Structural, phylogenetic and docking studies of D-amino acid oxidase activator (DAOA), a candidate schizophrenia gene.

Q1 Mathematics
Sheikh Arslan Sehgal, Naureen Aslam Khattak, Asif Mir
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引用次数: 0

Abstract

Background: Schizophrenia is a neurodegenerative disorder that occurs worldwide and can be difficult to diagnose. It is the foremost neurological disorder leading to suicide among patients in both developed and underdeveloped countries. D-amino acid oxidase activator (DAOA), also known as G72, is directly implicated in the glutamateric hypothesis of schizophrenia. It activates D-amino acid oxidase, which oxidizes D-serine, leading to modulation of the N-methyl-D-aspartate receptor.

Methods: MODELLER (9v10) was utilized to generate three dimensional structures of the DAOA candidate gene. The HOPE server was used for mutational analysis. The Molecular Evolutionary Genetics Analysis (MEGA5) tool was utilized to reconstruct the evolutionary history of the candidate gene DAOA. AutoDock was used for protein-ligand docking and Gramm-X and PatchDock for protein-protein docking.

Results: A suitable template (1ZCA) was selected by employing BLASTp on the basis of 33% query coverage, 27% identity and E-value 4.9. The Rampage evaluation tool showed 91.1% favored region, 4.9% allowed region and 4.1% outlier region in DAOA. ERRAT demonstrated that the predicted model had a 50.909% quality factor. Mutational analysis of DAOA revealed significant effects on hydrogen bonding and correct folding of the DAOA protein, which in turn affect protein conformation. Ciona was inferred as the outgroup. Tetrapods were in their appropriate clusters with bifurcations. Human amino acid sequences are conserved, with chimpanzee and gorilla showing more than 80% homology and bootstrap value based on 1000 replications. Molecular docking analysis was employed to elucidate the binding mode of the reported ligand complex for DAOA. The docking experiment demonstrated that DAOA is involved in major amino acid interactions: the residues that interact most strongly with the ligand C28H28N3O5PS2 are polar but uncharged (Gln36, Asn38, Thr 122) and non-polar hydrophobic (Ile119, Ser171, Ser21, Ala31). Protein-protein docking simulation demonstrated two ionic bonds and one hydrogen bond involving DAOA. Lys-7 of the receptor protein interacted with Lys-163 and Asp-2037. Tyr-03 interacted with Arg-286 of the ligand protein and formed a hydrogen bond.

Conclusion: The predicted interactions might serve to inhibit the disease-related allele. It is assumed that current bioinformatics methods will contribute significantly to identifying, analyzing and curing schizophrenia. There is an urgent need to develop effective drugs for schizophrenia, and tools for examining candidate genes more accurately and efficiently are required.

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候选精神分裂症基因--D-氨基酸氧化酶激活剂(DAOA)的结构、系统发育和对接研究。
背景:精神分裂症是一种神经退行性疾病,在世界各地均有发生,而且很难诊断。无论是在发达国家还是在不发达国家,它都是导致患者自杀的最主要的神经系统疾病。D- 氨基酸氧化酶激活剂(DAOA),又称 G72,与精神分裂症的谷氨酸假说直接相关。它能激活 D-氨基酸氧化酶,从而氧化 D-丝氨酸,导致对 N-甲基-D-天冬氨酸受体的调节:方法:利用 MODELLER(9v10)生成 DAOA 候选基因的三维结构。使用 HOPE 服务器进行突变分析。利用分子进化遗传学分析(MEGA5)工具重建候选基因DAOA的进化历史。AutoDock用于蛋白质-配体对接,Gramm-X和PatchDock用于蛋白质-蛋白质对接:结果:通过使用 BLASTp,在查询覆盖率为 33%、同一性为 27%、E 值为 4.9 的基础上选择了一个合适的模板(1ZCA)。Rampage 评估工具显示,在 DAOA 中,91.1% 的区域为有利区域,4.9% 的区域为允许区域,4.1% 的区域为离群区域。ERRAT表明,预测模型的质量因子为50.909%。DAOA 的突变分析表明,DAOA 蛋白的氢键和正确折叠会受到显著影响,进而影响蛋白质的构象。Ciona被推断为外群。四足动物在其适当的群中有分叉。人类的氨基酸序列是保守的,黑猩猩和大猩猩的同源性超过 80%,基于 1000 次重复的引导值。分子对接分析被用来阐明所报道的配体复合物与 DAOA 的结合模式。对接实验表明,DAOA参与了主要的氨基酸相互作用:与配体C28H28N3O5PS2相互作用最强的残基是极性但不带电的(Gln36、Asn38、Thr 122)和非极性疏水的(Ile119、Ser171、Ser21、Ala31)。蛋白质-蛋白质对接模拟显示,DAOA 与受体蛋白之间存在两个离子键和一个氢键。受体蛋白的 Lys-7 与 Lys-163 和 Asp-2037 相互作用。Tyr-03与配体蛋白的Arg-286相互作用并形成一个氢键:结论:预测的相互作用可能会抑制与疾病相关的等位基因。我们认为,目前的生物信息学方法将大大有助于精神分裂症的识别、分析和治疗。目前急需开发治疗精神分裂症的有效药物,因此需要更准确、更高效地研究候选基因的工具。
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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
自引率
0.00%
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0
审稿时长
6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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