| Abstract Scope |
Tendinopathy is characterized by impaired tendon remodeling and limited intrinsic healing. We present a machine-learning-guided M13 bacteriophage bio-piezoelectric architecture implant designed to provide wireless electrical stimulation to pathological tendon tissue under ultrasound. M13 phage offers a genetically programmable, intrinsically polar nanofiber composed of repeating major coat proteins. To enhance electromechanical activity, machine learning was used to screen engineered phage variants by integrating sequence-derived dipole descriptors, secondary-structure propensity, and predicted structural features, enabling selection of candidates with improved molecular polarity and α-helical stability. The selected phage was further organized into a unidirectional anisotropic hydrogel using magnetic-field-assisted alignment, and the resulting architecture was confirmed by SEM, polarized optical microscopy, and second-harmonic generation microscopy. The aligned structure exhibited enhanced ultrasound-induced piezoelectric output compared with non-aligned controls, supporting its potential as an implantable, biologically derived, electroactive platform for tendinopathy treatment. |