ABI Innovation: Protein Functional Sites Identification Using Sequence Variation

普渡大学获得了一项拨款，用于开发新颖的计算方法，通过揭示受功能要求限制的突变模式来识别蛋白质的功能区域。蛋白质序列和三级结构已经以指数速度积累，部分原因是大规模基因组测序和结构基因组学项目。生物信息学的一项紧迫任务包括开发方法来注释大量新序列和结构及其功能以及这些功能发生的位点的位置。将开发两种类型的方法：第一类方法检查特定于已知蛋白质功能区域的氨基酸突变。第二种方法识别蛋白质中以相互约束的方式同时突变的位置。该方法的一个强大优点是它足够通用，可以轻松扩展以预测蛋白质的多种功能位点和结构特征。该项目利用了实验序列和结构测定所取得的巨大努力和进展，开发了新一代计算工具来检测蛋白质序列中的结构变异，而不是传统的保守性。该项目将结构变异的概念纳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，例如序列比对、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专业的研究生和本科生将接受多个院系交叉列出的课程的培训。普渡大学现有的几个招收少数族裔学生和本科生的项目将有助于该项目的广泛参与。总体而言，拟议的项目利用了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.