摘要
探讨了脂肪酶MAS1对长链底物4-硝基苯酚豆蔻肉酸酯(pNP-C14)催化的最适温度、最适pH、pH稳定性,并通过分子动力学模拟计算得出结合自由能,然后根据结合自由能的差值初步筛选出了G145W及T141L两个单点突变体。实验表明,与野生型MAS1相比,结合自由能降低显著的突变体G145W的米氏常数(Km)减小了11%,催化常数(kcat)与Km的比值(kcat/Km)为野生型MAS1的1.29倍,突变体G145W对长链底物的亲和能力和催化效率均有提高;而结合自由能降低较小的突变体T141L的Km值增大了22%,kcat/Km为野生型MAS1的0.88倍,说明酶与底物分子结合自由能的降低并非准确的突变筛选标准。通过分子力学/广义波恩表面积(MM/GBSA)残基拆解分析得出:残基T38、F39、L149、F153、V202、V233对脂肪酶活性口袋与长链底物结合的稳定性贡献较其他残基大;残基T38、G40、N41、N45和T237对提高长链底物的亲和能力具有重要贡献,预测为热点残基,其结合自由能的降低可以作为突变筛选的参考标准。
The optimum temperature,pH,and pH stability of lipase MAS1 for the catalysis of the long-chain substrate 4-nitrophenol myristate(pNP-C14)were determined.Two single point mutations,G145W and T141L,were initially screened out according to the decrease of binding free energy by molecular dynamics simulation(Molecular Dynamics,MD).Experiments showed that Km of the mutant G145W with a significant decrease in binding free energy was reduced by 11%compared with the wild-type MAS1,and kcat/Km was 1.29 times that of the wild-type MAS1.Compared with the wild-type MAS1,Km of the mutant T141L with a smaller decrease in binding free energy was increased by 22%,and kcat/Km was 0.88 times that of the wild-type MAS1.Compared with the wild type,the affinity and catalytic efficiency of G145W for long-chain substrates were improved,while those of T141L were lower than the wild type.It showed that the absolute zero value of the binding energy difference was not an accurate mutation screening criterion.In-depth analysis was performed by Molecular Mechanics/Generalized Born Surface Area(MM/GBSA)residue disassembly.It was concluded that residues T38,F39,L149,F153,V202 and V233 contributed more to the binding stability of the lipase active pocket and long-chain substrates than other residues;Residues T38,G40,N41,N45,and T237 showed important contributions to the improvement of lipase affinity for long-chain substrates and were predicted to be hotspot residues whose reduced binding free energy can serve as a reference standard for mutation screening.
作者
唐秀云
王嘉伟
张建国
缪雨露
赵玥
高蓓
张鲁嘉
TANG Xiuyun;WANG Jiawei;ZHANG Jianguo;MIAO Yulu;ZHAO Yue;GAO Bei;ZHANG Lujia(State Key Laboratory of Bioreactor Engineering,School of Biotechnology,East China University of Science and Technology,Shanghai 200237,China;School of Health Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development,School of Chemistry and Molecular Engineering,East China Normal University,Shanghai 200241,China;NYU-ECNU Center for Computational Chemistry at NYU Shanghai,Shanghai 200122,China)
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2023年第4期529-535,共7页
Journal of East China University of Science and Technology
基金
国家重点研发计划(2020YFA0908400)
国家自然科学基金(31772007,U1805235)
上海市自然科学基金(21ZR1416500)
纽约大学全球种子基金
纽约大学-华东师范大学计算化学中心(ECNU创新001公众平台)。
