A decade of machine learning in protein corona research: Innovations and challenges

Abstract

Nanomaterials, with their diverse biomedical applications spanning drug delivery to molecular imaging, undergo the adsorption of a protein corona (PC) layer upon exposure to biological environments. This dynamic layer, shaped by intricate interactions, significantly influences immune recognition, biodistribution, and nanoparticle toxicity. Traditional proteomic methods, such as liquid chromatography-tandem mass spectrometry, are effective but limited by low throughput, high costs, and the requirement for specialized expertise. The transition from unintentional PC analysis during polymer evaluations to a deliberate investigation of its role in drug targeting underscores the need for more efficient analytical approaches. The integration of machine learning (ML) into PC research has emerged as a promising solution. This computational methodology, which learns from datasets of characterized protein layers on specific nanoparticles, offers a more streamlined and resource-efficient alternative to traditional methods. Recent studies highlight ML’s ability to predict PC dynamics and biological effects, achieving notable accuracy in forecasting organ accumulation patterns. However, challenges remain, including the need for larger and more diverse datasets, significant computational demands, and the necessity for interdisciplinary collaboration between biologists, chemists, and data scientists. In addition, the development of standardized experimental protocols is crucial to ensure reproducibility and comparability across studies. Ethical considerations, such as potential job displacement in traditional fields, such as chemistry, also warrant careful attention as ML continues to evolve in this domain. In summary, while ML shows immense potential to revolutionize PC research, further refinement of methodologies and enhanced collaboration across disciplines are essential to fully realize its application in clinical nanomedicine.