Kasam Vinod, Salzemann Jean, Botha Marli, Dacosta Ana, Degliesposti Gianluca, Isea Raul, Kim Doman, Maass Astrid, Kenyon Colin, Rastelli Giulio, Hofmann-Apitius Martin, Breton Vincent
Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany.
Malar J. 2009 May 1;8:88. doi: 10.1186/1475-2875-8-88.
Despite continuous efforts of the international community to reduce the impact of malaria on developing countries, no significant progress has been made in the recent years and the discovery of new drugs is more than ever needed. Out of the many proteins involved in the metabolic activities of the Plasmodium parasite, some are promising targets to carry out rational drug discovery.
Recent years have witnessed the emergence of grids, which are highly distributed computing infrastructures particularly well fitted for embarrassingly parallel computations like docking. In 2005, a first attempt at using grids for large-scale virtual screening focused on plasmepsins and ended up in the identification of previously unknown scaffolds, which were confirmed in vitro to be active plasmepsin inhibitors. Following this success, a second deployment took place in the fall of 2006 focussing on one well known target, dihydrofolate reductase (DHFR), and on a new promising one, glutathione-S-transferase.
In silico drug design, especially vHTS is a widely and well-accepted technology in lead identification and lead optimization. This approach, therefore builds, upon the progress made in computational chemistry to achieve more accurate in silico docking and in information technology to design and operate large scale grid infrastructures.
On the computational side, a sustained infrastructure has been developed: docking at large scale, using different strategies in result analysis, storing of the results on the fly into MySQL databases and application of molecular dynamics refinement are MM-PBSA and MM-GBSA rescoring. The modeling results obtained are very promising. Based on the modeling results, In vitro results are underway for all the targets against which screening is performed.
The current paper describes the rational drug discovery activity at large scale, especially molecular docking using FlexX software on computational grids in finding hits against three different targets (PfGST, PfDHFR, PvDHFR (wild type and mutant forms) implicated in malaria. Grid-enabled virtual screening approach is proposed to produce focus compound libraries for other biological targets relevant to fight the infectious diseases of the developing world.
尽管国际社会不断努力减轻疟疾对发展中国家的影响，但近年来并未取得显著进展，因此比以往任何时候都更需要发现新药。在疟原虫代谢活动涉及的众多蛋白质中，有些是进行合理药物研发的有前景的靶点。
近年来出现了网格，它是高度分布式的计算基础设施，特别适合像对接这样的易于并行的计算。2005年，首次尝试使用网格进行大规模虚拟筛选，重点是疟原蛋白酶，最终鉴定出了以前未知的骨架，体外实验证实它们是活性疟原蛋白酶抑制剂。在此成功之后，2006年秋季进行了第二次部署，重点是一个著名的靶点二氢叶酸还原酶（DHFR）和一个新的有前景的靶点谷胱甘肽-S-转移酶。
计算机辅助药物设计，尤其是虚拟高通量筛选（vHTS）是在先导化合物发现和优化中广泛且被认可的技术。因此，这种方法基于计算化学取得的进展，以实现更准确的计算机对接，并基于信息技术来设计和运行大规模网格基础设施。
在计算方面，已开发出一个持续的基础设施：大规模对接，在结果分析中使用不同策略，将结果实时存储到MySQL数据库中，并应用分子动力学精修，如MM-PBSA和MM-GBSA重评分。获得的建模结果非常有前景。基于建模结果，针对所有进行筛选的靶点的体外实验结果正在进行中。
本文描述了大规模的合理药物研发活动，特别是在计算网格上使用FlexX软件进行分子对接，以寻找针对疟疾中涉及的三种不同靶点（PfGST、PfDHFR、PvDHFR（野生型和突变型））的活性化合物。提出了基于网格的虚拟筛选方法，以生成针对与抗击发展中世界传染病相关的其他生物靶点的聚焦化合物库。
