Machine learning enables comprehensive prediction of the relative protein abundance of multiple proteins on the protein corona

The small size of nanoparticles is associated with several unique properties that mediate a profound impact on the development of many technological fields, including medicine. The nanoparticles in question are sufficiently small to allow access to almost all areas of the body. This has led to the development of a novel approach to medicine known as nanomedicine. Upon entering biological fluids, nanoparticles interact with a range of biopolymers, including proteins, which bind to them, forming the “protein corona” associated with the nanoparticle. Protein corona formation can be viewed as an interaction between nanoparticles and proteins. The protein corona composition reflects the relative protein abundance (RPA), which accounts for the total proteins in the corona, and is an important parameter for describing the protein corona. Artificial Intelligence could facilitate research related to protein corona.

Prof. Zilong Zhang,Feifei Cui's team from Hainan University and Quan Zou's team from University of Electronic Science and Technology of China collaborated to build a model that can comprehensively predict the relative abundance of multiple proteins in the protein corona by using machine learning, and provide the corresponding web service, so as to make the established model have practical application value.