Energy-based prediction of amino acid-nucleotide base recognition.
Energy-based prediction of amino acid-nucleotide base recognition.
复制标题
基于能量的氨基酸-核苷酸碱基识别预测。
DOI：
10.1002/jcc.20954
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发表时间：
2008
影响因子：
3
通讯作者：
Mozzarelli,Andrea
中科院分区：
文献类型：
--
作者：
Marabotti,Anna;Spyrakis,Francesca;Facchiano,Angelo;Cozzini,Pietro;Alberti,Saverio;Kellogg,GlenE;Mozzarelli,Andrea
尽管经过了几十年的研究，但尚不清楚是否存在规定氨基酸和核苷酸碱基之间相互作用特异性的规则。这个问题是通过在一个由100个高分辨率蛋白质- DNA结构组成的数据集中，确定每个氨基酸和碱基之间相互作用的频率和能量，以及水介导的相互作用的能量来解决的。分析使用了包含焓和熵贡献的非牛顿力场HINT和基于几何的氢键相互作用评估工具Rank进行。Arg和Lys与G、Asp和Glu与C、Asn和Gln与A之间存在基于频率和能量的优先相互作用。不仅有利的接触是保守的，而且不利的接触也是保守的。水介导的相互作用极大地增加了Thr‐A、Lys‐A和Lys‐C接触的可能性。使用频率、相互作用能和与每个氨基酸碱基对相关的水增强因子来预测45个锌指中螺旋基序识别的碱基对三重体，这是分析一对一氨基酸碱基对接触的理想案例研究。该模型正确预测了135对氨基酸碱基对中的70.4%，通过加权每个氨基酸碱基对与整体识别能量的能量相关性，其预测率为89.7%。©2008 Wiley期刊公司计算机学报，2008
Despite decades of investigations, it is not yet clear whether there are rules dictating the specificity of the interaction between amino acids and nucleotide bases. This issue was addressed by determining, in a dataset consisting of 100 high‐resolution protein‐DNA structures, the frequency and energy of interaction between each amino acid and base, and the energetics of water‐mediated interactions. The analysis was carried out using HINT, a non‐Newtonian force field encoding both enthalpic and entropic contributions, and Rank, a geometry‐based tool for evaluating hydrogen bond interactions. A frequency‐ and energy‐based preferential interaction of Arg and Lys with G, Asp and Glu with C, and Asn and Gln with A was found. Not only favorable, but also unfavorable contacts were found to be conserved. Water‐mediated interactions strongly increase the probability of Thr‐A, Lys‐A, and Lys‐C contacts. The frequency, interaction energy, and water enhancement factors associated with each amino acid–base pair were used to predict the base triplet recognized by the helix motif in 45 zinc fingers, which represents an ideal case study for the analysis of one‐to‐one amino acid–base pair contacts. The model correctly predicted 70.4% of 135 amino acid–base pairs, and, by weighting the energetic relevance of each amino acid–base pair to the overall recognition energy, it yielded a prediction rate of 89.7%. © 2008 Wiley Periodicals, Inc. J Comput Chem 2008