Wadood Abdul, Riaz Muhammad, Mulk Amir Ul, Khan Momin, Haleem Sobia Ahsan, Shams Sulaiman, Gul Sahib, Ahmed Ayaz, Qasim Muhammad, Ali Farman, Ul-Haq Zaheer
Computational Medicinal Chemistry Laboratory, Department of Biochemistry, Abdul Wali Khan University, Mardan, Pakistan.
Department of Chemistry, Abdul Wali Khan University Mardan, Pakistan.
Bioinformation. 2014 May 20;10(5):299-307. doi: 10.6026/97320630010299. eCollection 2014.
Urease is an important enzyme both in agriculture and medicine research. Strategies based on urease inhibition is critically considered as the first line treatment of infections caused by urease producing bacteria. Since, urease possess agro-chemical and medicinal importance, thus, it is necessary to search for the novel compounds capable of inhibiting this enzyme. Several computational methods were employed to design novel and potent urease inhibitors in this work. First docking simulations of known compounds consists of a set of arylidine barbiturates (termed as reference) were performed on the Bacillus pasteurii (BP) urease. Subsequently, two fold strategies were used to design new compounds against urease. Stage 1 comprised of the energy minimization of enzyme-ligand complexes of reference compounds and the accurate prediction of the molecular mechanics generalized born (MMGB) interaction energies. In the second stage, new urease inhibitors were then designed by the substitution of different groups consecutively in the aryl ring of the thiobarbiturates and N, N-diethyl thiobarbiturates of the reference ligands.. The enzyme-ligand complexes with lowest interaction energies or energies close to the calculated interaction energies of the reference molecules, were selected for the consequent chemical manipulation. This was followed by the substitution of different groups on the 2 and 5 positions of the aryl ring. As a result, several new and potent diethyl thiobarbiturates were predicted as urease inhibitors. This approach reflects a logical progression for early stage drug discovery that can be exploited to successfully identify potential drug candidates.
脲酶在农业和医学研究中都是一种重要的酶。基于脲酶抑制的策略被严格视为治疗由产脲酶细菌引起的感染的一线治疗方法。由于脲酶具有农业化学和医学重要性,因此,有必要寻找能够抑制这种酶的新型化合物。在这项工作中,采用了几种计算方法来设计新型强效脲酶抑制剂。首先,对一组芳亚基巴比妥酸盐(称为参考物)的已知化合物在巴氏芽孢杆菌(BP)脲酶上进行对接模拟。随后,采用双重策略设计针对脲酶的新化合物。第一阶段包括参考化合物的酶 - 配体复合物的能量最小化以及分子力学广义玻恩(MMGB)相互作用能的精确预测。在第二阶段,通过在参考配体的硫代巴比妥酸盐和N,N - 二乙基硫代巴比妥酸盐的芳环中连续取代不同基团来设计新的脲酶抑制剂。选择具有最低相互作用能或接近参考分子计算出的相互作用能的酶 - 配体复合物进行后续化学操作。接着在芳环的2位和5位取代不同基团。结果,预测了几种新型强效二乙基硫代巴比妥酸盐作为脲酶抑制剂。这种方法反映了早期药物发现的合理进展,可用于成功识别潜在的药物候选物。