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一种利用机器学习和生物信息学开发针对前列腺癌的有效候选疫苗构建体的综合方法。

An Integrated Approach to Develop a Potent Vaccine Candidate Construct Against Prostate Cancer by Utilizing Machine Learning and Bioinformatics.

作者信息

Albutti Aqel

机构信息

Department of Basic Health Sciences, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia.

出版信息

Cancer Rep (Hoboken). 2024 Dec;7(12):e70079. doi: 10.1002/cnr2.70079.

DOI:10.1002/cnr2.70079
PMID:39651594
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11626413/
Abstract

BACKGROUND

Prostate cancer is the most common malignancy among males. Prostaglandin G/H synthase (PGHS) is an essential enzyme in the synthesis of prostaglandins, and its activation has been linked to many malignancies, including colorectal cancer.

AIMS

Due to the limited effectiveness and specificity of existing prostate cancer therapies, this study was designed to formulate improved treatment techniques.

METHODS

Several immunoinformatic, reverse vaccinology, and molecular modeling methodologies were used to discover B- and T-cell epitopes for the glioblastoma multiforme tumor PGH2_HUMAN. This research evaluated Prostaglandin G/H synthase 2 protein as a potential vaccine candidate against the malignancy. The multi-epitope vaccine architecture is engineered to activate the immune system, with each epitope docked to its respective HLAs. Further, MD simulations analysis was performed to validate the findings.

RESULTS

A multi-epitope subunit vaccine candidate was developed by concatenating the chosen B- and T-cell epitopes. Results yield a codon adaptive index (CAI) of 0.93 and a GC content of 56.77%. Thus, it conforms to a biological requirement for effective protein expression, suggesting competent vaccine efficacy inside the Escherichia coli system. Significant interleukin and cytokine responses were seen, characterized by elevated levels of IL-2 and IFN-γ in the immune system's response to the immunization. Molecular docking demonstrated an efficient binding affinity of -278 kcal/mol, with hydrogen bonding to several residues. Furthermore, the system total root mean square deviation (RMSD) reached 3.23 Å, with a maximum of up to 5.0 Å at the 100 ns time point but remains stable till 400 ns time intervals followed by stable root mean square fluctuation (RMSF) and radius of gyration values. The hydrogen bond cloud residues are the critical sites that significantly influence the binding energies of MMPBSA and MMGBSA via substantial van der Waals interactions.

CONCLUSION

It has been determined that these in silico analyses will further augment the comprehension necessary for advancing the creation of targeted therapies for chemotherapeutic cancer treatments.

摘要

背景

前列腺癌是男性中最常见的恶性肿瘤。前列腺素G/H合酶(PGHS)是前列腺素合成中的一种关键酶,其激活与许多恶性肿瘤有关,包括结直肠癌。

目的

由于现有前列腺癌治疗方法的有效性和特异性有限,本研究旨在制定改进的治疗技术。

方法

采用多种免疫信息学、反向疫苗学和分子建模方法来发现多形性胶质母细胞瘤肿瘤PGH2_HUMAN的B细胞和T细胞表位。本研究评估了前列腺素G/H合酶2蛋白作为一种针对该恶性肿瘤的潜在疫苗候选物。多表位疫苗结构经过设计以激活免疫系统,每个表位与各自的人类白细胞抗原(HLA)对接。此外,进行了分子动力学(MD)模拟分析以验证研究结果。

结果

通过连接所选的B细胞和T细胞表位,开发了一种多表位亚单位疫苗候选物。结果显示密码子适应指数(CAI)为0.93,GC含量为56.77%。因此,它符合有效蛋白质表达的生物学要求,表明在大肠杆菌系统内疫苗效力良好。观察到显著的白细胞介素和细胞因子反应,其特征是免疫系统对免疫接种的反应中IL-2和IFN-γ水平升高。分子对接显示有效结合亲和力为-278 kcal/mol,与多个残基形成氢键。此外,系统总均方根偏差(RMSD)达到3.23 Å,在100 ns时间点最大可达5.0 Å,但直到400 ns时间间隔都保持稳定,随后均方根波动(RMSF)和回转半径值也保持稳定。氢键云残基是通过大量范德华相互作用显著影响MMPBSA和MMGBSA结合能的关键位点。

结论

已确定这些计算机模拟分析将进一步增强推进化疗癌症治疗靶向疗法创建所需的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87bd/11626413/70f13ffe9af1/CNR2-7-e70079-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87bd/11626413/be690e000eef/CNR2-7-e70079-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87bd/11626413/e49699ab3b7a/CNR2-7-e70079-g011.jpg
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