0% Complete
the 4th international edition and 13th Iranian Conference on Bioinformatics
international edition and Iranian Conference on Bioinformatics
صفحه اصلی
/
4th international edition and 13th Iranian Conference on Bioinformatics
Applying immunoinformatics methods using gb41 and gp120 genes of HIV virus to design a multi-epitope vaccine
نویسندگان :
Zahra Hassanzadeh
1
Fatemeh Hassanzadeh
2
Ava Hashempour
3
1- دانشگاه علوم پزشکی شیراز
2- دانشگاه علوم پزشکی شیراز
3- دانشگاه علوم پزشکی شیراز
کلمات کلیدی :
Immunoinformatics،Multi epitope based vaccines،HIV،gp120 and gp41
چکیده :
Background and Objective HIV remains one of the major global health concerns, with over 37 million people infected. It targets mainly the CD4+ T cells, inducing a systematic deterioration in the immune system, and eventually leading to AIDS. However, in spite of the progress achieved with antiretroviral therapies, an efficient vaccine would be the best long-term strategy against HIV. gp120 and gp41 viral envelope glycoproteins are crucial for the virus to get into host cells; thus, they are considered attractive candidates for vaccine development. Advanced immunoinformatics and bioinformatics approaches were used in designing a multi-epitope vaccine capable of eliciting robust and durable immune responses. Materials and Methods Bioinformatics prediction of linear B-cell, CTL, and HTL epitopes by Bepipred 2.0, NetCTL 1.2, and IEDB, respectively Identification of conserved regions in gp120 and gp41 was done as the first step for broad coverage of the vaccine with minimum chances of immune evasion. Strict validation was done to ensure that selected epitopes are antigenic, non-allergenic, and non-toxic with no homology to the human proteome.The final vaccine construct included eight CTL, six HTL, and eight B-cell epitopes, which were joined with EAAAK, AAY, GGGGS, and KK linkers. An adjuvant included was the Cholera Toxin Subunit B, CTxB, to enhance immunogenicity for a better immune response. I-TASSER was employed to model the 3D structure of the vaccine construct, and further refinement by GalaxyRefine was completed to assure correct spatial arrangement and structural integrity. Molecular docking for evaluating the affinity of vaccine epitopes to MHC alleles was done for both class I and II, with very good interactions resulting, which is important for effective immune stimulation. Immune responses were simulated on the C-ImmSim platform in order to predict the efficacy of the vaccine candidate in inducing humoral and cellular immunity. Finally, codon optimization and in-silico cloning were performed for effective expression of the vaccine in prokaryotic and eukaryotic systems. Findings The multi-epitope vaccine construct manifested a number of desirable features. It was validated to be highly antigenic, nontoxic, and non-allergenic, bearing high predicted immunogenicity. Molecular docking results revealed robust binding affinities with MHC molecules, therefore suggesting effective presentation of epitopes to the immune system. Immune simulations indicated that an immune response was high and long-lasting, characterized by the high production of antibodies and the activation of T cells. This was further supported by structural validation, which indicated stability with very favorable Ramachandran plots and quality scores from tools such as ProSA-web and QMEAN. Moreover, population coverage analysis predicted that the vaccine would be able to recognize various HLA alleles in global populations, thus targeting a wide pool of applications. Conclusion The current research has successfully designed a multi-epitope vaccine against gp120 and gp41 of HIV through advanced biocomputational approaches. Therefore, the vaccine construct has great potential for inducing broad immune responses and global coverage. The findings presented here form a promising basis for experimental validation and future development, contributing to the effort toward an effective vaccine against HIV infection.
لیست مقالات
لیست مقالات بایگانی شده
Tailoring Stability of Keratinocyte Growth Factor Variants for Effective Wound Healing in Acidic Conditions
Mansoureh Shahbazi Dastjerdeh - Mohammadtaghi Borjian Boroujeni
Predicting drug response using omics data and artificial intelligence approach in cancer
Abdolhamid Nazerpanahi - Mohammad Ali Hossein Beigy - Amirhossein Keyhanipour - Kaveh Kavousi - Siavash Kavousi
Identifying the expression profile of genes involved in human brain development using transcriptome data
Amirhosein Akhoondi - Mostafa Rafeipour - Farinaz Behfarjam
Predicting Adverse Drug Reactions with Advanced Machine Learning Techniques
AlI Mohammadian - Sara Haghighi Bardine - Fatemezahra Alizade
Novel Anti-ageing Strategy Via Targeting CST With Vitamin B1
SeyedMobin Mousavi Ghomi - Maryam Azimzadeh Irani - Aida Arezoumandchafi
Combination therapy synergism prediction for virus treatment using machine learning models
Shayan Majidifar - Arash Zabihian - Mohsen Hooshmand
Identification of Antigenic Proteins of Acinetobacter baumannii as Potential Novel Vaccine Candidates Through a Reverse Vaccinology Approach
Amirhossein Ghadiri - Abbas Doosti - Mostafa Shakhsi-Niaei
Bioinformatics and computational Studies on Highly Conserved Neurocalcin Protein
Ali Eyvazi - Khosrow Khalifeh - Emran Heshmati
Using interpretable deep learning models and multi-objective data for computational discovery of new drugs
Masoud Ahmadlou
Identification of Essential Genes and Suitable Drug Combinations for Colorectal Cancer Treatment Based on Systems Biology Approaches
Yasna Kazemghamsari
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.5.0