增强可解释的SARS-CoV-2疫苗开发利用蜂群优化的Bi-LSTM,Bi-GRU模型和生物信息分析来提高疫苗的开发效率
Dilber Uzun Ozsahin1,2,3, Zubaida Said Ameen3,4, Abdurrahman Shuaibu Hassan5
1Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah, UAE.
Scientific reports
|March 21, 2024
概括
使用蜂群优化与双向长期短期记忆 (BCO-Bi-LSTM) 的新型深度学习方法有效预测疫苗开发的SARS-CoV-2表位. 这种方法加速了针对COVID-19的潜在疫苗候选人的识别.
科学领域:
- 计算生物学和生物信息学
- 病毒学和免疫学 病毒学和免疫学
- 人工智能在药物发现中的作用
背景情况:
- 严重急性呼吸道综合征冠状病毒2 (SARS-CoV-2) 导致了COVID-19大流行,需要有效的疫苗.
- 鉴定SARS-CoV-2表位对疫苗设计至关重要,但仍然是一个复杂和资源密集的过程.
- 目前用于表位预测的方法通常是缓慢和昂贵的,阻碍了疫苗的快速开发.
研究的目的:
- 开发一个高效的深度学习模型来预测来自SARS-CoV-2蛋白质的表位.
- 使用蜂群优化 (BCO) 来优化循环神经网络 (RNN) 架构,特别是双向长期短期内存 (Bi-LSTM) 和双向门式循环单元 (Bi-GRU).
- 创建和评估基于使用生物信息工具预测的表位素的多表位素疫苗.
主要方法:
- 独立循环神经网络,包括Bi-LSTM和Bi-GRU,用于表位预测.
- 使用蜂群优化 (BCO) 来优化Bi-LSTM和Bi-GRU模型的性能.
- 可解释的AI (形状添加式解释 - SHAP) 用于解释模型预测.
- 生物信息工具用于评估疫苗毒性,过敏反应,抗原性和免疫模拟.
主要成果:
- 经BCO优化的Bi-LSTM模型 (BCO-Bi-LSTM) 显示出卓越的性能,达到0.92准确度和0.944 AUC.
- 可解释的人工智能 (SHAP) 提供了黑子深度学习模型的决策过程的见解.
- 预计开发的多类表位候选疫苗是无毒的,具有高度抗原作用.
结论:
- 深度学习,特别是BCO-Bi-LSTM,为SARS-CoV-2表位预测提供了一个高度准确和高效的方法.
- 该研究成功设计了一种具有有利特征的有希望的多表位候选疫苗.
- 这些发现为加速开发SARS-CoV-2疫苗提供了有价值的框架.
相关概念视频
Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Vaccine Production
Vaccine production involves a sequence of upstream and downstream processes to generate a safe and effective immunological product. It begins with cultivating microorganisms, such as viruses or bacteria, to obtain antigenic material. For viral vaccines, mammalian host cells are grown in bioreactors and subsequently infected with the target virus. The virus replicates within the host cells, which are lysed to release viral particles. This lysate is then clarified through filtration or...


