Thai Nguyen Quoc, Nguyen Hoang Linh, Linh Huynh Quang, Li Mai Suan
Institute for Computational Sciences and Technology,SBI building, Quang Trung Software City, Tan Chanh Hiep Ward, District 12, Ho Chi Minh City, Viet Nam; Dong Thap University,783 Pham Huu Lau Street, Ward 6, Cao Lanh City, Dong Thap, Viet Nam; Biomedical Engineering Department, University of Technology -VNU HCM, 268 Ly Thuong Kiet Str., Distr. 10, Ho Chi Minh City, Viet Nam.
Institute for Computational Sciences and Technology,SBI building, Quang Trung Software City, Tan Chanh Hiep Ward, District 12, Ho Chi Minh City, Viet Nam.
J Mol Graph Model. 2017 Oct;77:121-129. doi: 10.1016/j.jmgm.2017.08.002. Epub 2017 Aug 18.
The treatment of many diseases may require drugs that are capable to attack multiple targets simultaneously. Obviously, the virtual screening of multi-target drug candidates is much more time consuming compared to the single-target case. This, in particular, concerns the last step of virtual screening where the binding free energy is computed by conventional molecular dynamics simulation. To overcome this difficulty we propose a simple protocol which is relied on the fast steered molecular dynamics simulation and on available experimental data on binding affinity of reference ligand to a given target. Namely, first we compute non-equilibrium works generated during pulling ligands from the binding site using the steered molecular dynamics method. Then as top leads we choose only those compounds that have the non-equilibrium work larger than that of a reference compound for which the binding free energy has been already known from experiment. Despite many efforts no cures for AD (Alzheimer's disease) have been found. One of possible reasons for this failure is that drug candidates were developed for a single target, while there are exist many possible pathways to AD. Applying our new protocol to five targets including amyloid beta fibril, peroxisome proliferator-activated receptor γ, retinoic X receptor α, β- and γ-secretases, we have found two potential drugs (CID 16040294 and CID 9998128) for AD from the large PubChem database. We have also shown that these two ligands can interfere with the activity of popular Acetylcholinesterase target through strong binding towards it.
许多疾病的治疗可能需要能够同时作用于多个靶点的药物。显然,与单靶点情况相比,多靶点候选药物的虚拟筛选要耗时得多。这尤其涉及虚拟筛选的最后一步,即通过传统分子动力学模拟计算结合自由能。为了克服这一困难,我们提出了一种简单的方案,该方案依赖于快速引导分子动力学模拟以及参考配体与给定靶点结合亲和力的现有实验数据。具体来说,首先我们使用引导分子动力学方法计算从结合位点拉拽配体过程中产生的非平衡功。然后,作为顶级先导物,我们只选择那些非平衡功大于参考化合物的化合物,而参考化合物的结合自由能已从实验中得知。尽管付出了很多努力,但尚未找到治疗阿尔茨海默病(AD)的方法。这种失败的一个可能原因是候选药物是针对单一靶点开发的,而导致AD的可能途径有很多。将我们的新方案应用于包括淀粉样β纤维、过氧化物酶体增殖物激活受体γ、视黄酸X受体α、β和γ分泌酶在内的五个靶点,我们从庞大的PubChem数据库中找到了两种治疗AD的潜在药物(化合物登记号16040294和化合物登记号9998128)。我们还表明,这两种配体可以通过与常用的乙酰胆碱酯酶靶点紧密结合来干扰其活性。