Improving accuracy in binding site comparison with homology modeling

B. Godshall, B. Chen
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引用次数: 7

Abstract

Conformational changes make the comparison of protein structures difficult. Algorithms that identify small differences in protein structures to identify influences on specificity are particularly affected by molecular flexibility. However, such algorithms typically compare proteins with identical function and varying specificity, causing them to focus on closely related proteins rather than the remote evolutionary homologs sought by most comparison algorithms. This focus inspired us to ask if structure prediction algorithms, which more accurately predict the structures of close evolutionary neighbors, can be used to "remodel" existing structures with the same template, to make the comparison of their binding sites more accurate. Our results, on the enolase superfamily and the tyrosine kinases, reveal that this approach to error reduction is indeed possible, enabling our methods to identify influences on specificity in protein structures that originally could not be compared.
利用同源性建模提高结合位点比较的准确性
构象的变化使蛋白质结构的比较变得困难。识别蛋白质结构中的微小差异以确定对特异性的影响的算法特别受分子灵活性的影响。然而,这种算法通常比较具有相同功能和不同特异性的蛋白质,导致它们专注于密切相关的蛋白质,而不是大多数比较算法所寻求的远程进化同源物。这一重点激发了我们的思考,即结构预测算法是否可以更准确地预测进化近邻的结构,从而用相同的模板“重塑”现有结构,从而更准确地比较它们的结合位点。我们关于烯醇化酶超家族和酪氨酸激酶的研究结果表明,这种减少误差的方法确实是可能的,使我们的方法能够确定最初无法比较的蛋白质结构对特异性的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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