FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information.
FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information.
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FLEXc：使用基于上下文的统计、预测的结构特征和序列信息进行蛋白质灵活性预测
DOI：
10.1186/s12859-016-1117-3
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发表时间：
2016-08-31
影响因子：
3
通讯作者：
Li Y
中科院分区：
文献类型：
--
作者：
Yaseen A;Nijim M;Williams B;Qian L;Li M;Wang J;Li Y
蛋白质结构中原子围绕其平均位置的涨落提供了有关蛋白质动力学的重要信息。蛋白质结构的这种灵活性与各种生物过程有关。从蛋白质序列中预测残基的柔性对于分析蛋白质的动力学性质、预测蛋白质的功能具有重要意义。提出了一种三状态柔性预测的神经网络方法。该方法结合了序列和进化信息，基于上下文的分数，预测的二级结构和溶剂可及性，和氨基酸的性质。基于上下文的统计得分推导，使用平均场电位的方法，在考虑氨基酸背景的情况下，描述蛋白质残基在柔性状态下的不同偏好。当基于背景的分数和预测的结构状态被纳入柔性预测器的训练过程中时，7倍交叉验证的准确率达到61%。结论计算结果表明，基于预测结构状态的统计分数是提高蛋白质柔性预测性能的重要特征。我们的预测方法作为名为“FLEXc”的Web服务实现，并可在线访问： http://hpcr.cs.odu.edu/flexc .
BackgroundThe fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.ResultsIn this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.ConclusionsIncorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc .
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影响因子：
1.9
作者：
Sonavane S;Jaybhaye AA;Jadhav AG
通讯作者：
Jadhav AG
影响因子：
56.9
作者：
Boehr, David D.;McElheny, Dan;Wright, Peter E.
通讯作者：
Wright, Peter E.
影响因子：
64.8
作者：
Eisenmesser, EZ;Millet, O;Kern, D
通讯作者：
Kern, D
影响因子：
5.7
作者：
Tartaglia, Gian Gaetano;Cavalli, Andrea;Vendruscolo, Michele
通讯作者：
Vendruscolo, Michele
DOI：
10.1093/protein/1.6.477
发表时间：
1987-12-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
VIHINEN, M
通讯作者：
VIHINEN, M