Biospectra analysis: Model proteome characterizations for linking molecular structure and biological response
Biospectra analysis: Model proteome characterizations for linking molecular structure and biological response
复制标题
DOI：
10.1021/jm050494g
复制
发表时间：
2005-11-03
影响因子：
7.3
通讯作者：
Volkmann, RA
中科院分区：
文献类型：
--
作者：
Fliri, AF;Loging, WT;Volkmann, RA
建立分子结构与广泛生物效应之间的定量关系一直是药物发现的长期目标。评估分子调节蛋白质功能的能力是理解分子结构与体内生物反应之间关系的先决条件。在这些研究中，一个特别的挑战是推导出分子在不同蛋白质中的功能活动模式的定量测量。在这里，我们描述了一种操作简单的概率结构-活性关系（SAR）方法，称为生物光谱分析，用于通过使用分子生物活性光谱（生物光谱）之间的模式相似性作为决定因素来识别药物的激动剂和拮抗剂效应。因此，体外结合数据（在代表蛋白质组横截面的一系列测定中，在单一高药物浓度下测定的分子的抑制百分比值）对于确定药物之间的功能效应谱相似性是有用的。为了说明这一发现，通过对1567个分子数据集的分层聚类鉴定，24个分子的生物光谱相似性与神经递质多巴胺最接近，并探讨了它们的激动剂或拮抗剂特性之间的关系。将本研究描述的结果与基于亲和力的方法获得的结果区分开来，即使从数据集中去除假定的药物靶点(四种多巴胺能[D-1/D-2/D-3/D-4]和两种肾上腺素能[α（1)和α(2)]受体），所观察到的生物光谱和生物反应谱相似性之间的关联仍然完整。这些发现表明，生物光谱分析为预测结构-反应关系和将广泛的生物效应信息转化为化学结构设计提供了一种公正的新工具。
Establishing quantitative relationships between molecular structure and broad biological effects has been a long-standing goal in drug discovery. Evaluation of the capacity of molecules to modulate protein functions is a prerequisite for understanding the relationship between molecular structure and in vivo biological response. A particular challenge in these investigations is to derive quantitative measurements of a molecule's functional activity pattern across different proteins. Herein we describe an operationally simple probabilistic structure-activity relationship (SAR) approach, termed biospectra analysis, for identifying agonist and antagonist effect profiles of medicinal agents by using pattern similarity between biological activity spectra (biospectra) of molecules as the determinant. Accordingly, in vitro binding data (percent inhibition values of molecules determined at single high drug concentration in a battery of assays representing a cross section of the proteome) are useful for identifying functional effect profile similarity between medicinal agents. To illustrate this finding, the relationship between biospectra similarity of 24 molecules, identified by hierarchical clustering of a 1567 molecule dataset as being most closely aligned with the neurotransmitter dopamine, and their agonist or antagonist properties was probed. Distinguishing the results described in this study from those obtained with affinity-based methods, the observed association between biospectra and biological response profile similarity remains intact even upon removal of putative drug targets from the dataset (four dopaminergic [D-1/D-2/D-3/D-4] and two adrenergic [alpha(1), and alpha(2)] receptors). These findings indicate that biospectra analysis provides an unbiased new tool for forecasting structure-response relationships and for translating broad biological effect information into chemical structure design.