SCRATCH: a protein structure and structural feature prediction server

Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused COVID-19 disease in China. So far, no vaccine has licensed to protect against infection with COVID-19, therefore an effective COVID-19 vaccine needed. The aim of this study was to predict antigenic peptides of SARS-CoV-2 for designing the COVID-19 vaccine using immunoinformatic analysis. In this study, T and B-cell epitopes of S protein were predicted and screened based on the antigenicity, toxicity, allergenicity, and cross-reactivity with human proteomes. The epitopes were joined by the appropriate linker. LT-IIc as an adjuvant was attached to the end of the structure. The secondary and 3D structure of the vaccine was predicted. The refinement process was performed to improve the quality of the 3D model structure; the validation process is performed using the Ramachandran plot and ProSA z-score. The proposed vaccine's binding affinity to the HLA-A11:01 and HLA-DRB1_01:01 molecule was evaluated by molecular docking. Using molecular dynamics, the stability of vaccine-HLA complexes was also evaluated. Finally, in silico gene cloning was performed in the pET30a (+) vector. The findings suggest that the current vaccine may be a promising vaccine to prevent SARS-CoV-2 infection.

A single-model quality assessment method for poor quality protein structure

Background

Quality assessment of protein tertiary structure prediction models, in which structures of the best quality are selected from decoys, is a major challenge in protein structure prediction, and is crucial to determine a model’s utility and potential applications. Estimating the quality of a single model predicts the model’s quality based on the single model itself. In general, the Pearson correlation value of the quality assessment method increases in tandem with an increase in the quality of the model pool. However, there is no consensus regarding the best method to select a few good models from the poor quality model pool.

Results

We introduce a novel single-model quality assessment method for poor quality models that uses simple linear combinations of six features. We perform weighted search and linear regression on a large dataset of models from the 12th Critical Assessment of Protein Structure Prediction (CASP12) and benchmark the results on CASP13 models. We demonstrate that our method achieves outstanding performance on poor quality models.

Conclusions

According to results of poor protein structure assessment based on six features, contact prediction and relying on fewer prediction features can improve selection accuracy.

Yersinia pestis Antigen F1 but Not LcrV Induced Humoral and Cellular Immune Responses in Humans Immunized with Live Plague Vaccine-Comparison of Immunoinformatic and Immunological Approaches

The recent progress in immunoinformatics provided the basis for an accelerated development of target-specific peptide vaccines as an alternative to the traditional vaccine concept. However, there is still limited information on whether the in silico predicted immunoreactive epitopes correspond to those obtained from the actual experiments. Here, humoral and cellular immune responses to two major Yersinia pestis protective antigens, F1 and LcrV, were studied in human donors immunized with the live plague vaccine (LPV) based on the attenuated Y. pestis strain EV line NIIEG. The F1 antigen provided modest specific cellular (mixed T helper 1 (Th1)/Th2 type) and humoral immune responses in vaccinees irrespective of the amount of annual vaccinations and duration of the post-vaccination period. The probing of the F1 overlapping peptide library with the F1-positive sera revealed the presence of seven linear B cell epitopes, which were all also predicted by in silico assay. The immunoinformatics study evaluated their antigenicity, toxicity, and allergenic properties. The epitope TSQDGNNH was mostly recognized by the sera from recently vaccinated donors rather than antibodies from those immunized decades ago, suggesting the usefulness of this peptide for differentiation between recent and long-term vaccinations. The in silico analysis predicted nine linear LcrV-specific B-cell epitopes; however, weak antibody and cellular immune responses prevented their experimental evaluation, indicating that LcrV is a poor marker of successful vaccination. No specific Th17 immune response to either F1 or LcrV was detected, and there were no detectable serum levels of F1-specific immunoglobulin A (IgA) in vaccinees. Overall, the general approach validated in the LPV model could be valuable for the rational design of vaccines against other neglected and novel emerging infections with high pandemic potency.

Development of multi-epitope subunit vaccine for protection against the norovirus' infections based on computational vaccinology

Human Norovirus belongs to a family Calciviridae, and was identified in the outbreak of gastroenteritis in Norwalk, due to its seasonal prevalence known as "winter vomiting disease." Treatment of Norovirus infection is still mysterious because there is no effective antiviral drugs or vaccine developed to protect against the infection, to eradicate the infection an effective vaccine should be developed. In this study, capsid protein (A7YK10), small protein (A7YK11), and polyprotein (A7YK09) were utilized. These proteins were subjected to B and T cell epitopes prediction by using reliable immunoinformatics tools. The antigenic and non-allergenic epitopes were selected for the subunit vaccine, which can activate cellular and humoral immune responses. Linkers joined these epitopes together. The vaccine structure was modelled and validated by using Errat, ProSA, and rampage servers. The modelled vaccine was docked with TLR-7. The stability of the docked complex was evaluated by MD simulation. To apply the concept in a wet lab, the reverse translated vaccine sequence was cloned in pET28a (+). The vaccine developed in this study requires experimental validation to ensure its effectiveness against the disease.Communicated by Ramaswamy H. Sarma.

Designing of a novel multi-epitope peptide based vaccine against Brugia malayi: An in silico approach

In spite of the tremendous efforts of the World Health Organization, scientific and medical community to eradicate lymphatic filariasis (LF) within 2020, the disease is still taking a huge toll on mankind throughout the globe. The current therapeutic strategies and solution measures against this alarming condition are suffering from a number of limitations such as inadequate effectiveness of the drugs against the adult-stage parasites, low bioavailability, and emergence of resistance. Considering this situation, development of the new therapeutics are urgently needed to combat human LF, especially targeting the adult filarial nematodes. Brugia malayi, the causative parasite for the human brugian filariasis majorly found in the countries of the South-Asia. In this study, we have designed a vaccine candidate using B-cell and T-cell epitopes derived from the aspartic protease of B. malayi (BmASP-1) and found to display significant humoral and cell mediated immune responses using in-silico approaches. Protein-protein docking between the human Toll-like receptor 4 (TLR4) and the vaccine candidate helped us to predict the way of inductive signaling that leads to immune-response. Molecular dynamics (MD) simulation studies further confirmed the proper docking between the TLR4 and vaccine candidate. Moreover, in-silico cloning of the vaccine element within the expression vector was found useful to optimize the restriction sites as well as to determine the primer location. Taken together, the in-silico vaccine candidate depicted in this study promises could be a useful therapeutic option for treating LF and experimental validation of this study is expected to strengthen the candidature of the said vaccine in the future.

Protein-ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data

MOTIVATION

Knowledge of protein-ligand binding residues is important for understanding the functions of proteins and their interaction mechanisms. From experimentally solved protein structures, how to accurately identify its potential binding sites of a specific ligand on the protein is still a challenging problem. Compared with structure-alignment-based methods, machine learning algorithms provide an alternative flexible solution which is less dependent on annotated homogeneous protein structures. Several factors are important for an efficient protein-ligand prediction model, e.g. discriminative feature representation and effective learning architecture to deal with both the large-scale and severely imbalanced data.

RESULTS

In this study, we propose a novel deep-learning-based method called DELIA for protein-ligand binding residue prediction. In DELIA, a hybrid deep neural network is designed to integrate 1D sequence-based features with 2D structure-based amino acid distance matrices. To overcome the problem of severe data imbalance between the binding and nonbinding residues, strategies of oversampling in mini-batch, random undersampling and stacking ensemble are designed to enhance the model. Experimental results on five benchmark datasets demonstrate the effectiveness of proposed DELIA pipeline.

AVAILABILITY AND IMPLEMENTATION

The web server of DELIA is available at www.csbio.sjtu.edu.cn/bioinf/delia/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Toxoplasma gondii GRA60 is an effector protein that modulates host cell autonomous immunity and contributes to virulence

Toxoplasma gondii infects virtually any nucleated cell and resides inside a non-phagocytic vacuole surrounded by a parasitophorous vacuolar membrane (PVM). Pivotal to the restriction of T. gondii dissemination upon infection in murine cells is the recruitment of immunity regulated GTPases (IRGs) and guanylate binding proteins (GBPs) to the PVM that leads to pathogen elimination. The virulent T. gondii type I RH strain secretes a handful of effectors including the dense granule protein GRA7, the serine-threonine kinases ROP17 and ROP18, and a pseudo-kinase ROP5, that synergistically inhibit the recruitment of IRGs to the PVM. Here, we characterise GRA60, a novel dense granule effector, which localises to the vacuolar space and PVM and contributes to virulence of RH in mice, suggesting a role in the subversion of host cell defence mechanisms. Members of the host cell IRG defence system Irgb10 and Irga6 are recruited to the PVM of RH parasites lacking GRA60 as observed previously for the avirulent RHΔrop5 mutant, with RH preventing such recruitment. Deletion of GRA60 in RHΔrop5 leads to a recruitment of IRGs comparable to the single knockouts. GRA60 therefore represents a novel parasite effector conferring resistance to IRGs in type I parasites, and found associated to ROP18, a member of the virulence complex.

Protein profiles: Biases and protocols

The use of evolutionary profiles to predict protein secondary structure, as well as other protein structural features, has been standard practice since the 1990s. Using profiles in the input of such predictors, in place or in addition to the sequence itself, leads to significantly more accurate predictions. While profiles can enhance structural signals, their role remains somewhat surprising as proteins do not use profiles when folding in vivo. Furthermore, the same sequence-based redundancy reduction protocols initially derived to train and evaluate sequence-based predictors, have been applied to train and evaluate profile-based predictors. This can lead to unfair comparisons since profiles may facilitate the bleeding of information between training and test sets. Here we use the extensively studied problem of secondary structure prediction to better evaluate the role of profiles and show that: (1) high levels of profile similarity between training and test proteins are observed when using standard sequence-based redundancy protocols; (2) the gain in accuracy for profile-based predictors, over sequence-based predictors, strongly relies on these high levels of profile similarity between training and test proteins; and (3) the overall accuracy of a profile-based predictor on a given protein dataset provides a biased measure when trying to estimate the actual accuracy of the predictor, or when comparing it to other predictors. We show, however, that this bias can be mitigated by implementing a new protocol (EVALpro) which evaluates the accuracy of profile-based predictors as a function of the profile similarity between training and test proteins. Such a protocol not only allows for a fair comparison of the predictors on equally hard or easy examples, but also reduces the impact of choosing a given similarity cutoff when selecting test proteins. The EVALpro program is available in the SCRATCH suite ( www.scratch.proteomics.ics.uci.edu) and can be downloaded at: www.download.igb.uci.edu/#evalpro.

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time- and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal features based on protein sequences. After trying 20 different types of features, the best performance was achieved when all features were fed into XGBoost by the 5-fold cross validation in comparison with other machine learning methods. Then, the ReliefF algorithm was adopted to get the optimal feature set on our dataset, which further improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and outperformed other published prediction tools. Furthermore, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework can provide instructive guidance for similar studies to construct prediction methods on related biological problems. The data and source code of this study can be freely accessed at https://github.com/CT001002/T4SE-XGB.

Impact of protein dynamics on secondary structure prediction

Protein 3D structures support their biological functions. As the number of protein structures is negligible in regards to the number of available protein sequences, prediction methodologies relying only on protein sequences are essential tools. In this field, protein secondary structure prediction (PSSPs) is a mature area, and is considered to have reached a plateau. Nonetheless, proteins are highly dynamical macromolecules, a property that could impact the PSSP methods. Indeed, in a previous study, the stability of local protein conformations was evaluated demonstrating that some regions easily changed to another type of secondary structure. The protein sequences of this dataset were used by PSSPs and their results compared to molecular dynamics to investigate their potential impact on the quality of the secondary structure prediction. Interestingly, a direct link is observed between the quality of the prediction and the stability of the assignment to the secondary structure state. The more stable a local protein conformation is, the better the prediction will be. The secondary structure assignment not taken from the crystallized structures but from the conformations observed during the dynamics slightly increase the quality of the secondary structure prediction. These results show that evaluation of PSSPs can be done differently, but also that the notion of dynamics can be included in development of PSSPs and other approaches such as de novo approaches.

BCrystal: an interpretable sequence-based protein crystallization predictor

MOTIVATION

X-ray crystallography has facilitated the majority of protein structures determined to date. Sequence-based predictors that can accurately estimate protein crystallization propensities would be highly beneficial to overcome the high expenditure, large attrition rate, and to reduce the trial-and-error settings required for crystallization.

RESULTS

In this study, we present a novel model, BCrystal, which uses an optimized gradient boosting machine (XGBoost) on sequence, structural and physio-chemical features extracted from the proteins of interest. BCrystal also provides explanations, highlighting the most important features for the predicted crystallization propensity of an individual protein using the SHAP algorithm. On three independent test sets, BCrystal outperforms state-of-the-art sequence-based methods by more than 12.5% in accuracy, 18% in recall and 0.253 in Matthew's correlation coefficient, with an average accuracy of 93.7%, recall of 96.63% and Matthew's correlation coefficient of 0.868. For relative solvent accessibility of exposed residues, we observed higher values to associate positively with protein crystallizability and the number of disordered regions, fraction of coils and tripeptide stretches that contain multiple histidines associate negatively with crystallizability. The higher accuracy of BCrystal enables it to accurately screen for sequence variants with enhanced crystallizability.

AVAILABILITY AND IMPLEMENTATION

Our BCrystal webserver is at https://machinelearning-protein.qcri.org/ and source code is available at https://github.com/raghvendra5688/BCrystal.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

QMEANDisCo-distance constraints applied on model quality estimation

Motivation

Methods that estimate the quality of a 3D protein structure model in absence of an experimental reference structure are crucial to determine a model’s utility and potential applications. Single model methods assess individual models whereas consensus methods require an ensemble of models as input. In this work, we extend the single model composite score QMEAN that employs statistical potentials of mean force and agreement terms by introducing a consensus-based distance constraint (DisCo) score.

Results

DisCo exploits distance distributions from experimentally determined protein structures that are homologous to the model being assessed. Feed-forward neural networks are trained to adaptively weigh contributions by the multi-template DisCo score and classical single model QMEAN parameters. The result is the composite score QMEANDisCo, which combines the accuracy of consensus methods with the broad applicability of single model approaches. We also demonstrate that, despite being the de-facto standard for structure prediction benchmarking, CASP models are not the ideal data source to train predictive methods for model quality estimation. For performance assessment, QMEANDisCo is continuously benchmarked within the CAMEO project and participated in CASP13. For both, it ranks among the top performers and excels with low response times.

Availability and implementation

QMEANDisCo is available as web-server at https://swissmodel.expasy.org/qmean. The source code can be downloaded from https://git.scicore.unibas.ch/schwede/QMEAN.

Supplementary information

Supplementary data are available at Bioinformatics online.

Immunoinformatics characterization of SARS-CoV-2 spike glycoprotein for prioritization of epitope based multivalent peptide vaccine

The COVID-19 pandemic caused by SARS-CoV-2 is a public health emergency of international concern and thus calling for the development of effective and safe therapeutics and prophylactics particularly a vaccine to protect against the infection. SARS-CoV-2 spike glycoprotein is an attractive candidate for a vaccine, antibodies, and inhibitors development because of the many roles it plays in attachment, fusion and entry into the host cell. In the present investigation, we characterized the SARS-CoV-2 spike glycoprotein by immunoinformatics techniques to put forward potential B and T cell epitopes, followed by the use of epitopes in construction of a multi-epitope peptide vaccine construct (MEPVC). The MEPVC revealed robust host immune system simulation with high production of immunoglobulins, cytokines and interleukins. Stable conformation of the MEPVC with a representative innate immune TLR3 receptor was observed involving strong hydrophobic and hydrophilic chemical interactions, along with enhanced contribution from salt-bridges towards inter-molecular stability. Molecular dynamics simulation in aqueous milieu aided further in interpreting strong affinity of the MEPVC for TLR3. This stability is the attribute of several vital residues from both TLR3 and MEPVC as shown by radial distribution function (RDF) and a novel axial frequency distribution (AFD) analytical tool. Comprehensive binding free energies estimation was provided at the end that concluded major domination by electrostatic and minor from van der Waals. Summing all, the designed MEPVC has tremendous potential of providing protective immunity against COVID-19 and thus could be considered in experimental studies.

Toxoplasma gondii ROP38 protein: Bioinformatics analysis for vaccine design improvement against toxoplasmosis

Rhoptry proteins (ROPs) play a significant role in various stages of Toxoplasma gondii (T. gondii) life cycle, being critical for both invasion and intracellular survival. ROP38 is a key manipulator of host gene expression and has a function in tachyzoite to bradyzoite conversion. In this study, we've employed various bioinformatics online tools for immunogenicity prediction of ROP38 protein, comprising physico-chemical, antigenic and allergenic profiles, transmembrane domain, subcellular localization, post-translational modification (PTM) sites, secondary and 3D structure, B-cell, MHC-binding and cytotoxic T-lymphocyte (CTL) epitopes. The findings showed 54 PTM sites without a transmembrane domain. Also, ROP38 was proved a non-allergenic and antigenic protein. The protein had Sec signal peptide (Sec/SPI) with 0.8762 likelihood. The secondary structure included 52.68% random coil, 29.57% alpha helix and 17.74% extended strand. Based on Ramachandran plot output for refined model, 95.3%, 3.4%, and 1.4% of amino acid residues were incorporated in the favored, allowed, and outlier regions, respectively. B-cell epitopes TFPGDDIQTSS (67-72) and KAKNKWGRTRYTLQG (207-221) as well as T-cell epitope LSPVGFFTAL (6-15) possessed the highest antigenic index in the protein sequence. This paper is a premise for further researches, and provides insights for the development of a suitable vaccine against toxoplasmosis. More empirical studies are required using the ROP38 alone or in combination with other antigens/epitopes in the future.

Notable sequence homology of the ORF10 protein introspects the architecture of SARS-COV-2.. [DOI: 10.1101/2020.09.06.284976]

The global public health is endangered due to COVID-19 pandemic, which is caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Despite having similar pathology to MERS and SARS-CoV, the infection fatality rate of SARS-CoV-2 is likely lower than 1%. SARS-CoV-2 has been reported to be uniquely characterized by the accessory protein ORF10, which contains eleven cytotoxic T lymphocyte (CTL) epitopes of nine amino acids length each, across various human leukocyte antigen (HLA) subtypes. In this study, all missense mutations found in sequence databases were examined across twnety-two unique SARS-CoV-2 ORF10 variants that could possibly alter viral pathogenicity. Some of these mutations decrease the stability of ORF10, e.g. I4L and V6I were found in the MoRF region of ORF10 which may also possibly contribute to Intrinsic protein disorder. Furthermore, a physicochemical and structural comparative analysis was carried out on SARS-CoV-2 and Pangolin-CoV ORF10 proteins, which share 97.37% amino acid homology. The high degree of physicochemical and structural similarity of ORF10 proteins of SARS-CoV-2 and Pangolin-CoV open questions about the architecture of SARS-CoV-2 due to the disagreement of these two ORF10 proteins over their sub-structure (loop/coil region), solubility, antigenicity and change from the strand to coil at amino acid position 26, where tyrosine is present. Altogether, SARS-CoV-2 ORF10 is a promising pharmaceutical target and a protein which should be monitored for changes which correlate to change pathogenesis and clinical course of COVID-19 infection.

Characterization of the Pilotin-Secretin Complex from the Salmonella enterica Type III Secretion System Using Hybrid Structural Methods

The type III secretion system (T3SS) is a multi-membrane-spanning protein channel used by Gram-negative pathogenic bacteria to secrete effectors directly into the host cell cytoplasm. In the many species reliant on the T3SS for pathogenicity, proper assembly of the outer membrane secretin pore depends on a diverse family of lipoproteins called pilotins. We present structural and biochemical data on the Salmonella enterica pilotin InvH and the S domain of its cognate secretin InvG. Characterization of InvH by X-ray crystallography revealed a dimerized, α-helical pilotin. Size-exclusion-coupled multi-angle light scattering and small-angle X-ray scattering provide supporting evidence for the formation of an InvH homodimer in solution. Structures of the InvH-InvG heterodimeric complex determined by X-ray crystallography and NMR spectroscopy indicate a predominantly hydrophobic interface. Knowledge of the interaction between InvH and InvG brings us closer to understanding the mechanisms by which pilotins assemble the secretin pore.

Structural Requirements of the Phytoplasma Effector Protein SAP54 for Causing Homeotic Transformation of Floral Organs

Phytoplasmas are intracellular bacterial plant pathogens that cause devastating diseases in crops and ornamental plants by the secretion of effector proteins. One of these effector proteins, termed SECRETED ASTER YELLOWS WITCHES' BROOM PROTEIN 54 (SAP54), leads to the degradation of a specific subset of floral homeotic proteins of the MIKC-type MADS-domain family via the ubiquitin-proteasome pathway. In consequence, the developing flowers show the homeotic transformation of floral organs into vegetative leaf-like structures. The molecular mechanism of SAP54 action involves binding to the keratin-like domain of MIKC-type proteins and to some RAD23 proteins, which translocate ubiquitylated substrates to the proteasome. The structural requirements and specificity of SAP54 function are poorly understood, however. Here, we report, based on biophysical and molecular biological analyses, that SAP54 folds into an α-helical structure. Insertion of helix-breaking mutations disrupts correct folding of SAP54 and compromises SAP54 binding to its target proteins and, concomitantly, its ability to evoke disease phenotypes in vivo. Interestingly, dynamic light scattering data together with electrophoretic mobility shift assays suggest that SAP54 preferentially binds to multimeric complexes of MIKC-type proteins rather than to dimers or monomers of these proteins. Together with data from literature, this finding suggests that MIKC-type proteins and SAP54 constitute multimeric α-helical coiled coils. Our investigations clarify the structure-function relationship of an important phytoplasma effector protein and may thus ultimately help to develop treatments against some devastating plant diseases.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

BACKGROUND

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts.

OBJECTIVE

A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools.

METHODS

Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data.

RESULTS

Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields.

CONCLUSION

This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Designing an efficient multi-epitope vaccine against Campylobacter jejuni using immunoinformatics and reverse vaccinology approach

Campylobacterjejuni causes acute diarrhea as a leading cause of morbidity and mortality in children especially in the developing countries of Asia and Africa. C.jejuni has been identified as a member of the priority pathogens category due to the sudden emergence of multidrug-resistant isolates. Therefore, it is important to develop a protective vaccine against this pathogen. In the present study, the Reverse vaccinology approach was used to identify vaccine targets from the proteome of diarrheagenic C. jejuni strain NCTC11168 for the development of chimeric vaccine candidates against C. jejuni. Pathogen proteins that have adhesin like properties and role in virulence but not present in the human host were selected for the design of a multi-epitope vaccine. MHC class I & II and B-cell epitopes present in the selected vaccine target proteins were screened using different immunoinformatics tools. The commonly predicted epitopes from their corresponding different servers were selected and further shortlisted based on their immunogenicity, antigenicity, and toxicity analysis. Shortlisted peptides were joined by GPGPG linkers to design a multi-epitope vaccine construct. Immune-modulating adjuvant monophosphoryl lipid sequence was added with the vaccine construct's N terminal using EAAAK linkers to enhance the immunogenicity. The designed vaccine construct was evaluated by antigenicity, allergenicity, solubility, and physicochemical analysis using various bioinformatics tools. A three-dimensional model of vaccine construct was modeled by the Phyre2 server and refinement by the GalaxyRefine tool. Constructed model quality was validated by the ProSA-web error-detection tool and the Ramachandran plot. After that vaccine model was docked with TLR-4 protein and complex stability confirmed by molecular dynamics simulation studies. Finally, In-silico cloning of vaccine constructs into a vector was performed to ensuring its effective expression in the microbial system.

Anti-COVID-19 multi-epitope vaccine designs employing global viral genome sequences

BACKGROUND

The coronavirus SARS-CoV-2 is a member of the Coronaviridae family that has caused a global public health emergency. Currently, there is no approved treatment or vaccine available against it. The current study aimed to cover the diversity of SARS-CoV-2 strains reported from all over the world and to design a broad-spectrum multi-epitope vaccine using an immunoinformatics approach.

METHODS

For this purpose, all available complete genomes were retrieved from GISAID and NGDC followed by genome multiple alignments to develop a global consensus sequence to compare with the reference genome. Fortunately, comparative genomics and phylogeny revealed a significantly high level of conservation between the viral strains. All the Open Reading Frames (ORFs) of the reference sequence NC_045512.2 were subjected to epitope mapping using CTLpred and HLApred, respectively. The predicted CTL epitopes were then screened for antigenicity, immunogenicity and strong binding affinity with HLA superfamily alleles. HTL predicted epitopes were screened for antigenicity, interferon induction potential, overlapping B cell epitopes and strong HLA DR binding potential. The shortlisted epitopes were arranged into two multi-epitope sequences, Cov-I-Vac and Cov-II-Vac, and molecular docking was performed with Toll-Like Receptor 8 (TLR8).

RESULTS

The designed multi-epitopes were found to be antigenic and non-allergenic. Both multi-epitopes were stable and predicted to be soluble in an Escherichia coli expression system. The molecular docking with TLR8 also demonstrated that they have a strong binding affinity and immunogenic potential. These in silico analyses suggest that the proposed multi-epitope vaccine can effectively evoke an immune response.

Design of a Novel Multi Epitope-Based Vaccine for Pandemic Coronavirus Disease (COVID-19) by Vaccinomics and Probable Prevention Strategy against Avenging Zoonotics

The emergence and rapid expansion of the coronavirus disease (COVID-19) require the development of effective countermeasures especially a vaccine to provide active acquired immunity against the virus. This study presented a comprehensive vaccinomics approach applied to the complete protein data published so far in the National Center for Biotechnological Information (NCBI) coronavirus data hub. We identified non-structural protein 8 (Nsp8), 3C-like proteinase, and spike glycoprotein as potential targets for immune responses to COVID-19. Epitopes prediction illustrated both B-cell and T-cell epitopes associated with the mentioned proteins. The shared B and T-cell epitopes: DRDAAMQRK and QARSEDKRA of Nsp8, EDMLNPNYEDL and EFTPFDVVR of 3C-like proteinase, and VNNSYECDIPI of the spike glycoprotein are regions of high potential interest and have a high likelihood of being recognized by the human immune system. The vaccine construct of the epitopes shows stimulation of robust primary immune responses and high level of interferon gamma. Also, the construct has the best conformation with respect to the tested innate immune receptors involving vigorous molecular mechanics and solvation energy. Designing of vaccination strategies that target immune response focusing on these conserved epitopes could generate immunity that not only provide cross protection across Betacoronaviruses but additionally resistant to virus evolution.

Polyvalent therapeutic vaccine for type 2 diabetes mellitus: Immunoinformatics approach to study co-stimulation of cytokines and GLUT1 receptors

Background

Type 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route.

Methodology

Immunoinformatics based approach was used to design a polyvalent vaccine for T2DM that involved data accession, antigenicity analysis, T-cell epitopes prediction, conservation and proteasomal evaluation, functional annotation, interactomic and in silico binding affinity analysis.

Results

We found the binding affinity of antigenic peptides with major histocompatibility complex (MHC) Class-I molecules for immune activation to control T2DM. We found 13-epitopes of 9 amino acid residues for multiple alleles of MHC class-I bears significant binding affinity. The downstream signaling resulted by T-cell activation is directly regulated by the molecular weight, amino acid properties and affinity of these epitopes. Each epitope has important percentile rank with significant ANN IC₅₀ values. These high score potential epitopes were linked using AAY, EAAAK linkers and HBHA adjuvant to generate T-cell polyvalent vaccine with a molecular weight of 35.6 kDa containing 322 amino acids residues. In silico analysis of polyvalent construct showed the significant binding affinity (− 15.34 Kcal/mol) with MHC Class-I. This interaction would help to understand our hypothesis, potential activation of T-cells and stimulatory factor of cytokines and GLUT1 receptors.

Conclusion

Our system-level immunoinformatics approach is suitable for designing potential polyvalent therapeutic vaccine candidates for T2DM by reducing hyperglycemia and enhancing metabolic activities through the immune system.

In silico functional and structural characterization of hepatitis B virus PreS/S-gene in Iranian patients infected with chronic hepatitis B virus genotype D

Objective

Chronic hepatitis B (CHB) virus infection is the most prevalent chronic liver disease and has become a serious threat to human health. In this study, we attempted to specify and predict several properties including physicochemical, mutation sites, B-cell epitopes, phosphorylation sites, N-link, O-link glycosylation sites, and protein structures of S protein isolated from Ahvaz.

Materials and methods

Initially, hepatitis B virus DNA (HBV DNA) was extracted from five sera samples of untreated chronic hepatitis B patients. The full-length HBV genomes were amplified and then cloned in pTZ57 R/T vector. The full sequences of HBV were registered in the GenBank with accessions numbers (MK355500), (MK355501) and (MK693107-9). PROTSCALE, Expasy's ProtParam, immuneepitope, ABCpred, BcePred, Bepipred, Algpred, VaxiJen, SCRATCH, DiANNA, plus a number of online analytical processing tools were used to analyse and predict the preS/S gene of genotype D sequences. The present study is the first analytical research on samples obtained from Ahvaz.

Results

We found major hydrophilic region (MHR) mutations at "a" determining region that included K122R, N131T, F134Y, P142L, and T126N mutations. Moreover, Ahvaz sequences revealed four sites (4, 112, 166, and 309) in the preS/S gene for N-glycosylation that could possibly be a potential target for anti-HBV therapy.

Conclusion

In the present study, mutations were identified at positions T113S and N131T within the MHR region of S protein; these mutations can potentially decrease the effect of hepatitis B vaccination in vaccine recipients.

Designing Multi-Epitope Vaccines to Combat Emerging Coronavirus Disease 2019 (COVID-19) by Employing Immuno-Informatics Approach

A recent pandemic caused by a single-stranded RNA virus, COVID-19, initially discovered in China, is now spreading globally. This poses a serious threat that needs to be addressed immediately. Genome analysis of SARS-CoV-2 has revealed its close relation to SARS-coronavirus along with few changes in its spike protein. The spike protein aids in receptor binding and viral entry within the host and therefore represents a potential target for vaccine and therapeutic development. In the current study, the spike protein of SARS-CoV-2 was explored for potential immunogenic epitopes to design multi-epitope vaccine constructs. The S1 and S2 domains of spike proteins were analyzed, and two vaccine constructs were prioritized with T-cell and B-cell epitopes. We adapted a comprehensive predictive framework to provide novel insights into immunogenic epitopes of spike proteins, which can further be evaluated as potential vaccine candidates against COVID-19. Prioritized epitopes were then modeled using linkers and adjuvants, and respective 3D models were constructed to evaluate their physiochemical properties and their possible interactions with ACE2, HLA Superfamily alleles, TLR2, and TLR4.

Refined template selection and combination algorithm significantly improves template-based modeling accuracy

In contrast to ab-initio protein modeling methodologies, comparative modeling is considered as the most popular and reliable algorithm to model protein structure. However, the selection of the best set of templates is still a major challenge. An effective template-ranking algorithm is developed to efficiently select only the reliable hits for predicting the protein structures. The algorithm employs the pairwise as well as multiple sequence alignments of template hits to rank and select the best possible set of templates. It captures several key sequences and structural information of template hits and converts into scores to effectively rank them. This selected set of templates is used to model a target. Modeling accuracy of the algorithm is tested and evaluated on TBM-HA domain containing CASP8, CASP9 and CASP10 targets. On an average, this template ranking and selection algorithm improves GDT-TS, GDT-HA and TM_Score by 3.531, 4.814 and 0.022, respectively. Further, it has been shown that the inclusion of structurally similar templates with ample conformational diversity is crucial for the modeling algorithm to maximally as well as reliably span the target sequence and construct its near-native model. The optimal model sampling also holds the key to predict the best possible target structure.

Diagnosis of human and canine Brucella canis infection: development and evaluation of indirect enzyme-linked immunosorbent assays using recombinant Brucella proteins

Brucella canis, a Gram-negative coccobacilli belonging to the genus Brucellae, is a pathogenic bacterium that can produce infections in dogs and humans. Multiple studies have been carried out to develop diagnostic techniques to detect all zoonotic Brucellae. Diagnosis of Brucella canis infection is challenging due to the lack of highly specific and sensitive diagnostic assays. This work was divided in two phases: in the first one, were identified antigenic proteins in B. canis that could potentially be used for serological diagnosis of brucellosis. Human sera positive for canine brucellosis infection was used to recognize immunoreactive proteins that were then identified by performing 2D-GEL and immunoblot assays. These spots were analyzed using MALDI TOF MS and predicted proteins were identified. Of the 35 protein spots analyzed, 14 proteins were identified and subsequently characterized using bioinformatics, two of this were selected for the next phase. In the second phase, we developed and validated an indirect enzyme-linked immunosorbent assays using those recombinant proteins: inosine 5' phosphate dehydrogenase, pyruvate dehydrogenase E1 subunit beta (PdhB) and elongation factor Tu (Tuf). These genes were PCR-amplified from genomic DNA of B. canis strain Oliveri, cloned, and expressed in Escherichia coli. Recombinant proteins were purified by metal affinity chromatography, and used as antigens in indirect ELISA. Serum samples from healthy and B. canis-infected humans and dogs were used to evaluate the performance of indirect ELISAs. Our results suggest that PdhB and Tuf proteins could be used as antigens for serologic detection of B. canis infection in humans, but not in dogs. The use of recombinant antigens in iELISA assays to detect B. canis-specific antibodies in human serum could be a valuable tool to improve diagnosis of human brucellosis caused by B. canis.

Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment

To date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key importance for protein function elucidation. However, it remains a challenging task, especially for multiple sites proteins known to shuttle between cell compartments to perform their proper biological functions and proteins which do not have significant homology to proteins of known subcellular locations. Due to their low-cost and reasonable accuracy, machine learning-based methods have gained much attention in this context with the availability of a plethora of biological databases and annotated proteins for analysis and benchmarking. Various predictive models have been proposed to tackle the SCL problem, using different protein sequence features pertaining to the subcellular localization, however, the overwhelming majority of them focuses on single localization and cover very limited cellular locations. The prediction was basically established on sorting signals, amino acids compositions, and homology. To improve the prediction quality, focus is actually on knowledge information extracted from annotation databases, such as protein-protein interactions and Gene Ontology (GO) functional domains annotation which has been recently a widely adopted and essential information for learning systems. To deal with such problem, in the present study, we considered SCL prediction task as a multi-label learning problem and tried to label both single site and multiple sites unannotated bacterial protein sequences by mining proteins homology relationships using both GO terms of protein homologs and PSI-BLAST profiles. The experiments using 5-fold cross-validation tests on the benchmark datasets showed a significant improvement on the results obtained by the proposed consensus multi-label prediction model which discriminates six compartments for Gram-negative and five compartments for Gram-positive bacterial proteins.

DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks

MOTIVATION

Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability.

RESULTS

We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.

AVAILABILITY AND IMPLEMENTATION

Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data

Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom.

Predicting mucin-type O-Glycosylation using enhancement value products from derived protein features

Mucin-type O-glycosylation is one of the most common post-translational modifications of proteins. This glycosylation is initiated in the Golgi by the addition of the sugar N-acetylgalactosamine (GalNAc) onto protein Ser and Thr residues by a family of polypeptide GalNAc transferases. In humans there are 20 isoforms that are differentially expressed across tissues that serve multiple important biological roles. Using random peptide substrates, isoform specific amino acid preferences have been obtained in the form of enhancement values (EV). These EVs alone have previously been used to predict O-glycosylation sites via the web based ISOGlyP (Isoform Specific O-Glycosylation Prediction) tool. Here we explore additional protein features to determine whether these can complement the random peptide derived enhancement values and increase the predictive power of ISOGlyP. The inclusion of additional protein substrate features (such as secondary structure and surface accessibility) was found to increase sensitivity with minimal loss of specificity, when tested with three different published in vivo O-glycoproteomics data sets, thus increasing the overall accuracy of the ISOGlyP predictions.

Development of a multivalent enterovirus subunit vaccine based on immunoinformatic design principles for the prevention of HFMD

Hand, foot and mouth disease (HFMD) is mainly caused by EV-A71 and CV-A16. An increasing number of cases have been found to be caused by CV-A10, CV-A6, CV-B3 and the outbreaks are becoming increasingly more complex, often accompanied by the prevalence of a variety of enteroviruses. Based on the principle of synthetic peptide vaccines, we applied immune-informatics to design a highly efficient and safe multivalent epitope-based vaccine against EV-A71, CV-A16, CV-A10, CV-A6 and CV-B3. By screening B-cells, HTL and CTL cell antigen epitopes with high conservativity and immunogenicity that have no toxic effect on the host, further analysis confirmed that the vaccine built was IFN-γ inductive and IL-4 non-inductive HTL cell epitopes and had population coverage corresponding to MHC molecular alleles associated with T-cell phenotype. The multivalent enterovirus vaccine was constructed to connect the 50 s ribosomal protein L7/L12 adjuvant and candidate epitopes sequentially through appropriate linkers. Then, the antigenic, allergen and physical properties of the vaccine were evaluated, followed by a secondary structure analysis and tertiary structure modeling, disulfide engineering, refinement and validation. Moreover, the conformational B cell epitope of the vaccine was analyzed. The stability of the TLR4/MD2/Vaccine complex and details at atomic level were investigated by docking and molecular dynamics simulation. Finally, in silico immune simulation and in vivo immune experiments were done. This study provides a high cost-effective method of designing a multivalent enterovirus vaccine protect against a wide range of enterovirus pathogens.

Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes

BACKGROUND

The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work.

RESULTS

Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings.

CONCLUSION

We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone.

FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

Proteins are dynamic molecules which perform diverse molecular functions by adopting different three-dimensional structures. Recent progress in residue-residue contacts prediction opens up new avenues for the de novo protein structure prediction from sequence information. However, it is still difficult to predict more than one conformation from residue-residue contacts alone. This is due to the inability to deconvolve the complex signals of residue-residue contacts, i.e., spatial contacts relevant for protein folding, conformational diversity, and ligand binding. Here, we introduce a machine learning based method, called FingerprintContacts, for extending the capabilities of residue-residue contacts. This algorithm leverages the features of residue-residue contacts, that is, (1) a single conformation outperforms the others in the structural prediction using all the top ranking residue-residue contacts as structural constraints and (2) conformation specific contacts rank lower and constitute a small fraction of residue-residue contacts. We demonstrate the capabilities of FingerprintContacts on eight ligand binding proteins with varying conformational motions. Furthermore, FingerprintContacts identifies small clusters of residue-residue contacts which are preferentially located in the dynamically fluctuating regions. With the rapid growth in protein sequence information, we expect FingerprintContacts to be a powerful first step in structural understanding of protein functional mechanisms.

Machine Learning Approaches for Quality Assessment of Protein Structures

Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach-support vector machine, artificial neural networks, ensemble learning, or Bayesian learning-and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area.

Phylogenetic Analyses of Sites in Different Protein Structural Environments Result in Distinct Placements of the Metazoan Root

Phylogenomics, the use of large datasets to examine phylogeny, has revolutionized the study of evolutionary relationships. However, genome-scale data have not been able to resolve all relationships in the tree of life; this could reflect, at least in part, the poor-fit of the models used to analyze heterogeneous datasets. Some of the heterogeneity may reflect the different patterns of selection on proteins based on their structures. To test that hypothesis, we developed a pipeline to divide phylogenomic protein datasets into subsets based on secondary structure and relative solvent accessibility. We then tested whether amino acids in different structural environments had distinct signals for the topology of the deepest branches in the metazoan tree. We focused on a dataset that appeared to have a mixture of signals and we found that the most striking difference in phylogenetic signal reflected relative solvent accessibility. Analyses of exposed sites (residues located on the surface of proteins) yielded a tree that placed ctenophores sister to all other animals whereas sites buried inside proteins yielded a tree with a sponge+ctenophore clade. These differences in phylogenetic signal were not ameliorated when we conducted analyses using a set of maximum-likelihood profile mixture models. These models are very similar to the Bayesian CAT model, which has been used in many analyses of deep metazoan phylogeny. In contrast, analyses conducted after recoding amino acids to limit the impact of deviations from compositional stationarity increased the congruence in the estimates of phylogeny for exposed and buried sites; after recoding amino acid trees estimated using the exposed and buried site both supported placement of ctenophores sister to all other animals. Although the central conclusion of our analyses is that sites in different structural environments yield distinct trees when analyzed using models of protein evolution, our amino acid recoding analyses also have implications for metazoan evolution. Specifically, our results add to the evidence that ctenophores are the sister group of all other animals and they further suggest that the placozoa+cnidaria clade found in some other studies deserves more attention. Taken as a whole, these results provide striking evidence that it is necessary to achieve a better understanding of the constraints due to protein structure to improve phylogenetic estimation.

Toward a chimeric vaccine against multiple isolates of Mycobacteroides - An integrative approach

AIM

Nontuberculous mycobacterial (NTM) infection such as endophthalmitis, dacryocystitis, and canaliculitis are pervasive across the globe and are currently managed by antibiotics. However, the recent cases of Mycobacteroides developing drug resistance reported along with the improper practice of medicine intrigued us to explore its genomic and proteomic canvas at a global scale and develop a chimeric vaccine against Mycobacteroides.

MAIN METHODS

We carried out a vivid genomic study on five recently sequenced strains of Mycobacteroides and explored their Pan-core genome/proteome in three different phases. The promiscuous antigenic proteins were identified via a subtractive proteomics approach that qualified for virulence causation, resistance and essentiality factors for this notorious bacterium. An integrated pipeline was developed for the identification of B-Cell, MHC (Major histocompatibility complex) class I and II epitopes.

KEY FINDINGS

Phase I identified the shreds of evidence of reductive evolution and propensity of the Pan-genome of Mycobacteroides getting closed soon. Phase II and Phase III produced 8 vaccine constructs. Our final vaccine construct, V6 qualified for all tests such as absence for allergenicity, presence of antigenicity, etc. V6 contains β-defensin as an adjuvant, linkers, Lysosomal-associated membrane protein 1 (LAMP1) signal peptide, and PADRE (Pan HLA-DR epitopes) amino acid sequence. Besides, V6 also interacts with a maximum number of MHC molecules and the TLR4/MD2 (Toll-like receptor 4/Myeloid differentiation factor 2) complex confirmed by docking and molecular dynamics simulation studies.

SIGNIFICANCE

The knowledge harnessed from the current study can help improve the current treatment regimens or in an event of an outbreak and propel further related studies.

Synthesis, In Vitro Evaluation and Molecular Docking of the 5-Acetyl-2-aryl-6-hydroxybenzo[b]furans against Multiple Targets Linked to Type 2 Diabetes

The 5-acetyl-2-aryl-6-hydroxybenzo[b]furans 2a-h have been evaluated through in vitro enzymatic assay against targets which are linked to type 2 diabetes (T2D), namely, α-glucosidase, protein tyrosine phosphatase 1B (PTP1B) and β-secretase. These compounds have also been evaluated for antioxidant activity using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free-radical scavenging method. The most active compounds against α-glucosidase and/or PTP1B, namely, 4-fluorophenyl 2c, 4-methoxyphenyl 2g and 3,5-dimethoxyphenyl substituted 2h derivatives were also evaluated for potential anti-inflammatory properties against cyclooxygenase-2 activity. The Lineweaver-Burk and Dixon plots were used to determine the type of inhibition on compounds 2c and 2h against α-glucosidase and PTP1B receptors. The interactions were investigated in modelled complexes against α-glucosidase and PTP1B via molecular docking.

Multiepitope Subunit Vaccine Design against COVID-19 Based on the Spike Protein of SARS-CoV-2: An In Silico Analysis

The global health crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID-19, has resulted in a negative impact on human health and on social and economic activities worldwide. Researchers around the globe need to design and develop successful therapeutics as well as vaccines against the novel COVID-19 disease. In the present study, we conducted comprehensive computer-assisted analysis on the spike glycoprotein of SARS-CoV-2 in order to design a safe and potent multiepitope vaccine. In silico epitope prioritization shortlisted six HLA I epitopes and six B-cell-derived HLA II epitopes. These high-ranked epitopes were all connected to each other via flexible GPGPG linkers, and at the N-terminus side, the sequence of Cholera Toxin β subunit was attached via an EAAAK linker. Structural modeling of the vaccine was performed, and molecular docking analysis strongly suggested a positive association of a multiepitope vaccine with Toll-like Receptor 3. The structural investigations of the vaccine-TLR3 complex revealed the formation of fifteen interchain hydrogen bonds, thus validating its integrity and stability. Moreover, it was found that this interaction was thermodynamically feasible. In conclusion, our data supports the proposition that a multiepitope vaccine will provide protective immunity against COVID-19. However, further in vivo and in vitro experiments are needed to validate the immunogenicity and safety of the candidate vaccine.

The possible regions to design Human Papilloma Viruses vaccine in Iranian L1 protein

Human Papilloma Virus (HPV) genome encodes several proteins, as L1is major capsid protein and L2 is minor capsid protein. Among all HPV types HPV-16 and HPV-18 are the most common high-risk HPV (HR-HPV) types globally and the majority of cases are infected with these types. HPV entry and the initial interaction with the host cell are mainly related to the L1 protein which is the main component of HPV vaccines. The aim of this research was comparison analysis among all Iranian L1 protein sequences submitted in NCBI GenBank to find the major substitutions as well as structural and immune properties of this protein. All sequences HPV L1 protein from Iranian isolates from 2014 to 2016 were selected and obtained from NCBI data bank. "CLC Genomics Workbench" was used to translate alignment. To predict B cell epitopes, we employed several programs. Modification sites such as phosphorylation, glycosylation, and disulfide bonds were determined. Secondary and tertiary structures of all sequences were analyzed. Several mutations were found and major mutations were in amino acid residues 102, 202, 207, 292, 379, and 502. The mentioned mutations showed the minor effect on B cell and physicochemical properties of the L1 protein. Six disulfide bonds were determined in L1 protein and also in several N-link glycosylation and phosphorylation sites. Five L1 loops were determined, which had great potential to be B cell epitopes with high antigenic properties. All in all, this research as the first report from Iran described the tremendous potential of two L1 loops (BC and FG) to induce immune system which can be used as the descent candidate to design a new vaccine against HPV in the Iranian population. In addition, some differences between the reference sequence and Iranian patients' sequences were determined. It is essential to consider these differences to monitor the effectiveness and efficacy of the vaccine for the Iranian population. Our results provide a vast understanding of L1 protein that can be useful for further studies on HPV infections and new vaccine generations.

DEPICTER: Intrinsic Disorder and Disorder Function Prediction Server

Computational predictions of the intrinsic disorder and its functions are instrumental to facilitate annotation for the millions of unannotated proteins. However, access to these predictors is fragmented and requires substantial effort to find them and to collect and combine their results. The DEPICTER (DisorderEd PredictIon CenTER) server provides first-of-its-kind centralized access to 10 popular disorder and disorder function predictions that cover protein and nucleic acids binding, linkers, and moonlighting regions. It automates the prediction process, runs user-selected methods on the server side, visualizes the results, and outputs all predictions in a consistent and easy-to-parse format. DEPICTER also includes two accurate consensus predictors of disorder and disordered protein binding. Empirical tests on an independent (low similarity) benchmark dataset reveal that the computational tools included in DEPICTER generate accurate predictions that are significantly better than the results secured using sequence alignment. The DEPICTER server is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER/.

Rational Design of Chymotrypsin Inhibitor 2 by Optimizing Non-Native Interactions

Rational design of proteins via mutagenesis is crucial for several biotechnological applications. A significant challenge of the computational strategies used to predict optimized mutations is to understand the influence of each amino acid during the folding process. In the present work, chymotrypsin inhibitor 2 (CI2) and several of its designed mutants have been simulated using a non-native hydrophobic and electrostatic potential as a structure-based Cα model. Through these simulations, we could identify the most critical folding stage to accelerate CI2 and also the charged residues responsible for providing its thermostability. The replacement of ionizable residues for hydrophobic ones tended to promote the formation of the CI2 secondary structure in the early transition state, which speeds up folding. However, this same replacement destabilized the native structure, and there was a decrease in the protein thermostability. Such a simple method proved to be capable of providing valuable information about thermodynamics and kinetics of CI2 and its mutations, thus being a fast alternative to the study of rational protein design.

Comparative in silico analysis of ftsZ gene from different bacteria reveals the preference for core set of codons in coding sequence structuring and secondary structural elements determination

The deluge of sequence information in the recent times provide us with an excellent opportunity to compare organisms on a large genomic scale. In this study we have tried to decipher the variation in the gene organization and structuring of a vital bacterial gene called ftsZ which codes for an integral component of the bacterial cell division, the FtsZ protein. FtsZ is homologous to tubulin protein and has been found to be ubiquitous in eubacteria. FtsZ is showing increasing promise as a target for antibacterial drug discovery. Our study of ftsZ protein from 143 different bacterial species spanning a wider range of morphological and physiological type demonstrates that the ftsZ gene of about ninety three percent of the organisms show relatively biased codon usage profile and significant GC deviation from their genomic GC content. Comparative codon usage analysis of ftsZ and a core housekeeping gene rpoB demonstrated that codon usage pattern of ftsZ CDS is shaped by natural selection to a large extent and mimics that of a housekeeping gene. We have also detected a tendency among the different organisms to utilize a core set of codons in structuring the ftsZ coding sequence. We observed that the compositional frequency of the amino acid serine in the FtsZ protein appears to be a indicator of the bacterial lifestyle. Our meticulous analysis of the ftsZ gene linked with the corresponding FtsZ protein show that there is a bias towards the use of specific synonymous codons particularly in the helix and strand regions of the multi-domain FtsZ protein. Overall our findings suggest that in an indispensable and vital protein such as FtsZ, there is an inherent tendency to maintain form for optimized performance in spite of the extrinsic variability in coding features.

Advances in protein structure prediction and design

The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.

Immunoinformatics approaches to explore Helicobacter Pylori proteome (Virulence Factors) to design B and T cell multi-epitope subunit vaccine

Helicobacter Pylori is a known causal agent of gastric malignancies and peptic ulcers. The extremophile nature of this bacterium is protecting it from designing a potent drug against it. Therefore, the use of computational approaches to design antigenic, stable and safe vaccine against this pathogen could help to control the infections associated with it. Therefore, in this study, we used multiple immunoinformatics approaches along with other computational approaches to design a multi-epitopes subunit vaccine against H. Pylori. A total of 7 CTL and 12 HTL antigenic epitopes based on c-terminal cleavage and MHC binding scores were predicted from the four selected proteins (CagA, OipA, GroEL and cagA). The predicted epitopes were joined by AYY and GPGPG linkers. Β-defensins adjuvant was added to the N-terminus of the vaccine. For validation, immunogenicity, allergenicity and physiochemical analysis were conducted. The designed vaccine is likely antigenic in nature and produced robust and substantial interactions with Toll-like receptors (TLR-2, 4, 5, and 9). The vaccine developed was also subjected to an in silico cloning and immune response prediction model, which verified its efficiency of expression and the immune system provoking response. These analyses indicate that the suggested vaccine may produce particular immune responses against H. pylori, but laboratory validation is needed to verify the safety and immunogenicity status of the suggested vaccine design.