利用氨基酸的理化性质改进蛋白质二级结构的预测

Q2 Medicine
P. Chatterjee, Subhadip Basu, M. Nasipuri
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引用次数: 9

摘要

蛋白质结构预测很重要,因为它有助于扩展对蛋白质结构和功能的理解。这些知识对于预测与药物发现相关的应用所需的未知蛋白质的二级结构至关重要。本文提出了一种新的蛋白质二级结构预测方法。在这项工作中,使用了两层多层前馈神经网络。在第一级网络中,使用来自PSI-BLAST的序列剖面和氨基酸的物理化学性质进行序列结构预测。从第一级网络中获得的成形螺旋、板材和线圈的置信度值,然后与第二级网络一起用于结构对结构的预测。通过实验得到的总体预测精度在75.58% ~ 77.48%之间。该方法在nrDSSP数据集上进行了四倍交叉验证的训练和测试。并在蛋白结构预测关键评估实验(CASP3)的靶蛋白上进行了测试,在部分靶蛋白上取得了比PSIPRED更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving prediction of protein secondary structure using physicochemical properties of amino acids
Protein Structure Prediction is important in the sense that it helps to extend knowledge about the understanding of protein structures and functions. The knowledge is essential for prediction of secondary structures of unknown proteins required for applications related to drug discovery. A novel technique for protein secondary structure prediction is presented here. In this work, two levels of multi-layer feed forward neural networks are used. In the first level network, sequence profiles from PSI-BLAST and physicochemical properties of amino acids are used for sequence to structure predictions. Confidence values of forming helix, sheet and coil, obtained from the first level network are then used with the second level network for structure to structure predictions. The overall prediction accuracy as obtained through experimentation is in the range of 75.58% to 77.48%. This method is trained and tested with nrDSSP datasets using four folds cross validation. It is also tested on target proteins of Critical Assessment of Protein Structure Prediction Experiment (CASP3) and achieves better results than PSIPRED over some target proteins.
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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