Free Energy Estimation and Automated Docking of Carbohydrates to Proteins

该项目设想在两个相互关联的领域开展工作：1)扩展和改进部分完成的计算方法，以估计碳水化合物配体与蛋白质（酶或凝集素）结合的自由能；2)使用AutoDock将许多不同的碳水化合物底物与糖苷水解酶家族1 （b -葡萄糖苷酶、b -半乳糖糖苷酶、b -甘露糖糖苷酶、6-磷酸- b -半乳糖糖苷酶、6-磷酸- b -葡萄糖苷酶和黑芥子酶）、2 （b -半乳糖糖苷酶、b -葡萄糖醛酸酶和b -甘露糖糖苷酶）、6（纤维素生物水解酶和内切葡聚糖酶）和7（纤维素生物水解酶和内切葡聚糖酶）进行计算对接。这个项目的潜在影响如下：1)一个非常有用的计算模型，直到最近预测碳水化合物与蛋白质结合的一般能量将变得更加精确，并将更好地预测实际的结合自由能；2)在更一般的方式下，计算工具对补充和扩展实验蛋白质结构数据的重要性将进一步得到证明；3)对几个非常广泛和重要的糖基水解酶家族的酶的作用机制将得到更好的阐明，为研究这些酶的社区成员提供实验研究难以获得的新见解；4)一些研究生和本科生将在计算酶结构-功能测定领域接受培训，该领域缺乏训练有素的从业者，并且需要处理和理解大量的原始和部分数据。

This project envisions work in two interrelated areas: 1) the extension and improvement of a partially completed computational method to estimate the free energies of binding of carbohydrate ligands to proteins, either enzymes or lectins; and 2) computational docking using AutoDock of many different carbohydrates substrates into glycoside hydrolase Families 1 (B-glucosidases, B-galactosidases, B-mannosidases, 6-phospho-B-galactosidases, 6-phospho-B-glucosidases, and myrosinases), 2 (B-galactosidases, B-glucuronidases, and B-mannosidases), 6 (cellobiohydrolases and endoglucanases), and 7 (cellobiohydrolases and endoglucanases).The impacts of this project are potentially the following: 1) A very useful computational model that until recently predicted general energies of binding of carbohydrates to proteins will be made much more precise, and will better predict actual binding free energies; 2) in a more general fashion, the importance of computational tools to supplement and extend experimental protein structural data will be further demonstrated; 3) the mechanisms of the enzymes in several very extensive and important glycosyl hydrolase families will be better elucidated, giving members of the communities researching these enzymes new insights not easily obtained by experimental studies; 4) a number of graduate and undergraduate students will be trained in the area of computational enzyme structure-function determination, where there is a paucity of well-trained practitioners and an overwhelming amount of raw and partially data to be processed and understood.