Department of Applied Microbiology, Sri Padmavati Mahila Visvavidyalayam, Padmavathi Nagar, Tirupati, Andhra Pradesh, 517502, India.
Dr. Buddolla's Institute of Life Sciences (A Unit of Dr. Buddolla's Research and Educational Society), Tirupati, 517506, India.
Curr Microbiol. 2024 Jun 21;81(8):234. doi: 10.1007/s00284-024-03750-5.
Viral-like particles (VLPs) represent versatile nanoscale structures mimicking the morphology and antigenic characteristics of viruses, devoid of genetic material, making them promising candidates for various biomedical applications. The integration of artificial intelligence (AI) into VLP research has catalyzed significant advancements in understanding, production, and therapeutic applications of these nanostructures. This comprehensive review explores the collaborative utilization of AI tools, computational methodologies, and state-of-the-art technologies within the VLP domain. AI's involvement in bioinformatics facilitates sequencing and structure prediction, unraveling genetic intricacies and three-dimensional configurations of VLPs. Furthermore, AI-enabled drug discovery enables virtual screening, demonstrating promise in identifying compounds to inhibit VLP activity. In VLP production, AI optimizes processes by providing strategies for culture conditions, nutrient concentrations, and growth kinetics. AI's utilization in image analysis and electron microscopy expedites VLP recognition and quantification. Moreover, network analysis of protein-protein interactions through AI tools offers an understanding of VLP interactions. The integration of multi-omics data via AI analytics provides a comprehensive view of VLP behavior. Predictive modeling utilizing machine learning algorithms aids in forecasting VLP stability, guiding optimization efforts. Literature mining facilitated by text mining algorithms assists in summarizing information from the VLP knowledge corpus. Additionally, AI's role in laboratory automation enhances experimental efficiency. Addressing data security concerns, AI ensures the protection of sensitive information in the digital era of VLP research. This review serves as a roadmap, providing insights into AI's current and future applications in VLP research, thereby guiding innovative directions in medicine and beyond.
病毒样颗粒（VLPs）是一类具有多种功能的纳米结构，能够模拟病毒的形态和抗原特征，同时不含有遗传物质，因此它们是各种生物医学应用的有前途的候选者。将人工智能（AI）集成到 VLP 研究中，推动了对这些纳米结构的理解、生产和治疗应用的重大进展。这篇综述全面探讨了 AI 工具、计算方法和最先进技术在 VLP 领域中的协同应用。AI 在生物信息学中的应用有助于进行测序和结构预测，揭示 VLPs 的遗传复杂性和三维结构。此外，AI 辅助药物发现能够进行虚拟筛选，有望识别出抑制 VLP 活性的化合物。在 VLP 生产中，AI 通过提供培养条件、营养浓度和生长动力学等方面的策略来优化生产过程。AI 在图像分析和电子显微镜中的应用加速了 VLP 的识别和定量。此外，通过 AI 工具进行蛋白质-蛋白质相互作用的网络分析有助于理解 VLP 相互作用。通过 AI 分析整合多组学数据可以全面了解 VLP 的行为。利用机器学习算法进行预测建模有助于预测 VLP 的稳定性，指导优化工作。文本挖掘算法进行的文献挖掘有助于从 VLP 知识库中总结信息。此外，AI 在实验室自动化中的应用提高了实验效率。AI 解决了数据安全问题，确保了 VLP 研究数字化时代敏感信息的保护。本综述提供了一个路线图，深入了解 AI 在 VLP 研究中的当前和未来应用，从而为医学和其他领域的创新方向提供指导。
