We compiled the available information on allosteric transcription factors (aTFs) from multiple databases and the literature. We present an approach to assign new targets for aTFs that is based on sequence homology and machine learning. We further propose a structure-based analysis pipeline for predicting ligand binding and regulation and present the methodology to experimentally validate our predictions. Two synthetic biology applications of aTFs are described focused on bioproduction. Finally, our proposed in-vivo approach using aTFs to improve biomanufacturing of naringenin in Escherichia coli is presented.