Exploring the Bottleneck in Cryo-EM Dynamic Disorder Feature and Advanced Hybrid Prediction Model

Cryo-electron microscopy single-particle analysis (cryo-EM SPA) has advanced three-dimensional protein structure determination, yet resolving intrinsically disordered proteins and regions (IDPs/IDRs) remains challenging due to conformational heterogeneity. This research evaluates cryo-EM’s capacity to map dynamic regions, assesses the adaptability of disorder prediction tools, and explores optimization strategies for dynamic structure prediction. Cryo-EM SPA datasets from 2000 to 2024 were categorized into different periods, forming a database integrating sequence data and disorder indices. Established prediction tools—AlphaFold2 (pLDDT), flDPnn, and IUPred—were evaluated for transferability, while a multi-level CLTC hybrid model (combining CNN, LSTM, Transformer, and CRF architectures) was developed to link local conformational fluctuations with global sequence contexts. Analyses revealed consistent advancements in average resolution and model counts over the past decade, although mapping disordered regions remained technically demanding. Both the adapted AlphaFold pLDDT and the CLTC model demonstrated efficacy in predicting structurally variable and poorly resolved regions. A subset of the cryo-EM missing residues exhibited intermediate conformational features, suggesting classification ambiguities potentially influenced by experimental conditions. These findings systematically outline the evolving capabilities of cryo-EM in resolving dynamic regions, benchmark the adaptability of computational tools, and introduce a hybrid model to enhance prediction accuracy. This study provides a framework for addressing conformational heterogeneity, contributing to methodological advancements in structural biology.