TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
João Carneiro, Rita P. Magalhães, Victor M. de la Oliva Roque, Manuel Simões, Diogo Pratas, Sérgio F. Sousa
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Abstract

Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.

Abstract Image

TargIDe:一种机器学习工作流程,用于对铜绿假单胞菌具有抗生素膜活性的分子进行目标识别
细菌生物膜是传染性人类疾病的一个来源,与抗生素耐药性密切相关。铜绿假单胞菌是一种广泛存在的多重耐药细菌,与几种医院获得性感染有关。近年来,通过干扰铜绿假单胞菌形成生物膜的能力来抑制铜绿假单胞菌的新药物的开发已成为一种很有前途的药物发现策略。识别能够干扰生物膜形成的分子是困难的,但通过合理提高其活性来进一步开发这些分子尤其具有挑战性,因为它需要了解被抑制的特定蛋白质靶标。这项工作描述了一种机器学习多技术共识工作流的发展,以预测对铜绿假单胞菌生物膜形成具有确定抑制活性的分子的蛋白质靶标。它使用了一个专门的数据库,其中包含了铜绿假单胞菌形成生物膜所涉及的所有已知目标。实验证实的ChEMBL上可用的抑制剂,连同化学描述符,被用作九种不同分类模型组合的输入特征,从而产生一种共识方法来预测配体最可能的靶标。在GNU通用公共许可证(GPL)版本3的许可下,实现的算法可以在https://github.com/BioSIM-Research-Group/TargIDe上免费获得,并且可以很容易地随着更多数据的可用性而改进。
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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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