Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022, Valencia, Spain.
Institute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC, 46980, Paterna, Spain.
BMC Bioinformatics. 2023 Feb 28;24(1):71. doi: 10.1186/s12859-023-05201-7.
Allosteric transcription factor (aTF) based biosensors can be used to engineer genetic circuits for a wide range of applications. The literature and online databases contain hundreds of experimentally validated molecule-TF pairs; however, the knowledge is scattered and often incomplete. Additionally, compared to the number of compounds that can be produced in living systems, those with known associated TF-compound interactions are low. For these reasons, new tools that help researchers find new possible TF-ligand pairs are called for. In this work, we present Sensbio, a computational tool that through similarity comparison against a TF-ligand reference database, is able to identify putative transcription factors that can be activated by a given input molecule. In addition to the collection of algorithms, an online application has also been developed, together with a predictive model created to find new possible matches based on machine learning.
别构转录因子（aTF）基生物传感器可用于为广泛的应用工程遗传电路。文献和在线数据库中包含数百种经过实验验证的分子-TF 对；然而，这些知识是分散的，而且往往不完整。此外，与可以在活系统中产生的化合物数量相比，具有已知相关 TF-化合物相互作用的化合物数量较低。由于这些原因，需要新的工具来帮助研究人员找到新的可能的 TF-配体对。在这项工作中，我们提出了 Sensbio，这是一种计算工具，它通过与 TF-配体参考数据库进行相似性比较，能够识别可以被给定输入分子激活的假定转录因子。除了算法集外，还开发了一个在线应用程序，以及一个基于机器学习创建的预测模型，用于根据新的可能匹配项进行查找。
