首页> 外文期刊>Fuzzy sets and systems >A fuzzy sets based generalization of contact maps for the overlap of protein structures
【24h】

A fuzzy sets based generalization of contact maps for the overlap of protein structures

机译:基于模糊集的蛋白质结构重叠接触图的一般化

获取原文
获取原文并翻译 | 示例

摘要

The comparison of protein structures is an important problem in bioinformatics. As a protein biological role is derived from its three-dimensional native state, the comparison of a new protein structure (with unknown function) with other protein structures (with known biological activity) can shed light into the biological role of the former. Consequently, advances in the comparison (and clustering) of proteins according to their three-dimensional configurations might also have an impact on drug discovery and other biomedical research that relies on understanding the inter-relations between structure and function in proteins. The contributions described in this paper are: Firstly, we propose a generalization of the maximum contact map overlap problem (MAX-CMO) by means of fuzzy sets and systems. The MAX-CMO is a model for protein structure comparison. In our new model, named generalized maximum fuzzy contact map overlap (GMAX-FCMO), a contact map is defined by means of one (or more) fuzzy thresholds and one (or more) membership functions. The advantages and limitations of our new model are discussed. Secondly, we show how a fuzzy sets based metaheuristic can be used to compute protein similarities based on the new model. Finally, we compute the protein structure similarity of real-world proteins and show how our new model correctly measures their (di)similarity.
机译:蛋白质结构的比较是生物信息学中的重要问题。由于蛋白质的生物学作用源自其三维自然状态,因此将新的蛋白质结构(功能未知)与其他蛋白质结构(具有已知的生物学活性)进行比较可以阐明前者的生物学作用。因此,根据蛋白质的三维构型比较(和聚类)蛋白质的进展也可能对药物开发和其他依赖于了解蛋白质结构与功能之间相互关系的生物医学研究产生影响。本文描述的贡献是:首先,我们提出了通过模糊集和系统对最大接触图重叠问题(MAX-CMO)的推广。 MAX-CMO是用于蛋白质结构比较的模型。在名为广义最大模糊接触图重叠(GMAX-FCMO)的新模型中,接触图是通过一个(或多个)模糊阈值和一个(或多个)隶属函数定义的。讨论了我们新模型的优缺点。其次,我们展示了如何使用基于模糊集的元启发式算法来基于新模型计算蛋白质相似度。最后,我们计算现实世界中蛋白质的蛋白质结构相似性,并展示我们的新模型如何正确测量其(di)相似性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号