In silico proteomic characterization of human epidermal growth factor receptor 2 (HER-2) for the mapping of high affinity antigenic determinants against breast cancer

Genome-wide scan for potential CD4+ T-cell vaccine candidates in Candida auris by exploiting reverse vaccinology and evolutionary information

Candida auris is a globally emerging fungal pathogen responsible for causing nosocomial outbreaks in healthcare associated settings. It is known to cause infection in all age groups and exhibits multi-drug resistance with high potential for horizontal transmission. Because of this reason combined with limited therapeutic choices available, C. auris infection has been acknowledged as a potential risk for causing a future pandemic, and thus seeking a promising strategy for its treatment is imperative. Here, we combined evolutionary information with reverse vaccinology approach to identify novel epitopes for vaccine design that could elicit CD4+ T-cell responses against C. auris. To this end, we extensively scanned the family of proteins encoded by C. auris genome. In addition, a pathogen may acquire substitutions in epitopes over a period of time which could cause its escape from the immune response thus rendering the vaccine ineffective. To lower this possibility in our design, we eliminated all rapidly evolving genes of C. auris with positive selection. We further employed highly conserved regions of multiple C. auris strains and identified two immunogenic and antigenic T-cell epitopes that could generate the most effective immune response against C. auris. The antigenicity scores of our predicted vaccine candidates were calculated as 0.85 and 1.88 where 0.5 is the threshold for prediction of fungal antigenic sequences. Based on our results, we conclude that our vaccine candidates have the potential to be successfully employed for the treatment of C. auris infection. However, in vivo experiments are imperative to further demonstrate the efficacy of our design.

Immuno-Informatics Based Peptides: An Approach for Vaccine Development Against Outer Membrane Proteins of Pseudomonas Genus

Pseudomonas genus is among the top nosocomial pathogens known to date. Being highly opportunistic, members of pseudomonas genus are most commonly connected with nosocomial infections of urinary tract and ventilator-associated pneumonia. Nevertheless, vaccine development for this pathogenic genus is slow because of no information regarding immunity correlated functional mechanism. In this present work, an immunoinformatics pipeline is used for vaccine development based on epitope-based peptide design, which can result in crucial immune response against outer membrane proteins of pseudomonas genus. A total of 127 outer membrane proteins were analysed, studied and out of them three sequences were obtained to be the producer of non-allergic, highly antigenic T-cell and B-cell epitopes which show good binding affinity towards class II HLA molecules. After performing rigorous screening utilizing docking, simulation, modelling techniques, we had one nonameric peptide (WLLATGIFL)as a good vaccine candidate. The predicted epitopes needs to be further validated for its apt use as vaccine. This work paves a new way with extensive therapeutic application against Pseudomonas genus and their associated diseases.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Designing of an epitope-based peptide vaccine against walking pneumonia: an immunoinformatics approach

Mycoplasma pneumoniae is a substantial respiratory pathogen that develops not only pneumonia but also other respiratory diseases, which mimic viral respiratory syndromes. Nevertheless, vaccine development for this pathogen delays behind as immunity correlated with protection is now predominantly unknown. In the present study, an immunoinformatics pipeline is utilized for epitope-based peptide vaccine design, which can trigger a critical immune response against M. pneumoniae. A total of 105 T-cell epitopes from 12 membrane associated proteins and 7 T-cell epitopes from 5 cytadherence proteins of M. pneumoniae were obtained and validated. Thus, 18 peptides with 9-mer core sequence were identified as best T-cell epitopes by considering the number of residues with > 75% in favored region. Further, the crucial screening studies predicted three peptides with good binding affinity towards HLA molecules as best T-cell and B-cell epitopes. Based on this result, visualization, and dynamic simulation for the three epitopes (WIHGLILLF, VILLFLLLF, and LLAWMLVLF) were assessed. The predicted epitopes needs to be further validated for their adept use as vaccine. Collectively, the study opens up a new horizon with extensive therapeutic application against M. pneumoniae and its associated diseases.

Prediction of Structural and Functional Aspects of Protein

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

An antibiotic target ranking and prioritization pipeline combining sequence, structure and network-based approaches exemplified for Serratia marcescens

We investigate a drug target screening pipeline comparing sequence, structure and network-based criteria for prioritization. Serratia marcescens, an opportunistic pathogen, serves as test case. We rank according to (i) availability of three dimensional structures and lead compounds, (ii) not occurring in man and general sequence conservation information, and (iii) network information on the importance of the protein (conserved protein-protein interactions; metabolism; reported to be an essential gene in other organisms). We identify 45 potential anti-microbial drug targets in S. marcescens with KdsA involved in LPS biosynthesis as top candidate drug target. LpxC and FlgB are further top-ranked targets identified by interactome analysis not suggested before for S. marcescens. Pipeline, targets and complementarity of the three approaches are evaluated by available experimental data and genetic evidence and against other antibiotic screening pipelines. This supports reliable drug target identification and prioritization for infectious agents (bacteria, parasites, fungi) by these bundled complementary criteria.

Sequence-based approach for rapid identification of cross-clade CD8+ T-cell vaccine candidates from all high-risk HPV strains

Human papilloma virus (HPV) is the primary etiological agent responsible for cervical cancer in women. Although in total 16 high-risk HPV strains have been identified so far. Currently available commercial vaccines are designed by targeting mainly HPV16 and HPV18 viral strains as these are the most common strains associated with cervical cancer. Because of the high level of antigenic specificity of HPV capsid antigens, the currently available vaccines are not suitable to provide cross-protection from all other high-risk HPV strains. Due to increasing reports of cervical cancer cases from other HPV high-risk strains other than HPV16 and 18, it is crucial to design vaccine that generate reasonable CD8+ T-cell responses for possibly all the high-risk strains. With this aim, we have developed a computational workflow to identify conserved cross-clade CD8+ T-cell HPV vaccine candidates by considering E1, E2, E6 and E7 proteins from all the high-risk HPV strains. We have identified a set of 14 immunogenic conserved peptide fragments that are supposed to provide protection against infection from any of the high-risk HPV strains across globe.

Withanolide A offers neuroprotection, ameliorates stress resistance and prolongs the life expectancy of Caenorhabditis elegans

Withanolide A (steroidal lactone) forms the major constituent of the most popular herbal drug in Ayurvedic medicine, Ashwagandha. It has been used since ancient times as an alternative medicine for the treatment of a variety of age related disorders. Here we provide multiple lines of evidence indicating that Withanolide A improves healthspan, delays age-associated physiological changes and also extends the lifespan of Caenorhabditis elegans. We also report several neuroprotective benefits of this natural product, including its anti-amyloidogenic effects, alleviation of α-synuclein aggregation and neuroprotection through modulation of neural mediators like acetylcholine. We observed that Withanolide A mediates lifespan extension and promotes stress resistance via insulin/insulin-like growth factor signaling pathway. Such findings could be helpful to develop a therapeutic medicine from this natural product for the prevention or reversal of age-related ailments and to improve the survival of patients suffering from Alzheimer's or Parkinson's disease.

Understanding the mechanism of atovaquone drug resistance in Plasmodium falciparum cytochrome b mutation Y268S using computational methods

The rapid appearance of resistant malarial parasites after introduction of atovaquone (ATQ) drug has prompted the search for new drugs as even single point mutations in the active site of Cytochrome b protein can rapidly render ATQ ineffective. The presence of Y268 mutations in the Cytochrome b (Cyt b) protein is previously suggested to be responsible for the ATQ resistance in Plasmodium falciparum (P. falciparum). In this study, we examined the resistance mechanism against ATQ in P. falciparum through computational methods. Here, we reported a reliable protein model of Cyt bc1 complex containing Cyt b and the Iron-Sulphur Protein (ISP) of P. falciparum using composite modeling method by combining threading, ab initio modeling and atomic-level structure refinement approaches. The molecular dynamics simulations suggest that Y268S mutation causes ATQ resistance by reducing hydrophobic interactions between Cyt bc1 protein complex and ATQ. Moreover, the important histidine contact of ATQ with the ISP chain is also lost due to Y268S mutation. We noticed the induced mutation alters the arrangement of active site residues in a fashion that enforces ATQ to find its new stable binding site far away from the wild-type binding pocket. The MM-PBSA calculations also shows that the binding affinity of ATQ with Cyt bc1 complex is enough to hold it at this new site that ultimately leads to the ATQ resistance.

In silico identification of novel protective VSG antigens expressed by Trypanosoma brucei and an effort for designing a highly immunogenic DNA vaccine using IL-12 as adjuvant

African trypanosomiasis continues to be a major health problem, with more adults dying from this disease world-wide. As the sequence diversity of Trypanosoma brucei is extreme, with VSGs having 15-25% identity with most other VSGs, hence it displays a huge diversity of adaptations and host specificities. Therefore the need for an improved vaccine has become an international priority. The highly conserved and specific epitopes acting as both CD8+ and CD4+ T-cell epitopes (FLINKKPAL and FTALCTLAA) were predicted from large bunch of VSGs of T. brucei. Besides, some other potential epitopes with very high affinity for MHC I and II molecules were also determined while taking consideration on the most common HLA in the general population which accounts for major ethnicities. The vaccine candidates were found to be effective even for non-african populations as predicted by population coverage analysis. Hence the migrating travelers acting as a spread means of the infection can probably also be treated successfully after injection of such a multiepitopic vaccine. Exploiting the immunoinformatics approaches, we designed a potential vaccine by using the consensus epitopic sequence of 388 VSG proteins of T. brucei and performed in silico cloning of multiepitopic antigenic DNA sequence in pBI-CMV1 vector. Moreover, various techniques like codon adaptation, CpG optimization, removal of self recognized epitopes, use of adjuvant and co-injection with plasmids expressing immune-stimulatory molecules were implemented to enhance the immunogenicity of the proposed in silico vaccine.