Generation of functional oligopeptides that promote osteogenesis based on unsupervised deep learning of protein IDRs.

ABSTRACT: Deep learning (DL) is currently revolutionizing peptide drug development due to both computational advances and the substantial recent expansion of digitized biological data. However, progress in oligopeptide drug development has been limited, likely due to the lack of suitable datasets and difficulty in identifying informative features to use as inputs for DL models. Here, we utilized an unsupervised deep learning model to learn a semantic pattern based on the intrinsically disordered regions of ~171 known osteogenic proteins. Subsequently, oligopeptides were generated from this semantic pattern based on Monte Carlo simulation, followed by in vivo functional characterization. A five amino acid oligopeptide (AIB5P) had strong bone-formation-promoting effects, as determined in multiple mous

SUBMITTER: Cai M 

PROVIDER: S-EPMC8885677 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature