Template-Based Protein Structure Prediction Beyond Sequence Homology

“该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。“普渡大学获得了一笔赠款，用于开发用于构建蛋白质结构模型的新型计算算法和软件，这些算法和软件可以识别和利用非常遥远的模板结构进行建模。由于结构基因组学研究产生的蛋白质结构越来越多，充分利用这些结构作为模板进行建模对于蛋白质结构预测方法具有重要意义。然而，现有的方法在搜索模板结构和计算与模板的比对时过于依赖序列相似性。该项目的目的是推动模板搜索和结构建模的限制，通过广泛使用的结构信息在计算目标模板比对，并通过引入一个细化过程，它使用了先进的从头折叠。该项目的主要创新包括：（1）新的线程算法与模板蛋白质中残基接触的概率处理，（2）使用次优比对和多个模板来提高模型的准确性，以及（3）使用从头折叠方法来改进模型，该方法考虑了模型每个区域的估计误差。提高对目标蛋白质建模模板的识别将利用实验解决的结构的价值，并通过减少生物体蛋白质组结构建模所需的模板结构的数量来显着降低结构基因组学项目的成本。拟议项目是与蛋白质结构预测领域的世界专家进行国际合作。生物科学和计算机科学的研究生和本科生将在几个部门的交叉课程中接受培训。普渡大学现有的几个招收少数民族学生和本科生的项目将有助于广泛参与该项目。总体而言，拟议的项目利用普渡大学？在跨学科的计算生命科学和工程的努力。在此资助下开发的软件工具可从PI的实验室网站http://dragon.bio.purdue.edu/访问。

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."Purdue University is awarded a grant to develop novel computational algorithms and software for building protein structure models, which can identify and utilize very distantly related template structures for the modeling. Considering the growing number of protein structures produced by structural genomics projects, it is important for protein structure prediction methods to take full advantage of those structures as templates in modeling. However, existing methods rely too heavily on sequence similarity in searching template structures and computing alignments with templates. The proposed project is aimed to push the limit of template searching and structure modeling by extensive use of structural information in computing target-template alignments and by introducing a refinement procedure, which uses an advanced ab initio folding. Key innovations of this project include (1) novel threading algorithms with a probabilistic handling of residue contacts in template proteins, (2) use of suboptimal alignments and multiple templates to improve accuracy of a model, and (3) use of an ab initio folding method for refining the model, which takes estimated errors at each region of the model into account. Improvement of recognition of a template for modeling a target protein will leverage the value of experimentally solved structures and will also lead to a significant reduction of the cost of structural genomics projects by reducing the number of template structures needed for modeling the structures of proteome of organisms.The proposed project is carried out in international collaboration with world experts in the protein structure prediction field. Graduate and undergraduate students in biological sciences and computer science will be trained in cross-listed courses among several departments. Several existing programs at Purdue for recruiting minority students and undergraduate students will contribute to broad participation in the project. Overall the proposed project leverages Purdue University?s efforts in interdisciplinary computational life science and engineering. Software tools developed under this funding may be accessed from the PI's lab website at http://dragon.bio.purdue.edu/.