Rajat Pant, Ravi Kumar, Shilpa Sharma, Ramanathan Karuppasamy, Shanthi Veerappapillai
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The best-generated model from Alphafold with a confidence of 0.95 was validated from ERRAT and Verify 3D scores of 89.95 and 91.80%, respectively. The Ramachandran plot generated using the PROCHECK server further predicted the accuracy of the model with 93.7% and 5.9% of residues present in most favored and additional allowed regions of the plot respectively. The active sites, ion binding sites, and subcellular localization of laccase were also predicted. The generated model was docked with 121 pollutants (pesticides, insecticides, herbicides, fungicides, and rodenticides) for its degradation potential towards these pollutants. Two ligands chlorophacinone (based on the highest binding energy) and endosulfan (based on agricultural uses) were selected for molecular dynamic simulation studies. 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引用次数: 0
摘要
农药在农业中广泛使用,但与此同时,大多数农药对健康和环境造成严重危害。在最近的过去,漆酶被报道为具有通过将污染物转化为毒性较低的形式来降解污染物的能力的关键酶。本研究对嗜极多的嗜盐嗜碱杆菌C-125中的漆酶进行了结构、物理化学和功能表征。所述酶的三维模型未知;因此,使用ROBETTA、I-TASSER和Alphafold服务器通过模板独立建模生成模型。Alphafold生成的最佳模型置信度为0.95,ERRAT和Verify 3D评分分别为89.95和91.80%。使用PROCHECK服务器生成的Ramachandran图进一步预测了模型的准确性,在图的最有利区域和附加允许区域分别存在93.7%和5.9%的残差。预测了漆酶的活性位点、离子结合位点和亚细胞定位。生成的模型与121种污染物(杀虫剂、杀虫剂、除草剂、杀菌剂和灭鼠剂)对接,以测定其对这些污染物的降解潜力。选择两种配体氯哌酮(基于最高结合能)和硫丹(基于农业用途)进行分子动力学模拟研究。硫丹作为农药是被禁止的,但在一些国家,政府允许将其用于特殊目的,这些目的需要认真考虑开发硫丹降解的生物修复方法。MD模拟研究表明,氯喹酮和硫丹均与漆酶活性位点形成氢键和疏水键,氯喹酮-漆酶复合物比硫丹更稳定。本研究揭示了漆酶的结构特征及其降解农药的潜力,并可通过实验数据进一步验证。由Ramaswamy H. Sarma传达。
Exploring the potential of Halalkalibacterium halodurans laccase for endosulfan and chlorophacinone degradation: insights from molecular docking and molecular dynamics simulations.
Pesticides are widely used in agriculture but at the same time, a majority of them are known to cause serious harm to health and the environment. In the recent past, laccases have been reported as key enzymes having the ability to degrade pollutants by converting them into less toxic forms. In this investigation, laccase from polyextremophilic bacterium Halalkalibacterium halodurans C-125 was analyzed for its structural, physicochemical, and functional characterization using in silico approaches. The 3D model of the said enzyme is unknown; therefore, the model was generated by template-independent modeling using ROBETTA, I-TASSER, and Alphafold server. The best-generated model from Alphafold with a confidence of 0.95 was validated from ERRAT and Verify 3D scores of 89.95 and 91.80%, respectively. The Ramachandran plot generated using the PROCHECK server further predicted the accuracy of the model with 93.7% and 5.9% of residues present in most favored and additional allowed regions of the plot respectively. The active sites, ion binding sites, and subcellular localization of laccase were also predicted. The generated model was docked with 121 pollutants (pesticides, insecticides, herbicides, fungicides, and rodenticides) for its degradation potential towards these pollutants. Two ligands chlorophacinone (based on the highest binding energy) and endosulfan (based on agricultural uses) were selected for molecular dynamic simulation studies. Endosulfan as a pesticide is banned but in some countries governments allow its use for special purposes which need serious consideration on developing bioremediation approaches for endosulfan degradation. MD simulation studies revealed that both chlorophacinone and endosulfan form hydrogen bonds and hydrophobic bonds with the active site of laccase and chlorophacinone-laccase complex were more stable in comparison to endosulfan. The present investigation provides insight into the structural features of laccase and its potential for the degradation of pesticides which can be further validated by experimental data.Communicated by Ramaswamy H. Sarma.
期刊介绍:
The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.