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超高选择性的决定因素——生物学和医学应用的实用概念。

Determinants of Superselectivity─Practical Concepts for Application in Biology and Medicine.

机构信息

Département de Chimie Moléculaire (DCM), UMR 5250, Université Grenoble Alpes, CNRS, 38000 Grenoble, France.

Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.

出版信息

Acc Chem Res. 2023 Apr 4;56(7):729-739. doi: 10.1021/acs.accounts.2c00672. Epub 2023 Mar 14.

Abstract

Multivalent interactions are common in biological systems and are also widely deployed for targeting applications in biomedicine. A unique feature of multivalent binding is "superselectivity". Superselectivity refers to the sharp discrimination of surfaces (e.g., on cells or cell compartments) by their comparative surface densities of a given receptor. This feature is different from the conventional "type" selectivity, which discriminates surfaces by their distinct receptor types. In a broader definition, a probe is superselective if it converts a gradual change in any one interaction parameter into a sharp on/off dependency in probe binding.This Account describes our systematic experimental and theoretical efforts over the past decade to analyze the determinants of superselective binding. It aims to offer chemical biologists, biophysicists, biologists, and biomedical scientists a set of guidelines for the interpretation of multivalent binding data, and design rules for tuning superselective targeting. We first provide a basic introduction that identifies multiple low-affinity interactions and combinatorial entropy as the minimal set of conditions required for superselective recognition. We then introduce the main experimental and theoretical tools and analyze how salient features of the multivalent probes (i.e., their concentration, size, ligand valency, and scaffold type), of the surface receptors (i.e., their affinity for ligands, surface density, and mobility), and of competitors and cofactors (i.e., their concentration and affinity for the ligands and/or receptors) influence the sharpness and the position of the threshold for superselective recognition.Emerging from this work are a set of relatively simple yet quantitative data analysis guidelines and superselectivity design rules that apply to a broad range of probe types and interaction systems. The key finding is the scaling variable which faithfully predicts the influence of the surface receptor density, probe ligand valency, receptor-ligand affinity, and competitor/cofactor concentrations and affinities on superselective recognition. The scaling variable is a simple yet versatile tool to quantitatively tune the on/off threshold of superselective probes. We exemplify its application by reviewing and reinterpreting literature data for selected biological and biomedical interaction systems where superselectivity clearly is important.Our guidelines can be deployed to generate a new mechanistic understanding of multivalent recognition events inside and outside cells and the downstream physiological/pathological implications. Moreover, the design rules can be harnessed to develop novel superselective probes for analytical purposes in the life sciences and for diagnostic/therapeutic intervention in biomedicine.

摘要

多价相互作用在生物系统中很常见,也广泛应用于生物医学中的靶向应用。多价结合的一个独特特征是“超选择性”。超选择性是指通过比较给定受体的相对表面密度来对表面(例如,细胞或细胞隔室上的表面)进行尖锐区分。该特征与传统的“类型”选择性不同,传统的选择性通过其独特的受体类型来区分表面。在更广泛的定义中,如果探针将任何一个相互作用参数的逐渐变化转换为探针结合的尖锐开/关依赖性,则该探针是超选择性的。

本账户描述了我们在过去十年中为分析超选择性结合的决定因素而进行的系统实验和理论工作。它旨在为化学生物学家、生物物理学家、生物学家和生物医学科学家提供一组用于解释多价结合数据的指南,以及用于调整超选择性靶向的设计规则。我们首先提供了一个基本介绍,确定了多个低亲和力相互作用和组合熵是超选择性识别所需的最小条件集。然后,我们介绍了主要的实验和理论工具,并分析了多价探针的显著特征(即它们的浓度、大小、配体价数和支架类型)、表面受体(即它们对配体的亲和力、表面密度和流动性)以及竞争物和辅因子(即它们的浓度和对配体和/或受体的亲和力)如何影响超选择性识别的锐度和阈值位置。

从这项工作中得出了一组相对简单但定量的数据分析准则和超选择性设计规则,这些准则和设计规则适用于广泛的探针类型和相互作用系统。关键发现是标度变量,它忠实地预测了表面受体密度、探针配体价数、受体-配体亲和力以及竞争物/辅因子浓度和亲和力对超选择性识别的影响。标度变量是一种简单而通用的工具,可用于定量调整超选择性探针的开/关阈值。我们通过回顾和重新解释选定的生物和生物医学相互作用系统中的文献数据来举例说明其应用,其中超选择性显然很重要。

我们的准则可以用于生成对细胞内外多价识别事件的新的机制理解以及下游的生理/病理影响。此外,可以利用设计规则开发用于生命科学中的分析目的以及用于生物医学中的诊断/治疗干预的新型超选择性探针。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ad/10077582/bc659b574694/ar2c00672_0001.jpg

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