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生物受体库中分子识别的概率模型:对嗅觉系统的意义。

Probability model for molecular recognition in biological receptor repertoires: significance to the olfactory system.

作者信息

Lancet D, Sadovsky E, Seidemann E

机构信息

Department of Membrane Research and Biophysics, Weizmann Institute of Science, Rehovot, Israel.

出版信息

Proc Natl Acad Sci U S A. 1993 Apr 15;90(8):3715-9. doi: 10.1073/pnas.90.8.3715.

Abstract

A generalized phenomenological model is presented for stereospecific recognition between biological receptors and their ligands. We ask what is the distribution of binding constants psi(K) between an arbitrary ligand and members of a large receptor repertoire, such as immunoglobulins or olfactory receptors. For binding surfaces with B potential subsite and S different types of subsite configurations, the number of successful elementary interactions obeys a binomial distribution. The discrete probability function psi(K) is then derived with assumptions on alpha, the free energy contribution per elementary interaction. The functional form of psi(K) may be universal, although the parameter values could vary for different ligand types. An estimate of the parameter values of psi(K) for iodovanillin, an analog of odorants and immunological haptens, is obtained by equilibrium dialysis experiments with nonimmune antibodies. Based on a simple relationship, predicted by the model, between the size of a receptor repertoire and its average maximal affinity toward an arbitrary ligand, the size of the olfactory receptor repertoire (Nolf) is calculated as 300-1000, in very good agreement with recent molecular biological studies. A very similar estimate, Nolf = 500, is independently derived by relating a theoretical distribution of maxima for psi(K) with published human olfactory threshold variations. The present model also has implications to the question of olfactory coding and to the analysis of specific anosmias, genetic deficits in perceiving particular odorants. More generally, the proposed model provides a better understanding of ligand specificity in biological receptors and could help in understanding their evolution.

摘要

提出了一种用于生物受体与其配体之间立体特异性识别的广义现象学模型。我们探讨了任意配体与大量受体库(如免疫球蛋白或嗅觉受体)成员之间结合常数ψ(K)的分布情况。对于具有B个潜在亚位点和S种不同亚位点构型的结合表面,成功的基本相互作用数量服从二项分布。然后,在假设α(每个基本相互作用的自由能贡献)的基础上推导出离散概率函数ψ(K)。ψ(K)的函数形式可能具有普遍性,尽管不同配体类型的参数值可能会有所不同。通过使用非免疫抗体的平衡透析实验,获得了碘香草醛(一种气味剂和免疫半抗原类似物)的ψ(K)参数值估计。基于该模型预测的受体库大小与其对任意配体的平均最大亲和力之间的简单关系,计算出嗅觉受体库的大小(Nolf)为300 - 1000,这与最近的分子生物学研究结果非常吻合。通过将ψ(K)最大值的理论分布与已发表的人类嗅觉阈值变化相关联,独立得出了一个非常相似的估计值Nolf = 500。本模型还对嗅觉编码问题以及特定嗅觉缺失(感知特定气味剂的遗传缺陷)的分析具有启示意义。更一般地说,所提出的模型有助于更好地理解生物受体中的配体特异性,并可能有助于理解它们的进化。

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本文引用的文献

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