PREDICTION OF PROTEIN FOLDING CLASS FROM AMINO-ACID-COMPOSITION
PREDICTION OF PROTEIN FOLDING CLASS FROM AMINO-ACID-COMPOSITION
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DOI：
10.1002/prot.340160109
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发表时间：
1993-05-01
期刊：
影响因子：
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通讯作者：
KIM, SH
中科院分区：
文献类型：
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作者：
DUBCHAK, I;HOLBROOK, SR;KIM, SH
已经确定了氨基酸组成与几种蛋白质的三维折叠模式之间的经验关系。计算机模拟的神经网络已被用于根据蛋白质的氨基酸组成和大小将蛋白质划分为以下类别之一：(1)4 -螺旋束，(2)平行（α / β）8桶，(3)核苷酸结合折叠，(4)免疫球蛋白折叠，或(5)这些都不是。在已知的晶体结构以及密切相关的蛋白质序列上训练的网络被证明可以正确地预测未在训练集中表示的蛋白质的折叠类别，平均准确率为87%。一旦有了更大的数据库，可以很容易地将其他折叠基序添加到预测方案中。对神经网络权重的分析表明，有利于预测折叠类别的氨基酸通常在该类别中被过度代表，而在组成中具有不利权重的氨基酸被低估。神经网络利用这些氨基酸组成的多个小变化的组合来进行预测。给定类别中的有利加权氨基酸也与该类别蛋白质中的其他残基形成最多的分子内相互作用。对这些氨基酸的接触的详细检查揭示了一些可能有助于稳定每个折叠类的一般模式。
An empirical relation between the amino acid composition and three-dimensional folding pattern of several classes of proteins has been determined. Computer simulated neural networks have been used to assign proteins to one of the following classes based on their amino acid composition and size: (1) 4alpha-helical bundles, (2) parallel (alpha/beta)8 barrels, (3) nucleotide binding fold, (4) immunoglobulin fold, or (5) none of these. Networks trained on the known crystal structures as well as sequences of closely related proteins are shown to correctly predict folding classes of proteins not represented in the training set with an average accuracy of 87%. Other folding motifs can easily be added to the prediction scheme once larger databases become available. Analysis of the neural network weights reveals that amino acids favoring prediction of a folding class are usually over represented in that class and amino acids with unfavorable weights are underrepresented in composition. The neural networks utilize combinations of these multiple small variations in amino acid composition in order to make a prediction. The favorably weighted amino acids in a given class also form the most intramolecular interactions with other residues in proteins of that class. A detailed examination of the contacts of these amino acids reveals some general patterns that may help stabilize each folding class.