HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm

A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset

Protein-peptide interactions have attracted the attention of many drug discovery scientists due to their possible druggability features on most key biological activities such as regulating disease-related signaling pathways and enhancing the immune system's responses. Different studies have utilized some protein-peptide-specific docking algorithms/methods to predict protein-peptide interactions. However, the existing algorithms/methods suffer from two serious limitations which make them unsuitable for protein-peptide docking problems. First, it seems that the prevalent approaches require to be modified and remodeled for weighting the unbounded forces between a protein and a peptide. Second, they do not employ state-of-the-art search algorithms for detecting the 3D pose of a peptide relative to a protein. To address these restrictions, the present study aims to introduce a novel multi-objective algorithm, which first generates some potential 3D poses of a peptide, and then, improves them through its operators. The candidate solutions are further evaluated using Multi-Objective Pareto Front (MOPF) optimization concepts. To this end, van der Waals, electrostatic, solvation, and hydrogen bond energies between the atoms of a protein and designated peptide are computed. To evaluate the algorithm, it is first applied to the LEADS-PEP dataset containing 53 protein-peptide complexes with up to 53 rotatable branches/bonds and then compared with three popular/efficient algorithms. The obtained results indicate that the MOPF-based approaches which reduce the backbone RMSD between the original and predicted states, achieve significantly better results in terms of the success rate in predicting the near-native conditions. Besides, a comparison between the different types of search algorithms reveals that efficient ones like the multi-objective Trader/differential evolution algorithm can predict protein-peptide interactions better than the popular algorithms such as the multi-objective genetic/particle swarm optimization algorithms.

NLDock: a Fast Nucleic Acid-Ligand Docking Algorithm for Modeling RNA/DNA-Ligand Complexes

Nucleic acid-ligand interactions play an important role in numerous cellular processes such as gene function expression and regulation. Therefore, nucleic acids such as RNAs have become more and more important drug targets, where the structural determination of nucleic acid-ligand complexes is pivotal for understanding their functions and thus developing therapeutic interventions. Molecular docking has been a useful computational tool in predicting the complex structure between molecules. However, although a number of docking algorithms have been developed for protein-ligand interactions, only a few docking programs were presented for nucleic acid-ligand interactions. Here, we have developed a fast nucleic acid-ligand docking algorithm, named NLDock, by implementing our intrinsic scoring function ITScoreNL for nucleic acid-ligand interactions into a modified version of the MDock program. NLDock was extensively evaluated on four test sets and compared with five other state-of-the-art docking algorithms including AutoDock, DOCK 6, rDock, GOLD, and Glide. It was shown that our NLDock algorithm obtained a significantly better performance than the other docking programs in binding mode predictions and achieved the success rates of 73%, 36%, and 32% on the largest test set of 77 complexes for local rigid-, local flexible-, and global flexible-ligand docking, respectively. In addition, our NLDock approach is also computationally efficient and consumed an average of as short as 0.97 and 2.08 min for a local flexible-ligand docking job and a global flexible-ligand docking job, respectively. These results suggest the good performance of our NLDock in both docking accuracy and computational efficiency.

A deep-learning framework for multi-level peptide-protein interaction prediction

Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Here, we present a deep learning framework for multi-level peptide-protein interaction prediction, called CAMP, including binary peptide-protein interaction prediction and corresponding peptide binding residue identification. Comprehensive evaluation demonstrated that CAMP can successfully capture the binary interactions between peptides and proteins and identify the binding residues along the peptides involved in the interactions. In addition, CAMP outperformed other state-of-the-art methods on binary peptide-protein interaction prediction. CAMP can serve as a useful tool in peptide-protein interaction prediction and identification of important binding residues in the peptides, which can thus facilitate the peptide drug discovery process.

Peptides Affecting the Outer Membrane Lipid Asymmetry System (MlaA-OmpC/F) Reduce Avian Pathogenic Escherichia coli (APEC) Colonization in Chickens

Avian pathogenic Escherichia coli (APEC), an extraintestinal pathogenic E. coli (ExPEC), causes colibacillosis in chickens and is reportedly associated with urinary tract infections and meningitis in humans. Development of resistance is a major limitation of current ExPEC antibiotic therapy. New antibacterials that can circumvent resistance problem such as antimicrobial peptides (AMPs) are critically needed. Here, we evaluated the efficacy of Lactobacillus rhamnosus GG (LGG)-derived peptides against APEC and uncovered their potential antibacterial targets. Three peptides (NPSRQERR [P1], PDENK [P2], and VHTAPK [P3]) displayed inhibitory activity against APEC. These peptides were effective against APEC in biofilm and chicken macrophage HD11 cells. Treatment with these peptides reduced the cecum colonization (0.5 to 1.3 log) of APEC in chickens. Microbiota analysis revealed two peptides (P1 and P2) decreased Enterobacteriaceae abundance with minimal impact on overall cecal microbiota of chickens. Bacterial cytological profiling showed peptides disrupt APEC membranes either by causing membrane shedding, rupturing, or flaccidity. Furthermore, gene expression analysis revealed that peptides downregulated the expression of ompC (>13.0-fold), ompF (>11.3-fold), and mlaA (>4.9-fold), genes responsible for the maintenance of outer membrane (OM) lipid asymmetry. Consistently, immunoblot analysis also showed decreased levels of OmpC and MlaA proteins in APEC treated with peptides. Alanine scanning studies revealed residues crucial (P1, N, E, R and P; P2, D and E; P3, T, P, and K) for their activity. Overall, our study identified peptides with a new antibacterial target that can be developed to control APEC infections in chickens, thereby curtailing poultry-originated human ExPEC infections. IMPORTANCE Avian pathogenic Escherichia coli (APEC) is a subgroup of extraintestinal pathogenic E. coli (ExPEC) and considered a foodborne zoonotic pathogen transmitted through consumption of contaminated poultry products. APEC shares genetic similarities with human ExPECs, including uropathogenic E. coli (UPEC) and neonatal meningitis E. coli (NMEC). Our study identified Lactobacillus rhamnosus GG (LGG)-derived peptides (P1 [NPSRQERR], P2 [PDENK], and P3 [VHTAPK]) effective in reducing APEC infection in chickens. Antimicrobial peptides (AMPs) are regarded as ideal candidates for antibacterial development because of their low propensity for resistance development and ability to kill resistant bacteria. Mechanistic studies showed peptides disrupt the APEC membrane by affecting the MlaA-OmpC/F system responsible for the maintenance of outer membrane (OM) lipid asymmetry, a promising new druggable target to overcome resistance problems in Gram-negative bacteria. Altogether, these peptides can provide a valuable approach for development of novel anti-ExPEC therapies, including APEC, human ExPECs, and other related Gram-negative pathogens. Furthermore, effective control of APEC infections in chickens can curb poultry-originated ExPEC infections in humans.

Source and exploration of the peptides used to construct peptide-drug conjugates

Peptide-drug conjugates (PDCs) are a class of novel molecules widely designed and synthesized for delivering payload drugs. The peptide part plays a vital role in the whole molecule, because they determine the ability of the molecules to penetrate the membrane and target to the specific targets. Here, we introduce the source of different kinds of cell-penetrating peptides (CPPs) and cell-targeting peptides (CTPs) that have been used or could be used in constructing PDCs as well as their latest application in delivering drugs. What's more, the approaches of developing CPPs and CTPs and the techniques to discover novel peptides are focused on and summarized in the review. This review aims to help relevant researchers fast understand the research status of peptides in PDCs and carry forward the process of novel peptides discovery.

Identification of 13 Guanidinobenzoyl- or Aminidinobenzoyl-Containing Drugs to Potentially Inhibit TMPRSS2 for COVID-19 Treatment

Positively charged groups that mimic arginine or lysine in a natural substrate of trypsin are necessary for drugs to inhibit the trypsin-like serine protease TMPRSS2 that is involved in the viral entry and spread of coronaviruses, including SARS-CoV-2. Based on this assumption, we identified a set of 13 approved or clinically investigational drugs with positively charged guanidinobenzoyl and/or aminidinobenzoyl groups, including the experimentally verified TMPRSS2 inhibitors Camostat and Nafamostat. Molecular docking using the C-I-TASSER-predicted TMPRSS2 catalytic domain model suggested that the guanidinobenzoyl or aminidinobenzoyl group in all the drugs could form putative salt bridge interactions with the side-chain carboxyl group of Asp435 located in the S1 pocket of TMPRSS2. Molecular dynamics simulations further revealed the high stability of the putative salt bridge interactions over long-time (100 ns) simulations. The molecular mechanics/generalized Born surface area-binding free energy assessment and per-residue energy decomposition analysis also supported the strong binding interactions between TMPRSS2 and the proposed drugs. These results suggest that the proposed compounds, in addition to Camostat and Nafamostat, could be effective TMPRSS2 inhibitors for COVID-19 treatment by occupying the S1 pocket with the hallmark positively charged groups.

Computational design of ultrashort peptide inhibitors of the receptor-binding domain of the SARS-CoV-2 S protein

Targeting the interaction between severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2)-receptor-binding domain (RBD) and angiotensin-converting enzyme 2 (ACE2) is believed to be an effective strategy for drug design to inhibit the infection of SARS-CoV-2. Herein, several ultrashort peptidase inhibitors against the RBD-ACE2 interaction were obtained by a computer-aided approach based on the RBD-binding residues on the protease domain (PD) of ACE2. The designed peptides were tested on a model coronavirus GX_P2V, which has 92.2 and 86% amino acid identity to the SARS-CoV-2 spike protein and RBD, respectively. Molecular dynamics simulations and binding free energy analysis predicted a potential binding pocket on the RBD of the spike protein, and this was confirmed by the specifically designed peptides SI5α and SI5α-b. They have only seven residues, showing potent antiviral activity and low cytotoxicity. Enzyme-linked immunosorbent assay result also confirmed their inhibitory ability against the RBD-ACE2 interaction. The ultrashort peptides are promising precursor molecules for the drug development of Corona Virus Disease 2019, and the novel binding pocket on the RBD may be helpful for the design of RBD inhibitors or antibodies against SARS-CoV-2.

Heparan Sulfate Binding Cationic Peptides Restrict SARS-CoV-2 Entry

A novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. While the world is striving for a treatment modality against SARS-CoV-2, our understanding about the virus entry mechanisms may help to design entry inhibitors, which may help to limit the virus spreading. Owing to the importance of cellular ACE2 and heparan sulfate in SARS-CoV-2 entry, we aimed to evaluate the efficacy of cationic G1 and G2 peptides in virus entry inhibition. In silico binding affinity studies revealed possible binding sites of G1 and G2 peptides on HS and ACE2, which are required for the spike–HS and spike–ACE2 interactions. Prophylactic treatment of G1 and G2 peptide was also proved to decrease the cell surface HS, an essential virus entry receptor. With these two mechanisms we confirm the possible use of cationic peptides to inhibit the entry of SARS-CoV-2.

Temporin G, an amphibian antimicrobial peptide against influenza and parainfluenza respiratory viruses: Insights into biological activity and mechanism of action

Treatment of respiratory viral infections remains a global health concern, mainly due to the inefficacy of available drugs. Therefore, the discovery of novel antiviral compounds is needed; in this context, antimicrobial peptides (AMPs) like temporins hold great promise. Here, we discovered that the harmless temporin G (TG) significantly inhibited the early life-cycle phases of influenza virus. The in vitro hemagglutinating test revealed the existence of TG interaction with the viral hemagglutinin (HA) protein. Furthermore, the hemolysis inhibition assay and the molecular docking studies confirmed a TG/HA complex formation at the level of the conserved hydrophobic stem groove of HA. Remarkably, these findings highlight the ability of TG to block the conformational rearrangements of HA2 subunit, which are essential for the viral envelope fusion with intracellular endocytic vesicles, thereby neutralizing the virus entry into the host cell. In comparison, in the case of parainfluenza virus, which penetrates host cells upon a membrane-fusion process, addition of TG to infected cells provoked ~1.2 log reduction of viral titer released in the supernatant. Nevertheless, at the same condition, an immunofluorescent assay showed that the expression of viral hemagglutinin/neuraminidase protein was not significantly reduced. This suggested a peptide-mediated block of some late steps of viral replication and therefore the impairment of the extracellular release of viral particles. Overall, our results are the first demonstration of the ability of an AMP to interfere with the replication of respiratory viruses with a different mechanism of cell entry and will open a new avenue for the development of novel therapeutic approaches against a large variety of respiratory viruses, including the recent SARS-CoV2.

Direct activation of the proton channel by albumin leads to human sperm capacitation and sustained release of inflammatory mediators by neutrophils

Human voltage-gated proton channels (hHv1) extrude protons from cells to compensate for charge and osmotic imbalances due metabolism, normalizing intracellular pH and regulating protein function. Human albumin (Alb), present at various levels throughout the body, regulates oncotic pressure and transports ligands. Here, we report Alb is required to activate hHv1 in sperm and neutrophils. Dose-response studies reveal the concentration of Alb in semen is too low to activate hHv1 in sperm whereas the higher level in uterine fluid yields proton efflux, allowing capacitation, the acrosomal reaction, and oocyte fertilization. Likewise, Alb activation of hHv1 in neutrophils is required to sustain production and release of reactive oxygen species during the immune respiratory burst. One Alb binds to both voltage sensor domains (VSDs) in hHv1, enhancing open probability and increasing proton current. A computational model of the Alb-hHv1 complex, validated by experiments, identifies two sites in Alb domain II that interact with the VSDs, suggesting an electrostatic gating modification mechanism favoring the active "up" sensor conformation. This report shows how sperm are triggered to fertilize, resolving how hHv1 opens at negative membrane potentials in sperm, and describes a role for Alb in physiology that will operate in the many tissues expressing hHv1.

The effect of the prospective information-based nursing quality management model on the improvement of management quality in emergency medicine nursing management

OBJECTIVE

This study aimed to explore the clinical effectiveness of applying a prospective information-based nursing quality management model in emergency medicine nursing management and its impact on the management quality.

METHODS

170 inpatients treated in the emergency department of our hospital from April 2018 to March 2019 were recruited as the study cohort and randomly divided into a control group and a study group, with 85 patients in each group. In the study, the patients in the control group were treated using the routine nursing mode, and the patients in the study group were treated using the prospective information-based nursing quality management model to compare the nursing management quality, the nursing satisfaction, the occurrence of adverse events, the nursing compliance, the emergency response times, and the survival rates between the two groups.

RESULTS

We found that the overall nursing management quality in the study group was better than it was in the control group. The nursing satisfaction rate in the study group (96.47%) was higher than the nursing satisfaction rate in the control group (83.53%) The rate of adverse events in the study group (5.88%) was lower than it was in the control group (16.47%), the nursing compliance rate in the study group (89.41%) was higher than it was in the control group (63.53%), the emergency response times in the study group were shorter than they were in the control group, and the postoperative survival rate in the study group was higher than it was in the control group, all with statistically significant differences (P<0.05).

CONCLUSION

The prospective information-based nursing quality management model has a significant influence on emergency medicine nursing management. It can significantly improve the nursing management quality, the patients' satisfaction with the nursing process, and the nursing compliance, and it can effectively avoid adverse events, so it is worthy of promotion.

Nutritional composition and proteomic analysis of soft-shelled turtle (Pelodiscus sinensis) egg and identification of oligopeptides with alpha-glucosidase inhibitory activity

This study aimed to explore nutritional compositions and proteomics of soft-shelled turtle (SST) egg, as well as identify potential antidiabetic oligopeptides with α-glucosidase inhibitory property. Results revealed that SST egg is a promising source of highly nutritious proteins and minerals (54.64% and 5.81% of dry matter, respectively). Further proteomic analysis showed SST egg proteins contained at least 9 protein families, such as transferrin/iron binding protein and immunoregulation-related protein. Hydrolysis by different enzymes, especially papain, remarkably increased α-glucosidase inhibitory activity and scavenging activity for ABTS, DPPH, hydroxyl and oxygen radicals of SST egg proteins. Peptides from papain hydrolysate were fractionated using ultrafiltration followed by reverse phase chromatography, and 16 peptides were identified in the most active fraction by LC-QTOF-MS/MS. Molecular docking revealed that 14 of these peptides could easily dock into the substrate-binding pocket and/or inhibitor binding sites of α-glucosidase with the docking score below -150 kcal/mol, indicating their potential α-glucosidase inhibitory properties. The five most abundant oligopeptides with potent interaction with α-glucosidase were further synthesized, and oligopeptides HNKPEVEVR, ARDASVLK and SGTLLHK strongly inhibited the activity of α-glucosidase (IC₅₀ of 56, 195 and 289 µmol/L, respectively). Therefore, oligopeptides from enzymatic hydrolysate of SST egg protein exhibit potential antidiabetic activity, making it a promising functional food ingredient.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

Computational designing of a peptide that potentially blocks the entry of SARS-CoV, SARS-CoV-2 and MERS-CoV

Last decade has witnessed three major pandemics caused by SARS-CoV, SARS-CoV-2 and MERS-CoV that belong to Coronavirus family. Currently, there are no effective therapies available for corona virus infections. Since the three viruses belong to the same family and share many common features, we can theoretically design a drug that can be effective on all the three of them. In this study, using computational approach, we designed a peptide (Peptide 7) that can bind to the Receptor Binding Domain (RBD) of SARS-CoV, SARS-CoV-2 and MERS-CoV thereby preventing the entry of the viruses into the host cell. The peptide inhibitor was designed as a consensus peptide from three different peptides that might individually bind to the RBD of the three viruses. Docking studies and molecular dynamic simulations using Peptide 7 has shown that it binds with higher affinity than the native receptors of the RBD and forms a stable complex thereby preventing further viral-receptor interaction and inhibiting their cellular entry. This effective binding is observed for the three RBDs, despite the Peptide 7 interactions being slightly different. Hence; this peptide inhibitor can be used as a potential candidate for the development of peptide based anti-viral therapy against Corona viruses.

An affinity prediction approach for the ligand of E3 ligase Cbl-b and an insight into substrate binding pattern

Protein-protein interactions (PPIs) are essentially fundamental to all cellular processes, so that developing small molecule inhibitors of PPIs have great significance despite representing a huge challenge. Studying PPIs with the help of peptide motifs could obtain the structural information and reference significance to reduce the difficulty in the development of small molecules. Computational methods are powerful tools to characterize peptide-protein interactions, especially molecular dynamics simulation and binding free energy calculation. Here, we established an affinity prediction model suitable for Casitas B lymphoma-b (Cbl-b) and phosphorylated motif system. According to the affinity data set of multiple truncated peptides, the force field, solvent model, and internal dielectric constant of molecular mechanics/generalized Born surface area (MM/GBSA) method were optimized. Further, we predicted the affinity of the rationally designed new sequences through this model and obtained a new 6-mer motif with a 7-fold increase in affinity and the comprehensive structure-activity relationship. Moreover, we proposed an insight of unexpected activity of the truncated 5-mer peptide and revealed the possible binding mode of the new highly active 6-mer motif by extended simulation. Our results showed that the activity enhancement of the truncated peptide was caused by the acetyl-mediated conformation change. The side chain of Arg and pTyr in the 6-mer motif co-occupied the site p1 to form numerous hydrogen bond interactions and increased hydrophobic interaction formed with Tyr266, leading to the higher affinity. The present work provided a reference to investigate the PPI of Cbl-b and phosphorylated substrates and guided the development of Cbl-b inhibitors.

Intestinal guard: Human CXCL17 modulates protective response against mycotoxins and CXCL17-mimetic peptides development

Mycotoxin contamination is an ongoing and growing issue that can create health risks and even cause death. Unfortunately, there is currently a lack of specific therapy against mycotoxins with few side effects. On the other hand, the strategic expression of CXCL17 in mucosal tissues suggests that it may be involved in immune response when exposed to mycotoxins, but the exact role of CXCL17 remains largely unknown. Using Caco-2 as a cell model of the intestinal epithelial barrier (the first line of defense against mycotoxins), we showed that a strong production of ROS-dependent CXCL17 was triggered by mycotoxins via p38 and JNK pathways. Under the mycotoxins stress, CXCL17 modulated enhanced immuno-protective response with a remission of inflammation and apoptosis through PI3K/AKT/mTOR. Based on our observed feedback of CXCL17 to the mycotoxins, we developed the CXCL17-mimetic peptides in silico (CX1 and CX2) that possessed the safety and the capability to ameliorate mycotoxins-inducible inflammation and apoptosis. In this study, the identification of detoxifying feature of CXCL17 is a prominent addition to the chemokine field, pointing out a new direction for curing the mycotoxins-caused damage.

Identification of Highly Conserved SARS-CoV-2 Antigenic Epitopes with Wide Coverage Using Reverse Vaccinology Approach

One of the most effective strategies for eliminating new and emerging infectious diseases is effective immunization. The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) warrants the need for a maximum coverage vaccine. Moreover, mutations that arise within the virus have a significant impact on the vaccination strategy. Here, we built a comprehensive in silico workflow pipeline to identify B-cell- and T-cell-stimulating antigens of SARS-CoV-2 viral proteins. Our in silico reverse vaccinology (RV) approach consisted of two parts: (1) analysis of the selected viral proteins based on annotated cellular location, antigenicity, allele coverage, epitope density, and mutation density and (2) analysis of the various aspects of the epitopes, including antigenicity, allele coverage, IFN-γ induction, toxicity, host homology, and site mutational density. After performing a mutation analysis based on the contemporary mutational amino acid substitutions observed in the viral variants, 13 potential epitopes were selected as subunit vaccine candidates. Despite mutational amino acid substitutions, most epitope sequences were predicted to retain immunogenicity without toxicity and host homology. Our RV approach using an in silico pipeline may potentially reduce the time required for effective vaccine development and can be applicable for vaccine development for other pathogenic diseases as well.

Introduction of a Glycine Linker Connecting the Heavy and Light Chains in Synthetic Cardosin B-Derived Rennet Changes the Specificity of Subpocket S3'

The development of plant-based synthetic rennets is of high commercial interest, due to the current great consumer demand for animal product alternatives. A previously developed recombinant form of the aspartic protease cardosin B with a three-glycine linker showed great potential due to its good performance in milk coagulation. This enzyme was found to be more specific and less proteolytically active than the native form for milk clotting, but the underlying structural causes for these activity changes were not completely clear. Here, we have performed molecular dynamics simulations with the recombinant enzyme with and without the linker. Our results showed that the introduction of the linker changes the subpocket S3', which is located more than 4 nm away. These results showcase how small modifications in proteins can have significant effects in distant regions in the protein structure that affect their biotechnological applications.

In Silico Identification of Multi-target Anti-SARS-CoV-2 Peptides from Quinoa Seed Proteins

Peptides are promising antagonists against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). To expedite drug discovery, a computational approach is widely employed for the initial screening of anti-SARS-CoV-2 candidates. This study aimed to investigate the potential of peptides from quinoa seed proteins as multi-target antagonists against SARS-CoV-2 spike glycoprotein receptor-binding domain, main protease, and papain-like protease. Five quinoa proteins were hydrolyzed in silico by papain and subtilisin. Among the 1465 peptides generated, seven could interact stably with the key binding residues and catalytic residues of the viral targets, mainly via hydrogen bonds and hydrophobic interactions. The seven peptides were comparable or superior to previously reported anti-SARS-CoV-2 peptides based on docking scores. Key residues in the seven peptides contributing to binding to viral targets were determined by computational alanine scanning. The seven peptides were predicted in silico to be non-toxic and non-allergenic. The peptides ranged between 546.66 and 3974.87 g/mol in molecular mass, besides exhibiting basic and cationic properties (isoelectric points: 8.26-12.10; net charges: 0.1-4.0). Among the seven peptides, VEDKGMMHQQRMMEKAMNIPRMCGTMQRKCRMS was found to bind the largest number of key residues on the targets. In conclusion, seven putative non-toxic, non-allergenic, multi-target anti-SARS-CoV-2 peptides were identified from quinoa seed proteins. The in vitro and in vivo efficacies of the seven peptides against SARS-CoV-2 deserve attention in future bench-top testing.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10989-021-10214-y.

Cytotoxic T-lymphocyte elicited vaccine against SARS-CoV-2 employing immunoinformatics framework

Development of effective counteragents against the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains, requires clear insights and information for understanding the immune responses associated with it. This global pandemic has pushed the healthcare system and restricted the movement of people and succumbing of the available therapeutics utterly warrants the development of a potential vaccine to contest the deadly situation. In the present study, highly efficacious, immunodominant cytotoxic T-lymphocyte (CTL) epitopes were predicted by advanced immunoinformatics assays using the spike glycoprotein of SARS-CoV2, generating a robust and specific immune response with convincing immunological parameters (Antigenicity, TAP affinity, MHC binder) engendering an efficient viral vaccine. The molecular docking studies show strong binding of the CTL construct with MHC-1 and host membrane specific TLR2 receptors. The molecular dynamics simulation in an explicit system confirmed the stable and robust binding of CTL epitope with TLR2. Steep magnitude RMSD variation and compelling residual fluctuations existed in terminal residues and various loops of the β linker segments of TLR2-epitope (residues 105-156 and 239-254) to about 0.4 nm. The reduced R_g value (3.3 nm) and stagnant SASA analysis (275 nm/S²/N after 8 ns and 5 ns) for protein surface and its orientation in the exposed and buried regions suggests more compactness due to the strong binding interaction of the epitope. The CTL vaccine candidate establishes a high capability to elicit the critical immune regulators, like T-cells and memory cells as proven by the in silico immunization assays and can be further corroborated through in vitro and in vivo assays.

Characterization of ACE Inhibitory Peptides Prepared from Pyropia pseudolinearis Protein

More than 7000 red algae species have been classified. Although most of them are underused, they are a protein-rich marine resource. The hydrolysates of red algal proteins are good candidates for the inhibition of the angiotensin-I-converting enzyme (ACE). The ACE is one of the key factors for cardiovascular disease, and the inhibition of ACE activity is related to the prevention of high blood pressure. To better understand the relationship between the hydrolysates of red algal proteins and the inhibition of ACE activity, we attempted to identify novel ACE inhibitory peptides from Pyropia pseudolinearis. We prepared water soluble proteins (WSP) containing phycoerythrin, phycocyanin, allophycocyanin, and ribulose 1,5-bisphosphate carboxylase/oxygenase. In vitro analysis showed that the thermolysin hydrolysate of the WSP had high ACE inhibitory activity compared to that of WSP. We then identified 42 peptides in the hydrolysate by high-performance liquid chromatography and mass spectrometry. Among 42 peptides, 23 peptides were found in chloroplast proteins. We then synthesized the uncharacterized peptides ARY, YLR, and LRM and measured the ACE inhibitory activity. LRM showed a low IC₅₀ value (0.15 μmol) compared to ARY and YLR (1.3 and 5.8 μmol). In silico analysis revealed that the LRM sequence was conserved in cpcA from Bangiales and Florideophyceae, indicating that the novel ACE inhibitory peptide LRM was highly conserved in red algae.

Immuno-informatics design of a multimeric epitope peptide based vaccine targeting SARS-CoV-2 spike glycoprotein

Developing an efficacious vaccine for SARS-CoV-2 infection is critical to stemming COVID-19 fatalities and providing the global community with immune protection. We have used a bioinformatic approach to aid in designing an epitope peptide-based vaccine against the spike protein of the virus. Five antigenic B cell epitopes with viable antigenicity and a total of 27 discontinuous B cell epitopes were mapped out structurally in the spike protein for antibody recognition. We identified eight CD8+ T cell 9-mers and 12 CD4+ T cell 14-15-mer as promising candidate epitopes putatively restricted by a large number of MHC I and II alleles, respectively. We used this information to construct an in silico chimeric peptide vaccine whose translational rate was highly expressed when cloned in pET28a (+) vector. With our In silico test, the vaccine construct was predicted to elicit high antigenicity and cell-mediated immunity when given as a homologous prime-boost, triggering of toll-like receptor 5 by the adjuvant linker. The vaccine was also characterized by an increase in IgM and IgG and an array of Th1 and Th2 cytokines. Upon in silico challenge with SARS-CoV-2, there was a decrease in antigen levels using our immune simulations. We, therefore, propose that potential vaccine designs consider this approach.

Mapping specificity, cleavage entropy, allosteric changes and substrates of blood proteases in a high-throughput screen

Proteases are among the largest protein families and critical regulators of biochemical processes like apoptosis and blood coagulation. Knowledge of proteases has been expanded by the development of proteomic approaches, however, technology for multiplexed screening of proteases within native environments is currently lacking behind. Here we introduce a simple method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter, their analysis in a mass spectrometer and a custom data analysis pipeline. The method is significantly faster, cheaper, technically less demanding, easy to multiplex and produces accurate protease fingerprints. Using the blood cascade proteases as a case study, we obtain protease substrate profiles that can be used to map specificity, cleavage entropy and allosteric effects and to design protease probes. The data further show that protease substrate predictions enable the selection of potential physiological substrates for targeted validation in biochemical assays.

Characterizing proteases in their native environment is still challenging. Here, the authors develop a proteomics workflow for analyzing protease-specific peptides from cell lysates in 96-well format, providing mechanistic insights into blood proteases and enabling the prediction of protease substrates.

A Novel Machine Learning Strategy for the Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

Strategies to screen antihypertensive peptides with high throughput and rapid speed will doubtlessly contribute to the treatment of hypertension. Food-derived antihypertensive peptides can reduce blood pressure without side effects. In the present study, a novel model based on the eXtreme Gradient Boosting (XGBoost) algorithm was developed and compared with the dominating machine learning models. To further reflect on the reliability of the method in a real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of the k-mer peptides cutting from six key proteins in bovine milk, and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance, with an accuracy of 86.50% and area under the receiver operating characteristic curve of 94.11%, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using the XGBoost algorithm as a novel auxiliary tool is feasible to screen for antihypertensive peptides derived from food, with high throughput and high efficiency.

Immunoinformatic based identification of cytotoxic T lymphocyte epitopes from the Indian isolate of SARS-CoV-2

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has turned into a pandemic with about thirty million confirmed cases worldwide as of September 2020. Being an airborne infection, it can be catastrophic to populous countries like India. This study sets to identify potential cytotoxic T lymphocyte (CTL) epitopes in the SARS-CoV-2 Indian isolate which can act as an effective vaccine epitope candidate for the majority of the Indian population. The immunogenicity and the foreignness of the epitopes towards the human body have to be studied to further confirm their candidacy. The top-scoring epitopes were subjected to molecular docking studies to study their interactions with the corresponding human leukocyte antigen (HLA) system. The CTL epitopes were observed to bind at the peptide-binding groove of the corresponding HLA system, indicating their potency as an epitope candidate. The candidacy was further analyzed using sequence conservation studies and molecular dynamics simulation. The identified epitopes can be subjected to further studies for the development of the SARS-CoV-2 vaccine.

SARS-CoV-2 spike protein-, main protease- and papain-like-protease-targeting peptides from seed proteins following gastrointestinal digestion: An in silico study

BACKGROUND

The anti-COVID-19 potential of phytochemicals was investigated in numerous studies, but efficacy of peptides released by seed proteins upon gastrointestinal (GI) digestion is underexplored.

PURPOSE

This study investigated whether multi-target anti-COVID-19 peptides could be released from edible seeds following GI digestion, by using in silico and molecular docking approaches.

METHODS

Nineteen seed storage proteins from Chenopodium quinoa (quinoa), Sesamum indicum (sesame), Brassica napus (rape), Helianthus annuus (sunflower) and Cucurbita maxima (pumpkin) seeds were subjected to in silico GI digestion, in order to detect the released peptides with high GI absorption that concurrently target the spike protein, main protease and papain-like protease of SARS-CoV-2.

RESULTS

Molecular docking study revealed that 36 peptides with high GI absorption, out of the 1593 peptides released from seed proteins, could bind to the binding or catalytic sites of the spike protein, main protease and papain-like protease of SARS-CoV-2, after GI digestion. Among the five seeds, quinoa was predicted to release the largest number (27) of multi-target peptides. When compared with PIY (Pro-Ile-Tyr), a high-GI-absorption fragment released from a potential anti-COVID-19 peptide, pumpkin-derived peptide PW (Pro-Trp) could bind more strongly to SARS-CoV-2 spike protein. PW was superior to some previously reported anti-SARS-CoV-2 phytochemicals when binding affinities towards the three viral targets were compared.

CONCLUSION

Edible seeds are a potential source of anti-COVID-19 peptides upon GI digestion, hence they should be considered as an alternative to assist in the treatment and management of COVID-19.

Could Dermaseptin Analogue be a Competitive Inhibitor for ACE2 Towards Binding with Viral Spike Protein Causing COVID19?: Computational Investigation

Initial phase of COVID-19 infection is associated with the binding of viral spike protein S1 receptor binding domain (RBD) with the host cell surface receptor, ACE2. Peptide inhibitors typically interact with spike proteins in order to block its interaction with ACE2, and this knowledge would promote the use of such peptides as therapeutic scaffolds. The present study examined the competitive inhibitor activity of a broad spectrum antimicrobial peptide, Dermaseptin-S4 (S4) and its analogues. Three structural S4 analogues viz., S4 (K₄), S4 (K₂₀) and S4 (K₄K₂₀) were modelled by substituting charged lysine for non-polar residues in S4 and subsequently, docked with S1. Further, the comparative analysis of inter-residue contacts and non-covalent intermolecular interactions among S1–S4 (K₄), S1–S4 (K₄K₂₀) and S1–ACE2 complexes were carried out to explore their mode of binding with S1. Interestingly, S1–S4 (K₄) established more inter-molecular interactions compared to S4 (K₄K₂₀) and S1–ACE2. In order to substantiate this study, the normal mode analysis (NMA) was conducted to show how the structural stability of the flexible loop region in S1 is affected by atomic displacements in unbound S1 and docked complexes. Markedly, the strong interactions consistently maintained by S1–S4 (K₄) complex revealed their conformational transition over the harmonic motion period. Moreover, S1–S4 (K₄) peptide complex showed a higher energy deformation profile compared to S1–S4 (K₄K₂₀), where the higher energy deformation suggests the rigidity of the docked complex and thus it’s harder deformability, which is also substantiated by molecular dynamics simulation. In conclusion, S1–S4 (K₄) complex has definitely exhibited a functionally significant dynamics compared to S1–ACE2 complex; this peptide inhibitor, S4 (K₄) will need to be considered as the best therapeutic scaffold to block SARS-CoV-2 infection.

Design and Evaluation of Peptide Dual-Agonists of GLP-1 and NPY2 Receptors for Glucoregulation and Weight Loss with Mitigated Nausea and Emesis

There is a critical unmet need for therapeutics to treat the epidemic of comorbidities associated with obesity and type 2 diabetes, ideally devoid of nausea/emesis. This study developed monomeric peptide agonists of glucagon-like peptide 1 receptor (GLP-1R) and neuropeptide Y2 receptor (Y2-R) based on exendin-4 (Ex-4) and PYY_3–36. A novel peptide, GEP44, was obtained via in vitro receptor screens, insulin secretion in islets, stability assays, and in vivo rat and shrew studies of glucoregulation, weight loss, nausea, and emesis. GEP44 in lean and diet-induced obese rats produced greater reduction in body weight compared to Ex-4 without triggering nausea associated behavior. Studies in the shrew demonstrated a near absence of emesis for GEP44 in contrast to Ex-4. Collectively, these data demonstrate that targeting GLP-1R and Y2-R with chimeric single peptides offers a route to new glucoregulatory treatments that are well-tolerated and have improved weight loss when compared directly to Ex-4.

The Mechanism of NEDD8 Activation of CUL5 Ubiquitin E3 Ligases

Cullin RING E3 ligases (CRLs) ubiquitylate hundreds of important cellular substrates. Here we have assembled and purified the Ankyrin repeat and SOCS Box protein 9 CUL5 RBX2 ligase (ASB9-CRL) in vitro and show how it ubiquitylates one of its substrates, CKB. CRLs occasionally collaborate with RING between RING E3 ligases (RBRLs), and indeed, mass spectrometry analysis showed that CKB is specifically ubiquitylated by the ASB9-CRL-ARIH2-UBE2L3 complex. Addition of other E2s such as UBE2R1 or UBE2D2 contributes to polyubiquitylation but does not alter the sites of CKB ubiquitylation. Hydrogen–deuterium exchange mass spectrometry (HDX-MS) analysis revealed that CUL5 neddylation allosterically exposes its ARIH2 binding site, promoting high-affinity binding, and it also sequesters the NEDD8 E2 (UBE2F) binding site on RBX2. Once bound, ARIH2 helices near the Ariadne domain active site are exposed, presumably relieving its autoinhibition. These results allow us to propose a model of how neddylation activates ASB-CRLs to ubiquitylate their substrates.

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ARIH2 is required for ASB9CRL to polyubiquitylate 4/18 lysines on one creatine kinase subunit.

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HDX-MS reveals long-range allosteric opening of a cleft in CUL5 where the ARIH2 RBRL binds.

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HDX-MS reveals that neddylation of CUL5 alters the RBX2 conformation away from binding the E2∼NEDD8.

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HDX-MS reveals opening of the ARIH2 active site upon binding CUL5, thus releasing its autoinhibition.

Binding Ensembles of p53-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations

Designing peptide inhibitors of the p53-MDM2 interaction against cancer is of wide interest. Computational modeling and virtual screening are a well established step in the rational design of small molecules. But they face challenges for binding flexible peptide molecules that fold upon binding. We look at the ability of five different peptides, three of which are intrinsically disordered, to bind to MDM2 with a new Bayesian inference approach (MELD × MD). The method is able to capture the folding upon binding mechanism and differentiate binding preferences between the five peptides. Processing the ensembles with statistical mechanics tools depicts the most likely bound conformations and hints at differences in the binding mechanism. Finally, the study shows the importance of capturing two driving forces to binding in this system: the ability of peptides to adopt bound conformations (ΔGconformation) and the interaction between interface residues (ΔGinteraction).

ITScore-NL: An Iterative Knowledge-Based Scoring Function for Nucleic Acid-Ligand Interactions

Nucleic acid-ligand complexes underlie numerous cellular processes, such as gene function expression and regulation, in which their three-dimensional structures are important to understand their functions and thus to develop therapeutic interventions. Given the high cost and technical difficulties in experimental methods, computational methods such as molecular docking have been actively used to investigate nucleic acid-ligand interactions in which an accurate scoring function is crucial. However, because of the limited number of experimental nucleic acid-ligand binding data and structures, the scoring function development for nucleic acid-ligand interactions falls far behind that for protein-protein and protein-ligand interactions. Here, based on our statistical mechanics-based iterative approach, we have developed an iterative knowledge-based scoring function for nucleic acid-ligand interactions, named as ITScore-NL, by explicitly including stacking and electrostatic potentials. Our ITScore-NL scoring function was extensively evaluated for its ability in the binding mode and binding affinity predictions on three diverse test sets and compared with state-of-the-art scoring functions. Overall, ITScore-NL obtained significantly better performance than the other 12 scoring functions and predicted near-native poses with rmsd ≤ 1.5 Å for 71.43% of the cases when the top three binding modes were considered and a good correlation of R = 0.64 in binding affinity prediction on the large test set of 77 nucleic acid-ligand complexes. These results suggested the accuracy of ITScore-NL and the necessity of explicitly including stacking and electrostatic potentials.

New Putative Antimicrobial Candidates: In silico Design of Fish-Derived Antibacterial Peptide-Motifs

Antimicrobial resistance remains a great threat to global health. In response to the World Health Organizations’ global call for action, nature has been explored for novel and safe antimicrobial candidates. To date, fish have gained recognition as potential source of safe, broad spectrum and effective antimicrobial therapeutics. The use of computational methods to design antimicrobial candidates of industrial application has however, been lagging behind. To fill the gap and contribute to the current fish-derived antimicrobial peptide repertoire, this study used Support Vector Machines algorithm to fish out fish-antimicrobial peptide-motif candidates encrypted in 127 peptides submitted at the Antimicrobial Peptide Database (APD3), steered by their physico-chemical characteristics (i.e., positive net charge, hydrophobicity, stability, molecular weight and sequence length). The best two novel antimicrobial peptide-motifs (A15_B, A15_E) with the lowest instability index (−28.25, −22.49, respectively) and highest isoelectric point (pI) index (10.48 for each) were selected for further analysis. Their 3D structures were predicted using I-TASSER and PEP-FOLD servers while ProSA, PROCHECK, and ANOLEA were used to validate them. The models predicted by I-TASSER were found to be better than those predicted by PEP-FOLD upon validation. Two I-TASSER models with the lowest c-score of −0.10 and −0.30 for A15_B and A15_E peptide-motifs, respectively, were selected for docking against known bacterial-antimicrobial target-proteins retrieved from protein databank (PDB). Carbapenam-3-carboxylate synthase (PDB ID; 4oj8) yielded the lowest docking energy (−8.80 and −7.80 Kcal/mol) against motif A15_B and A15_E, respectively, using AutoDock VINA. Further, in addition to Carbapenam-3-carboxylate synthase, these peptides (A15_B and A15_E) were found to as well bind to membrane protein (PDB ID: 1by3) and Carbapenem synthetase (PDB: 1q15) when ClusPro and HPEPDOCK tools were used. The membrane protein yielded docking energy scores (DES): −290.094, −270.751; coefficient weight (CW): −763.6, 763.3 for A15_B and A15_E) whereas, Carbapenem synthetase (PDB: 1q15) had a DES of −236.802, −262.75 and a CW of −819.7, −829.7 for peptides A15_B and A15_E, respectively. Motif A15_B of amino acid positions 2–19 in Pleurocidin exhibited the strongest in silico antimicrobial potentials. This segment could be a good biological candidate of great application in pharmaceutical industries as an antimicrobial drug candidate.

Investigation of beta-lactoglobulin derived bioactive peptides against SARS-CoV-2 (COVID-19): In silico analysis

The coronavirus disease of 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which started in late 2019 in Wuhan, China spread to the whole world in a short period of time, and thousands of people have died due to this epidemic. Although scientists have been searching for methods to manage SARS-CoV-2, there is no specific medication against COVID-19 as of yet. Two main approaches should be followed in the treatment of SARS-CoV-2; one of which is to neutralize the virus, and the other is to inhibit the host cell membrane receptors, where SARS-CoV-2 will bind. In this study, peptides derived from beta-lactoglobulin, which inactivates both the virus and its receptors in the host cell, were identified using computer-based in silico analysis. The beta-lactoglobulin derived peptides used in this study were obtained by the treatment of goat milk whey fraction with trypsin. The structure of the peptides was characterized by the liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF/MS), and six beta-lactoglobulin derived peptides were selected as candidate peptides. Subsequently, the effects of peptides on SARS-CoV-2 and host cells were identified using virtual screening. According to the results of this in silico analysis, Ala-Leu-Pro-Met-His-Ile-Arg (ALMPHIR) and Ile-Pro-Ala-Val-Phe-Lys (IPAVFK) peptides were evaluated as potential candidates to be used in the treatment of SARS-CoV-2 after the future in vitro and in vivo studies.

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This in silico study used a hypothesis-driven peptidomics strategy.

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The beta-lactoglobulin derived peptides have potential effects against SARS-CoV-2.

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ALMPHIR and IPAVFK are potential candidates among these peptides.

Human papillomavirus E6: Host cell receptor, GRP78, binding site prediction

Human papillomavirus (HPV) is the main cervical cancer-promoting element that is transmitted through sexual routes. Anal, head, and throat cancers are also reported to be accompanied by HPV infection. E6 is one of the HPV nonstructural proteins, which is responsible for cell differentiation by targeting tumor suppressor genes, p105Rb and p53. E6 was reported to be stabilized by two chaperone proteins; glucose-regulated protein 78 (GRP78) and heat shock protein 90. GRP78 is responsible for the unfolded protein response of the cells, and it was reported to be upregulated in many cancers, including cervical cancer. It was reported that knocking out GRP78 destabilizes E6 leading to faster degradation of E6 in vivo. The current work predicts the possible binding mode between E6 and GRP78 based on sequence and structural similarities.

Potential of Plant Proteins Digested In Silico by Gastrointestinal Enzymes as Nutritional Supplement for COVID-19 Patients

Currently, no specific drug and vaccine are available for the new coronavirus SARS-CoV-2, and nutritional supplementation should be helpful. This study tried to provide reference for protein supplementation. Specifically, in silico method was employed to simulate protein degradation by gastrointestinal enzymes and to produce a large number of active peptides, then, the binding ability of these peptides to SARS-CoV-2 spike protein receptor-binding domain (RBD) was evaluated. The results showed that wheat-derived alpha/beta-gliadin, oat-derived avenin, and ribulose bisphosphate carboxylase small chain of different origin could be good protein source in generating potent binders to SARS-CoV-2 spike RBD. In addition, some high-affinity oligopeptides (such as PISCR, VQVVN, PQQQF, etc.) were identified as potential binders of SARS-CoV-2 spike RBD. In summary, a number of plant proteins could be helpful for COVID-19 patients when supplemented with these proteins, the identified oligopeptides could be used as lead compound to design potential entry inhibitors against SARS-CoV-2.

Peptide and peptide-based inhibitors of SARS-CoV-2 entry

To date, no effective vaccines or therapies are available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pandemic agent of the coronavirus disease 2019 (COVID-19). Due to their safety, efficacy and specificity, peptide inhibitors hold great promise for the treatment of newly emerging viral pathogens. Based on the known structures of viral proteins and their cellular targets, antiviral peptides can be rationally designed and optimized. The resulting peptides may be highly specific for their respective targets and particular viral pathogens or exert broad antiviral activity. Here, we summarize the current status of peptides inhibiting SARS-CoV-2 entry and outline the strategies used to design peptides targeting the ACE2 receptor or the viral spike protein and its activating proteases furin, transmembrane serine protease 2 (TMPRSS2), or cathepsin L. In addition, we present approaches used against related viruses such as SARS-CoV-1 that might be implemented for inhibition of SARS-CoV-2 infection.

A multiple peptides vaccine against COVID-19 designed from the nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the immunoinformatics approach

Due to the current Coronavirus (COVID-19) pandemic, the rapid discovery of a safe and effective vaccine is an essential issue. Consequently, this study aims to predict a potential COVID-19 peptide-based vaccine utilizing the Nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the Immunoinformatics approach. To achieve this goal, several Immune Epitope Database (IEDB) tools, molecular docking, and safety prediction servers were used. According to the results, The Spike peptide SQCVNLTTRTQLPPAYTNSFTRGVY is predicted to have the highest binding affinity to the B-Cells. The Spike peptide FTISVTTEI has the highest binding affinity to the Major Histocompatibility Complex class 1 (MHC I) Human Leukocyte Allele HLA-B*1503 (according to the MDockPeP and HPEPDOCK servers, docking scores were −153.9 and −229.356, respectively). The Nucleocapsid peptides KTFPPTEPK and RWYFYYLGTGPEAGL have the highest binding affinity to the MHC I HLA-A0202 allele and the three the Major Histocompatibility Complex class 2 (MHC II) Human Leukocyte Allele HLA-DPA1*01:03/DPB1*02:01, HLA-DQA1*01:02/DQB1-*06:02, HLA-DRB1, respectively. Docking scores of peptide KTFPPTEPK were −153.9 and −220.876. In contrast, docking scores of peptide RWYFYYLGTGPEAGL were ranged from 218 to 318. Furthermore, those peptides were predicted as non-toxic and non-allergen. Therefore, the combination of those peptides is predicted to stimulate better immunological responses with respectable safety.

Cardiovascular Active Peptides of Marine Origin with ACE Inhibitory Activities: Potential Role as Anti-Hypertensive Drugs and in Prevention of SARS-CoV-2 Infection

Growing interest in hypertension-one of the main factors characterizing the cardiometabolic syndrome (CMS)-and anti-hypertensive drugs raised from the emergence of a new coronavirus, SARS-CoV-2, responsible for the COVID19 pandemic. The virus SARS-CoV-2 employs the Angiotensin-converting enzyme 2 (ACE2), a component of the RAAS (Renin-Angiotensin-Aldosterone System) system, as a receptor for entry into the cells. Several classes of synthetic drugs are available for hypertension, rarely associated with severe or mild adverse effects. New natural compounds, such as peptides, might be useful to treat some hypertensive patients. The main feature of ACE inhibitory peptides is the location of the hydrophobic residue, usually Proline, at the C-terminus. Some already known bioactive peptides derived from marine resources have potential ACE inhibitory activity and can be considered therapeutic agents to treat hypertension. Peptides isolated from marine vertebrates, invertebrates, seaweeds, or sea microorganisms displayed important biological activities to treat hypertensive patients. Here, we reviewed the anti-hypertensive activities of bioactive molecules isolated/extracted from marine organisms and discussed the associated molecular mechanisms involved. We also examined ACE2 modulation in sight of SARS2-Cov infection prevention.

Computational structure modeling for diverse categories of macromolecular interactions

Computational protein-protein docking is one of the most intensively studied topics in structural bioinformatics. The field has made substantial progress through over three decades of development. The development began with methods for rigid-body docking of two proteins, which have now been extended in different directions to cover the various macromolecular interactions observed in a cell. Here, we overview the recent developments of the variations of docking methods, including multiple protein docking, peptide-protein docking, and disordered protein docking methods.

Topology-independent and global protein structure alignment through an FFT-based algorithm

MOTIVATION

Protein structure alignment is one of the fundamental problems in computational structure biology. A variety of algorithms have been developed to address this important issue in the past decade. However, due to their heuristic nature, current structure alignment methods may suffer from suboptimal alignment and/or over-fragmentation and thus lead to a biologically wrong alignment in some cases. To overcome these limitations, we have developed an accurate topology-independent and global structure alignment method through an FFT-based exhaustive search algorithm, which is referred to as FTAlign.

RESULTS

Our FTAlign algorithm was extensively tested on six commonly used datasets and compared with seven state-of-the-art structure alignment approaches, TMalign, DeepAlign, Kpax, 3DCOMB, MICAN, SPalignNS and CLICK. It was shown that FTAlign outperformed the other methods in reproducing manually curated alignments and obtained a high success rate of 96.7 and 90.0% on two gold-standard benchmarks, MALIDUP and MALISAM, respectively. Moreover, FTAlign also achieved the overall best performance in terms of biologically meaningful structure overlap (SO) and TMscore on both the sequential alignment test sets including MALIDUP, MALISAM and 64 difficult cases from HOMSTRAD, and the non-sequential sets including MALIDUP-NS, MALISAM-NS, 199 topology-different cases, where FTAlign especially showed more advantage for non-sequential alignment. Despite its global search feature, FTAlign is also computationally efficient and can normally complete a pairwise alignment within one second.

AVAILABILITY AND IMPLEMENTATION

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

Insights into the Microscopic Structure of RNF4-SIM-SUMO Complexes from MD Simulations

Post-translational modification with one of the isoforms of the small ubiquitin-like modifier (SUMO) affects thousands of proteins in the human proteome. The binding of SUMO to SUMO interacting motifs (SIMs) can translate the SUMOylation event into functional consequences. The E3 ubiquitin ligase RNF4 contains multiple SIMs and connects SUMOylation to the ubiquitin pathway. SIM2 and SIM3 of RNF4 were shown to be the most important motifs to recognize SUMO chains. However, the study of SIM-SUMO complexes is complicated by their typically low affinity and variable binding of the SIMs in parallel and antiparallel orientations. We investigated properties of complexes formed by SUMO3 with peptides containing either SIM2 or SIM3 using molecular dynamics simulations. The affinities of the complexes were determined using a state-of-the-art free energy protocol and were found to be in good agreement with experimental data, thus corroborating our method. Long unrestrained simulations allowed a new interpretation of experimental results regarding the structure of the SIM-SUMO interface. We show that both SIM2 and SIM3 bind SUMO3 in parallel and antiparallel orientations and identified main interaction sites for acidic residues flanking the SIM. We noticed unusual SIM-SUMO interfaces in a previously reported NMR structure (PDB: 2mp2) of a complex formed by a SUMO3 dimer with the bivalent SIM2-SIM3 peptide. Computational determination of the individual SIM-SUMO affinities based on these structural arrangements yielded significantly higher dissociation constants. To our knowledge, our approach adds new opportunities to characterize individual SIM-SUMO complexes and suggests that further studies will be necessary to understand these interactions when occurring in multivalent form.

The AAA + ATPase valosin-containing protein (VCP)/p97/Cdc48 interaction network in Leishmania

Valosin-containing protein (VCP)/p97/Cdc48 is an AAA + ATPase associated with many ubiquitin-dependent cellular pathways that are central to protein quality control. VCP binds various cofactors, which determine pathway selectivity and substrate processing. Here, we used co-immunoprecipitation and mass spectrometry studies coupled to in silico analyses to identify the Leishmania infantum VCP (LiVCP) interactome and to predict molecular interactions between LiVCP and its major cofactors. Our data support a largely conserved VCP protein network in Leishmania including known but also novel interaction partners. Network proteomics analysis confirmed LiVCP-cofactor interactions and provided novel insights into cofactor-specific partners and the diversity of LiVCP complexes, including the well-characterized VCP-UFD1-NPL4 complex. Gene Ontology analysis coupled with digitonin fractionation and immunofluorescence studies support cofactor subcellular compartmentalization with either cytoplasmic or organellar or vacuolar localization. Furthermore, in silico models based on 3D homology modeling and protein-protein docking indicated that the conserved binding modules of LiVCP cofactors, except for NPL4, interact with specific binding sites in the hexameric LiVCP protein, similarly to their eukaryotic orthologs. Altogether, these results allowed us to build the first VCP protein interaction network in parasitic protozoa through the identification of known and novel interacting partners potentially associated with distinct VCP complexes.

Development of an LDL Receptor-Targeted Peptide Susceptible to Facilitate the Brain Access of Diagnostic or Therapeutic Agents

Blood-brain barrier (BBB) crossing and brain penetration are really challenging for the delivery of therapeutic agents and imaging probes. The development of new crossing strategies is needed, and a wide range of approaches (invasive or not) have been proposed so far. The receptor-mediated transcytosis is an attractive mechanism, allowing the non-invasive penetration of the BBB. Among available targets, the low-density lipoprotein (LDL) receptor (LDLR) shows favorable characteristics mainly because of the lysosome-bypassed pathway of LDL delivery to the brain, allowing an intact discharge of the carried ligand to the brain targets. The phage display technology was employed to identify a dodecapeptide targeted to the extracellular domain of LDLR (ED-LDLR). This peptide was able to bind the ED-LDLR in the presence of natural ligands and dissociated at acidic pH and in the absence of calcium, in a similar manner as the LDL. In vitro, our peptide was endocytosed by endothelial cells through the caveolae-dependent pathway, proper to the LDLR route in BBB, suggesting the prevention of its lysosomal degradation. The in vivo studies performed by magnetic resonance imaging and fluorescent lifetime imaging suggested the brain penetration of this ED-LDLR-targeted peptide.

MDockPeP: A Web Server for Blind Prediction of Protein-Peptide Complex Structures

Protein-peptide interactions mediate a wide range of important cellular tasks. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. Recently, we developed a docking-based method for predicting protein-peptide complex structures, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. The produced modes are then evaluated with a statistical potential-based scoring function. The method has been implemented into an online server, MDockPeP server, which is freely available at http://zougrouptoolkit.missouri.edu/mdockpep . The server can be used for protein-peptide complex structure prediction. The server can also be used for initial-stage sampling of the protein-peptide binding modes for computational-demanding simulation or docking methods.