In Journal of drug targeting
Bone morphogenetic protein-2 (BMP-2) is a key regulator of bone formation, growth and regeneration, which contains a conformational wrist epitope and a linear knuckle epitope that are functionally responsible for the protein by mediating its interaction with type-I and type-II receptors, respectively. Previously, a long (19-mer) knuckle peptide derived from the knuckle epitope region (residues 73-92) has been found to promote osteogenesis and bone repair. Here, we attempt to rationally redesign the knuckle peptide by using bioinformatics and machine learning-guided evolution to obtain structurally simplified, potent osteogenic peptides that are capable of targeting type-II receptor. Complex analysis reveals that only a fraction of the epitope region can directly interact with type-II receptor, which represents a small (12-mer) knuckle-derived peptide (KDP0 peptide). Glycine scanning further identifies three KDP0 anchor residues Ser88, Leu90 and Tyr91 that are fundamentally important in the peptide-receptor binding. Systematic mutation, amino acid combination and uniform design of other nine KDP0 non-anchor residues generate 32 new knuckle-derived peptides (KDP1-KDP32); their binding affinities to recombinant protein of human type-II receptor are determined using fluorescence spectroscopy assay. The resulting affinity values (Kd) are used to train six regression models developed by combination of two machine learning methods and three amino acids descriptors. The best SVM/VHSE predictor is then utilised to guide the genetic evolution of a knuckle-derived peptide population. Eight peptides (KDP33-KDP40) with high affinity scores are selected from the improved population, and their osteogenic activities on bone marrow stromal cells are measured using alkaline phosphatase assay. Consequently, six out of the 8 tested peptides exhibit increased activity relative to KDP0 peptide. The KDP34 (DFQTWSFLYVEN) is found as the most potent peptide with APL activities of 195% and 279% at 0.01 and 0.1 µg/ml, respectively, which shares a similar binding mode with the native knuckle epitope and can form diverse nonbonded interactions of hydrogen bond, hydrophobic contact, cation-π/π-π stacking and salt bridge with type-II receptor.
Zhang Wei, Liu Jiazhi, Shan Haojie, Yin Fuli, Zhong Biao, Zhang Chi, Yu Xiaowei
Bone morphogenetic protein-2, knuckle epitope, machine learning, osteogenic peptide, rational peptide design, receptor targeting