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Cooperative versus key residues in globular protein folding: an artificial neural network approach

机译:球形蛋白质折叠中的协作残基与关键残基:一种人工神经网络方法

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Neural networks have been applied with success for protein sequence analysis. Besides, the protein folding process involves many scientific aspects that are not completely understood yet. We present two new artificial neural network approaches (forward and reverse) focusing on the capability of each amino acid to cause a specific folding-either by itself or with the cooperativity of other residues that are close in the primary structure. The forward approach looks for an association between the protein primary structure fragments and its folded structure; this approach should retain the cooperative features of residues. The reverse approach looks for an association between a fragment of protein tertiary structure and the amino acid placed in the center of the fragment; this approach should retain the singular influence that a key residue by itself has on that folding. The results obtained emphasize the cooperative nature of regular structures and the specific role that some residues plays in the folded structures.
机译:神经网络已成功应用于蛋白质序列分析。此外,蛋白质折叠过程涉及许多尚未完全了解的科学方面。我们提出了两种新的人工神经网络方法(正向和反向),着眼于每种氨基酸自身或与一级结构中其他残基的协同作用引起特异性折叠的能力。正向方法寻找蛋白质一级结构片段与其折叠结构之间的联系。这种方法应保留残基的协同特征。相反的方法是寻找蛋白质三级结构的片段和位于片段中间的氨基酸之间的联系。这种方法应该保留关键残基本身对该折叠具有的独特影响。获得的结果强调了规则结构的协同性质以及某些残基在折叠结构中发挥的特定作用。

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