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首页> 外文期刊>Pharmaceuticals >Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues
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Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues

机译：通过动力学热残基的分析识别异二聚体结合支架。

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Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM’s weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol’s ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner’s interacting scaffold is more flexible and adaptable.
机译：蛋白质之间的物理相互作用通常很难破译。本文的目的是提出一种算法，该算法旨在利用高斯网络模型（GNM）识别相互作用的异二聚体蛋白的结合斑块和支持结构的支架。该识别基于动力学热残基的（自）可调节识别以及它们与可能的结合支架的连接。动力学上热的残基是具有最低熵的残基，即，对通过GNM提取的每链最快模式的加权总和的最大贡献。该算法使用目标残留物（接触和邻近的第一层残留物）的预测数量与预期数量之比来调整GNM加权总和计算中的快速模式数量。当应用于具有高蛋白质序列长度比率的二聚体时，这种方法会产生非常好的结果。使用Sternberg和Vakser诱饵二聚体组，将该协议识别近乎本地诱饵的能力与Lu和Skolnick的残留水平统计潜力的能力进行了比较。统计潜力产生了更好的总体结果，但是在许多情况下，其预测能力与可调式GNM方法的预测能力相当甚至更低。本文提出的结果表明，在异二聚体中，至少一种蛋白质具有由固定的，动力学上热的残基决定的相互作用支架。在许多情况下，相互作用的蛋白质（尤其是大小明显不同的蛋白质）要么表现为刚性的锁和钥匙，要么表现出相反的动态行为。尽管一种蛋白质的结合表面是刚性且稳定的，但其伴侣的相互作用支架却更具弹性和适应性。

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