Deep Learning Approaches for Protein Structure Prediction in Regenerative Therapies

Abstract

Protein structure prediction -- the computational determination of a protein's three-dimensional structure from its amino acid sequence -- has been transformed by the advent of deep learning methods, most dramatically by AlphaFold2 (Jumper et al., 2021), which achieved near-experimental accuracy for single-chain protein structure prediction. In regenerative medicine, accurate protein structure prediction is critical for three application domains: growth factor engineering (optimising the structure and stability of therapeutic growth factors for cell therapy protocols); biomaterial scaffold design (predicting protein-biomaterial interaction geometries for extracellular matrix engineering); and therapeutic protein design (de novo design of novel proteins with regenerative activity). While AlphaFold2 and related methods have substantially advanced single-protein structure prediction, their application to the specific challenges of regenerative medicine -- multi-protein complex assembly, protein-surface interactions, and de novo design -- requires domain-specific adaptation and extension. This paper proposes the Regenerative Protein Structure (RPS) framework, a suite of deep learning methods adapted from AlphaFold2-class architectures for the three regenerative medicine application domains: RPS-GF (growth factor engineering), RPS-BS (biomaterial scaffold design), and RPS-TP (therapeutic protein de novo design). RPS is evaluated on benchmark tasks covering all three domains against AlphaFold2 and RoseTTAFold baselines. RPS-GF achieves TM-score = 0.94 (SD = 0.02) for growth factor structure prediction -- 6.8% improvement over AlphaFold2 (0.88) for thermostability-optimised variants. RPS-BS predicts protein- biomaterial interaction geometries with a mean binding energy prediction error of 1.84 kcal/mol (SD = 0.28) -- 38.2% improvement over docking baselines. RPS-TP designs novel pro-angiogenic peptides with 74.6% experimental validation rate (SD = 6.2%), substantially exceeding random sequence baseline (8.4%). The study contributes the RPS framework specification, three domain- specific benchmarks, and experimentally validated de novo protein designs with potential therapeutic application.