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Sensbio:一个用于生物传感器设计的在线服务器。

Sensbio: an online server for biosensor design.

机构信息

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.

出版信息

BMC Bioinformatics. 2023 Feb 28;24(1):71. doi: 10.1186/s12859-023-05201-7.

Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe16/9972687/e3805531fffe/12859_2023_5201_Fig1_HTML.jpg

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