UM-CPP: A Universal Model for Efficient Classification of Protein Particles in cryo-EM Micrographs with Feature Engineering

Cryo-electron microscopy (cryo-EM) is a powerful tool for high-resolution structural analysis of proteins and viruses. However, a major challenge in cryo-EM data processing is the presence of heterogeneous samples, IC contamination, and extraneous impurities, which hinder accurate target protein identification. To address this issue, we propose the Universal Model for Cryo-electron Microscopy Particle Picking (UM-CPP), a novel framework that integrates feature engineering with deep learning to enhance particle detection in cryo-EM micrographs. The key contribution of UM-CPP lies in its hybrid approach, which combines classical machine learning features with state-of-the-art deep learning techniques. This fusion enables robust and adaptable performance across diverse protein structures while maintaining high accuracy. In comparative evaluations, UM-CPP outperforms existing deep-learning-based methods in detection precision. Additionally, our model provides interpretable feature analysis, offering researchers deeper insights into the decision-making process of particle selectiona critical advancement for improving trust and usability in cryo-EM data analysis. By improving both accuracy and interpretability, UM-CPP advances the field of cryo-EM, facilitating more reliable and efficient structural studies of biological macromolecules.