Your browser doesn't support javascript.
loading
Sensbio: an online server for biosensor design.
Tellechea-Luzardo, Jonathan; Martín Lázaro, Hèctor; Moreno López, Raúl; Carbonell, Pablo.
Afiliação
  • Tellechea-Luzardo J; Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022, Valencia, Spain.
  • Martín Lázaro H; Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022, Valencia, Spain.
  • Moreno López R; Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022, Valencia, Spain.
  • Carbonell P; Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022, Valencia, Spain. pablo.carbonell@upv.es.
BMC Bioinformatics ; 24(1): 71, 2023 Feb 28.
Article em En | MEDLINE | ID: mdl-36855083
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.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Computadores Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Computadores Idioma: En Ano de publicação: 2023 Tipo de documento: Article