Integration of Evolutionary Analysis With RFdiffusion for De Novo Design of Aggregation-Resistant Frataxin

Friedreich's ataxia (FRDA) is a neurodegenerative disorder caused by frataxin (FXN) deficiency, where protein replacement therapy is hampered by the inherent instability and aggregation propensity of wild-type (WT) FXN. The structural flexibility of Loop-1 (residues 115-123), a critical region within the acidic ridge, represents a key determinant of protein stability. This study introduces a computational pipeline integrating evolutionary conservation analysis (ConSurf) with diffusion-based de novo design (RFdiffusion) to redesign both the backbone and sequence of Loop-1. Through systematic filtration of 1000 ProteinMPNN-generated variants using aggregation propensity screening (AGGRESCAN) and 450 ns of molecular dynamics (MD) simulations, four lead candidates were identified. Design_188 (EERVGGREI) demonstrated optimal performance with 2.3-fold improvement in aggregation resistance (Na4vSS: -53.8 vs. -23.5 for WT), superior structural stability (RMSD: 0.486 nm), reduced conformational diversity (62.3% dominant cluster occupancy), and 93% retention of ISCU binding capacity (ΔΔG: +6.4 kcal/mol). Experimental validation through ¹⁵N NMR relaxation analysis confirmed computational predictions, with Design_188 exhibiting uniform backbone rigidification (S² = 0.81-0.95) and strong MD-NMR correlation (Pearson r = 0.675, p = 0.003). SEC-MALS analysis demonstrated near-complete monomeric behavior (> 98% monomer content) compared to WT's heterogeneous oligomerization (68% monomer, 32% oligomers), directly confirming the predicted anti-aggregation properties. K-means clustering analysis revealed an inverse relationship between conformational heterogeneity and stability, while correlation analysis identified a fundamental trade-off between aggregation resistance and structural stability (r = -0.82, p < 0.01). This work establishes a generalizable framework for therapeutic protein engineering where backbone redesign enables conformational ensemble modulation beyond the limitations of sequence optimization alone.