scholarly journals In Silico Analysis of Ixodid Tick Aqauporin-1 Protein as a Candidate Anti-Tick Vaccine Antigen

2019 ◽  
Author(s):  
Christian Ndekezi ◽  
Joseph Nkamwesiga ◽  
Sylvester Ochwo ◽  
Magambo Phillip Kimuda ◽  
Frank Norbert Mwiine ◽  
...  
Keyword(s):  
In Silico ◽  
Prediction Models ◽  
Vaccine Development ◽  
Sequence Similarity ◽  
Vaccine Antigen ◽  
Systematic Research ◽  
Motif Analysis ◽  
Peptide Motifs ◽  
Starting Point ◽  
Tick Vaccine

AbstractTicks are arthropod vectors of pathogens of both Veterinary and Public health importance. Ticks are largely controlled by acaricide application. However, acaricide efficacy is hampered by high cost, the need for regular application and selection of multi-acaricide resistant tick populations. In light of this, future tick control approaches are poised to rely on integration of rational acaricide application and other methods such as vaccination. To contribute to systematic research-guided efforts to produce anti-tick vaccines, we carried out an in silico tick Aquaporin-1 protein (AQP1) analysis to identify unique tick AQP1 peptide motifs that can be used in future peptide anti-tick vaccine development. We used multiple sequence alignment (MSA), motif analysis, homology modeling, and structural analysis to identify unique tick AQP1 peptide motifs. BepiPred, Chou & Fasman-Turn, Karplus & Schulz Flexibility and Parker-Hydrophilicity prediction models were used to asses these motifs’ abilities to induce antibody mediated immune responses. Tick AQP1 (MK334178) protein homology was largely similar to the bovine AQP1 (PDB:1J4N) (23% sequence similarity; Structural superimposition RMS=1.475). The highest similarities were observed in the transmembrane domains while differences were observed in the extra and intra cellular protein loops. Two unique tick AQP1 (MK334178) motifs, M7 (residues 106-125, p=5.4e-25) and M8 (residues 85-104, p=3.3e-24) were identified. These two motifs are located on the extra-cellular AQP1 domain and showed the highest Parker-Hydrophilicity prediction immunogenic scores of 1.153 and 2.612 respectively. The M7 and M8 motifs are a good starting point for the development of potential peptide-based anti-tick vaccine. Further analyses such as in vivo immunization assays are required to validate these findings.

scholarly journals In silico modeling and interactive profiling of BPH resistant R genes with elicitor molecules of rice planthoppers

2021 ◽  
Author(s):  
Krishnamanikumar Premachandran ◽  
Thanga Suja Srinivasan
Keyword(s):  
In Silico ◽  
Sequence Similarity ◽  
Brown Planthopper ◽  
Chromosome 12 ◽  
R Genes ◽  
Motif Analysis ◽  
Signaling Mechanism ◽  
Bph Genes ◽  
Rice Planthoppers ◽  
In Silico Studies

Brown planthopper resistant NBS-LRR specific R genes (Bph9, Bph14, Bph18, Bph26) have been reported in rice. BPH specific R genes were clustered with other R genes of rice on chromosome 12 (Bph9, Bph18, Bph26) and 3 (Bph14). Motif analysis of BPH specific R genes showed the predominant motifs as CC, NBS and LRR regions. Bph9, Bph18 and Bph26 R genes exhibited high degree of sequence similarity in their CC and NBS region and are considered as functional alleles of BPH resistance at chromosome 12. LRR region of BPH genes were interacting with the elicitor  molecules of planthoppers and are the potential lignad binding site. Bph14 exhibited more number of LRR repeats and were interacting efficiently with all the tested salivary elictor molecules of planthoppers. Bph18 with no LRR region exhibited reduced interaction efficiency with the tested elicitor molecules of planthoppers. Our in silico studies confirms that Bph14 R gene resistance protein to be a promising candidate for providing broad spectrum resistance against planthoppers of rice. The study further provides new avenues to investigate the mechanism of receptor-ligand recognition and signaling mechanism against rice planthoppers.

scholarly journals Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds

Cells ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1431
Author(s):  
Réau ◽  
Lagarde ◽  
Zagury ◽  
Montes
Keyword(s):  
Androgen Receptor ◽  
In Silico ◽  
High Performance ◽  
Prediction Models ◽  
Endocrine System ◽  
Pharmacophore Modeling ◽  
Activity Data ◽  
Starting Point ◽  
Exogenous Compounds ◽  
Modeling Strategy

The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of endogenous agonist ligands and whether restore or alter the AR endocrine system functions. The activation of AR must be correctly balanced and identifying potent AR agonist compounds is of high interest to both propose treatments for certain diseases, or to predict the risk related to agonist chemicals exposure. The development of in silico approaches and the publication of structural, affinity and activity data provide a good framework to develop rational AR hits prediction models. Herein, we present a docking and a pharmacophore modeling strategy to help identifying AR agonist compounds. All models were trained on the NR-DBIND that provides high quality binding data on AR and tested on AR-agonist activity assays from the Tox21 initiative. Both methods display high performance on the NR-DBIND set and could serve as starting point for biologists and toxicologists. Yet, the pharmacophore models still need data feeding to be used as large scope undesired effect prediction models.

scholarly journals Immunogenic SARS-CoV-2 Epitopes: In Silico Study Towards Better Understanding of COVID-19 Disease—Paving the Way for Vaccine Development

Vaccines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 408 ◽  
Author(s):  
Vipin Ranga ◽  
Erik Niemelä ◽  
Mahlet Z. Tamirat ◽  
John E. Eriksson ◽  
Tomi T. Airenne ◽  
...  
Keyword(s):  
Amino Acids ◽  
In Silico ◽  
Vaccine Development ◽  
Sequence Similarity ◽  
T Cell Response ◽  
Immune Defense ◽  
Structural Explanation ◽  
High Sequence Similarity ◽  
Human Major Histocompatibility Complex ◽  
Mhc Molecules

The emergence of the COVID-19 outbreak at the end of 2019, caused by the novel coronavirus SARS-CoV-2, has, to date, led to over 13.6 million infections and nearly 600,000 deaths. Consequently, there is an urgent need to better understand the molecular factors triggering immune defense against the virus and to develop countermeasures to hinder its spread. Using in silico analyses, we showed that human major histocompatibility complex (MHC) class I cell-surface molecules vary in their capacity for binding different SARS-CoV-2-derived epitopes, i.e., short sequences of 8-11 amino acids, and pinpointed five specific SARS-CoV-2 epitopes that are likely to be presented to cytotoxic T-cells and hence activate immune responses. The identified epitopes, each one of nine amino acids, have high sequence similarity to the equivalent epitopes of SARS-CoV virus, which are known to elicit an effective T cell response in vitro. Moreover, we give a structural explanation for the binding of SARS-CoV-2-epitopes to MHC molecules. Our data can help us to better understand the differences in outcomes of COVID-19 patients and may aid the development of vaccines against SARS-CoV-2 and possible future outbreaks of novel coronaviruses.

scholarly journals Identification of Unique Peptides for SARS-CoV-2 Diagnostics and Vaccine Development by an In Silico Proteomics Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Veerbhan Kesarwani ◽  
Rupal Gupta ◽  
Ramesh Raju Vetukuri ◽  
Sandeep Kumar Kushwaha ◽  
Sonu Gandhi
Keyword(s):  
Cell Lines ◽  
In Silico ◽  
Vaccine Development ◽  
Sequence Similarity ◽  
Cell Receptor ◽  
Human Saliva ◽  
Antigenic Peptides ◽  
Genomics And Proteomics ◽  
Human Proteins ◽  
Antigen Antibody

Ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus strains is posing new COVID-19 diagnosis and treatment challenges. To help efforts to meet these challenges we examined data acquired from proteomic analyses of human SARS-CoV-2-infected cell lines and samples from COVID-19 patients. Initially, 129 unique peptides were identified, which were rigorously evaluated for repeats, disorders, polymorphisms, antigenicity, immunogenicity, toxicity, allergens, sequence similarity to human proteins, and contributions from other potential cross-reacting pathogenic species or the human saliva microbiome. We also screened SARS-CoV-2-infected NBHE and A549 cell lines for presence of antigenic peptides, and identified paratope peptides from crystal structures of SARS-CoV-2 antigen-antibody complexes. We then selected four antigen peptides for docking with known viral unbound T-cell receptor (TCR), class I and II peptide major histocompatibility complex (pMHC), and identified paratope sequences. We also tested the paratope binding affinity of SARS-CoV T- and B-cell peptides that had been previously experimentally validated. The resultant antigenic peptides have high potential for generating SARS-CoV-2-specific antibodies, and the paratope peptides can be directly used to develop a COVID-19 diagnostics assay. The presented genomics and proteomics-based in-silico approaches have apparent utility for identifying new diagnostic peptides that could be used to fight SARS-CoV-2.

HIV-1 Accessory Proteins: Which one is Potentially Effective in Diagnosis and Vaccine Development?

2020 ◽  
Vol 28 ◽  
Author(s):  
Alireza Milani ◽  
Kazem Baesi ◽  
Elnaz Agi ◽  
Ghazal Marouf ◽  
Maryam Ahmadi ◽  
...  
Keyword(s):  
Immune Response ◽  
Vaccine Development ◽  
Treated Group ◽  
Vaccine Antigen ◽  
Accessory Proteins ◽  
Serological Method ◽  
Th2 Immune Response ◽  
Untreated Individuals ◽  
Immunogenic Properties ◽  
Hiv 1

Background:: The combination antiretroviral therapy (cART) could increase the number of circulating naive CD4 T lymphocytes, but was not able to eradicate human immunodeficiency virus-1 (HIV-1) infection. Objective:: Thus, induction of strong immune responses is important for control of HIV-1 infection. Furthermore, a simple and perfect serological method is required to detect virus in untreated-, treated- and drug resistant- HIV-1 infected individuals. Methods:: This study was conducted to assess and compare immunogenic properties of Nef, Vif, Vpr and Vpu accessory proteins as an antigen candidate in mice and their diagnostic importance in human as a biomarker. Results:: Our data showed that in mice, all heterologous prime/ boost regimens were more potent than homologous prime/ boost regimens in eliciting Th1 response and Granzyme B secretion as CTL activity. Moreover, the Nef, Vpu and Vif proteins could significantly increase Th1 immune response. In contrast, the Vpr protein could considerably induce Th2 immune response. On the other hand, among four accessory proteins, HIV-1 Vpu could significantly detect treated group from untreated group as a possible biomarker in human. Conclusion:: Generally, among accessory proteins, Nef, Vpu and Vif antigens were potentially more suitable vaccine antigen candidates than Vpr antigen. Human antibodies against all these proteins were higher in HIV-1 different groups than healthy group. Among them, Vpu was known as a potent antigen in diagnosis of treated from untreated individuals. The potency of accessory proteins as an antigen candidate in an animal model and a human cohort study are underway.

scholarly journals Identification of linear epitopes on the flagellar proteins of Clostridioides difficile

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Razim ◽  
K. Pacyga ◽  
P. Naporowski ◽  
D. Martynowski ◽  
A. Szuba ◽  
...  
Keyword(s):  
Digestive Tract ◽  
In Silico ◽  
Anaerobic Bacterium ◽  
Vaccine Antigen ◽  
Protective Properties ◽  
Clostridioides Difficile ◽  
Vaccine Antigens ◽  
Linear Epitopes ◽  
Flagellar Proteins

AbstractClostridioides difficile (C. difficile) is an opportunistic anaerobic bacterium that causes severe diseases of the digestive tract of humans and animals. One of the possible methods of preventing C. difficile infection is to develop a vaccine. The most promising candidates for vaccine antigens are the proteins involved in the adhesion phenomena. Among them, the FliC and FliD are considered to be suitable candidates. In this paper, the FliC and FliD protein polypeptide epitopes were mapped in silico and by using PEPSCAN procedure. We identified four promising epitopes: 117QRMRTLS123, 205MSKAG209 of FliC and 226NKVAS230, 306TTKKPKD312 of FliD protein. We showed that 117QRMRTLS123 sequence is not only located in TLR5-binding and activating region, as previously shown, but forms an epitope recognized by C. difficile-infected patients’ antibodies. 205MSKAG209 is a C. difficile-unique, immunogenic sequence that forms an exposed epitope on the polymerized flagella structure which makes it a suitable vaccine antigen. 226NKVAS230 and 306TTKKPKD312 are well exposed and possess potential protective properties according to VaxiJen analysis. Our results open the possibility to use these epitopes as suitable anti-C. difficile vaccine antigens.

scholarly journals Computational Analysis of African Swine Fever Virus Protein Space for the Design of an Epitope-Based Vaccine Ensemble

Pathogens ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 1078 ◽  
Author(s):  
Albert Ros-Lucas ◽  
Florencia Correa-Fiz ◽  
Laia Bosch-Camós ◽  
Fernando Rodriguez ◽  
Julio Alonso-Padilla
Keyword(s):  
T Cell ◽  
Vaccine Development ◽  
De Novo ◽  
African Swine Fever Virus ◽  
African Swine Fever ◽  
Fever Virus ◽  
Economic Losses ◽  
Virus Protein ◽  
Hemorrhagic Disease ◽  
Starting Point

African swine fever virus is the etiological agent of African swine fever, a transmissible severe hemorrhagic disease that affects pigs, causing massive economic losses. There is neither a treatment nor a vaccine available, and the only method to control its spread is by extensive culling of pigs. So far, classical vaccine development approaches have not yielded sufficiently good results in terms of concomitant safety and efficacy. Nowadays, thanks to advances in genomic and proteomic techniques, a reverse vaccinology strategy can be explored to design alternative vaccine formulations. In this study, ASFV protein sequences were analyzed using an in-house pipeline based on publicly available immunoinformatic tools to identify epitopes of interest for a prospective vaccine ensemble. These included experimentally validated sequences from the Immune Epitope Database, as well as de novo predicted sequences. Experimentally validated and predicted epitopes were prioritized following a series of criteria that included evolutionary conservation, presence in the virulent and currently circulating variant Georgia 2007/1, and lack of identity to either the pig proteome or putative proteins from pig gut microbiota. Following this strategy, 29 B-cell, 14 CD4+ T-cell and 6 CD8+ T-cell epitopes were selected, which represent a starting point to investigating the protective capacity of ASFV epitope-based vaccines.

scholarly journals Live Attenuated African Swine Fever Viruses as Ideal Tools to Dissect the Mechanisms Involved in Cross-Protection

Viruses ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1474
Author(s):  
Elisabeth Lopez ◽  
Juanita van Heerden ◽  
Laia Bosch-Camós ◽  
Francesc Accensi ◽  
Maria Jesus Navas ◽  
...  
Keyword(s):  
Vaccine Development ◽  
African Swine Fever Virus ◽  
Virulent Strain ◽  
Sequence Similarity ◽  
Clinical Signs ◽  
African Swine Fever ◽  
Cross Protection ◽  
Sub Saharan Africa ◽  
Genotype I ◽  
Genotype Ii

African swine fever (ASF) has become the major threat for the global swine industry. Furthermore, the epidemiological situation of African swine fever virus (ASFV) in some endemic regions of Sub-Saharan Africa is worse than ever, with multiple virus strains and genotypes currently circulating in a given area. Despite the recent advances on ASF vaccine development, there are no commercial vaccines yet, and most of the promising vaccine prototypes available today have been specifically designed to fight the genotype II strains currently circulating in Europe, Asia, and Oceania. Previous results from our laboratory have demonstrated the ability of BA71∆CD2, a recombinant LAV lacking CD2v, to confer protection against homologous (BA71) and heterologous genotype I (E75) and genotype II (Georgia2007/01) ASFV strains, both belonging to same clade (clade C). Here, we extend these results using BA71∆CD2 as a tool trying to understand ASFV cross-protection, using phylogenetically distant ASFV strains. We first observed that five out of six (83.3%) of the pigs immunized once with 106 PFU of BA71∆CD2 survived the tick-bite challenge using Ornithodoros sp. soft ticks naturally infected with RSA/11/2017 strain (genotype XIX, clade D). Second, only two out of six (33.3%) survived the challenge with Ken06.Bus (genotype IX, clade A), which is phylogenetically more distant to BA71∆CD2 than the RSA/11/2017 strain. On the other hand, homologous prime-boosting with BA71∆CD2 only improved the survival rate to 50% after Ken06.Bus challenge, all suffering mild ASF-compatible clinical signs, while 100% of the pigs immunized with BA71∆CD2 and boosted with the parental BA71 virulent strain survived the lethal challenge with Ken06.Bus, without almost no clinical signs of the disease. Our results confirm that cross-protection is a multifactorial phenomenon that not only depends on sequence similarity. We believe that understanding this complex phenomenon will be useful for designing future vaccines for ASF-endemic areas.

scholarly journals Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaghoub Dabiri ◽  
Alex Van der Velden ◽  
Kevin L. Sack ◽  
Jenny S. Choy ◽  
Julius M. Guccione ◽  
...  
Keyword(s):  
Real Time ◽  
Material Properties ◽  
In Silico ◽  
Prediction Models ◽  
Heart Function ◽  
Active Material ◽  
Experimental Studies ◽  
Left Ventricular ◽  
Feed Forward ◽  
Ventricular Mechanics

AbstractAn understanding of left ventricle (LV) mechanics is fundamental for designing better preventive, diagnostic, and treatment strategies for improved heart function. Because of the costs of clinical and experimental studies to treat and understand heart function, respectively, in-silico models play an important role. Finite element (FE) models, which have been used to create in-silico LV models for different cardiac health and disease conditions, as well as cardiac device design, are time-consuming and require powerful computational resources, which limits their use when real-time results are needed. As an alternative, we sought to use deep learning (DL) for LV in-silico modeling. We used 80 four-chamber heart FE models for feed forward, as well as recurrent neural network (RNN) with long short-term memory (LSTM) models for LV pressure and volume. We used 120 LV-only FE models for training LV stress predictions. The active material properties of the myocardium and time were features for the LV pressure and volume training, and passive material properties and element centroid coordinates were features of the LV stress prediction models. For six test FE models, the DL error for LV volume was 1.599 ± 1.227 ml, and the error for pressure was 1.257 ± 0.488 mmHg; for 20 LV FE test examples, the mean absolute errors were, respectively, 0.179 ± 0.050 for myofiber, 0.049 ± 0.017 for cross-fiber, and 0.039 ± 0.011 kPa for shear stress. After training, the DL runtime was in the order of seconds whereas equivalent FE runtime was in the order of several hours (pressure and volume) or 20 min (stress). We conclude that using DL, LV in-silico simulations can be provided for applications requiring real-time results.

Abstract P437: Deep Learning-based Models For Complete Atrioventricular Block Heart Rhythm Analysis

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Dahim Choi ◽  
Nam Kyun Kim ◽  
Young H Son ◽  
Yuming Gao ◽  
Christina Sheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Atrioventricular Block ◽  
Prediction Models ◽  
Pacemaker Implantation ◽  
Heart Rhythm ◽  
High Sensitivity ◽  
Ventricular Myocardium ◽  
True Positive Rate ◽  
Starting Point

Atrioventricular block (AVB), caused by impairment in the heart conduction system, presents extreme diversity and is associated with other complications. Only half of AVB patients require a permanent pacemaker, and the process determining the pacemaker implantation is associated with an increase in cost and patient morbidity and mortality. Thus, there is a need for models capable of accurately identifying transient or reversible causes for conduction disturbances and predicting the patient risks and the necessity of a pacemaker. Deep learning (DL) is brought to the forefront due to its prediction accuracy, and the DL-based electrocardiogram (ECG) analysis can be a breakthrough to analyze a massive amount of data. However, the current DL models are unsuitable for AVB-ECG, where the P waves are decoupled from the QRS/T waves, and a black-box nature of the DL-based model lowers the credibility of prediction models to physicians. Here, we present a real-time-capable DL-based algorithm that can identify AVB-ECG waves and automate AVB phenotyping for arrhythmogenic risk assessment. Our algorithm can analyze unformatted ECG records with abnormal patterns by integrating the two representative DL algorithms: convolutional neural networks (CNN) and recurrent neural networks (RNN). This hybrid CNN/RNN network can memorize local patterns, spatial hierarchies, and long-range temporal dependencies of ECG signals. Furthermore, by integrating parameters derived from dimension reduction analysis and heart rate variability into the hybrid layers, the algorithm can capture the P/QRS/T-specific morphological and temporal features in ECG waveforms. We evaluated the algorithm using the six AVB porcine models, where TBX18, a pacemaker transcription factor, was transduced into the ventricular myocardium to form a biological pacemaker, and an additional electronic pacemaker was transplanted as a backup pacemaker. We achieved high sensitivity (95% true positive rate) and quantified the potential risks of various pathological ECG patterns. This study may be a starting point in conducting both retrospective and prospective patient studies and will help physicians understand its decision-making workflow and find the incorrect recommendations for AVB patients.

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