HawkRank: a new scoring function for protein-protein docking based on weighted energy terms

Designing a multi-epitope subunit vaccine against Orf virus using molecular docking and molecular dynamics

Orf virus (ORFV) is an acute contact, epitheliotropic, zoonotic, and double-stranded DNA virus that causes significant economic losses in the livestock industry. The objective of this study is to design an immunoinformatics-based multi-epitope subunit vaccine against ORFV. Various immunodominant cytotoxic T lymphocytes (CTL), helper T lymphocytes (HTL), and B-cell epitopes from the B2L, F1L, and 080 protein of ORFV were selected and linked by short connectors to construct a multi-epitope subunit vaccine. Immunogenicity was enhanced by adding an adjuvant β-defensin to the N-terminal of the vaccine using the EAAAK linker. The vaccine exhibited a significant degree of antigenicity and solubility, without allergenicity or toxicity. The 3D formation of the vaccine was subsequently anticipated, improved, and verified. The optimized model exhibited a lower Z-score of -4.33, indicating higher quality. Molecular docking results demonstrated that the vaccine strongly binds to TLR2 and TLR4. Molecular dynamics results indicated that the docked vaccine-TLR complexes were stable. Immune simulation analyses further confirmed that the vaccine can induce a marked increase in IgG and IgM antibody titers, and elevated levels of IFN-γ and IL-2. Finally, the optimized DNA sequence of the vaccine was cloned into the vector pET28a (+) for high expression in the E.coli expression system. Overall, the designed multi-epitope subunit vaccine is highly stable and can induce robust humoral and cellular immunity, making it a promising vaccine candidate against ORFV.

Multi-epitope peptide vaccines targeting dengue virus serotype 2 created via immunoinformatic analysis

The Middle East has witnessed a greater spread of infectious Dengue viruses, with serotype 2 (DENV-2) being the most prevalent form. Through this work, multi-epitope peptide vaccines against DENV-2 that target E and nonstructural (NS1) proteins were generated through an immunoinformatic approach. MHC class I and II and LBL epitopes among NS1 and envelope E proteins sequences were predicted and their antigenicity, toxicity, and allergenicity were investigated. Studies of the population coverage denoted the high prevalence of NS1 and envelope-E epitopes among different countries where DENV-2 endemic. Further, both the CTL and HTL epitopes retrieved from NS1 epitopes exhibited high conservancies' percentages with other DENV serotypes (1, 3, and 4). Three vaccine constructs were created and the expected immune responses for the constructs were estimated using C-IMMSIM and HADDOCK (against TLR 2,3,4,5, and 7). Molecular dynamics simulation for vaccine construct 2 with TLR4 denoted high binding affinity and stability of the construct with the receptor which might foretell favorable in vivo interaction and immune responses.

Unveiling the Binding between the Armadillo-Repeat Domain of Plakophilin 1 and the Intrinsically Disordered Transcriptional Repressor RYBP

Plakophilin 1 (PKP1), a member of the p120ctn subfamily of the armadillo (ARM)-repeat-containing proteins, is an important structural component of cell-cell adhesion scaffolds although it can also be ubiquitously found in the cytoplasm and the nucleus. RYBP (RING 1A and YY1 binding protein) is a multifunctional intrinsically disordered protein (IDP) best described as a transcriptional regulator. Both proteins are involved in the development and metastasis of several types of tumors. We studied the binding of the armadillo domain of PKP1 (ARM-PKP1) with RYBP by using in cellulo methods, namely immunofluorescence (IF) and proximity ligation assay (PLA), and in vitro biophysical techniques, namely fluorescence, far-ultraviolet (far-UV) circular dichroism (CD), and isothermal titration calorimetry (ITC). We also characterized the binding of the two proteins by using in silico experiments. Our results showed that there was binding in tumor and non-tumoral cell lines. Binding in vitro between the two proteins was also monitored and found to occur with a dissociation constant in the low micromolar range (~10 μM). Finally, in silico experiments provided additional information on the possible structure of the binding complex, especially on the binding ARM-PKP1 hot-spot. Our findings suggest that RYBP might be a rescuer of the high expression of PKP1 in tumors, where it could decrease the epithelial-mesenchymal transition in some cancer cells.

Molecular docking and dynamics simulation studies uncover the host-pathogen protein-protein interactions in Penaeus vannamei and Vibrio parahaemolyticus

Shrimp aquaculture contributes significantly to global economic growth, and the whiteleg shrimp, Penaeus vannamei, is a leading species in this industry. However, Vibrio parahaemolyticus infection poses a major challenge in ensuring the success of P. vannamei aquaculture. Despite its significance in this industry, the biological knowledge of its pathogenesis remains unclear. Hence, this study was conducted to identify the interaction sites and binding affinity between several immune-related proteins of P. vannamei with V. parahaemolyticus proteins associated with virulence factors. Potential interaction sites and the binding affinity between host and pathogen proteins were identified using molecular docking and dynamics (MD) simulation. The P. vannamei-V. parahaemolyticus protein-protein interaction of Complex 1 (Ferritin-HrpE/YscL family type III secretion apparatus protein), Complex 2 (Protein kinase domain-containing protein-Chemotaxis CheY protein), and Complex 3 (GPCR-Chemotaxis CheY protein) was found to interact with -4319.76, -5271.39, and -4725.57 of the docked score and the formation of intermolecular bonds at several interacting residues. The docked scores of Complex 1, Complex 2, and Complex 3 were validated using MD simulation analysis, which revealed these complexes greatly contribute to the interactions between P. vannamei and V. parahaemolyticus proteins, with binding free energies of -22.50 kJ/mol, -30.20 kJ/mol, and -26.27 kJ/mol, respectively. This finding illustrates the capability of computational approaches to search for molecular binding sites between host and pathogen, which could increase the knowledge of Vibrio spp. infection on shrimps, which then can be used to assist in the development of effective treatment.

Docking Foundations: From Rigid to Flexible Docking

Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.

Identification of thrombin inhibiting antithrombin-III like protein from Punica granatum using in silico approach and in vitro validation of thrombin inhibition activity in crude protein

Thrombosis is characterized by the formation of clots in the blood vessels. Antithrombin-III deficiency in the blood causes thrombus formation. Supplementing antithrombin-III may serve as anticoagulant therapy. In the present studies, an antithrombin like Protein from Punica granatum has been identified and characterized using in silico approach. Based on sequence homology, an ALPP was selected depending upon its highest binding affinity of -41.28 kcal/mol with thrombin. Thrombin structure complexed with ALPP was docked with TAME using AutoDock Vina. No binding was observed for TAME at Ser195 of thrombin. MD simulation (50 ns) was performed to evaluate the flexibility and stability of docked complexes. In vitro assays with crude protein showed 78% thrombin inhibition at 5 µg and calculated IC50 value was 0.188 µg. The presence of thrombin inhibitors in crude protein was also confirmed by reverse zymography. Thus, it is very likely that the protein identified from P. granatum may act as thrombin inhibitor.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Assessment of neutralization susceptibility of Omicron subvariants XBB.1.5 and BQ.1.1 against broad-spectrum neutralizing antibodies through epitopes mapping

The emergence of new variants of the SARS-CoV-2 virus has posed a significant challenge in developing broadly neutralizing antibodies (nAbs) with guaranteed therapeutic potential. Some nAbs, such as Sotrovimab, have exhibited varying levels of efficacy against different variants, while others, such as Bebtelovimab and Bamlanivimab-etesevimab are ineffective against specific variants, including BQ.1.1 and XBB. This highlights the urgent need for developing broadly active monoclonal antibodies (mAbs) providing prophylactic and therapeutic benefits to high-risk patients, especially in the face of the risk of reinfection from new variants. Here, we aimed to investigate the feasibility of redirecting existing mAbs against new variants of SARS-CoV-2, as well as to understand how BQ.1.1 and XBB.1.5 can evade broadly neutralizing mAbs. By mapping epitopes and escape sites, we discovered that the new variants evade multiple mAbs, including FDA-approved Bebtelovimab, which showed resilience against other Omicron variants. Our approach, which included simulations, endpoint free energy calculation, and shape complementarity analysis, revealed the possibility of identifying mAbs that are effective against both BQ.1.1 and XBB.1.5. We identified two broad-spectrum mAbs, R200-1F9 and R207-2F11, as potential candidates with increased binding affinity to XBB.1.5 and BQ.1.1 compared to the reference (Wu01) strain. Additionally, we propose that these mAbs do not interfere with Angiotensin Converting Enzyme 2 (ACE2) and bind to conserved epitopes on the receptor binding domain of Spike that are not-overlapping, potentially providing a solution to neutralize these new variants either independently or as part of a combination (cocktail) treatment.

Rational strategies for enhancing mAb binding to SARS-CoV-2 variants through CDR diversification and antibody-escape prediction

Since the emergence of SARS-CoV-2, dozens of variants of interest and half a dozen variants of concern (VOCs) have been documented by the World Health Organization. The emergence of these VOCs due to the continuous evolution of the virus is a major concern for COVID-19 therapeutic antibodies and vaccines because they are designed to target prototype/previous strains and lose effectiveness against new VOCs. Therefore, there is a need for time- and cost-effective strategies to estimate the immune escape and redirect therapeutic antibodies against newly emerging variants. Here, we computationally predicted the neutralization escape of the SARS-CoV-2 Delta and Omicron variants against the mutational space of RBD-mAbs interfaces. Leveraging knowledge of the existing RBD-mAb interfaces and mutational space, we fine-tuned and redirected CT-p59 (Regdanvimab) and Etesevimab against the escaped variants through complementarity-determining regions (CDRs) diversification. We identified antibodies against the Omicron lineage BA.1 and BA.2 and Delta variants with comparable or better binding affinities to that of prototype Spike. This suggests that CDRs diversification by hotspot grafting, given an existing insight into the Ag-Abs interface, is an exquisite strategy to redirect antibodies against preselected epitopes and combat the neutralization escape of emerging SARS-CoV-2 variants.

Computational designing of a novel subunit vaccine for human cytomegalovirus by employing the immunoinformatics framework

Human cytomegalovirus (HCMV) is a widespread virus that can cause serious and irreversible neurological damage in newborns and even death in children who do not have the access to much-needed medications. While some vaccines and drugs are found to be effective against HCMV, their extended use has given rise to dose-limiting toxicities and the development of drug-resistant mutants among patients. Despite half a century's worth of research, the lack of a licensed HCMV vaccine heightens the need to develop newer antiviral therapies and vaccine candidates with improved effectiveness and reduced side effects. In this study, the immunoinformatics approach was utilized to design a potential polyvalent epitope-based vaccine effective against the four virulent strains of HCMV. The vaccine was constructed using seven CD8+ cytotoxic T lymphocytes epitopes, nine CD4+ helper T lymphocyte epitopes, and twelve linear B-cell lymphocyte epitopes that were predicted to be antigenic, non-allergenic, non-toxic, fully conserved, and non-human homologous. Subsequently, molecular docking study, protein-protein interaction analysis, molecular dynamics simulation (including the root mean square fluctuation (RMSF) and root mean square deviation (RMSD)), and immune simulation study rendered promising results assuring the vaccine to be stable, safe, and effective. Finally, in silico cloning was conducted to develop an efficient mass production strategy of the vaccine. However, further in vitro and in vivo research studies on the proposed vaccine are required to confirm its safety and efficacy.Communicated by Ramaswamy H. Sarma.

EhSir2c, a Sir2 homolog from the human pathogen Entamoeba histolytica interacts with a DNA repair protein, EhRAD23: Protein-protein interaction, docking and functional study

Chromosome segregation is a crucial phenomenon in the cell cycle and defects in genome segregation result in an abnormality in various cellular events. Unlike higher eukaryotes, chromosome segregation and a number of cell cycle events are unusual in the protozoan parasite Entamoeba histolytica (E. histolytica). Characterization of Sir2 proteins from E. histolytica may reveal its unique cellular events as they play role in diverse cellular processes including chromosome segregation. E. histolytica has four homologs of Sir2 proteins. EhSir2a and EhSir2b show sequence similarity towards eukaryotic Sir2 homologs, whereas EhSir2c and EhSir2d are more like prokaryotic sirtuins. Using both computational and experimental methods, EhSir2c has been characterized in this study. The three-dimensional structure of EhSir2c is predicted by homology modelling. The protein interactors of EhSir2c have been identified by yeast-two-hybrid screening against the cDNA library of E. histolytica. We have identified a novel interactor, EhRAD23 which is a homolog of UV excision repair protein RAD23. The interaction of EhSir2c and EhRAD23 was validated by pull-down assay. UV-C irradiation up-regulates the relative expression of EhSir2c, suggesting the necessity of EhSir2c in UV-induced stress in this parasite.Communicated by Ramaswamy H. Sarma.

Immunoinformatics-guided designing and in silico analysis of epitope-based polyvalent vaccines against multiple strains of human coronavirus (HCoV)

OBJECTIVES

The group of human coronaviruses (HCoVs) consists of some highly pathogenic viruses that have caused several outbreaks in the past. The newly emerged strain of HCoV, the SARS-CoV-2 is responsible for the recent global pandemic that has already caused the death of hundreds of thousands of people due to the lack of effective therapeutic options.

METHODS

In this study, immunoinformatics methods were used to design epitope-based polyvalent vaccines which are expected to be effective against four different pathogenic strains of HCoV i.e., HCoV-OC43, HCoV-SARS, HCoV-MERS, and SARS-CoV-2.

RESULTS

The constructed vaccines consist of highly antigenic, non-allergenic, nontoxic, conserved, and non-homologous T-cell and B-cell epitopes from all the four viral strains. Therefore, they should be able to provide strong protection against all these strains. Protein-protein docking was performed to predict the best vaccine construct. Later, the MD simulation and immune simulation of the best vaccine construct also predicted satisfactory results. Finally, in silico cloning was performed to develop a mass production strategy of the vaccine.

CONCLUSION

If satisfactory results are achieved in further in vivo and in vitro studies, then the vaccines designed in this study might be effective as preventative measures against the selected HCoV strains.

Distribution of intra-host variations and mutations in the genomes of SARS-CoV-2 and their implications on detection and therapeutics

The ongoing circulation of SARS-CoV-2 variants of concern (VOCs) has caused global concerns, because VOCs could escape current vaccines, antiviral drugs, and diagnosis. Analyzing mutations and intra-host diversities in different and widespread VOCs can provide important insights to virus adaptive evolution and validity of vaccines, antiviral drugs, and diagnosis. In this study, by analyzing 1744 high-throughput sequencing data for intra-host single-nucleotide variations (iSNVs) and 3,668,205 genome sequences for mutations in different VOCs, it was found that Omicron variant is still evolving at high speed, especially having high iSNVs frequency in its S and N genes. The efficacies of antibodies or detection primers targeting these two genes are at high risks to be invalid. Instead, highly conserved regions such as NSP8 gene could be better therapeutic and detection targets. Furthermore, mutations in later VOCs could be traced to the minor alleles in the previous variant samples such as Alpha and Delta in different countries. Finally, it was found that mutations C14408T in RdRp and A18163G in NSP14 gene might be associated with the higher genetic diversity in Omicron. Our findings not only contribute to understanding the adaptive evolution of SARS-CoV-2 VOCs, but also provide useful information for both drugs and diagnostic kits development.

Immunoinformatics Approach to Design Novel Subunit Vaccine against the Epstein-Barr Virus

Epstein-Barr virus (EBV) is a lymphotropic virus responsible for numerous epithelial and lymphoid cell malignancies, including gastric carcinoma, Hodgkin's lymphoma, nasopharyngeal carcinoma, and Burkitt lymphoma. Hundreds of thousands of people worldwide get infected with this virus, and in most cases, this viral infection leads to cancer. Although researchers are trying to develop potential vaccines and drug therapeutics, there is still no effective vaccine to combat this virus. In this study, the immunoinformatics approach was utilized to develop a potential multiepitope subunit vaccine against the two most common subtypes of EBV, targeting three of their virulent envelope glycoproteins. Eleven cytotoxic T lymphocyte (CTL) epitopes, 11 helper T lymphocyte (HTL) epitopes, and 10 B-cell lymphocyte (BCL) epitopes were predicted to be antigenic, nonallergenic, nontoxic, and fully conserved among the two subtypes, and nonhuman homologs were used for constructing the vaccine after much analysis. Later, further validation experiments, including molecular docking with different immune receptors (e.g., Toll-like receptors [TLRs]), molecular dynamics simulation analyses (including root means square deviation [RMSD], root mean square fluctuation [RMSF], radius of gyration [Rg], principal-component analysis [PCA], dynamic cross-correlation [DCC], definition of the secondary structure of proteins [DSSP], and Molecular Mechanics Poisson-Boltzmann Surface Area [MM-PBSA]), and immune simulation analyses generated promising results, ensuring the safe and stable response of the vaccine with specific immune receptors after potential administration within the human body. The vaccine's high binding affinity with TLRs was revealed in the docking study, and a very stable interaction throughout the simulation proved the potential high efficacy of the proposed vaccine. Further, in silico cloning was also conducted to design an efficient mass production strategy for future bulk industrial vaccine production. IMPORTANCE Epstein-Barr virus (EBV) vaccines have been developing for over 30 years, but polyphyletic and therapeutic vaccines have failed to get licensed. Our vaccine surpasses the limitations of many such vaccines and remains very promising, which is crucial because the infection rate is higher than most viral infections, affecting a whopping 90% of the adult population. One of the major identifications covers a holistic analysis of populations worldwide, giving us crucial information about its effectiveness for everyone's unique immunological system. We targeted three glycoproteins that enhance the virulence of the virus to design an epitope-based polyvalent vaccine against two different strains of EBV, type 1 and 2. Our methodology in this study is nonconventional yet swift to show effective results while designing vaccines.

Studying protein-protein interaction through side-chain modeling method OPUS-Mut

Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65) and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.

An Immunoinformatic Strategy to Develop New Mycobacterium tuberculosis Multi-epitope Vaccine

Mycobacterium tuberculosis causes a life-threatening disease known as tuberculosis (TB). In 2021, tuberculosis was the second cause of death after COVID-19 among infectious diseases. Latent life cycle and development of multidrug resistance in one hand and lack of an effective vaccine in another hand have made TB a global health issue. Here, a multi-epitope vaccine have been designed against TB using five new antigenic protein and immunoinformatic tools. To do so, immunodominant MHC-I/MHC-II binding epitopes of Rv2346, Rv2347, Rv3614, Rv3615 and Rv2031 antigenic proteins have been selected using advanced computational procedures. The vaccine was designed by linking ten epitopes from the antigenic proteins and flagellin and TpD as adjuvant. Three-dimensional (3D) structure of the vaccine was modeled, was refined and was evaluated using bioinformatics tools. The 3D structure of the vaccine was docked into the toll-like-receptors (TLR3, 4, 8) to evaluate potential interaction between the vaccine and TLRs. Evaluation of immunological and physicochemical properties of the constructed vaccine have demonstrated the vaccine construct can induce significant humoral and cellular immune responses, the vaccine is non-allergenic and can be recognized by TLR proteins. The immunoinformatic results reported in the present study demonstrates that it is worth following the designed vaccine by experimental investigations.

Structural and functional characterization of an auxiliary domain-containing PET hydrolase from Burkholderiales bacterium

Biodegradation of polyethylene terephthalate (PET) is one of fundamental ways to solve plastic pollution. As various microbial hydrolases have an extra domain unlike PETase from Ideonella sakaiensis (IsPETase), research on the role of these extra domain in PET hydrolysis is crucial for the identification and selection of a novel PET hydrolase. Here, we report that a PET hydrolase from Burkholderiales bacterium RIFCSPLOWO2_02_FULL_57_36 (BbPETase) with an additional N-terminal domain (BbPETase^AND) shows a similar hydrolysis activity toward microcrystalline PET and a higher thermal stability than IsPETase. Based on detailed structural comparisons between BbPETase and IsPETase, we generated the BbPETase^{S335N/T338I/M363I/N365G} variant with an enhanced PET-degrading activity and thermal stability. We further revealed that BbPETase^AND contributes to the thermal stability of the enzyme through close contact with the core domain, but the domain might hinder the adhesion of enzyme to PET substrate. We suggest that BbPETase is an enzyme in the evolution of efficient PET degradation and molecular insight into a novel PET hydrolase provides a novel strategy for the development of biodegradation of PET.

Omicron: A Heavily Mutated SARS-CoV-2 Variant Exhibits Stronger Binding to ACE2 and Potently Escapes Approved COVID-19 Therapeutic Antibodies

The new SARS-CoV-2 variant of concern “Omicron” was recently spotted in South Africa and spread quickly around the world due to its enhanced transmissibility. The variant became conspicuous as it harbors more than 30 mutations in the Spike protein with 15 mutations in the receptor-binding domain (RBD) alone, potentially dampening the potency of therapeutic antibodies and enhancing the ACE2 binding. More worrying, Omicron infections have been reported in vaccinees in South Africa and Hong Kong, and that post-vaccination sera poorly neutralize the new variant. Here, we investigated the binding strength of Omicron with ACE2 and monoclonal antibodies that are either approved by the FDA for COVID-19 therapy or undergoing phase III clinical trials. Computational mutagenesis and free energy perturbation could confirm that Omicron RBD binds ACE2 ~2.5 times stronger than prototype SARS-CoV-2. Notably, three substitutions, i.e., T478K, Q493K, and Q498R, significantly contribute to the binding energies and almost doubled the electrostatic potential (ELE) of the RBDOmic–ACE2 complex. Omicron also harbors E484A substitution instead of the E484K that helped neutralization escape of Beta, Gamma, and Mu variants. Together, T478K, Q493K, Q498R, and E484A substitutions contribute to a significant drop in the ELE between RBDOmic–mAbs, particularly in etesevimab, bamlanivimab, and CT-p59. AZD1061 showed a slight drop in ELE and sotrovimab that binds a conserved epitope on the RBD; therefore, it could be used as a cocktail therapy in Omicron-driven COVID-19. In conclusion, we suggest that the Spike mutations prudently devised by the virus facilitate the receptor binding, weakening the mAbs binding to escape the immune response.

Repositioning of Fungal-based Peptides as Modulators of Angiotensin-converting Enzyme-related Carboxypeptidase, SARS-coronavirus HR2 Domain, and Coronavirus Disease 2019 Main Protease

Background and Objectives

Angiotensin-converting enzyme-related carboxypeptidase, SARS-Coronavirus HR2 Domain, and COVID-19 main protease are essential for the cellular entry and replication of coronavirus in the host. This study investigated the putative inhibitory action of peptides form medicinal mushrooms, namely Pseudoplectania nigrella, Russula paludosa, and Clitocybe sinopica, towards selected proteins through computational studies.

Materials and Methods

The respective physicochemical properties of selected peptides were predicted using ProtParam tool, while the binding modes and binding free energy of selected peptides toward proteins were computed through HawkDock server. The structural flexibility and stability of docked protein-peptide complexes were assessed through iMODS server.

Results

The peptides showed an optimum binding afinity with the molecular targets; plectasin from P. nigrella showed the highest binding free energy compared to peptides from R. paludosa and C. sinopica. Besides, molecular dynamic simulations showed all fungal-based peptides could influence the flexibility and stability of selected proteins.

Conclusion

The study revealed fungal-based peptides could be explored as functional modulators of essential proteins that are involved in the cellular entry of coronavirus.

Protein-Protein Docking: Past, Present, and Future

The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.

In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population's needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host-pathogen protein interactions (HPIs) have a promising role in identifying the pathogens' strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein-protein interactions, molecular docking, and followed by 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

Decreased Interactions between Calmodulin and a Mutant Huntingtin Model Might Reduce the Cytotoxic Level of Intracellular Ca2+: A Molecular Dynamics Study

Mutant huntingtin (m-HTT) proteins and calmodulin (CaM) co-localize in the cerebral cortex with significant effects on the intracellular calcium levels by altering the specific calcium-mediated signals. Furthermore, the mutant huntingtin proteins show great affinity for CaM that can lead to a further stabilization of the mutant huntingtin aggregates. In this context, the present study focuses on describing the interactions between CaM and two huntingtin mutants from a biophysical point of view, by using classical Molecular Dynamics techniques. The huntingtin models consist of a wild-type structure, one mutant with 45 glutamine residues and the second mutant with nine additional key-point mutations from glutamine residues into proline residues (9P(EM) model). Our docking scores and binding free energy calculations show higher binding affinities of all HTT models for the C-lobe end of the CaM protein. In terms of dynamic evolution, the 9P(EM) model triggered great structural changes into the CaM protein’s structure and shows the highest fluctuation rates due to its structural transitions at the helical level from α-helices to turns and random coils. Moreover, our proposed 9P(EM) model suggests much lower interaction energies when compared to the 45Qs-HTT mutant model, this finding being in good agreement with the 9P(EM)’s antagonistic effect hypothesis on highly toxic protein–protein interactions.

Protein-Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.

Can molecular mimicry explain the cytokine storm of SARS-CoV-2?: An in silico approach

PARP14 and PARP9 play a key role in macrophage immune regulation. SARS‐CoV‐2 is an emerging viral disease that triggers hyper‐inflammation known as a cytokine storm. In this study, using in silico tools, we hypothesize about the immunological phenomena of molecular mimicry between SARS‐CoV‐2 Nsp3 and the human PARP14 and PARP9. The results showed an epitope of SARS‐CoV‐2 Nsp3 protein that contains consensus sequences for both human PARP14 and PARP9 that are antigens for MHC Classes 1 and 2, which can potentially induce an immune response against human PARP14 and PARP9; while its depletion causes a hyper‐inflammatory state in SARS‐CoV‐2 patients.

Molecular mimicry could produce an inflammatory state in SARS‐CoV‐2 patients.

Human PARP14 and PARP9 are the proteins involves in this phenomena.

Exploring the Binding Interaction of Raf Kinase Inhibitory Protein With the N-Terminal of C-Raf Through Molecular Docking and Molecular Dynamics Simulation

Protein-protein interactions are indispensable physiological processes regulating several biological functions. Despite the availability of structural information on protein-protein complexes, deciphering their complex topology remains an outstanding challenge. Raf kinase inhibitory protein (RKIP) has gained substantial attention as a favorable molecular target for numerous pathologies including cancer and Alzheimer’s disease. RKIP interferes with the RAF/MEK/ERK signaling cascade by endogenously binding with C-Raf (Raf-1 kinase) and preventing its activation. In the current investigation, the binding of RKIP with C-Raf was explored by knowledge-based protein-protein docking web-servers including HADDOCK and ZDOCK and a consensus binding mode of C-Raf/RKIP structural complex was obtained. Molecular dynamics (MD) simulations were further performed in an explicit solvent to sample the conformations for when RKIP binds to C-Raf. Some of the conserved interface residues were mutated to alanine, phenylalanine and leucine and the impact of mutations was estimated by additional MD simulations and MM/PBSA analysis for the wild-type (WT) and constructed mutant complexes. Substantial decrease in binding free energy was observed for the mutant complexes as compared to the binding free energy of WT C-Raf/RKIP structural complex. Furthermore, a considerable increase in average backbone root mean square deviation and fluctuation was perceived for the mutant complexes. Moreover, per-residue energy contribution analysis of the equilibrated simulation trajectory by HawkDock and ANCHOR web-servers was conducted to characterize the key residues for the complex formation. One residue each from C-Raf (Arg398) and RKIP (Lys80) were identified as the druggable “hot spots” constituting the core of the binding interface and corroborated by additional long-time scale (300 ns) MD simulation of Arg398Ala mutant complex. A notable conformational change in Arg398Ala mutant occurred near the mutation site as compared to the equilibrated C-Raf/RKIP native state conformation and an essential hydrogen bonding interaction was lost. The thirteen binding sites assimilated from the overall analysis were mapped onto the complex as surface and divided into active and allosteric binding sites, depending on their location at the interface. The acquired information on the predicted 3D structural complex and the detected sites aid as promising targets in designing novel inhibitors to block the C-Raf/RKIP interaction.

PRMT3 interacts with ALDH1A1 and regulates gene-expression by inhibiting retinoic acid signaling

Protein arginine methyltransferase 3 (PRMT3) regulates protein functions by introducing asymmetric dimethylation marks at the arginine residues in proteins. However, very little is known about the interaction partners of PRMT3 and their functional outcomes. Using yeast-two hybrid screening, we identified Retinal dehydrogenase 1 (ALDH1A1) as a potential interaction partner of PRMT3 and confirmed this interaction using different methods. ALDH1A1 regulates variety of cellular processes by catalyzing the conversion of retinaldehyde to retinoic acid. By molecular docking and site-directed mutagenesis, we identified the specific residues in the catalytic domain of PRMT3 that facilitate interaction with the C-terminal region of ALDH1A1. PRMT3 inhibits the enzymatic activity of ALDH1A1 and negatively regulates the expression of retinoic acid responsive genes in a methyltransferase activity independent manner. Our findings show that in addition to regulating protein functions by introducing methylation modifications, PRMT3 could also regulate global gene expression through protein-protein interactions.

Here, the authors demonstrate that protein arginine methyltransferase 3 (PRMT3) interacts with and inhibits the retinal dehydrogenase ALDH1A1, negatively regulating the expression of retinoic acid responsive genes. This study shows that PRMT3 affects diverse biological processes not only by globally regulating protein function through methylation but also by regulating gene expression.

Biochemical Characterization and Functional Analysis of Heat Stable High Potential Protease of Bacillus amyloliquefaciens Strain HM48 from Soils of Dachigam National Park in Kashmir Himalaya

A novel temperature stable alkaline protease yielding bacteria was isolated from the soils of Dachigam National Park, which is known to be inhabited by a wide variety of endemic plant and animal species of Western Himalaya. This high-potential protease producing isolate was characterized and identified as Bacillus amyloliquefaciens strain HM48 by morphological, Gram’s staining and biochemical techniques followed by molecular characterization using 16S rRNA approach. The extracellular protease of B. amyloliquefaciens HM48 was purified by precipitating with ammonium sulfate (80%), followed by dialysis and Gel filtration chromatography increasing its purity by 5.8-fold. The SDS–PAGE analysis of the purified enzyme confirmed a molecular weight of about ≈25 kDa. The enzyme displayed exceptional activity in a broad temperature range (10–90 °C) at pH 8.0, retaining its maximum at 70 °C, being the highest reported for this proteolytic Bacillus sp., with K_M and V_max of 11.71 mg/mL and 357.14 µmol/mL/min, respectively. The enzyme exhibited remarkable activity and stability against various metal ions, surfactants, oxidizing agent (H₂O₂), organic solvents and displayed outstanding compatibility with widely used detergents. This protease showed effective wash performance by exemplifying complete blood and egg-yolk stains removal at 70 °C and efficiently disintegrated chicken feathers making it of vital importance for laundry purpose and waste management. For functional analysis, protease gene amplification of strain HM48 yielded a nucleotide sequence of about 700 bp, which, when checked against the available sequences in NCBI, displayed similarity with subtilisin-like serine protease of B. amyloliquefaciens. The structure of this protease and its highest-priority substrate β-casein was generated through protein modeling. These protein models were validated through futuristic algorithms following which protein–protein (protease from HM48 and β-casein) docking was performed. The interaction profile of these proteins in the docked state with each other was also generated, shedding light on their finer details. Such attributes make this thermally stable protease novel and suitable for high-temperature industrial and environmental applications.

Blueprint of epitope-based multivalent and multipathogenic vaccines: targeted against the dengue and zika viruses

Both dengue virus (DENV) and zika virus (ZIKV) belong to the highly infectious Flaviviridae family that has already caused several outbreaks and epidemics in many countries. DENV and ZIKV cause two of the most wide spread mosquito-borne viral diseases in the world, dengue fever (DENF) and zika fever (ZIKF), respectively. In many regions around the world, both of these diseases can outbreak together and can be lethal as well as life-threatening. Unfortunately, there is no functional and satisfactory vaccine available to combat these viruses. Therefore, in this study, we have attempted to design a blue print of potential multivalent and multipathogenic vaccines using immunoinformatics approach, which can combat both the DENV and ZIKV infections, simultaneously. Initially, three vaccines were designed; containing highly antigenic, non-allergenic, and non-toxic epitopes of T-cell (100% conserved) and B-cell from all the four DENV serotypes and ZIKV. In total, nine cytotoxic T-lymphocytic (CTL), nine helper T-lymphocytic (HTL), and seven B-cell lymphocytic (BCL) epitopes were used to construct three vaccines using three different adjuvants, designated as 'V1', 'V2', and 'V3'. Later, V3 was found to be the best vaccine construct, determined by molecular docking analysis. Thereafter, several in silico validation studies including molecular dynamics simulation and immune simulation were performed which indicated that V3 might be quite stable and should generate substantial immune response in the biological environment. However, further in vivo and in vitro validation might be required to finally confirm the safety and efficacy of our suggested vaccine constructs.Communicated by Ramaswamy H. Sarma.

An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity

The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method is constantly used to calculate the binding free energy of protein-ligand complexes, and has been shown to effectively balance computational cost against accuracy. The relative binding affinities obtained by the MM/PBSA approach are acceptable, while it usually overestimates the absolute binding free energy. This paper proposes four free energy estimators based on the MM/PBSA for enthalpy change combined with interaction entropy (IE) for entropy change using different weights for individual energy terms. The ΔG_{PBSA_IE} method is determined to be an optimal estimator based on its performance in terms of the correlation between experimental and theoretical values and error estimations. This approach is optimized using high-quality experimental values from a training set containing 84 protein-ligand systems, and the coefficients for the sum of electrostatic energy and polar solvation free energy, van der Waals (vdW) energy, non-polar solvation energy and entropy change are obtained by multivariate linear fitting to the corresponding experimental values. A comparison between the traditional MM/PBSA method and this method shows that the correlation coefficient is improved from 0.46 to 0.72 and the slope of the regression line increases from 0.10 to 1.00. More importantly, the mean absolute error (MAE) is significantly reduced from 22.52 to 1.59 kcal mol^-1. Furthermore, the numerical stability of this method is validated on a test set with a similar correlation coefficient, slope and MAE to those of the training set. Based on the above advantages, the ΔG_{PBSA_IE} method can be a powerful tool for a reliable and accurate estimation of binding free energy and plays a significant role in a detailed energetic investigation of protein-ligand interaction.

HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA

Protein–protein interactions (PPIs) play an important role in the different functions of cells, but accurate prediction of the three-dimensional structures for PPIs is still a notoriously difficult task. In this study, HawkDock, a free and open accessed web server, was developed to predict and analyze the structures of PPIs. In the HawkDock server, the ATTRACT docking algorithm, the HawkRank scoring function developed in our group and the MM/GBSA free energy decomposition analysis were seamlessly integrated into a multi-functional platform. The structures of PPIs were predicted by combining the ATTRACT docking and the HawkRank re-scoring, and the key residues for PPIs were highlighted by the MM/GBSA free energy decomposition. The molecular visualization was supported by 3Dmol.js. For the structural modeling of PPIs, HawkDock could achieve a better performance than ZDOCK 3.0.2 in the benchmark testing. For the prediction of key residues, the important residues that play an essential role in PPIs could be identified in the top 10 residues for ∼81.4% predicted models and ∼95.4% crystal structures in the benchmark dataset. To sum up, the HawkDock server is a powerful tool to predict the binding structures and identify the key residues of PPIs. The HawkDock server is accessible free of charge at http://cadd.zju.edu.cn/hawkdock/.

Exploiting the reverse vaccinology approach to design novel subunit vaccines against Ebola virus

Ebola virus is a highly pathogenic RNA virus that causes the Ebola haemorrhagic fever in human. This virus is considered as one of the dangerous viruses in the world with very high mortality rate. To date, no epitope-based subunit vaccine has yet been discovered to fight against Ebola although the outbreaks of this deadly virus took many lives in the past. In this study, approaches of reverse vaccinology were utilized in combination with different tools of immunoinformatics to design subunit vaccines against Ebola virus strain Mayinga-76. Three potential antigenic proteins of this virus i.e., matrix protein VP40, envelope glycoprotein and nucleoprotein were selected to construct the subunit vaccine. The MHC class-I, MHC class-II and B-cell epitopes were determined initially and after some robust analysis i.e., antigenicity, allergenicity, toxicity, conservancy and molecular docking study, EV-1, EV-2 and EV-3 were constructed as three potential vaccine constructs. These vaccine constructs are also expected to be effective on few other strains of Ebola virus since the highly conserved epitopes were used for vaccine construction. Thereafter, molecular docking study was conducted on these vaccines and EV-1 emerged as the best vaccine construct. Afterward, molecular dynamics simulation study revealed the good performances and stability of the intended vaccine protein. Finally, codon adaptation and in silico cloning were carried out to design a possible plasmid (pET-19b plasmid vector was used) for large scale production of the EV-1 vaccine. However, further in vitro and in vivo studies might be required on the predicted vaccines for final validation.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (r_p = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (r_p = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.

Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes

A significant number of protein-protein interactions (PPIs) are mediated through the interactions between proteins and peptide segments, and therefore determination of protein-peptide interactions (PpIs) is critical to gain an in-depth understanding of the PPI network and even design peptides or small molecules capable of modulating PPIs. Computational approaches, especially molecular docking, provide an efficient way to model PpIs, and a reliable scoring function that can recognize the correct binding conformations for protein-peptide complexes is one of the most important components in protein-peptide docking. The end-point binding free energy calculation methods, such as MM/GBSA and MM/PBSA, are theoretically more rigorous than most empirical and semi-empirical scoring functions designed for protein-peptide docking, but their performance in predicting binding affinities and binding poses for protein-peptide systems has not been systematically assessed. In this study, we first evaluated the capability of MM/GBSA and MM/PBSA with different solvation models, interior dielectric constants (εin) and force fields to predict the binding affinities for 53 protein-peptide complexes. For the 19 short peptides with 5-12 residues, MM/PBSA based on the minimized structures in explicit solvent with the ff99 force field and εin = 2 yields the best correlation between the predicted binding affinities and the experimental data (rp = 0.748), while for the 34 medium-size peptides with 20-25 residues, MM/GBSA based on 1 ns of molecular dynamics (MD) simulations in implicit solvent with the ff03 force field, the GBOBC1 model and a low interior dielectric constant (εin = 1) yields the best accuracy (rp = 0.735). Then, we assessed the rescoring capability of MM/PBSA and MM/GBSA to distinguish the correct binding conformations from the decoys for 112 protein-peptide systems. The results illustrate that MM/PBSA based on the minimized structures with the ff99 or ff14SB force field and MM/GBSA based on the minimized structures with the ff03 force field show excellent capability to recognize the near-native binding poses for the short and medium-size peptides, respectively, and they outperform the predictions given by two popular protein-peptide docking algorithms (pepATTRACT and HPEPDOCK). Therefore, MM/PBSA and MM/GBSA are powerful tools to predict the binding affinities and identify the correct binding poses for protein-peptide systems.

An account of solvent accessibility in protein-RNA recognition

Protein–RNA recognition often induces conformational changes in binding partners. Consequently, the solvent accessible surface area (SASA) buried in contact estimated from the co-crystal structures may differ from that calculated using their unbound forms. To evaluate the change in accessibility upon binding, we compare SASA of 126 protein-RNA complexes between bound and unbound forms. We observe, in majority of cases the interface of both the binding partners gain accessibility upon binding, which is often associated with either large domain movements or secondary structural transitions in RNA-binding proteins (RBPs), and binding-induced conformational changes in RNAs. At the non-interface region, majority of RNAs lose accessibility upon binding, however, no such preference is observed for RBPs. Side chains of RBPs have major contribution in change in accessibility. In case of flexible binding, we find a moderate correlation between the binding free energy and change in accessibility at the interface. Finally, we introduce a parameter, the ratio of gain to loss of accessibility upon binding, which can be used to identify the native solution among the flexible docking models. Our findings provide fundamental insights into the relationship between flexibility and solvent accessibility, and advance our understanding on binding induced folding in protein-RNA recognition.