Tau aggregation into amyloid fibrils is a central pathological feature of multiple neurodegenerative diseases, yet identical tau sequences can adopt structurally distinct fibril conformations associated with different disorders. How such disease-specific folds emerge from the same sequence remains poorly understood. Here, we use metadynamics and all-atom molecular dynamics simulations to elucidate the folding mechanisms of tau trimers comprising the R3-R4 region, the minimal aggregation nucleus of tau fibrils. By constructing the folding free-energy landscape within a pre-organized fibril-like scaffold, we identify a shared, partially folded intermediate from which two energetically comparable minimum free-energy pathways (MEPs) diverge, leading to either a compact AD-like or a more open CTE-like C-shaped conformation. Structural analysis reveals that the β4-β6 triangular region acts as the central determinant of this bifurcation. Hydrophobic-core rearrangements within this region provide the primary energetic driving force for compaction, whereas the conformational flexibility of the 332PGGG335 hinge modulates whether this tendency can be structurally realized. Additional simulations show that stable formation of the triangular region requires cooperative multichain interactions; reducing oligomer size destabilizes this scaffold and abolishes ordered folding. Together, our results establish a mechanistic framework in which identical tau sequences access alternative folding routes through a common intermediate, explaining the emergence of disease-specific fibril polymorphism at the oligomeric level and highlighting early folding intermediates as potential targets for therapeutic intervention.
Tong Zhang, Lingling Dai, Yanping Ma et al.· Biophysical Journal· 0 citations
Accurate identification of near-native ligand binding poses is a central challenge in structure-based drug design. From a physical point of view, the successful construction of a protein–ligand complex structure is dependent on whether protein and ligand can form enough atomically pairwise interactions that result in a global energy minimum. In this work, we report a machine learning scoring strategy for protein–ligand screening which explicitly considers the Native Contact Ratio (NCR), a topology inspired metric that quantifies the preservation of protein–ligand interfacial contacts as well as interaction energy. This physics-awared supervision strategy provides a simple but efficient gradient field that faithfully reflects the complicated protein energy landscape than conventional 3D coordinate-based objectives. Building on this principle, we present DeepNCR, an energy-informed Transformer framework that encodes approximate Coulombic and dispersive interaction potentials across the protein–ligand binding interface. Furthermore, we introduce a feature pruning step that compresses the interaction tensor from 1470 to 868 dimensions, further improving signal-to-noise ratio and directing model attention toward the interaction motifs critical for binding specificity. The model optimizes topological objectives and at inference drives pose refinement through a differentiable hybrid gradient field integrating predicted NCR and AutoDock Vina energetics. Extensive evaluation on the CASF-2016 benchmark and the 3D-DISCO cross-docking data set demonstrates consistently high performance: a Top-1 docking success rate of 94.7%, a 1% Enrichment Factor of 21.21 in virtual screening, and a Top-1 cross-docking success rate of 34.8%. Mechanistic analysis reveals that NCR-guided optimization enables decoy escaping from local energy minima and drives the recovery of disrupted native interactions, confirming that NCR captures the physical determinants of binding rather than mere geometric proximity.
Zhenqiang Zhang, Zhihao Wang, Yang Liu et al.· Journal of Chemical Informat...· 0 citations