Engineering an AI-based forward-reverse platform for the design of cross-ribosome binding sites of a transcription factor biosensor
Nana Ding, Guangkun Zhang, Linpei Zhang, Ziyun Shen, Lianghong Yin, Shenghu Zhou, Yu Deng
Abstract
cRBS design with selected TFB dynamic ranges. The platform demonstrated superior in processing unbalanced minority-class datasets and was guided by sequence characteristics from trained cRBSs. The platform identified correlations between cRBSs and dynamic ranges to mimic bidirectional design between these factors based on Wasserstein generative adversarial network (GAN) with a gradient penalty (GP) (WGAN-GP) and balancing GAN with GP (BAGAN-GP). For forward and reverse engineering, the predictive accuracy was up to 98% and 82%, respectively. Collectively, we generated an AI-based method for the rational design of TFBs with desired dynamic ranges.
Topics & Concepts
Rational designReverse engineeringComputer scienceSynthetic biologyDynamic rangeComputational biologyBiosensorTranscription factorRibosomeMachine learningArtificial intelligenceBiological systemBiologyBiochemistryGeneticsRNAGeneProgramming languageComputer visionRNA and protein synthesis mechanismsEvolutionary Algorithms and ApplicationsGenomics and Chromatin Dynamics