Reformulating Protein Corona Prediction as Universal Protein-conditioned Learning
When nanoparticles enter biological environments, their surfaces are rapidly coated by proteins, forming a protein corona that governs their biological identity and downstream responses. Existing computational methods treat each protein as an independent prediction task, preventing the learning of shared adsorption principles and limiting generalization beyond experimentally characterized…
Nanoparticles entering biological environments are quickly enveloped by proteins, forming a protein corona that determines their biological role and subsequent reactions. Existing computational approaches analyze each protein independently, hindering the discovery of common adsorption mechanisms and constraining the ability to generalize to new proteins.
Researchers have now developed NanoPCT, a universal protein-conditioned learning framework that integrates nanoparticle physical-chemical attributes with representations of protein sequences. Utilizing pre-trained ESM-2 embeddings and a joint interaction transformer, NanoPCT identifies transferable patterns of nano-bio interactions across a vast array of proteins, preserving accurate predictions even as protein complexity grows.
Moreover, the protein representation allows for accurate forecasting of protein outcomes for previously unencountered proteins and for protein groups with distinct biological functions, achieved through classifier reconstruction within the embedding space. In addition to prediction capabilities, NanoPCT facilitates broad-scale computational "design by inversion" and uncovers consistent tendencies in protein adsorption across both observed and unknown protein collections.
This suggests that the framework captures universal interaction patterns, rather than memorizing protein-specific details. NanoPCT offers a comprehensive approach to protein corona prediction, enables zero-shot generalization, and aids in the rational design of nanomaterials.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
