GalaxyRefine2: simultaneous refinement of inaccurate local regions and overall protein structure

Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template-based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi-level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta and D-I-TASSER for modelling the 3D structures. HIGHLIGHTS: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein. This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step. This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models. This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures.

Computational bioprospecting of phytoconstituents as potential inhibitors for peptide deformylase from Streptococcus oralis: An opportunistic pathogen

Streptococcus oralis an opportunistic bacterium has been reported to be involved in various blood borne infections like subacute bacterial endocarditis, septicemia, bacterial meningitis and in some cases dental caries too. Among various targets the peptide deformylase, of S.oralis appears to be most potent druggable target as it is involved in protein synthesis is opted for the current study. Due to unavailability of PDB structure of peptide deformylase from S. oralis the study initiates with homology modelling of the protein and 6OW2 of S pneumoniae is considered as the template. Thereafter, Molecular docking, Molecular dynamic simulation, ADME analysis, and MMPBSA analysis was carried out to explore the inhibitory potential of phyto-constituents as potential inhibitors for Peptide deformylase from S.oralis. Actinonin was considered as reference drug. Among 2370 phyto compounds the best observations were recorded for A1-Barrigenol (IMPHY010984) with binding affinity of -8.5 kcal/mol. Calculated RMSD, RMSF, Binding Free Energy for IMPHY010984 averaged at about 0.10 ± 0.03 nm, 0.08 ± 0.05 nm, 131 ± 21 kJ/mol respectively whereas the RMSD, RMSF, Binding Free Energy recorded for reference drug averaged at about 0.19 ± 0.04 nm, 0.11 ± 0.08 nm, -94 ± 18 kJ/mol respectively. Based on in silico observations IMPHY010984 is proved out as superior candidate over reference drug. The study reflects the potential of IMPHY010984 as prophylactic therapeutics for S.oralis.

Insights into the Gene Expression Profile of Classical Hodgkin Lymphoma: A Study towards Discovery of Novel Therapeutic Targets

Classical Hodgkin lymphoma (cHL) is a common B-cell cancer and a significant health concern, especially in Western and Asian countries. Despite the effectiveness of chemotherapy, many relapse cases are being reported, highlighting the need for improved treatments. This study aimed to address this issue by discovering biomarkers through the analysis of gene expression data specific to cHL. Additionally, potential anticancer inhibitors were explored to target the discovered biomarkers. This study proceeded by retrieving microarray gene expression data from cHL patients, which was then analyzed to identify significant differentially expressed genes (DEGs). Functional and network annotation of the upregulated genes revealed the active involvement of matrix metallopeptidase 12 (MMP12) and C-C motif metallopeptidase ligand 22 (CCL22) genes in the progression of cHL. Additionally, the mentioned genes were found to be actively involved in cancer-related pathways, i.e., oxidative phosphorylation, complement pathway, myc_targets_v1 pathway, TNFA signaling via NFKB, etc., and showed strong associations with other genes known to promote cancer progression. MMP12, topping the list with a logFC value of +6.6378, was selected for inhibition using docking and simulation strategies. The known anticancer compounds were docked into the active site of the MMP12 molecular structure, revealing significant binding scores of -7.7 kcal/mol and -7.6 kcal/mol for BDC_24037121 and BDC_27854277, respectively. Simulation studies of the docked complexes further supported the effective binding of the ligands, yielding MMGBSA and MMPBSA scores of -78.08 kcal/mol and -82.05 kcal/mol for MMP12-BDC_24037121 and -48.79 kcal/mol and -49.67 kcal/mol for MMP12-BDC_27854277, respectively. Our findings highlight the active role of MMP12 in the progression of cHL, with known compounds effectively inhibiting its function and potentially halting the advancement of cHL. Further exploration of downregulated genes is warranted, as associated genes may play a role in cHL. Additionally, CCL22 should be considered for further investigation due to its significant role in the progression of cHL.

Cellulolytic potential of mangrove bacteria Bacillus haynesii DS7010 and the effect of anthropogenic and environmental stressors on bacterial survivability and cellulose metabolism

Cellulose degrading bacterial diversity of Bhitarkanika mangrove ecosystem, India, was uncovered and the cellulose degradation mechanism in Bacillus haynesii DS7010 under the modifiers such as pH (pCO2), salinity and lead (Pb) was elucidated in the present study. The abundance of cellulose degrading heterotrophic bacteria was found to be higher in mangrove sediment than in water. The most potential strain, B. haynesii DS7010 showed the presence of endoglucanase, exoglucanase and β-glucosidase with the maximum degradation recorded at 48 h of incubation, with 1% substrate concentration at 41 °C incubation temperature. Two glycoside hydrolase genes, celA and celB were confirmed in this bacterium. 3D structure prediction of the translated CelA and CelB proteins showed maximum similarities with glycoside hydrolase 48 (GH48) and glycoside hydrolase 5 (GH5) respectively. Native PAGE followed by zymogram assay unveiled the presence of eight isoforms of cellulase ranged from 78 kDa to 245 kDa. Among the stressors, most adverse effect was observed under Pb stress at 1400 ppm concentration, followed by pH at pH 4. This was indicated by prolonged lag phase growth, higher reactive oxygen species (ROS) production, lower enzyme activity and downregulation of celA and celB gene expressions. Salinity augmented bacterial metabolism up to 3% NaCl concentration. Mangrove leaf litter degradation by B. haynesii DS7010 indicated a substantial reduction in cellulolytic potential of the bacterium in response to the synergistic effect of the stressors. Microcosm set up with the stressors exhibited 0.97% decrease in total carbon (C%) and 0.02% increase in total nitrogen (N%) after 35 d of degradation while under natural conditions, the reduction in C and the increase in N were 4.05% and 0.2%, respectively. The findings of the study suggest the cellulose degradation mechanism of a mangrove bacterium and its resilience to the future consequences of environmental pollution and climate change.

Understanding Genetic Risks: Computational Exploration of Human β-Synuclein nsSNPs and their Potential Impact on Structural Alteration

Synucleins are pivotal in neurodegenerative conditions. Beta-synuclein (β-synuclein) is part of the synuclein protein family alongside alpha-synuclein (α-synuclein) and gamma-synuclein (γ-synuclein). These proteins, found mainly in brain tissue and cancers, are soluble and unstructured. β-synuclein shares significant similarity with α-synuclein, especially in their N-terminus, with a 90% match. However, their aggregation tendencies differ significantly. While α-synuclein aggregation is believed to be counteracted by β-synuclein, which occurs in conditions like Parkinson's disease, β-synuclein may counteract α-synuclein's toxic effects on the nervous system, offering potential treatment for neurodegenerative diseases. Under normal circumstances, β-synuclein may guard against disease by interacting with α-synuclein. Yet, in pathological environments with heightened levels or toxic substances, it might contribute to disease. Our research aims to explore potential harmful mutations in the β-synuclein using computational tools to predict their destabilizing impact on protein structure. Consensus analysis revealed rs1207608813 (A63P), rs1340051870 (S72F), and rs1581178262 (G36C) as deleterious. These findings highlight the intricate relationship between nsSNPs and protein function, shedding light on their potential implications in disease pathways. Understanding the structural consequences of nsSNPs is crucial for elucidating their role in pathogenesis and developing targeted therapeutic interventions. Our results offer a robust computational framework for identifying neurodegenerative disorder-related mutations from SNP datasets, potentially reducing the costs associated with experimental characterization.

Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling

As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in protein structure prediction achieved through neural network training utilizing extensive sequence and structure data. However, substantial challenges persist in structure prediction due to limited data availability, with antibody structure prediction standing as one such challenge. In this paper, we propose a novel neural network architecture that effectively enables structure prediction by reflecting the inherent combinatorial nature involved in protein structure formation. The core idea of this neural network architecture is not solely to track and generate a single structure but rather to form a community of multiple structures and pursue accurate structure prediction by exchanging information among community members. Applying this concept to antibody CDR H3 loop structure prediction resulted in improved structure sampling. Such an approach could be applied in the structural and functional studies of proteins, particularly in exploring various physiological processes mediated by loops. Moreover, it holds potential in addressing various other types of combinatorial structure prediction and design problems.

Immune targeting of filarial glutaredoxin through a multi-epitope peptide-based vaccine: A reverse vaccinology approach

Despite the efforts of global programme to eliminate lymphatic filariasis (GPELF), the threat of lymphatic filariasis (LF) still looms over humanity in terms of long-term disabilities, and morbidities across the globe. In light of this situation, investigators have chosen to focus on the development of immunotherapeutics targeting the physiologically important filarial-specific proteins. Glutaredoxin (16.43 kDa) plays a pivotal role in filarial redox biology, serving as a vital contributor. In the context of the intra-host survival of filarial parasites, this antioxidant helps in mitigating the oxidative stress imposed by the host immune system. Given its significant contribution, the development of a vaccine targeting glutaredoxin holds promise as a new avenue for achieving a filaria-free world. Herein, multi-epitope-based vaccine was designed using advanced immunoinformatics approach. Initially, 4B-cell epitopes and 6 T-cell epitopes (4 MHC I and 2 MHC II) were identified from the 146 amino acid long sequence of glutaredoxin of the human filarid, Wuchereria bancrofti. Subsequent clustering of these epitopes with linker peptides finalized the vaccine structure. To boost TLR-mediated innate immunity, TLR-specific adjuvants were incorporated into the designed vaccine. After that, experimental analyses confirm the designed vaccine, Vac4 as anefficient ligand of human TLR5 to elicit protective innate immunity against filarial glutaredoxin. Immune simulation further demonstrated abundant levels of IgG and IgM as crucial contributors in triggering vaccine-induced adaptive responses in the recipients. Hence, to facilitate the validation of immunogenicity of the designed vaccine, Vac4 was cloned in silico in pET28a(+) expression vector for recombinant production. Taken together, our findings suggest that vaccine-mediated targeting of filarial glutaredoxin could be a future option for intervening LF on a global scale.

Inferring B-cell derived T-cell receptor induced multi-epitope-based vaccine candidate against enterovirus 71: a reverse vaccinology approach

Purpose

Enterovirus 71, a pathogen that causes hand-foot and mouth disease (HFMD) is currently regarded as an increasing neurotropic virus in Asia and can cause severe complications in pediatric patients with blister-like sores or rashes on the hand, feet, and mouth. Notwithstanding the significant burden of the disease, no authorized vaccine is available. Previously identified attenuated and inactivated vaccines are worthless over time owing to changes in the viral genome.

Materials and Methods

A novel vaccine construct using B-cell derived T-cell epitopes from the virulent polyprotein found the induction of possible immune response. In order to boost the immune system, a beta-defensin 1 preproprotein adjuvant with EAAAK linker was added at the N-terminal end of the vaccine sequence.

Results

The immunogenicity of the designed, refined, and verified prospective three-dimensional-structure of the multi-epitope vaccine was found to be quite high, exhibiting non-allergenic and antigenic properties. The vaccine candidates bound to toll-like receptor 3 in a molecular docking analysis, and the efficacy of the potential vaccine to generate a strong immune response was assessed through in silico immunological simulation.

Conclusion

Computational analysis has shown that the proposed multi-epitope vaccine is possibly safe for use in humans and can elicit an immune response.

Construction of an aerolysin-based multi-epitope vaccine against Aeromonas hydrophila: an in silico machine learning and artificial intelligence-supported approach

Aeromonas hydrophila, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroenteritis, skin and wound infections, meningitis, fulminating septicemia, enterocolitis, peritonitis, and endocarditis. It frequently occurs in aquatic environments and readily contacts humans, leading to high infection rates. This bacterium has exhibited resistance to numerous commercial antibiotics, and no vaccine has yet been developed. Aiming to combat the alarmingly high infection rate, this study utilizes in silico techniques to design a multi-epitope vaccine (MEV) candidate against this bacterium based on its aerolysin toxin, which is the most toxic and highly conserved virulence factor among the Aeromonas species. After retrieval, aerolysin was processed for B-cell and T-cell epitope mapping. Once filtered for toxicity, antigenicity, allergenicity, and solubility, the chosen epitopes were combined with an adjuvant and specific linkers to create a vaccine construct. These linkers and the adjuvant enhance the MEV's ability to elicit robust immune responses. Analyses of the predicted and improved vaccine structure revealed that 75.5%, 19.8%, and 1.3% of its amino acids occupy the most favored, additional allowed, and generously allowed regions, respectively, while its ERRAT score reached nearly 70%. Docking simulations showed the MEV exhibiting the highest interaction and binding energies (-1,023.4 kcal/mol, -923.2 kcal/mol, and -988.3 kcal/mol) with TLR-4, MHC-I, and MHC-II receptors. Further molecular dynamics simulations demonstrated the docked complexes' remarkable stability and maximum interactions, i.e., uniform RMSD, fluctuated RMSF, and lowest binding net energy. In silico models also predict the vaccine will stimulate a variety of immunological pathways following administration. These analyses suggest the vaccine's efficacy in inducing robust immune responses against A. hydrophila. With high solubility and no predicted allergic responses or toxicity, it appears safe for administration in both healthy and A. hydrophila-infected individuals.

Rational design of a multivalent vaccine targeting arthropod-borne viruses using reverse vaccinology strategies

Viruses transmitted by arthropods, such as Dengue, Zika, and Chikungunya, represent substantial worldwide health threats, particularly in countries like India. The lack of approved vaccines and effective antiviral therapies calls for developing innovative strategies to tackle these arboviruses. In this study, we employed immunoinformatics methodologies, incorporating reverse vaccinology, to design a multivalent vaccine targeting the predominant arboviruses. Epitopes of B and T cells were recognized within the non-structural proteins of Dengue, Zika, and Chikungunya viruses. The predicted epitopes were enhanced with adjuvants β-defensin and RS-09 to boost the vaccine's immunogenicity. Sixteen distinct vaccine candidates were constructed, each incorporating epitopes from all three viruses. FUVAC-11 emerged as the most promising vaccine candidate through molecular docking and molecular dynamics simulations, demonstrating favorable binding interactions and stability. Its effectiveness was further evaluated using computational immunological studies confirming strong immune responses. The in silico cloning performed using the pET-28a(+) plasmid facilitates the future experimental implementation of this vaccine candidate, paving the way for potential advancements in combating these significant arboviral threats. However, further in vitro and in vivo studies are warranted to confirm the results obtained in this computational study, which highlights the effectiveness of immunoinformatics and reverse vaccinology in creating vaccines against major Arboviruses, offering a promising model for developing vaccines for other vector-borne diseases and enhancing global health security.

In silico characterization of five novel disease-resistance proteins in Oryza sativa sp. japonica against bacterial leaf blight and rice blast diseases

In the current study, gene network analysis revealed five novel disease-resistance proteins against bacterial leaf blight (BB) and rice blast (RB) diseases caused by Xanthomonas oryzae pv. oryzae (Xoo) and Magnaporthe oryzae (M. oryzae), respectively. In silico modeling, refinement, and model quality assessment were performed to predict the best structures of these five proteins and submitted to ModelArchive for future use. An in-silico annotation indicated that the five proteins functioned in signal transduction pathways as kinases, phospholipases, transcription factors, and DNA-modifying enzymes. The proteins were localized in the nucleus and plasma membrane. Phylogenetic analysis showed the evolutionary relation of the five proteins with disease-resistance proteins (XA21, OsTRX1, PLD, and HKD-motif-containing proteins). This indicates similar disease-resistant properties between five unknown proteins and their evolutionary-related proteins. Furthermore, gene expression profiling of these proteins using public microarray data showed their differential expression under Xoo and M. oryzae infection. This study provides an insight into developing disease-resistant rice varieties by predicting novel candidate resistance proteins, which will assist rice breeders in improving crop yield to address future food security through molecular breeding and biotechnology.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03893-5.

Translational and structural vaccinomics approach to design a multi-epitope vaccine against NOL4 autologous antigen of small cell lung cancer

Small cell lung cancer (SCLC) is one of the most common cancers and it is the sixth common cause for cancer-related deaths. The high plasticity and metastasis have been a major challenge for humanity to treat the disease. Hence, a vaccine for SCLC has become an urgent need of the hour due to public health concern. Implementation of immunoinformatics technique is one of the best way to find a suitable vaccine candidate. Immunoinformatics tools can be used to overcome the limitations and difficulties of traditional vaccinological techniques. Multi-epitope cancer vaccines have become a next-generation technique in vaccinology which can be used to stimulate more potent immune response against a particular antigen by eliminating undesirable molecules. In this study, we used multiple computational and immunoinformatics approach to design a novel multi-epitope vaccine for small cell lung cancer. Nucleolar protein 4 (NOL4) is an autologous cancer-testis antigen overexpressed in SCLC cells. Seventy-five percent humoral immunity have been identified for this particular antigen. In this study, we mapped immunogenic cytotoxic T lymphocyte, helper T lymphocyte, and interferon-gamma epitopes present in NOL4 antigen and designed a multi-epitope-based vaccine using the predicted epitopes. The designed vaccine was antigenic, non-allergenic, and non-toxic with 100% applicability on human population. The chimeric vaccine construct showed stable and significant interaction with endosomal and plasmalemmal toll-like receptors in molecular docking and protein-peptide interaction analysis, thus assuring a strong potent immune response against the vaccine upon administration. Therefore, these preliminary results can be used to carry out further experimental investigations.

Genome sequencing of cucumber mosaic virus (CMV) isolates infecting chilli and its interaction with host ferredoxin protein of different host for causing mosaic symptoms

Chilli (Capsicum annuum L.) is an important vegetable crop grown in the Indian sub-continent and is prone to viral infections under field conditions. During the field survey, leaf samples from chilli plants showing typical symptoms of disease caused by cucumber mosaic virus (CMV) such as mild mosaic, mottling and leaf distortion were collected. DAC-ELISA analysis confirmed the presence of CMV in 71 out of 100 samples, indicating its widespread prevalence in the region. Five CMV isolates, named Gu1, Gu2, BA, Ho, and Sal were mechanically inoculated onto cucumber and Nicotiana glutinosa plants to study their virulence. Inoculated plants expressed the characteristic symptoms of CMV such as chlorotic spots followed by mild mosaic and leaf distortion. Complete genomes of the five CMV isolates were amplified, cloned, and sequenced, revealing RNA1, RNA2, and RNA3 sequences with 3358, 3045, and 2220 nucleotides, respectively. Phylogenetic analysis classified the isolates as belonging to the CMV-IB subgroup, distinguishing them from subgroup IA and II CMV isolates. Recombination analysis showed intra and interspecific recombination in all the three RNA segments of these isolates. In silico protein-protein docking approach was used to decipher the mechanism behind the production of mosaic symptoms during the CMV-host interaction in 13 host plants. Analysis revealed that the production of mosaic symptoms could be due to the interaction between the coat protein (CP) of CMV and chloroplast ferredoxin proteins. Further, in silico prediction was validated in 13 host plants of CMV by mechanical sap inoculation. Twelve host plants produced systemic symptoms viz., chlorotic spot, chlorotic ringspot, chlorotic local lesion, mosaic and mild mosaic and one host plant, Solanum lycopersicum produced mosaic followed by shoestring symptoms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03777-8.

Computational exploration and design of a multi-epitopes vaccine construct against Chlamydia psittaci

Chlamydia psittaci is an intracellular pathogen and causes variety of deadly infections in humans. Antibiotics are effective against C. psittaci however high percentage of resistant strains have been reported in recent times. As there is no licensed vaccine, we used in-silico techniques to design a multi-epitopes vaccine against C. psittaci. Following a step-wise protocol, the proteome of available 26 strains was retrieved and filtered for subcellular localized proteins. Five proteins were selected (2 extracellular and 3 outer membrane) and were further analyzed for B-cell and T-cell epitopes prediction. Epitopes were further checked for antigenicity, solubility, stability, toxigenicity, allergenicity, and adhesive properties. Filtered epitopes were linked via linkers and the 3D structure of the designed vaccine construct was predicted. Binding of the designed vaccine with immune receptors: MHC-I, MHC-II, and TLR-4 was analyzed, which resulted in docking energy scores of -4.37 kcal/mol, -0.20 kcal/mol and -22.38 kcal/mol, respectively. Further, the docked complexes showed stable dynamics with a maximum value of vaccine-MHC-I complex (7.8 Å), vaccine-MHC-II complex (6.2 Å) and vaccine-TLR4 complex (5.2 Å). As per the results, the designed vaccine construct reported robust immune responses to protect the host against C. psittaci infections. In the study, the C. psittaci proteomes were considered in pan-genome analysis to extract core proteins. The pan-genome analysis was conducted using bacterial pan-genome analysis (BPGA) software. The core proteins were checked further for non-redundant proteins using a CD-Hit server. Surface localized proteins were investigated using PSORTb v 3.0. The surface proteins were BLASTp against Virulence Factor Data Base (VFDB) to predict virulent factors. Antigenicity prediction of the shortlisted proteins was further done using VAXIGEN v 2.0. The epitope mapping was done using the immune epitope database (IEDB). A multi-epitopes vaccine was built and a 3D structure was generated using 3Dprot online server. The docking analysis of the designed vaccine with immune receptors was carried out using PATCHDOCK. Molecular dynamics and post-simulation analyses were carried out using AMBER v20 to decipher the dynamics stability and intermolecular binding energies of the docked complexes.Communicated by Ramaswamy H. Sarma.

Identification of potential flavonoid compounds as antibacterial therapeutics against Klebsiella pneumoniae infection using structure-based virtual screening and molecular dynamics simulation

Klebsiella pneumoniae, which is among the top three pathogens on WHO's priority list, is one of the gram-negative bacteria that doctors and researchers around the world have fought for decades. Capsular polysaccharide (CPS) protein is extensively recognized as an important K. pneumoniae virulence factor. Thus, CPS has become the most characterized target for the discovery of novel drug candidates. The ineffectiveness of currently existing antibiotics urges the search for potent antimicrobial compounds. Flavonoids are a group of plant metabolites that have antibacterial potential and can enhance the present medications to elicit improved results against diverse diseases without adverse reactions. Henceforth, the present study aims to illustrate the inhibitory potential of flavonoids with varying pharmacological properties, targeting the CPS protein of K. pneumoniae by in silico approaches. The flavonoid compounds (n = 169) were retrieved from the PubChem database and screened using the structure-based virtual screening approach. Compounds with the highest binding score were estimated through their pharmacokinetic effects by ADMET descriptors. Finally, four potential inhibitors with PubChem CID: (4301534, 5213, 5481948, and 637080) were selected after molecular docking and drug-likeness analysis. All four lead compounds were employed for the MDS analysis of a 100 ns time period. Various studies were undertaken to assess the stability of the protein-ligand complexes. The binding free energy was computed using MM-PBSA, and the outcomes indicated that the molecules are having stable interactions with the binding site of the target protein. The results revealed that all four compounds can be employed as potential therapeutics against K. pneumoniae.

Roles for the Synechococcus elongatus RNA-Binding Protein Rbp2 in Regulating the Circadian Clock

The cyanobacterial circadian oscillator, consisting of KaiA, KaiB, and KaiC proteins, drives global rhythms of gene expression and compaction of the chromosome and regulates the timing of cell division and natural transformation. While the KaiABC posttranslational oscillator can be reconstituted in vitro, the Kai-based oscillator is subject to several layers of regulation in vivo. Specifically, the oscillator proteins undergo changes in their subcellular localization patterns, where KaiA and KaiC are diffuse throughout the cell during the day and localized as a focus at or near the pole of the cell at night. Here, we report that the CI domain of KaiC, when in a hexameric state, is sufficient to target KaiC to the pole. Moreover, increased ATPase activity of KaiC correlates with enhanced polar localization. We identified proteins associated with KaiC in either a localized or diffuse state. We found that loss of Rbp2, found to be associated with localized KaiC, results in decreased incidence of KaiC localization and long-period circadian phenotypes. Rbp2 is an RNA-binding protein, and it appears that RNA-binding activity of Rbp2 is required to execute clock functions. These findings uncover previously unrecognized roles for Rbp2 in regulating the circadian clock and suggest that the proper localization of KaiC is required for a fully functional clock in vivo.

Immunization of sheep with a recombinant vaccine containing immunogenic nontoxic domains of Clostridium perfringens alpha and beta toxins

Clostridium perfringens (types A and C) can cause several diseases by secreting alpha (CPA) and beta (CPB) exotoxins in the gastrointestinal tract. Although vaccination is the main measure of immunization against C. perfringens, available vaccines have limitations in terms of productivity and safety. Thus, recombinant vaccines are an important, more effective, practical, and safer strategy in the immunization of animals. In this study, we evaluated the immunization of sheep with recombinant Escherichia coli bacterins expressing CPA and CPB complete proteins (co-administered), the immunogenic nontoxic domains rCPA-C247-370 and rCPB-C143-311 co-administered or fused as a bivalent chimera (rCPBcAc). For this, in silico analysis was performed to design rCPBcAc, considering the stability of the mRNA (-278.80 kcal/mol), the degree of antigenicity (0.7557), the epitopes of the B cell ligand, and different physicochemical characteristics. All proteins were expressed in vitro. In vivo, animals vaccinated with the co-administered antigens rCPA + rCPB and rCPA-C+ rCPB-C (200 μg each) had mean CPA and CPB neutralizing antitoxin titers of 4, 10, 4.8, and 14.4 IU/mL, respectively, while those vaccinated with 200 μg of rCPBcAc chimera (approximately 100 μg of each antigen) had titers of <4 and 12 IU/mL of CPA and CPB antitoxins, respectively, 56 days after the administration of the first dose. In addition, the chimera was considered to be immunogenic for inducing antitoxin titers using the half dose. In this study, we presented a new recombinant antigen potentially applicable for vaccines against the CPA and CPB toxins for preventing diseases caused by Clostridium perfringens.

Spike protein mutations and structural insights of pangolin lineage B.1.1.25 with implications for viral pathogenicity and ACE2 binding affinity

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of COVID -19, is constantly evolving, requiring continuous genomic surveillance. In this study, we used whole-genome sequencing to investigate the genetic epidemiology of SARS-CoV-2 in Bangladesh, with particular emphasis on identifying dominant variants and associated mutations. We used high-throughput next-generation sequencing (NGS) to obtain DNA sequences from COVID-19 patient samples and compared these sequences to the Wuhan SARS-CoV-2 reference genome using the Global Initiative for Sharing All Influenza Data (GISAID). Our phylogenetic and mutational analyzes revealed that the majority (88%) of the samples belonged to the pangolin lineage B.1.1.25, whereas the remaining 11% were assigned to the parental lineage B.1.1. Two main mutations, D614G and P681R, were identified in the spike protein sequences of the samples. The D614G mutation, which is the most common, decreases S1 domain flexibility, whereas the P681R mutation may increase the severity of viral infections by increasing the binding affinity between the spike protein and the ACE2 receptor. We employed molecular modeling techniques, including protein modeling, molecular docking, and quantum mechanics/molecular mechanics (QM/MM) geometry optimization, to build and validate three-dimensional models of the S_D614G-ACE2 and S_P681R-ACE2 complexes from the predominant strains. The description of the binding mode and intermolecular contacts of the referenced systems suggests that the P681R mutation may be associated with increased viral pathogenicity in Bangladeshi patients due to enhanced electrostatic interactions between the mutant spike protein and the human ACE2 receptor, underscoring the importance of continuous genomic surveillance in the fight against COVID -19. Finally, the binding profile of the S_D614G-ACE2 and S_P681R-ACE2 complexes offer valuable insights to deeply understand the binding site characteristics that could help to develop antiviral therapeutics that inhibit protein-protein interactions between SARS-CoV-2 spike protein and human ACE2 receptor.

Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph transformer

MOTIVATION

The state-of-art protein structure prediction methods such as AlphaFold are being widely used to predict structures of uncharacterized proteins in biomedical research. There is a significant need to further improve the quality and nativeness of the predicted structures to enhance their usability. In this work, we develop ATOMRefine, a deep learning-based, end-to-end, all-atom protein structural model refinement method. It uses a SE(3)-equivariant graph transformer network to directly refine protein atomic coordinates in a predicted tertiary structure represented as a molecular graph.

RESULTS

The method is first trained and tested on the structural models in AlphaFoldDB whose experimental structures are known, and then blindly tested on 69 CASP14 regular targets and 7 CASP14 refinement targets. ATOMRefine improves the quality of both backbone atoms and all-atom conformation of the initial structural models generated by AlphaFold. It also performs better than two state-of-the-art refinement methods in multiple evaluation metrics including an all-atom model quality score-the MolProbity score based on the analysis of all-atom contacts, bond length, atom clashes, torsion angles, and side-chain rotamers. As ATOMRefine can refine a protein structure quickly, it provides a viable, fast solution for improving protein geometry and fixing structural errors of predicted structures through direct coordinate refinement.

AVAILABILITY AND IMPLEMENTATION

The source code of ATOMRefine is available in the GitHub repository (https://github.com/BioinfoMachineLearning/ATOMRefine). All the required data for training and testing are available at https://doi.org/10.5281/zenodo.6944368.

Cell Surface Fibroblast Activation Protein-2 (Fap2) of Fusobacterium nucleatum as a Vaccine Candidate for Therapeutic Intervention of Human Colorectal Cancer: An Immunoinformatics Approach

Colorectal cancer (CRC) is one of the most common cancers and is the second-highest in cancer-related deaths worldwide. The changes in gut homeostasis and microbial dysbiosis lead to the initiation of the tumorigenesis process. Several pathogenic gram-negative bacteria including Fusobacterium nucleatum are the principal contributors to the induction and pathogenesis of CRC. Thus, inhibiting the growth and survival of these pathogens can be a useful intervention strategy. Fibroblast activation protein-2 (Fap2) is an essential membrane protein of F. nucleatum that promotes the adherence of the bacterium to the colon cells, recruitment of immune cells, and induction of tumorigenesis. The present study depicts the design of an in silico vaccine candidate comprising the B-cell and T-cell epitopes of Fap2 for improving cell-mediated and humoral immune responses against CRC. Notably, this vaccine participates in significant protein–protein interactions with human Toll-like receptors, especially with TLR6 reveals, which is most likely to be correlated with its efficacy in eliciting potential immune responses. The immunogenic trait of the designed vaccine was verified by immune simulation approach. The cDNA of the vaccine construct was cloned in silico within the expression vector pET30ax for protein expression. Collectively, the proposed vaccine construct may serve as a promising therapeutic in intervening F. nucleatum-induced human CRC.

Reverse vaccinology assisted design of a novel multi-epitope vaccine to target Wuchereria bancrofti cystatin: An immunoinformatics approach

Proteases are the critical mediators of immunomodulation exerted by the filarial parasites to bypass and divert host immunity. Cystatin is a small (∼15 kDa) immunomodulatory filarial protein and known to contribute in the immunomodulation strategy by inducing anti-inflammatory response through alternative activation of macrophages. Recently, Wuchereria bancrofti cystatin has been discovered as a ligand of human toll-like receptor 4 which is key behind the cystatin-induced anti-inflammatory response in major human antigen-presenting cells. Considering the pivotal role of cystatin in the immunobiology of filariasis, cystatin could be an efficacious target for developing vaccine. Herein, we present the design and in-silico analyses of a multi-epitope-based peptide vaccine to target W. bancrofti cystatin through immune-informatics approaches. The 262 amino acid long antigen construct comprises 9 MHC-I epitopes and MHC-II epitopes linked together by GPGPG peptide alongside an adjuvant (50S ribosomal protein L7/L12) at N terminus and 6 His tags at C terminus. Molecular docking study reveals that the peptide could trigger TLR4-MD2 to induce protective innate immune responses while the induced adaptive responses were found to be mediated by IgG, IgM and Th1 mediated responses. Notably, the designed vaccine exhibits high stability and no allergenicity in-silico. Furthermore, the muti epitope-vaccine was also predicted for its RNA structure and cloned in pET30ax for further experimental validation. Taken together, this study presents a novel multi-epitope peptide vaccine for triggering efficient innate and adaptive immune responses against W. bancrofti to intervene LF through immunotherapy.

Reducing the Immunogenicity of Pulchellin A-Chain, Ribosome-Inactivating Protein Type 2, by Computational Protein Engineering for Potential New Immunotoxins

Pulchellin is a plant biotoxin categorized as a type 2 ribosome-inactivating protein (RIPs) which potentially kills cells at very low concentrations. Biotoxins serve as targeting immunotoxins (IT), consisting of antibodies conjugated to toxins. ITs have two independent protein components, a human antibody and a toxin with a bacterial or plant source; therefore, they pose unique setbacks in immunogenicity. To overcome this issue, the engineering of epitopes is one of the beneficial methods to elicit an immunological response. Here, we predicted the tertiary structure of the pulchellin A-chain (PAC) using five common powerful servers and adopted the best model after refining. Then, predicted structure using four distinct computational approaches identified conformational B-cell epitopes. This approach identified some amino acids as a potential for lowering immunogenicity by point mutation. All mutations were then applied to generate a model of pulchellin containing all mutations (so-called PAM). Mutants’ immunogenicity was assessed and compared to the wild type as well as other mutant characteristics, including stability and compactness, were computationally examined in addition to immunogenicity. The findings revealed a reduction in immunogenicity in all mutants and significantly in N146V and R149A. Furthermore, all mutants demonstrated remarkable stability and validity in Molecular Dynamic (MD) simulations. During docking and simulations, the most homologous toxin to pulchellin, Abrin-A was applied as a control. In addition, the toxin candidate containing all mutations (PAM) disclosed a high level of stability, making it a potential model for experimental deployment. In conclusion, by eliminating B-cell epitopes, our computational approach provides a potential less immunogenic IT based on PAC.

Contact-Assisted Threading in Low-Homology Protein Modeling

The ability to successfully predict the three-dimensional structure of a protein from its amino acid sequence has made considerable progress in the recent past. The progress is propelled by the improved accuracy of deep learning-based inter-residue contact map predictors coupled with the rising growth of protein sequence databases. Contact map encodes interatomic interaction information that can be exploited for highly accurate prediction of protein structures via contact map threading even for the query proteins that are not amenable to direct homology modeling. As such, contact-assisted threading has garnered considerable research effort. In this chapter, we provide an overview of existing contact-assisted threading methods while highlighting the recent advances and discussing some of the current limitations and future prospects in the application of contact-assisted threading for improving the accuracy of low-homology protein modeling.

Pathogen-driven gene expression patterns lead to a novel approach to the identification of common therapeutic targets

Developing a common medication strategy for disease control and management could be greatly beneficial. Investigating the differences between diseased and healthy states using differentially expressed genes aids in understanding disease pathophysiology and enables the exploration of protein-drug interactions. This study aimed to find the most common genes in diarrhea-causing bacteria such as Salmonella enterica serovar Typhimurium, Campylobacter jejuni, Escherichia coli, Shigella dysenteriae (CESS) to find new drugs. Thus, differential gene expression datasets of CESS were screened through computational algorithms and programming. Subsequently, hub and common genes were prioritized from the analysis of extensive protein-protein interactions. Binding predictions were performed to identify the common potential therapeutic targets of CESS. We identified a total of 827 dysregulated genes that are highly linked to CESS. Notably, no common gene interaction was found among all CESS bacteria, but we identified 3 common genes in both Salmonella-Escherichia and Escherichia-Campylobacter infections. Later, out of 73 protein complexes, molecular simulations confirmed 5 therapeutic candidates from the CESS. We have developed a new pipeline for identifying therapeutic targets for a common medication strategy against CESS. However, further wet-lab validation is needed to confirm their effectiveness.

Comparative Modelling of Organic Anion Transporting Polypeptides: Structural Insights and Comparison of Binding Modes

To better understand the functionality of organic anion transporting polypeptides (OATPs) and to design new ligands, reliable structural data of each OATP is needed. In this work, we used a combination of homology model with molecular dynamics simulations to generate a comprehensive structural dataset, that encompasses a diverse set of OATPs but also their relevant conformations. Our OATP models share a conserved transmembrane helix folding harbouring a druggable binding pocket in the shape of an inner pore. Our simulations suggest that the conserved salt bridges at the extracellular region between residues on TM1 and TM7 might influence the entrance of substrates. Interactions between residues on TM1 and TM4 within OATP1 family shown their importance in transport of substrates. Additionally, in transmembrane (TM) 1/2, a known conserved element, interact with two identified motifs in the TM7 and TM11. Our simulations suggest that TM1/2-TM7 interaction influence the inner pocket accessibility, while TM1/2-TM11 salt bridges control the substrate binding stability.

Exploiting reverse vaccinology approach for the design of a multiepitope subunit vaccine against the major SARS-CoV-2 variants

The current COVID-19 pandemic, an infectious disease caused by the novel coronavirus (SARS-CoV-2), poses a threat to global health because of its high rate of spread and death. Currently, vaccination is the most effective method to prevent the spread of this disease. In the present study, we developed a novel multiepitope vaccine against SARS-CoV-2 containing Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (BA.1) variants. To this end, we performed a robust immunoinformatics approach based on multiple epitopes of the four structural proteins of SARS-CoV-2 (S, M, N, and E) from 475 SARS-CoV-2 genomes sequenced from the regions with the highest number of registered cases, namely the United States, India, Brazil, France, Germany, and the United Kingdom. To investigate the best immunogenic epitopes for linear B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL), we evaluated antigenicity, allergenicity, conservation, immunogenicity, toxicity, human population coverage, IFN-inducing, post-translational modifications, and physicochemical properties. The tertiary structure of a vaccine prototype was predicted, refined, and validated. Through docking experiments, we evaluated its molecular coupling to the key immune receptor Toll-Like Receptor 3 (TLR3). To improve the quality of docking calculations, quantum mechanics/molecular mechanics calculations (QM/MM) were used, with the QM part of the simulations performed using the density functional theory formalism (DFT). Cloning and codon optimization were performed for the successful expression of the vaccine in E. coli. Finally, we investigated the immunogenic properties and immune response of our SARS-CoV-2 multiepitope vaccine. The results of the simulations show that administering our prototype three times significantly increases the antibody response and decreases the amount of antigens. The proposed vaccine candidate should therefore be tested in clinical trials for its efficacy in neutralizing SARS-CoV-2.

Multi-Epitope-Based Vaccine Candidate for Monkeypox: An In Silico Approach

Currently, there are limited treatment options available for the monkeypox disease. We used a computational strategy to design a specific antigenic vaccine against pathogens. After using various immunoinformatic tools and filters, cytotoxic T-cell lymphocyte (CTL)-, helper T-cell lymphocyte (HTL)-, and interferon gamma (IFN-γ)-inducing epitopes, which comprised the vaccine, in addition to other parameters, such as antigenic and allergic profiles, were assessed to confirm the safety of the vaccine. However, vaccine interaction and stability with Toll-like receptors (TLRs) were assessed by dynamic simulation methods, and it was found that the constructed vaccine was stable. In addition, C-IMMSIM tools were used to determine the immune-response-triggering capabilities of the vaccine. These immunoinformatic findings reveal that constructed vaccine candidates may be capable of triggering an efficient immune response for monkeypox viral infections. However, experimental evaluation is required to verify the safety and immunogenic profile of constructed vaccines.

Design of Multi-Epitope Vaccine for Staphylococcus saprophyticus: Pan-Genome and Reverse Vaccinology Approach

Staphylococcus saprophyticus is a Gram-positive coccus responsible for the occurrence of cystitis in sexually active, young females. While effective antibiotics against this organism exist, resistant strains are on the rise. Therefore, prevention via vaccines appears to be a viable solution to address this problem. In comparison to traditional techniques of vaccine design, computationally aided vaccine development demonstrates marked specificity, efficiency, stability, and safety. In the present study, a novel, multi-epitope vaccine construct was developed against S. saprophyticus by targeting fully sequenced proteomes of its five different strains, which were examined using a pangenome and subtractive proteomic strategy to characterize prospective vaccination targets. The three immunogenic vaccine targets which were utilized to map the probable immune epitopes were verified by annotating the entire proteome. The predicted epitopes were further screened on the basis of antigenicity, allergenicity, water solubility, toxicity, virulence, and binding affinity towards the DRB*0101 allele, resulting in 11 potential epitopes, i.e., DLKKQKEKL, NKDLKKQKE, QDKLKDKSD, NVMDNKDLE, TSGTPDSQA, NANSDGSSS, GSDSSSSNN, DSSSSNNDS, DSSSSDRNN, SSSDRNNGD, and SSDDKSKDS. All these epitopes have the efficacy to cover 99.74% of populations globally. Finally, shortlisted epitopes were joined together with linkers and three different adjuvants to find the most stable and immunogenic vaccine construct. The top-ranked vaccine construct was further scrutinized on the basis of its physicochemical characterization and immunological profile. The non-allergenic and antigenic features of modeled vaccine constructs were initially validated and then subjected to docking with immune receptor major histocompatibility complex I and II (MHC-I and II), resulting in strong contact. In silico cloning validations yielded a codon adaptation index (CAI) value of 1 and an ideal percentage of GC contents (46.717%), indicating a putative expression of the vaccine in E. coli. Furthermore, immune simulation demonstrated that, after injecting the proposed MEVC, powerful antibodies were produced, resulting in the sharpest peaks of IgM + IgG formation (>11,500) within 5 to 15 days. Experimental testing against S. saprophyticus can evaluate the safety and efficacy of these prophylactic vaccination designs.

Computationally Decoding NudF Residues To Enhance the Yield of the DXP Pathway

Terpenoids form a large pool of highly diverse organic compounds possessing several economically important properties, including nutritional, aromatic, and pharmacological properties. The 1-deoxy-d-xylulose 5-phosphate (DXP) pathway's end enzyme, nuclear distribution protein (NudF), interacting with isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), is critical for the synthesis of isoprenol/prenol/downstream compounds. The enzyme is yet to be thoroughly investigated to increase the overall yield of terpenoids in the Bacillus subtilis, which is widely used in industry and is generally regarded as a safe (GRAS) bacterium. The study aims to analyze the evolutionary conservation across the active site for mapping the key residues for mutagenesis studies. The 37-sequence data set, extracted from 103 Bacillus subtilis entries, shows a high phylogenetic divergence, and only six one-motif sequences ASB92783.1, ASB69297.1, ASB56714.1, AOR97677.1, AOL97023.1, and OAZ71765.1 show a monophyly relationship, unlike a complete polyphyly relationship between the other 31 three-motif sequences. Furthermore, only 47 of 179 residues of the representative sequence CUB50584.1 are observed to be significantly conserved. Docking analysis suggests a preferential bias of adenosine diphosphate (ADP)-ribose pyrophosphatase toward IPP, and a nearly threefold energetic difference is observed between IPP and DMAPP. The loops are hereby shown to play a regulatory role in guiding the promiscuity of NudF toward a specific ligand. Computational saturation mutagenesis of the seven hotspot residues identifies two key positions LYS78 and PHE116, orderly encoded within loop1 and loop7, majorly interacting with the ligands DMAPP and IPP, and their mutants K78I/K78L and PHE116D/PHE116E are found to stabilize the overall conformation. Molecular dynamics analysis shows that the IPP complex is significantly more stable than the DMAPP complex, and the NudF structure is very unstable. Besides showing a promiscuous binding of NudF with ligands, the analysis suggests its rate-limiting nature. The study would allow us to customize the metabolic load toward the synthesis of any of the downstream molecules. The findings would pave the way for the development of catalytically improved NudF mutants for the large-scale production of specific terpenoids with significant nutraceutical or commercial value.

Immunological investigation of a multiepitope peptide vaccine candidate based on main proteins of SARS-CoV-2 pathogen

Multiepitope vaccines could induce multiantigenic immunity against large complex pathogens with different strain variants. Herein, the in silico, in vitro and in vivo studies were used to design and develop a novel candidate antigenic multiepitope vaccine against SARS-CoV-2 pathogen. The designed multiepitope construct targets the spike glycoprotein (S), membrane protein (M), and nucleocapsid phosphoprotein (N) of SARS-CoV-2 (i.e., the S-N-M construct). This construct contains the cytotoxic T lymphocyte (CTL)-, helper T lymphocyte (HTL)-, and linear B lymphocyte (LBL)-inducing epitopes. The multiepitope s-n-m fusion gene was subcloned in prokaryotic (pET24a) and eukaryotic (pcDNA3.1) expression vectors. Its expression was evaluated in mammalian cell line using LL37 cell penetrating peptide. Moreover, the recombinant multiepitope S-N-M peptide was produced in E. coli strain. Finally, mice were immunized using homologous and heterologous regimens for evaluation of immune responses. Our data indicated that the multiepitope S-N-M peptide construct combined with Montanide 720 in homologous regimen significantly stimulated total IgG, IgG2a, IFN-γ, TNF-α, IL-15, IL-21 and IL-6, and Granzyme B secretion as compared to other groups. Moreover, the pcDNA-s-n-m/ LL37 nanoparticles significantly induced higher immune responses than the naked DNA in both homologous and heterologous regimens. In general, our designed multiepitope vaccine construct can be considered as a vaccine candidate in SARS-CoV-2 infection model.

Execution and Design of an Anti HPIV-1 Vaccine with Multiple Epitopes Triggering Innate and Adaptive Immune Responses: An Immunoinformatic Approach

Human Parainfluenza Virus (HPIV) Type-1, which is an anti-sense ribonucleic acid (RNA) virus belonging to the paramyxoviridae family, induces upper and lower respiratory tract infections. The infections caused by the HPIV Type-1 virus are usually confined to northwestern regions of America. HPIV-1 causes infections through the virulence of the hemagglutinin-neuraminidase (HN) protein, which plays a key role in the attachment of the viral particle with the host’s receptor cells. To the best of our knowledge, there is no effective antiviral drugs or vaccines being developed to combat the infection caused by HPIV-1. In the current study, a multiple epitope-based vaccine was designed against HPIV-1 by taking the viral HN protein as a probable vaccine candidate. The multiple epitopes were selected in accordance with their allergenicity, antigenicity and toxicity scoring. The determined epitopes of the HN protein were connected simultaneously using specific conjugates along with an adjuvant to construct the subunit vaccine, with an antigenicity score of 0.6406. The constructed vaccine model was docked with various Toll-like Receptors (TLRs) and was computationally cloned in a pET28a (+) vector to analyze the expression of vaccine sequence in the biological system. Immune stimulations carried out by the C-ImmSim Server showed an excellent result of the body’s defense system against the constructed vaccine model. The AllerTop tool predicted that the construct was non-allergen with and without the adjuvant sequence, and the VaxiJen 2.0 with 0.4 threshold predicted that the construct was antigenic, while the Toxinpred predicted that the construct was non-toxic. Protparam results showed that the selected protein was stable with 36.48 instability index (II) scores. The Grand average of Hydropathicity or GRAVY score indicated that the constructed protein was hydrophilic in nature. Aliphatic index values (93.53) confirmed that the construct was thermostable. This integrated computational approach shows that the constructed vaccine model has a potential to combat laryngotracheobronchitis infections caused by HPIV-I.

Reverse vaccinology approach to design a multi-epitope vaccine construct based on the Mycobacterium tuberculosis biomarker PE_PGRS17

Mycobacterium tuberculosis (Mtb) is responsible for high mortality rates in many low- and middle-income countries. This infectious disease remains accountable for around 1.4 million deaths yearly. Finding effective control measures against Mtb has become imperative. Vaccination has been regarded as the safe and lasting control measure to curtail the impact of Mtb. This study used the Mtb protein biomarker PE_PGRS17 to design a multi-epitope vaccine. A previous study predicted a strong antigenic property of PE_PGRS17. Immunogenic properties such as antigenicity, toxicity, and allergenicity were predicted for the PE_PGRS17 biomarker, specific B- and T-cell epitope sequences, and the final multiple epitope vaccine (MEV) construct. Algorithmic tools predicted the T- and B-cell epitopes and those that met the immunogenic properties were selected to construct the MEV candidate for predicted vaccine development. The epitopes were joined via linkers and an adjuvant was attached to the terminals of the entire vaccine construct. Immunogenic properties, and physicochemical and structural predictions gave insight into the MEV construct. The assembled vaccine candidate was docked with a receptor and validated using web-based tools. An immune simulation was performed to imitate the immune response after exposure to a dosed administrated predicted MEV subunit. An in silico cloning and codon optimisation gave insight into optimal expression conditions regarding the MEV candidate. In conclusion, the generated MEV construct may potentially emit both cellular and humoral responses which are vital in the development of a peptide-based vaccine against Mtb; nonetheless, further experimental validation is still required.

Reannotation of Fly Amanita l-DOPA Dioxygenase Gene Enables Its Cloning and Heterologous Expression

The l-DOPA dioxygenase of Amanita muscaria (AmDODA) participates in the biosynthesis of betalain- and hygroaurin-type natural pigments. AmDODA is encoded by the dodA gene, whose DNA sequence was inferred from cDNA and gDNA libraries almost 30 years ago. However, reports on its heterologous expression rely on either the original 5'-truncated cDNA plasmid or artificial gene synthesis. We provide unequivocal evidence that the heterologous expression of AmDODA from A. muscaria specimens is not possible by using the coding sequence previously inferred for dodA. Here, we rectify and reannotate the full-length coding sequence for AmDODA and express a 205-aa His-tagged active enzyme, which was used to produce the l-DOPA hygroaurin, a rare fungal pigment. Moreover, AmDODA and other isozymes from bacteria were submitted to de novo folding using deep learning algorithms, and their putative active sites were inferred and compared. The wide catalytic pocket of AmDODA and the presence of the His-His-His and His-His-Asp motifs can provide insight into the dual cleavage of l-DOPA at positions 2,3 and 4,5 as per the mechanism proposed for nonheme dioxygenases.

In Silico Prediction of Cross-Reactive Epitopes of Tropomyosin from Shrimp and Other Arthropods Involved in Allergy

Tropomyosin in shellfish is considered a major cross-reactive allergen in house dust mites and cockroaches; however, the specific epitopes have not been elucidated. Therefore, this study aimed to identify the consensus antigenic determinant among shrimp, house dust mites, and cockroaches using in silico methods. The protein sequences of tropomyosin, including Der f 10, Mac r 1, Pen a 1, Pen m 1, Per a 7, and Bla g 7, were retrieved from the UniProt database. The 3D structures were derived from the AlphaFold or modeled using the Robetta. The determination of linear epitopes was performed by AlgPRED and BepiPRED for B cell epitope, and NetMHCIIpan and NetMHCII for T cell epitope, while Ellipro was used to evaluate conformational epitopes. Fourteen peptides were discovered as the consensus linear B cell epitopes, while seventeen peptides were identified as linear T cell epitopes specific to high-frequency HLA-DR and HLA-DQ alleles. The conformational determination of B cell epitopes provided nine peptides, in which residues 209, 212, 255–256, and 258–259 were found in both linear B cell and linear T cell epitope analysis. This data could be utilized for further in vitro study and may contribute to immunotherapy for allergic diseases associated with tropomyosin.

Identification of the most damaging nsSNPs in the human CFL1 gene and their functional and structural impacts on cofilin-1 protein

The cofilin-1 protein, encoded by CFL1, is an actin-binding protein that regulates F-actin depolymerization and nucleation activity through phosphorylation and dephosphorylation. CFL1 has been implicated in the development of neurodegenerative diseases (Alzheimer's disease and Huntington's disease), neuronal migration disorders (lissencephaly, epilepsy, and schizophrenia), and neural tube closure defects. Mutations in CFL1 have been associated with impaired neural crest cell migration and neural tube closure defects. In our study, various computational approaches were utilized to explore single-nucleotide polymorphisms (SNPs) in CFL1. The Variation Viewer and gnomAD databases were used to retrieve CFL1 SNPs, including 46 nonsynonymous SNPs (nsSNPs). The functional and structural annotation of SNPs was performed using 12 sequence-based web applications, which identified 20 nsSNPs as being the most likely to be deleterious or disease-causing. The conservation of cofilin-1 protein structures was illustrated using the ConSurf and PROSITE web servers, which projected the 12 most deleterious nsSNPs onto conserved domains, with the potential to disrupt the protein's functionality. These 12 nsSNPs were selected for protein structure construction, and the DynaMut/DUET servers predicted that the protein variants V7G, L84P, and L99A were the most likely to be damaging to the cofilin-1 protein structure or function. The evaluation of molecular docking studies demonstrated that the L99A and L84P cofilin-1 variants reduce the binding affinity for actin compared with the native cofilin-1 structure, and molecular dynamic simulation studies confirmed that these variants might destabilize the protein structure. The consequences of putative mutations on protein-protein interactions and post-translational modification sites in the cofilin-1 protein structure were analyzed. This study represents the first complete approach to understanding the effects of nsSNPs within the actin-depolymerizing factor/cofilin family, which suggested that SNPs resulting in L84P (rs199716082) and L99A (rs267603119) variants represent significant CFL1 mutations associated with disease development.

Combined Subcutaneous-Intranasal Immunization With Epitope-Based Antigens Elicits Binding and Neutralizing Antibody Responses in Serum and Mucosae Against PRRSV-2 and SARS-CoV-2

New vaccine design approaches, platforms, and immunization strategies might foster antiviral mucosal effector and memory responses to reduce asymptomatic infection and transmission in vaccinated individuals. Here, we investigated a combined parenteral and mucosal immunization scheme to induce local and serum antibody responses, employing the epitope-based antigens 3BT and NG19m. These antigens target the important emerging and re-emerging viruses PRRSV-2 and SARS-CoV-2, respectively. We assessed two versions of the 3BT protein, which contains conserved epitopes from the GP5 envelope protein of PRRSV-2: soluble and expressed by the recombinant baculovirus BacDual-3BT. On the other hand, NG19m, comprising the receptor-binding motif of the S protein of SARS-CoV-2, was evaluated as a soluble recombinant protein only. Vietnamese mini-pigs were immunized employing different inoculation routes: subcutaneous, intranasal, or a combination of both (s.c.-i.n.). Animals produced antigen-binding and neut1ralizing antibodies in serum and mucosal fluids, with varying patterns of concentration and activity, depending on the antigen and the immunization schedule. Soluble 3BT was a potent immunogen to elicit binding and neutralizing antibodies in serum, nasal mucus, and vaginal swabs. The vectored immunogen BacDual-3BT induced binding antibodies in serum and mucosae, but PRRSV-2 neutralizing activity was found in nasal mucus exclusively when administered intranasally. NG19m promoted serum and mucosal binding antibodies, which showed differing neutralizing activity. Only serum samples from subcutaneously immunized animals inhibited RBD-ACE2 interaction, while mini-pigs inoculated intranasally or via the combined s.c.-i.n. scheme produced subtle neutralizing humoral responses in the upper and lower respiratory mucosae. Our results show that intranasal immunization, alone or combined with subcutaneous delivery of epitope-based antigens, generates local and systemic binding and neutralizing antibodies. Further investigation is needed to evaluate the capability of the induced responses to prevent infection and reduce transmission.

HSP60-Derived Peptide as an LPS/TLR4 Modulator: An in silico Approach

As a part of innate immunity mechanisms, the Toll-like receptor (TLR) signaling pathway serves as one of the mainstay lines of defense against pathogenic microorganisms and cell dysfunction. Nevertheless, TLR overactivation induces a systemic proinflammatory environment compromising organ function or causing the patient’s death. TLRs modulators, specially those focused for TLR4, remain a promising approach for inflammatory diseases treatment, being peptide-based therapy a trendy approach. Heat shock protein 60 (HSP60) not only plays a pivotal role in the development of several maladies with strong inflammatory components but also HSP60 peptides possess anti-inflammatory properties in TLR4-mediated diseases, such as diabetes, arthritis, and atherosclerosis. The experimental treatment using HSP60 peptides has proven to be protective in preclinical models of the heart by hampering inflammation and modulating the activity of immune cells. Nonetheless, the effect that these peptides may exert directly on cells that express TLR and its role to inhibit overactivation remain elusive. The aim of this study is to evaluate by molecular docking, a 15 amino acid long-HSP60 peptide (Peptide-2) in the lipopolysaccharide (LPS) binding site of TLR4/MD2, finding most Peptide-2 resulting conformations posed into the hydrophobic pocket of MD2. This observation is supported by binding energy obtained for the control antagonist Eritoran, close to those of Peptide-2. This last does not undergo drastic structural changes, moving into a delimited space, and maintaining the same orientation during molecular dynamics simulation. Based on the two computational techniques applied, interaction patterns were defined for Peptide-2. With these results, it is plausible to propose a peptidic approach for TLR4 modulation as a new innovative therapy to the treatment of TLR4-related cardiovascular diseases.

Computational vaccinology guided design of multi-epitope subunit vaccine against a neglected arbovirus of the Americas

Mayaro virus (MAYV) is an arbovirus found in the Americas that can cause debilitating arthritogenic disease. Although it is an emerging virus, the only current approach is vector control, as there are no approved vaccines to prevent MAYV infection nor therapeutics to treat it. In search of an effective vaccine candidate against MAYV, we used immunoinformatics and molecular modeling to attempt to identify promiscuous T-cell epitopes of the nonstructural polyproteins (nsP1, nsP2, nsP3, and nsP4) from 127 MAYV genomes sequenced in the Americas (08 Bolivia, 72 Brazil, 04 French Guiana, 05 Haiti, 20 Peru, 04 Trinidad and Tobago, and 14 Venezuela). For this purpose, consensus sequences of 360 proteins were used to identify short protein sequences that can bind to MHC I class (MHC II). Our analysis revealed 56 potential MHC-I/TCD8+ (29 MHC-II/TCD4+) epitopes, but only 6 (16) TCD8+ (TCD4+) epitopes showed high antigenicity and conservation, non-allergenicity, non-toxicity, and excellent population coverage. Finally, classical and quantum mechanical calculations (QM:MM) were used to improve the quality of the docking calculations, with the QM part of the simulations performed using the density functional theory formalism (DFT). These results provide insights for the advancement of diagnostic platforms, vaccine development, and immunotherapeutic interventions.Communicated by Ramaswamy H. Sarma.

Designing efficient multi-epitope peptide-based vaccine by targeting the antioxidant thioredoxin of bancroftian filarial parasite

Thioredoxin is a low molecular weight redox-active protein of filarial parasite that plays a crucial role in downregulating the host immune response to prolong the survival of the parasite within the host body. It has the ability to cope up with the oxidative challenges posed by the host. Hence, the antioxidant protein of the filarial parasite has been suggested to be a useful target for immunotherapeutic intervention of human filariasis. In this study, we have designed a multi-epitope peptide-based vaccine using thioredoxin of Wuchereria bancrofti. Different MHC-I and MHC-II epitopes were predicted using various web servers to construct the vaccine model as MHC-I and MHC-II epitopes are crucial for the development of both humoral and cellular immune responses. Moreover, TLRs specific adjuvants were also incorporated into the vaccine candidates as TLRs are the key immunomodulator to execute innate immunity. Protein-protein molecular docking and simulation analysis between the vaccine and human TLR was performed. TLR5 is the most potent receptor to convey the vaccine-mediated inductive signal for eliciting an innate immune response. A satisfactory immunogenic report from an in-silico immune simulation experiment directed us to propose our vaccine model for experimental and clinical validation. The reverse translated vaccine sequence was also cloned in pET28a(+) to apply the concept in a wet lab experiment in near future. Taken together, this in-silico study on the design of a vaccine construct to target W. bancrofti thioredoxin is predicted to be a future hope in saving human-being from the threat of filariasis.

In silico and in vivo approaches to recombinant multi-epitope immunogen of GroEL provides efficient cross protection against S. Typhimurium, S. flexneri, and S. dysenteriae

OBJECTIVES

Stress or Heat Shock Proteins (HSPs) have been included in various operations like protein folding, autophagy, and apoptosis. HSP families recognize as protective antigens in a wide range of bacteria because they have been conserved through evolution. Due to their homology as well as antigenicity they are competent for applying in cross-protection against bacterial diseases.

METHODS

In the present study, bioinformatics approaches utilized to design epitope-based construction of Hsp60 (or GroEL) protein. In this regard, potential B-cell and T-cell epitopes except for allergenic sequences were selected by immunoinformatic tools. The structural and functional aspects of the DNA, RNA, and protein levels were assessed by bioinformatics software. Following in silico investigations, recombinant GroEL multi-epitope of Salmonella typhi was expressed, purified, and validated. Mouse groups were immunized with recombinant protein and humoral immune response was measured by enzyme linked immunosorbent assay (ELISA). Animal challenge against Salmonella Typhimurium, Shigella flexneri, and Shigella dysenteriae was evaluated.

RESULTS

recombinant protein expression and purification with 14.3 kilodaltons (kDa) was confirmed by SDS-PAGE and western blotting. After animal administration, the immunoglobulins evaluated increase after each immunization. Immunized antisera exhibited 80%, 40%, and 40% protection against the lethal dose infection by S. Typhimurium, S. flexneri, and S. dysenteriae respectively. Passive immunization conferred 50%, 30%, and 30% protection in mice against S. Typhimurium, S. flexneri and S. dysentery respectively. In addition, bacterial organ load had exhibited a significant decrease in colony forming unit (CFU) in the liver and spleen of the immunized mice compared to the control.

CONCLUSION

Our study demonstrates the efficacy of S. Typhi recombinant GroEL multi-epitope to consider as a universal immunogen candidate versus multiple bacterial pathogens.

Molecular characterization and structural dynamics of Aquaporin1 from walking catfish in lipid bilayers

Aquaporin's (AQPs) are the major superfamily of small integral membrane proteins that facilitates transportation of water, urea, ammonia, glycerol and ions across biological cell membranes. Despite of recent advancements made in understanding the biology of Aquaporin's, only few isoforms of aquaporin 1 (AQP1) some of the teleost fish species have been characterized at molecular scale. In this study, we made an attempt to elucidate the molecular mechanism of water transportation in AQP1 from walking catfish (Clarias batrachus), a model species capable of breathing in air and inhabits in challenging environments. Using state-of-the-art computational modelling and all-atoms molecular dynamics simulation, we explored the structural dynamics of full-length aquaporin 1 from walking catfish (CbAQP1) in lipid mimetic bilayers. Unlike AQP1 of human and bovine, structural ensembles of CbAQP1 from MD revealed discrete positioning of pore lining residues at the intracellular end. Snapshots from MD simulation displayed differential dynamics of aromatic/arginine (ar/R) filter and extracellular loop C bridging transmembrane (TM) helix H3 and H4. Distinct conformation of large extracellular loops, loop bridging TM2 domain and HB helix along with positioning of selectivity filter lining residues controls the permeability of water across the bilayer. Moreover, the identified unique and conserved lipid binding sites with 100% lipid occupancy signifies lipid mediated structural dynamics of CbAQP1. All-together, this is the first ever report on structural-dynamics of aquaporin 1 in walking catfish which will be useful to understand the molecular basis of transportation of water and other small molecules under varying degree of hyperosmotic environment.

Immunoinformatics and molecular docking studies reveal a novel Multi-Epitope peptide vaccine against pneumonia infection

Pneumonia is a major endemic disease around the world, and an effective vaccine is the need of the hour to fight against the disease. When there are no appropriate antiviral and associated therapies available, vaccine development becomes even more essential. Therefore, in the present study, a variety of immunoinformatics techniques was utilized to develop a novel multi-epitope vaccine that targets the highly immunodominant type 3 fimbrial protein of Klebsiella pneumoniae, the causal organism for pneumonia. The putative B and T cell epitopes were predicted from the protein and screened for antigenicity, toxicity, allergenicity, and cross-reactivity with human proteomes. Subsequently, the selected epitopes were joined with the help of linkers to form a robust vaccine construct. In addition, an adjuvant was applied to the N-terminal of the construct to improve the immunogenicity of the vaccine. The physicochemical properties, solubility, the secondary and tertiary structure of the final vaccine were also established. MD simulations for 100 ns were employed to assess the stability of the vaccine-TLR-2 docked complex. The final vaccine was optimized and cloned in pET28a (+) vector with His-tag to achieve maximum vaccine protein expression for ease of purification. Immune simulation results indicated the potency of this vaccine candidate as a probable therapeutic agent. In conclusion, the overall results of various immunoinformatics tools and methods employed revealed that the constructed multi-epitope vaccine exhibits a high potential for stimulating both B and T-cells immune responses against pneumonia infection. However, experimental immunological studies are required to corroborate the viability of the novel multi-epitope construct as a commercial vaccine.

Protein sequence-to-structure learning: Is this the end(-to-end revolution)?

The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.

Protein oligomer structure prediction using GALAXY in CASP14

Proteins perform their functions by interacting with other biomolecules. For these interactions, proteins often form homo- or hetero-oligomers as well. Thus, oligomer protein structures provide important clues regarding the biological roles of proteins. To this end, computational prediction of oligomer structures may be a useful tool in the absence of experimentally resolved structures. Here, we describe our server and human-expert methods used to predict oligomer structures in the CASP14 experiment. Examples are provided for cases in which manual domain-splitting led to improved oligomeric domain structures by ab initio docking, automated oligomer structure refinement led to improved subunit orientation and terminal structure, and manual oligomer modeling utilizing literature information generated a reasonable oligomer model. We also discussed the results of post-prediction docking calculations with AlphaFold2 monomers as input in comparison to our blind prediction results. Overall, ab initio docking of AlphaFold2 models did not lead to better oligomer structure prediction, which may be attributed to the interfacial structural difference between the AlphaFold2 monomer structures and the crystal oligomer structures. This result poses a next-stage challenge in oligomer structure prediction after the success of AlphaFold2. For successful protein assembly structure prediction, a different approach that exploits further evolutionary information on the interface and/or flexible docking taking the interfacial conformational flexibilities of subunit structures into account is needed.

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach

Currently, no approved vaccine is available against the Middle East respiratory syndrome coronavirus (MERS-CoV), which causes severe respiratory disease. The spike glycoprotein is typically considered a suitable target for MERS-CoV vaccine candidates. A computational strategy can be used to design an antigenic vaccine against a pathogen. Therefore, we used immunoinformatics and computational approaches to design a multi-epitope vaccine that targets the spike glycoprotein of MERS-CoV. After using numerous immunoinformatics tools and applying several immune filters, a poly-epitope vaccine was constructed comprising cytotoxic T-cell lymphocyte (CTL)-, helper T-cell lymphocyte (HTL)-, and interferon-gamma (IFN-γ)-inducing epitopes. In addition, various physicochemical, allergenic, and antigenic profiles were evaluated to confirm the immunogenicity and safety of the vaccine. Molecular interactions, binding affinities, and the thermodynamic stability of the vaccine were examined through molecular docking and dynamic simulation approaches, during which we identified a stable and strong interaction with Toll-like receptors (TLRs). In silico immune simulations were performed to assess the immune-response triggering capabilities of the vaccine. This computational analysis suggested that the proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat viral infections; however, experimental evaluations remain necessary to verify the exact safety and immunogenicity profile of this vaccine.

DeepRefiner: high-accuracy protein structure refinement by deep network calibration

The DeepRefiner webserver, freely available at http://watson.cse.eng.auburn.edu/DeepRefiner/, is an interactive and fully configurable online system for high-accuracy protein structure refinement. Fuelled by deep learning, DeepRefiner offers the ability to leverage cutting-edge deep neural network architectures which can be calibrated for on-demand selection of adventurous or conservative refinement modes targeted at degree or consistency of refinement. The method has been extensively tested in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments under the group name 'Bhattacharya-Server' and was officially ranked as the No. 2 refinement server in CASP13 (second only to 'Seok-server' and outperforming all other refinement servers) and No. 2 refinement server in CASP14 (second only to 'FEIG-S' and outperforming all other refinement servers including 'Seok-server'). The DeepRefiner web interface offers a number of convenient features, including (i) fully customizable refinement job submission and validation; (ii) automated job status update, tracking, and notifications; (ii) interactive and interpretable web-based results retrieval with quantitative and visual analysis and (iv) extensive help information on job submission and results interpretation via web-based tutorial and help tooltips.