PseudoResistance DB: A new Database of antibiotics related to Pseudomonas aeruginosa antibiotic resistance

This research addresses the pressing issue of antibiotic resistance, a global health challenge that undermines the efficacy of treatments against infectious diseases. Focusing on Pseudomonas aeruginosa—a Gram-negative bacterium known for causing opportunistic infections—this study emphasizes its prioritization by the World Health Organization (WHO) as a critical-level pathogen requiring new therapeutic approaches.

To identify antibiotics associated with P. aeruginosa, the study employed text mining techniques on the Scielo database. The resulting dataset comprises 98 antibiotics, each documented with detailed textual information and referencing data. Additionally, the dataset includes structural files of the antibiotics in several formats suitable for computational modeling and simulations. These formats encompass Protein Data Bank, Partial Charge & Atom Type (PDBQT), Simplified Molecular Input Line Entry System (SMI), IUPAC International Chemical Identifier (INCHI), Molecular Design Limited Molfile (MOL2), Structure-Data File (SDF), Chemical Markup Language (CML), Cartesian Coordinates File (XYZ), Scalable Vector Graphics (SVG), Molecular File (MOL) and Protein Data Bank (PDB) files, with molecular models generated via OpenBabel to facilitate advanced studies in drug development and resistance mechanisms.

本研究旨在解决抗生素耐药性的紧迫问题，这一问题作为全球性的健康挑战，削弱了针对传染病的治疗方案的有效性。聚焦于铜绿假单胞菌——一种以引起机会性感染而闻名的革兰氏阴性菌——本研究强调其被世界卫生组织（WHO）列为关键级别的病原体，并迫切需要新的治疗策略。为了识别与铜绿假单胞菌相关的抗生素，研究团队在Scielo数据库中运用了文本挖掘技术。由此产生的数据集包含98种抗生素，每种抗生素均配有详细的文本信息和参考文献数据。此外，该数据集还包括了多种格式的抗生素结构文件，适用于计算建模和模拟。这些格式包括蛋白质数据银行（PDB）、部分电荷与原子类型（PDBQT）、简化分子输入线性系统（SMI）、国际纯粹与应用化学联合会化学标识符（INCHI）、分子设计有限分子文件（MOL2）、结构数据文件（SDF）、化学标记语言（CML）、笛卡尔坐标文件（XYZ）、可缩放矢量图形（SVG）、分子文件（MOL）和蛋白质数据银行（PDB）文件，以及通过OpenBabel生成的分子模型，以促进药物开发和耐药机制的高级研究。