Suppr超能文献

从核磁共振化学位移到氨基酸类型:对原子核所具有的预测能力的研究。

From NMR chemical shifts to amino acid types: investigation of the predictive power carried by nuclei.

作者信息

Marin Antoine, Malliavin Thérèse E, Nicolas Pierre, Delsuc Marc-André

机构信息

Laboratoire de Biochimie Théorique, CNRS UPR 9080, Institut de Biologie Physico-Chimique, 13 rue P. et M. Curie, 75005 Paris, France.

出版信息

J Biomol NMR. 2004 Sep;30(1):47-60. doi: 10.1023/B:JNMR.0000042948.12381.88.

Abstract

An approach to automatic prediction of the amino acid type from NMR chemical shift values of its nuclei is presented here, in the frame of a model to calculate the probability of an amino acid type given the set of chemical shifts. The method relies on systematic use of all chemical shift values contained in the BioMagResBank (BMRB). Two programs were designed, one (BMRB stats) for extracting statistical chemical shift parameters from the BMRB and another one (RESCUE2) for computing the probabilities of each amino acid type, given a set of chemical shifts. The Bayesian prediction scheme presented here is compared to other methods already proposed: PROTYP RESCUE and PLATON and is found to be more sensitive and more specific. Using this scheme, we tested various sets of nuclei. The two nuclei carrying the most information are C(beta) and H(beta), in agreement with observations made in Grzesiek and Bax, 1993. Based on four nuclei: H(beta), C(beta), C(alpha) and C', it is possible to increase correct predictions to a rate of more than 75%. Taking into account the correlations between the nuclei chemical shifts has only a slight impact on the percentage of correct predictions: indeed, the largest correlation coefficients display similar features on all amino acids.

摘要

本文提出了一种基于核磁共振(NMR)化学位移值自动预测氨基酸类型的方法,该方法基于一个模型框架,用于在给定化学位移集的情况下计算氨基酸类型的概率。该方法依赖于系统地使用生物磁谱数据库(BMRB)中包含的所有化学位移值。设计了两个程序,一个(BMRB stats)用于从BMRB中提取统计化学位移参数,另一个(RESCUE2)用于在给定一组化学位移的情况下计算每种氨基酸类型的概率。本文提出的贝叶斯预测方案与已提出的其他方法(PROTYP RESCUE和PLATON)进行了比较,发现其更敏感且更具特异性。使用该方案,我们测试了各种原子核集。携带最多信息的两个原子核是C(β)和H(β),这与1993年Grzesiek和Bax的观察结果一致。基于四个原子核:H(β)、C(β)、C(α)和C′,可以将正确预测率提高到75%以上。考虑原子核化学位移之间的相关性对正确预测的百分比只有轻微影响:实际上,最大相关系数在所有氨基酸上显示出相似的特征。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验