the derivation of nanobody-derived peptides (NDPs) targeting active-state β2-adrenergic receptor (β2AR) conformation. Sequences of conformationally selective Nbs favoring the agonist-occupied β2AR were initially analyzed by the informational spectrum method (ISM). The derived NDPs in complex with β2AR were subjected to protein-peptide docking, molecular dynamics (MD) simulations, and metadynamics-based free-energy binding calculations. Computational analyses identified a 25-amino-acid-long CDR2-NDP of Nb71, designated P4, which exhibited the following binding free-energy for the formation of the β2AR:P4 complex (ΔG = -6.8 ± 0.8 kcal/mol or a Ki = 16.5 μM at 310 K) and mapped the β2AR:P4 amino acid interaction network. In vitro characterization showed that P4 (i) can cross the plasma membrane, (ii) reduces the maximum isoproterenol-induced cAMP level by approximately 40% and the isoproterenol potency by up to 20-fold at micromolar concentration, (iii) has a very low affinity to interact with unstimulated β2AR in the cAMP assay, and (iv) cannot reduce the efficacy and potency of the isoproterenol-mediated β2AR/β-arrestin-2 interaction in the BRET2-based recruitment assay. In summary, the CDR2-NDP, P4, binds preferentially to agonist-activated β2AR and disrupts Gαs-mediated signaling.

Computational Modeling of the Interactions between DPP IV and Hemorphins.

Type 2 diabetes is a chronic metabolic disorder characterized by high blood glucose levels due to either insufficient insulin production or ineffective utilization of insulin by the body. The enzyme dipeptidyl peptidase IV (DPP IV) plays a crucial role in degrading incretins that stimulate insulin secretion. Therefore, the inhibition of DPP IV is an established approach for the treatment of diabetes. Hemorphins are a class of short endogenous bioactive peptides produced by the enzymatic degradation of hemoglobin chains. Numerous in vitro and in vivo physiological effects of hemorphins, including DPP IV inhibiting activity, have been documented in different systems and tissues. However, the underlying molecular binding behavior of these peptides with DPP IV remains unknown. Here, computational approaches such as protein-peptide molecular docking and extensive molecular dynamics (MD) simulations were employed to identify the binding pose and stability of peptides in the active site of DPP IV. Findings indicate that hemorphins lacking the hydrophobic residues LVV and VV at the N terminal region strongly bind to the conserved residues in the active site of DPP IV. Furthermore, interactions with these critical residues were sustained throughout the duration of multiple 500 ns MD simulations. Notably, hemorphin 7 showed higher binding affinity and sustained interactions by binding to S1 and S2 pockets of DPP IV.

Molecular investigation of antimicrobial peptides against Helicobacter pylori proteins using a peptide-protein docking approach

The impact of H. pylori resistance on patient's treatment failure is a major concern. Therefore, the development of novel or alternative therapies for H. pylori is urgently needed. The purpose of this study was to investigate the molecular interactions of various antimicrobial peptides (AMPs) to H. pylori proteins. We performed the peptide-protein molecular docking using HADDOCK 2.4 webserver. Fourteen AMPs were tested for their binding efficacy against four H. pylori proteins. Simulation of the peptide-protein complex was performed using molecular dynamic software package AMBER20. From molecular docking analysis, five peptides (LL-37, Tilapia piscidin 4, napin, snakin-1 and EcAMP1) showed strong binding interactions against H. pylori proteins. The strongest binding affinity was observed in the interactions between Snakin-1 and PBP2, TP4 and type I HopQ and EcAMP1 and type I HopQ with −11.1, −13.6 and −13.8 kcal/mol, respectively. The dynamic simulation was performed for two complexes (snakin1-PBP2 and EcAMP1-HopQ). Results of the dynamics simulation showed that EcAMP1 had stable interaction and binding to type I HopQ protein without significant structural changes. In conclusion, both results of docking and simulation showed that EcAMP1 might be useful as a potential therapeutic agent for H. pylori treatment. This molecular approach provides deep understanding of the interaction insights between AMPs and H. pylori proteins. It paves the way for the development of novel anti-H. pylori using antimicrobial peptides.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking.

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational costwhile providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Reconstruction of Four-Body Statistical Pseudopotential for Protein-Peptide Docking

Reconstruction of Four-Body Statistical Pseudopotential for Protein-Peptide Docking

Identification of Potential Modulators of a Pathogenic G Protein-Gated Inwardly Rectifying K+ Channel 4 Mutant: In Silico Investigation in the Context of Drug Discovery for Hypertension.

Genetic abnormalities have been associated with primary aldosteronism, a major cause of secondary hypertension. This includes mutations in the KCNJ5 gene, which encodes G protein-gated inwardly rectifying K+ channel 4 (GIRK4). For example, the substitution of glycine with glutamic acid gives rise to the pathogenic GIRK4G151E mutation, which alters channel selectivity, making it more permeable to Na+ and Ca2+. While tertiapin and tertiapin-Q are well-known peptide inhibitors of the GIRK4WT channel, clinically, there is a need for the development of selective modulators of mutated channels, including GIRK4G151E. Using in silico methods, including homology modeling, protein-peptide docking, ligand-binding site prediction, and molecular docking, we aimed to explore potential modulators of GIRK4WT and GIRK4G151E. Firstly, protein-peptide docking was performed to characterize the binding site of tertiapin and its derivative to the GIRK4 channels. In accordance with previous studies, the peptide inhibitors preferentially bind to the GIRK4WT channel selectivity filter compared to GIRK4G151E. A ligand-binding site analysis was subsequently performed, resulting in the identification of two potential regions of interest: the central cavity and G-loop gate. Utilizing curated chemical libraries, we screened over 700 small molecules against the central cavity of the GIRK4 channels. Flavonoids, including luteolin-7-O-rutinoside and rutin, and the macrolides rapamycin and troleandomycin bound strongly to the GIRK4 channels. Similarly, xanthophylls, particularly luteoxanthin, bound to the central cavity with a strong preference towards the mutated GIRK4G151E channel compared to GIRK4WT. Overall, our findings suggest potential lead compounds for further investigation, particularly luteoxanthin, that may selectively modulate GIRK4 channels.

MM-GBSA and QM/MM simulation-based in silico approaches for the inhibition of Acinetobacter baumannii class D OXA-24 β-lactamase using antimicrobial peptides melittin and RP-1

The antimicrobial peptides (AMPs) have been regarded as the next generation antibiotics. This study aimed to explore the AMP inhibitors of β-lactamase enzyme employing computational biology methods. Protein-peptide docking of Acinetobacter baumannii OXA-24 class D β-lactamase with AMPs (melittin and RP-1) were performed using HawkDock webserver. The docked complexes were subjected to energy property analysis through MM/GBSA, and binding affinity (ΔG (kcal/mol)) and stability (dissociation constant, KD (M)) prediction using PRODIGY. Both the AMPs, melittin and RP-1, were well docked with A. baumannii OXA-24. The top ranked OXA-24-melittin and OXA-24-RP-1 complexes were detected on the basis of the HawkDock scores (-2974.08 and -2825.83, respectively), thereafter by rescoring with MM/GBSA-based binding free energy (BFE) of -33.85 and -29.29 kcal/mol, respectively. The PRODIGY-based respective BFE (-8.0 and -10.0 kcal/mol) and KD (1.4 × 10-6 and 5 × 10-8 M) of the complexes revealed excellent protein-peptide binding affinity and complex stability. The iMODS-based molecular dynamic simulation authenticated the stability and molecular motion flexibility of the protein-peptide complexes. The quantum mechanics and molecular mechanics energy estimated using density-functional theory with CP2K software, for the electrostatic interaction of OXA-24-RP-1 (-218862.95 kcal/mol), was more favourable than the H-bonded interaction of OXA-24-melittin (-200620.21 kcal/mol), nevertheless both the peptides were found effective to inhibit OXA-24. Both the AMPs had toxicity profiles within the acceptable limits as predicted through pkCSM. Current findings indicate the potential supplement or replacement of conventionally used antibiotics with melittin and RP-1 to treating or averting A. baumannii infection by escaping the β-lactamases-mediated resistance.

Designing a novel and combinatorial multi-antigenic epitope-based vaccine “MarVax” against Marburg virus—a reverse vaccinology and immunoinformatics approach

ContextMarburg virus (MARV) is a member of the Filoviridae family and causes Marburg virus disease (MVD) among humans and primates. With fatality rates going up to 88%, there is currently no commercialized cure or vaccine to combat the infection. The National Institute of Allergy and Infectious Diseases (NIAID) classified MARV as priority pathogen A, which presages the need for a vaccine candidate which can provide stable, long-term adaptive immunity. The surface glycoprotein (GP) and fusion protein (FP) mediate the adherence, fusion, and entry of the virus into the host cell via the TIM-I receptor. Being important antigenic determinants, studies reveal that GP and FP are prone to evolutionary mutations, underscoring the requirement of a vaccine construct capable of eliciting a robust and sustained immune response. In this computational study, a reverse vaccinology approach was employed to design a combinatorial vaccine from conserved and antigenic epitopes of essential viral proteins of MARV, namely GP, VP24, VP30, VP35, and VP40 along with an endogenous protein large polymerase (L). MethodsEpitopes for T-cell and B-cell were predicted using TepiTool and ElliPro, respectively. The surface-exposed TLRs like TLR2, TLR4, and TLR5 were used to screen high-binding affinity epitopes using the protein-peptide docking platform MdockPeP. The best binding epitopes were selected and assembled with linkers to design a recombinant multi-epitope vaccine construct which was then modeled in Robetta. The in silico biophysical and biochemical analyses of the recombinant vaccine were performed. The docking and MD simulation of the vaccine using WebGro and CABS-Flex against TLRs support the stable binding of vaccine candidates. A virtual immune simulation to check the immediate and long-term immunogenicity was carried out using the C-ImmSim server. ResultsThe biochemical characteristics and docking studies with MD simulation establish the recombinant protein vaccine construct MarVax as a stable, antigenic, and potent vaccine molecule. Immune simulation studies reveal 1-year passive immunity which needs to be validated by in vivo studies.

A dual-population multi-objective evolutionary algorithm driven by generative adversarial networks for benchmarking and protein-peptide docking

Multi-objective optimization problems (MOPs) are characterized as optimization problems in which multiple conflicting objective functions are optimized simultaneously. To solve MOPs, some algorithms used machine learning models to drive the evolutionary algorithms, leading to the design of a variety of model-based evolutionary algorithms. However, model collapse occurs during the generation of candidate solutions, which results in local optima and poor diversity in model-based evolutionary algorithms. To address this problem, we propose a dual-population multi-objective evolutionary algorithm driven by Wasserstein generative adversarial network with gradient penalty (DGMOEA), where the dual-populations coordinate and cooperate to generate high-quality solutions, thus improving the performance of the evolutionary algorithm. We compare the proposed algorithm with the 7 state-of-the-art algorithms on 20 multi-objective benchmark functions. Experimental results indicate that DGMOEA achieves significant results in solving MOPs, where the metrics IGD and HV outperform the other comparative algorithms on 15 and 18 out of 20 benchmarks, respectively. Our algorithm is evaluated on the LEADS-PEP dataset containing 53 protein-peptide complexes, and the experimental results on solving the protein-peptide docking problem indicated that DGMOEA can effectively reduce the RMSD between the generated and the original peptide's 3D poses and achieve more competitive results.

Investigation of active peptides from natural products that induce ER-stress-mediated apoptosis in cancer as potential therapeutics for kidney cancer, common in some genetic diseases: An in silico approach

Background: Endoplasmic reticulum (ER) stress initiates an unfolded protein response (UPR) to re-establish ER homeostasis as an adaptive pathway in cancer. However, persistent ER stress triggers the apoptotic pathway usually observed in kidney cancer (KC). The term KC actually refers to a number of distinct cancers. Kidney cancer is generally observed in a number of genetic diseases. Treatment options for KC have changed greatly over the years. The most commonly used tyrosine kinase inhibitors, TKI, show conflicting results regarding their beneficial effects on patients, as demonstrated in different KC patient cohort studies, indicating that the underlying molecular mechanisms involved in KC are more complex and likely need combined therapies able to modulate the activity of endoplasmic reticulum. Methods: The purpose of this research was to generate peptides from natural products in sillico that might be used as kidney cancer potential modulators. For this aim, several methods were used: Target prediction, protein hydrolysis, and protein-peptide molecular docking have all been used as techniques. Results: The network of critical KC genes is composed of C3AR1, CSNK2A2, ACE, DPP4, CAPN1, FPR2, HLA-A, and MMP2, together with predicted kinases such as RPS6KA5, MAPK14, CSNK2A1, PRKCD, CDK1, and HIPK2, in addition to transcription factors such as IRF8, TCF3, ERG, CREB1, EZH2, SPI1, IRF1, and SUZ12. The identified molecular targets of the isolated peptides are: HLA class I histocompatibility antigen A-3, lipoxin A4, dipeptidyl peptidase IV, angiotensinconverting enzyme, cyclooxygenase-2, C3a anaphylatoxin chemotactic receptor, melanocortin receptor 4, neurotensin receptor 1, mu opioid receptor, delta opioid receptor, and calpain 1. Conclusion: Overall, the results showed that GVSK, PGP, WQR, YGGF, and IF peptides are promising candidates for further study. Future work would be needed to test the therapeutic properties of these hydrolysate peptides using in vitro and in vivo approaches.

Using a novel structure/function approach to select diverse swine major histocompatibility complex 1 alleles to predict epitopes for vaccine development.

Swine leukocyte antigens (SLAs) (i.e. swine major histocompatibility complex proteins) conduct a fundamental role in swine immunity. To generate a protective vaccine across an outbred species, such as pigs, it is critical that epitopes that bind to diverse SLA alleles are used in the vaccine development process. We introduced a new strategy for epitope prediction. We employed molecular dynamics simulation to identify key amino acids for interactions with epitopes. We developed an algorithm wherein each SLA-1 is compared to a crystalized reference allele with unique weighting for non-conserved amino acids based on R group and position. We then performed homology modeling and electrostatic contact mapping to visualize how relatively small changes in sequences impacted the charge distribution in the binding site. We selected eight diverse SLA-1 alleles and performed homology modeling followed, by protein-peptide docking and binding affinity analyses, to identify porcine reproductive and respiratory syndrome virus matrix protein epitopes that bind with high affinity to these alleles. We also performed docking analysis on the epitopes identified as strong binders using NetMHCpan 4.1. Epitopes predicted to bind to our eight SLA-1 alleles had equivalent or higher energetic interactions than those predicted to bind to the NetMHCpan 4.1 allele repertoire. This approach of selecting diverse SLA-1 alleles, followed by homology modeling, and docking simulations, can be used as a novel strategy for epitope prediction that complements other available tools and is especially useful when available tools do not offer a prediction for SLAs/major histocompatibility complex. The data underlying this article are available in the online Supplementary Material.