ABI Innovation: Protein Functional Sites Identification Using Sequence Variation

普渡大学获得了一项资助，用于开发新的计算方法，通过揭示突变模式来识别蛋白质的功能区域，这些突变模式受到功能要求的限制。蛋白质序列和三级结构已经积累在一个指数的步伐，部分是由于大规模的基因组测序和结构基因组学计划。生物信息学的一项紧迫任务包括开发注释大量新序列和结构的方法，以及它们的功能和这些功能发生的位点的位置。将开发两种类型的方法：第一种类型的方法检查特定于蛋白质的已知功能区域的氨基酸突变。第二种方法是确定蛋白质中以相互约束的方式同时突变的位置。该方法的一个很大的优点是它具有足够的通用性，因此可以很容易地扩展到预测蛋白质的许多类型的功能位点和结构特征。该项目利用在实验序列和结构测定方面取得的巨大努力和进展，开发新一代的计算工具，检测蛋白质序列中的结构变异，而不是传统的保守性，将结构变异的概念融入生物序列分析领域。由于该方法具有通用性和通用性，因此该方法可以应用于其他生物信息学方法，如序列比对，蛋白质结构预测方法和单核苷酸多态性分析。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。总的来说，拟议的项目利用普渡大学在跨学科计算生命科学和工程方面的努力。有关该项目的更多信息，请访问PI实验室网站http://kiharalab.org。

Purdue University is awarded a grant to develop novel computational methods that identify functional regions of proteins by revealing mutations patterns, which are constrained by functional requirements. Protein sequences and the tertiary structures have been accumulated in an exponential pace partly due to large-scale genome sequencing and structural genomics projects. An urgent task for bioinformatics includes the development of methods for annotating the flood of new sequences and structures with their functions and the location of sites where these functions occur. Two types of methods will be developed: The first type of methods examine amino acid mutations specific to known functional regions of proteins. The second type of methods identifies positions in proteins that mutate simultaneously in a mutually constrained fashion. A strong advantage of the methods is that it is general enough so that it can be easily extended to predict many types of functional sites and structure features of proteins. The project capitalizes on tremendous efforts and progress made by experimental sequence and structure determination by developing a new generation of computational tools that detect structured variation, rather than conventional conservation, in protein sequences.The project incorporates the concept of the structured variation into the field of biological sequence analysis. Because the methodology is general and versatile, the method can be applied to other bioinformatics methods, such as sequence alignments, protein structure prediction methods, and single nucleotide polymorphism analysis. 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. For further information about this project visit the PI's lab website at http://kiharalab.org.