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蛋白质构象的统计力学处理。5. 氨基酸特定序列共聚物的多态模型。

Statistical mechanical treatment of protein conformation. 5. A multistate model for specific-sequence copolymers of amino acids.

作者信息

Tanaka S, Scheraga H A

出版信息

Macromolecules. 1977 Jan-Feb;10(1):9-20. doi: 10.1021/ma60055a002.

Abstract

One-dimensional short-range interaction models for specific-sequence copolymers of amino acids have been developed in this series of papers. In the present paper, a multistate model (involving right-handed helical (hR), extended (epsilon), chain-reversal (R and S), left-handed helical (hL), right-handed bridge-region (zota R), left-handed bridge-region (zota L), and coil (or other) (c) states) is developed for the prediction of protein backbone conformation. This model involves ten parameters (WhR, UPSILONHR, V epsilon, VR, VS, WhL, VhL, U zota R, U zota L, and Uc) and requires a 10X10 statistical weight matrix. Assuming that the left-handed helical sequence cannot occur in proteins, this 10X10 matrix can be reduced to a 9X9 matrix with nine parameters (WhR, VhR, V epsilon, VR, VS, VhL, U zota R, U zota L, and Uc). A nearest neighbor approximation of this multistate model is also formulated; with the omission of left-handed helical sequences, and the inclusion of the left-handed bridge region in the c state, this approximate model requires a 7X7 matrix with statistical weights WhR, VhR, VS, VhL, U zota R, and Uc, expressed as values relative to the statistical weight of the epsilon state. The statistical weights for the multistate model are evaluated from the atomic coordinates of the X-ray structures of 26 native proteins. These statistical weights and the multistate model are applied in the prediction of the backbone conformations of proteins. The conformational probabilities of finding a residue in hR, epsilon, R, S, hL, zota R, or c states, defined as relative values with respect to their average values over the whole molecule, are calculated for bovine pancreatic trypsin inhibitor and clostridial flavodoxin, in order to select the most probable conformation for each residue of these proteins. The predicted results are compared to experimental observations and are discussed together with the reliability of the statistical weights. In the Appendix, the property of asymmetric nucleation of helical sequences is introduced into the (nearest neighbor) multistate model.

摘要

在这一系列论文中,已经开发了用于氨基酸特定序列共聚物的一维短程相互作用模型。在本文中,为预测蛋白质主链构象开发了一种多状态模型(涉及右手螺旋(hR)、伸展(ε)、链反转(R和S)、左手螺旋(hL)、右手桥区域(ζR)、左手桥区域(ζL)以及卷曲(或其他)(c)状态)。该模型涉及十个参数(WhR、UPSILONHR、Vε、VR、VS、WhL、VhL、UζR、UζL和Uc),并且需要一个10×10的统计权重矩阵。假设左手螺旋序列不会出现在蛋白质中,这个10×10矩阵可以简化为一个9×9矩阵,具有九个参数(WhR、VhR、Vε、VR、VS、VhL、UζR、UζL和Uc)。还制定了这个多状态模型的最近邻近似;在忽略左手螺旋序列并将左手桥区域包含在c状态的情况下,这个近似模型需要一个7×7矩阵,其统计权重为WhR、VhR、VS、VhL、UζR和Uc,以相对于ε状态统计权重的值来表示。多状态模型的统计权重是根据26种天然蛋白质的X射线结构的原子坐标来评估的。这些统计权重和多状态模型被应用于预测蛋白质的主链构象。为牛胰蛋白酶抑制剂和梭菌黄素氧还蛋白计算了在hR、ε、R、S、hL、ζR或c状态中发现一个残基构象的概率,这些概率被定义为相对于整个分子平均值的相对值,以便为这些蛋白质的每个残基选择最可能的构象。将预测结果与实验观察结果进行比较,并结合统计权重的可靠性进行讨论。在附录中,螺旋序列的不对称成核特性被引入到(最近邻)多状态模型中。

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