Critical assessment of approaches for molecular docking to elucidate associations of HLA alleles with adverse drug reactions.
Critical assessment of approaches for molecular docking to elucidate associations of HLA alleles with adverse drug reactions.
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DOI：
10.1016/j.molimm.2018.08.003
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发表时间：
2018-09
影响因子：
3.6
通讯作者：
Rigden DJ
中科院分区：
文献类型：
--
作者：
Ramsbottom KA;Carr DF;Jones AR;Rigden DJ
所有接受评估的软件都可以将阿巴卡韦重新对接到风险等位基因结构中，但并不总是预测确切的结合模式。大多数被评估的对接软件可以区分风险和控制等位基因。使用同源模型可能会降低对接性能。对于复杂的HLA实例，受体的灵活性会对对接性能产生负面影响。使用AutoDockFR不能补偿停靠到未绑定目标所增加的困难。在许多不同的研究中，药物不良反应与人类白细胞抗原基因的遗传多态有关。作为获得性免疫反应的一部分，HL A蛋白在自我和非自我多肽的呈递中起着重要的作用。在相关的药物-等位基因组合中，抗HIV药物阿巴卡韦被证明与HLA-B*57：01等位基因相关，抗癫痫药物卡马西平与B*15：02相关，这两种情况下都可能遵循改变的多肽相互作用模型。在这种模式下，药物直接结合到抗原提呈区域，导致不同的自体多肽被提呈，从而引发不想要的免疫反应。人们越来越有兴趣利用生物信息学技术为其他ADR寻找支持这一模型的证据。在这项研究中，在电子对接被用来评估众所周知的对接计划的实用性和可靠性，当解决这些具有挑战性的人类白细胞抗原药物的情况。总体目标是通过完成对接软件的详细比较研究来解决对接程序给出不同结果的不确定性，对接软件的基础是阿巴卡韦和卡马西平的MHC-配体实验结构数据-以评估它们的性能。使用了四个对接程序：SwissDock、Rosie、AutoDock Vina和AutoDockFR，以调查每个软件是否能够将Abacavir准确地对接回晶体结构中，以获得来自已知风险等位基因的蛋白质，以及它们是否能够区分与人类白细胞抗原相关的和非人类白细胞抗原相关的(对照)等位基因。还研究了使用同源模型对对接性能的影响，以及使用不同的参数(如包括受体灵活性)对对接性能的影响，以模拟可能无法获得给定HLA等位基因的晶体结构的方法。然后使用最能预测Abacavir结合位置的程序来重建卡马西平与B*15：02和对照等位基因的对接。研究发现，所研究的程序有时能够正确预测Abacavir与B*57：01的结合模式，但并不总是如此。每个被评估的软件包都可以预测阿巴卡韦和卡马西平在正确的子袋中的结合，并且除了Rosie之外，能够正确地区分风险和控制等位基因。我们发现，对接到同源模型可能会产生较差的质量预测，特别是当序列差异影响预测的结合口袋的结构时。因此，必须谨慎使用，因为不准确的结构可能导致错误的对接预测。结合受体的灵活性被发现对所调查的例子的对接性能产生负面影响。综上所述，我们的发现有助于表征药物-人类白细胞抗原相互作用的计算预测的潜力和局限性。因此，这些对接技术应始终谨慎使用，并与其他调查方法一起使用，以便能够从给定的结果中得出强有力的结论。
All software assessed could dock Abacavir back into the risk allele structure but not always predict the exact binding mode. Most docking software assessed can distinguish between risk and control alleles. Docking performance can be degraded by using a homology model. Receptor flexibility can negatively affect the docking performance for complex HLA examples. Using AutoDockFR cannot compensate for the added difficulty of docking to the unbound target. Adverse drug reactions have been linked with genetic polymorphisms in HLA genes in numerous different studies. HLA proteins have an essential role in the presentation of self and non-self peptides, as part of the adaptive immune response. Amongst the associated drugs-allele combinations, anti-HIV drug Abacavir has been shown to be associated with the HLA-B*57:01 allele, and anti-epilepsy drug Carbamazepine with B*15:02, in both cases likely following the altered peptide repertoire model of interaction. Under this model, the drug binds directly to the antigen presentation region, causing different self peptides to be presented, which trigger an unwanted immune response. There is growing interest in searching for evidence supporting this model for other ADRs using bioinformatics techniques. In this study, in silico docking was used to assess the utility and reliability of well-known docking programs when addressing these challenging HLA-drug situations. The overall aim was to address the uncertainty of docking programs giving different results by completing a detailed comparative study of docking software, grounded in the MHC-ligand experimental structural data – for Abacavir and to a lesser extent Carbamazepine - in order to assess their performance. Four docking programs: SwissDock, ROSIE, AutoDock Vina and AutoDockFR, were used to investigate if each software could accurately dock the Abacavir back into the crystal structure for the protein arising from the known risk allele, and if they were able to distinguish between the HLA-associated and non-HLA-associated (control) alleles. The impact of using homology models on the docking performance and how using different parameters, such as including receptor flexibility, affected the docking performance were also investigated to simulate the approach where a crystal structure for a given HLA allele may be unavailable. The programs that were best able to predict the binding position of Abacavir were then used to recreate the docking seen for Carbamazepine with B*15:02 and controls alleles. It was found that the programs investigated were sometimes able to correctly predict the binding mode of Abacavir with B*57:01 but not always. Each of the software packages that were assessed could predict the binding of Abacavir and Carbamazepine within the correct sub-pocket and, with the exception of ROSIE, was able to correctly distinguish between risk and control alleles. We found that docking to homology models could produce poorer quality predictions, especially when sequence differences impact the architecture of predicted binding pockets. Caution must therefore be used as inaccurate structures may lead to erroneous docking predictions. Incorporating receptor flexibility was found to negatively affect the docking performance for the examples investigated. Taken together, our findings help characterise the potential but also the limitations of computational prediction of drug-HLA interactions. These docking techniques should therefore always be used with care and alongside other methods of investigation, in order to be able to draw strong conclusions from the given results.
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影响因子：
3
作者：
Bordogna, Annalisa;Pandini, Alessandro;Bonati, Laura
通讯作者：
Bonati, Laura
影响因子：
7.4
作者：
Hillisch A;Pineda LF;Hilgenfeld R
通讯作者：
Hilgenfeld R
影响因子：
3.3
作者：
Kløverpris HN;Cole DK;Fuller A;Carlson J;Beck K;Schauenburg AJ;Rizkallah PJ;Buus S;Sewell AK;Goulder P
通讯作者：
Goulder P
影响因子：
2.7
作者：
Berka, Noureddine;Gill, John M.;Khan, Faisal M.
通讯作者：
Khan, Faisal M.
DOI：
10.1093/cid/cit021
发表时间：
2013-05
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
--
作者：
Carr DF;Chaponda M;Jorgensen AL;Castro EC;van Oosterhout JJ;Khoo SH;Lalloo DG;Heyderman RS;Alfirevic A;Pirmohamed M
通讯作者：
Pirmohamed M