A comprehensive review of computational strategies in the rational design of biosensors based on allosteric transcription factors for pollutant detection

Allosteric transcription factor (aTF)-based biosensors are powerful analytical tools for the detection of different analytes such as environmental pollutants and are widely used in medical diagnostics and environmental monitoring. These biosensors rely on ligand-induced conformational changes in transcription factors that regulate gene expression, thereby positioning them as low-cost, sensitive and selective components in biosensor design. In recent times, computational biology and chemistry techniques such as homology modelling, molecular docking, molecular dynamics (MD) simulations, free energy perturbation (FEP), enhanced sampling techniques and machine learning have been widely employed in the design and optimization of aTF-based biosensors. With these methods, the structures of proteins could be predicted, the ligand-binding pockets could be identified and characterized, the interactions therein could be analyzed and binding affinities could be predicted. Also, the conformational dynamics, allosteric and transition pathways, free energy landscapes, relative binding free energies of the ligand-aTF complexes can be predicted. Taken together, these can facilitate the engineering of aTFs with improved sensitivity and specificity. Therefore, this review highlights the individual and complementary roles of these computational techniques in the design and optimization of aTF-based biosensors and discusses the recent advances, current limitations and future directions in the design of next-generation aTF-based biosensors.