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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Predicting protein folding rate from amino acid sequence
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Predicting protein folding rate from amino acid sequence

机译：从氨基酸序列预测蛋白质折叠率

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摘要

Predicting protein folding rate from amino acid sequence is an important challenge in computational and molecular biology. Over the past few years, many methods have been developed to reflect the correlation between the folding rates and protein structures and sequences. In this paper, we present an effective method, a combined neural network-genetic algorithm approach, to predict protein folding rates only from amino acid sequences, without any explicit structural information. The originality of this paper is that, for the first time, it tackles the effect of sequence order. The proposed method provides a good correlation between the predicted and experimental folding rates. The correlation coefficient is 0.80 and the standard error is 2.65 for 93 proteins, the largest such databases of proteins yet studied, when evaluated with leave-one-out jackknife test. The comparative results demonstrate that this correlation is better than most of other methods, and suggest the important contribution of sequence order information to the determination of protein folding rates.
机译：从氨基酸序列预测蛋白质折叠速率是计算和分子生物学中的重要挑战。在过去的几年中，已经开发出许多方法来反映折叠率与蛋白质结构和序列之间的相关性。在本文中，我们提出了一种有效的方法，即组合的神经网络-遗传算法方法，仅从氨基酸序列预测蛋白质折叠速率，而无需任何明确的结构信息。本文的独创性在于，它首次解决了序列顺序的影响。所提出的方法在预测折叠率和实验折叠率之间提供了良好的相关性。当使用留一刀切刀测试进行评估时，93种蛋白质的相关系数为0.80，标准误差为2.65，这是迄今为止研究的最大的此类蛋白质数据库。比较结果表明，这种相关性优于大多数其他方法，并且表明序列顺序信息对确定蛋白质折叠率具有重要作用。

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