Predicting DNA toehold-mediated strand displacement rate constants using a DNA-BERT transformer deep learning model.

ABSTRACT: Dynamic DNA nanotechnology is driving exciting developments in molecular computing, cargo delivery, sensing and detection. Combining this innovative area of research with the progress made in machine learning will aid in the design of sophisticated DNA machinery. Herein, we present a novel framework based on a transformer architecture and a deep learning model which can predict the rate constant of toehold-mediated strand displacement, the underlying process in dynamic DNA nanotechnology. Initially, a dataset of 4450 DNA sequences and corresponding rate constants were generated in-silico using KinDA. Subsequently, a 1D convolution neural network was trained using specific local features and DNA-BERT sequence embedding to produce predicted rate constants. As a result, the newly train

SUBMITTER: Akay A 

PROVIDER: S-EPMC10981123 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature