Protein energetic conformational analysis from NMR chemical shifts (PECAN) and its use in determining secondary structural elements

被引:101
作者
Eghbalnia, HR
Wang, LY
Bahrami, A
Assadi, A
Markley, JL
机构
[1] Natl Magnet Resonance Facil, Dept Biochem, Madison, WI 53706 USA
[2] Univ Wisconsin, Dept Math, Madison, WI 53706 USA
[3] Univ Wisconsin, Ctr Eukaryot Struct Genom, Madison, WI 53706 USA
[4] Univ Wisconsin, Grad Program Biophys, Madison, WI 53706 USA
基金
美国国家卫生研究院;
关键词
chemical shifts; protein secondary structure; statistical energy model; statistical decision;
D O I
10.1007/s10858-005-5705-1
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
We present an energy model that combines information from the amino acid sequence of a protein and available NMR chemical shifts for the purposes of identifying low energy conformations and determining elements of secondary structure. The model ("PECAN", Protein Energetic Conformational Analysis from NMR chemical shifts) optimizes a combination of sequence information and residue-specific statistical energy function to yield energetic descriptions most favorable to predicting secondary structure. Compared to prior methods for secondary structure determination, PECAN provides increased accuracy and range, particularly in regions of extended structure. Moreover, PECAN uses the energetics to identify residues located at the boundaries between regions of predicted secondary structure that may not fit the stringent secondary structure class definitions. The energy model offers insights into the local energetic patterns that underlie conformational preferences. For example, it shows that the information content for defining secondary structure is localized about a residue and reaches a maximum when two residues on either side are considered. The current release of the PECAN software determines the well-defined regions of secondary structure in novel proteins with assigned chemical shifts with an overall accuracy of 90%, which is close to the practical limit of achievable accuracy in classifying the states.
引用
收藏
页码:71 / 81
页数:11
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