COMPUTATIONAL & BIOLOGICAL CO-DESIGN-CRACKING UGT STRUCTURE-FUNCTION RELATIO

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 计算与生物协同设计-破解UGT结构-功能关系 随着工程蛋白在药物、载体、酶活性、受体、疫苗、抗体、生物材料和纳米技术、体外合成和检测系统中的应用，对了解蛋白质结构的迫切需要加剧。能够输入蛋白质的一级序列和预测蛋白质的结构-功能关系对于合理的蛋白质或药物设计有很大的实用性。目前的计算方法提供有限的准确度（~80%），通常不能处理大蛋白质，并且需要大量的计算时间。这项研究将开发新的蛋白质结构预测方法，以提高预测精度和计算效率。我们的策略结合了一个“共同设计”的过程，使计算预测直接进行测试，并通过一个简单的模型显着的医疗相关性进一步优化-UDP-葡萄糖醛酸转移酶（UGT）的家庭。 我们假设，集成蛋白质线程，图论和参数化的算法将提供直接用于评估复杂蛋白质（如UGT）的蛋白质结构-功能关系所需的蛋白质结构预测的准确性和效率。此外，将比较域建模与直接生物实验相结合将描绘UGT底物选择性的关键结构参数。这些信息将是设计特异性改变的UGT和开发UGT特异性抑制剂的关键。具体而言，我们将： + 通过整合蛋白质线程、侧链包装和参数化计算中的新思想，提高蛋白质结构算法的计算效率和预测精度。 + 对UGT和UGT变体进行建模，并通过诱变、蛋白表达和酶活性对UGT蛋白进行直接生物学评估进行验证。 + 扩展计算方法，包括蛋白质-底物和蛋白质-抑制剂相互作用，蛋白质-蛋白质相互作用，糖基化和膜缔合的影响建模。 开发有效的计算方法来精确地建模蛋白质结构，对蛋白质在介导细胞信号传导、生长和分化中的关键作用的研究具有重要的影响。这种建模和生物研究接口的成功结果可能会导致更有效的药物和疾病治疗。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Computational & Bio Co-design-Cracking UGT Structure-Function Relationships The immediate need for understanding protein structure intensifies as the applications for engineered proteins for drugs, carriers, enzymatic activities, receptors, vaccines, antibodies, biomaterials and nanotechnology, in vitro synthesis, and detection systems grow. There is great utility in being able to input primary protein sequences and predict protein structure-function relationships for rational protein or drug design. Current computational approaches provide limited accuracy (~80%), often cannot handle large proteins, and require significant computational time. The proposed research will develop novel approaches for protein structure prediction to improve predictive accuracy and computational efficiency. Our strategy incorporates a "co-design" process enabling computational predictions to be directly tested and further optimized through a facile model of significant medical relevance -the UDP-glucuronosyltransferase (UGT) family. We hypothesize that algorithms integrating protein threading, graph theory, and parameterization will provide the accuracy and efficiency of protein structure prediction required for direct utility in assessing protein structure-function relationships of complex proteins such as UGTs. Further, combining comparative domain modeling with direct biological experimentation will delineate key structural parameters for UGTs' substrate selectivity. This information will be key to designing UGTs with altered specificities and developing UGT-specific inhibitors. Specifically we will: + Enhance computational efficiency and predictive accuracy of protein structure algorithms by integrating protein threading, side-chain packing, and newly-developed ideas in parameterized computation. + Model UGTs and UGT variants and validate with direct biological assessment of UGT proteins through mutagenesis, protein expression, and enzymatic activity. + Expand computational approaches to encompass modeling of protein-substrate and protein-inhibitor interactions, protein-protein interaction, and impacts of glycosylation and membrane association. Developing effective computational approaches for accurately modeling protein structures has significant potential to impact research in proteins' critical roles in mediating cell signaling, growth, and differentiation. Successful outcomes from this modeling and biological research interface could lead to more effective medicines and disease treatments.