Extrapolating the effect of deleterious nsSNPs in the binding adaptability of flavopiridol with CDK7 protein: a molecular dynamics approach.
Extrapolating the effect of deleterious nsSNPs in the binding adaptability of flavopiridol with CDK7 protein: a molecular dynamics approach.
复制标题
推断有害 nsSNP 对黄吡醇与 CDK7 蛋白结合适应性的影响：分子动力学方法
DOI：
10.1186/1479-7364-7-10
复制
发表时间：
2013-04-05
期刊：
影响因子：
4.5
通讯作者：
Zhu H
中科院分区：
文献类型：
--
作者：
George Priya Doss C;Nagasundaram N;Chakraborty C;Chen L;Zhu H
研究背景细胞周期蛋白依赖性激酶7（cyclin-dependent kinase 7，CDK 7）基因的非同义单核苷酸多态性（nonsynonymous single nucleotide polymorphisms，nsSNPs）与DNA修复机制的缺陷有关，可能是导致癌症的危险因素。在迄今为止开发的各种抑制剂中，flavopiridol在慢性淋巴细胞白血病的III期临床试验中被证明是一种潜在的抗肿瘤药物。在这里，我们描述了一个理论评估的发现新的药物或药物靶点的CDK 7蛋白由于有害的nsSNPs引起的变化。MethodsThree nsSNPs（I63 R，H135 R，和T285 M）被预测有功能的影响蛋白质功能的SIFT，PolyPhen 2，I-Mux 3，PANTHER，SNPs和GO，PhD-SNP，和筛选不可接受的多态性（SNAP）。此外，我们分析了本机和建议突变模型在原子级10 ns模拟使用分子动力学（MD）的方法。最后，借助Autodock 4.0和PatchDock，我们分析了flavopiridol与CDK 7蛋白的结合效率，相对于有害的mutations.ResultsBy比较所有7个预测工具的结果，3个nsSNPs（I63 R，H135 R，和T285 M）被预测对蛋白功能有功能性影响。蛋白质稳定性分析的结果表明，I63 R和H135 R与天然蛋白和T285 M蛋白相比，在均方根偏差方面表现出较小的偏差。与天然蛋白相比，CDK 7蛋白的所有三种突变模型的灵活性是不同的。之后，对接研究揭示了活性位点残基的变化和减少flavopiridol与突变protein.ConclusionThis理论方法的结合亲和力是完全基于计算方法，它有能力确定疾病相关的SNP在复杂的疾病，通过对比他们的成本和能力的实验方法。细胞周期调控蛋白CDK 7与DNA修复机制密切相关，与肿瘤的发生、发展密切相关，是肿瘤发生、发展的重要因素。本研究的主要目的是推断nsSNPs与其药物结合能力之间的关系。在这项工作中，我们提出了一种新的方法，（1）有效地确定了有害的nsSNPs，往往有功能的影响蛋白质的功能突变后，通过计算工具，（2）分析d天然蛋白质和提出的突变模型在原子水平上使用MD方法，和（3）研究蛋白质-配体相互作用，分析结合能力通过对接分析。这种理论方法完全基于计算方法，通过将其成本和能力与实验方法进行对比，能够识别复杂疾病中与疾病相关的SNP。总的来说，这种方法有可能为疾病的诊断、预后和治疗创造个性化的工具。
BackgroundRecent reports suggest the role of nonsynonymous single nucleotide polymorphisms (nsSNPs) in cyclin-dependent kinase 7 (CDK7) gene associated with defect in the DNA repair mechanism that may contribute to cancer risk. Among the various inhibitors developed so far, flavopiridol proved to be a potential antitumor drug in the phase-III clinical trial for chronic lymphocytic leukemia. Here, we described a theoretical assessment for the discovery of new drugs or drug targets in CDK7 protein owing to the changes caused by deleterious nsSNPs.MethodsThree nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on protein function by SIFT, PolyPhen2, I-Mutant3, PANTHER, SNPs&GO, PhD-SNP, and screening for non-acceptable polymorphisms (SNAP). Furthermore, we analyzed the native and proposed mutant models in atomic level 10 ns simulation using the molecular dynamics (MD) approach. Finally, with the aid of Autodock 4.0 and PatchDock, we analyzed the binding efficacy of flavopiridol with CDK7 protein with respect to the deleterious mutations.ResultsBy comparing the results of all seven prediction tools, three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on the protein function. The results of protein stability analysis inferred that I63R and H135R exhibited less deviation in root mean square deviation in comparison with the native and T285M protein. The flexibility of all the three mutant models of CDK7 protein is diverse in comparison with the native protein. Following to that, docking study revealed the change in the active site residues and decrease in the binding affinity of flavopiridol with mutant proteins.ConclusionThis theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. The identification of disease related SNPs by computational methods has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases.Lay abstractCell cycle regulatory protein, CDK7, is linked with DNA repair mechanism which can contribute to cancer risk. The main aim of this study is to extrapolate the relationship between the nsSNPs and their effects in drug-binding capability. In this work, we propose a new methodology which (1) efficiently identified the deleterious nsSNPs that tend to have functional effect on protein function upon mutation by computational tools, (2) analyze d the native protein and proposed mutant models in atomic level using MD approach, and (3) investigated the protein-ligand interactions to analyze the binding ability by docking analysis. This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. Overall, this approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases.
登录
查看更多内容
影响因子：
2.1
作者：
Giovannetti, Elisa;Pacetti, Paola;Cantore, Maurizio
通讯作者：
Cantore, Maurizio
单点突变对蛋白质稳定性的三态预测发生了变化。
影响因子：
3
作者：
Capriotti, Emidio;Fariselli, Piero;Rossi, Ivan;Casadio, Rita
通讯作者：
Casadio, Rita
影响因子：
5.5
作者：
Hess, Berk;Kutzner, Carsten;Lindahl, Erik
通讯作者：
Lindahl, Erik
影响因子：
56.9
作者：
Hinds, DA;Stuve, LL;Cox, DR
通讯作者：
Cox, DR
影响因子：
14.9
作者：
Bromberg Y;Rost B
通讯作者：
Rost B