Suppr超能文献

用于确定蛋白质-蛋白质相互作用亲和力的细胞结合测定:技术与注意事项

Cell-Binding Assays for Determining the Affinity of Protein-Protein Interactions: Technologies and Considerations.

作者信息

Hunter S A, Cochran J R

机构信息

Stanford University, Stanford, CA, United States.

Stanford University, Stanford, CA, United States.

出版信息

Methods Enzymol. 2016;580:21-44. doi: 10.1016/bs.mie.2016.05.002. Epub 2016 Aug 8.

Abstract

Determining the equilibrium-binding affinity (Kd) of two interacting proteins is essential not only for the biochemical study of protein signaling and function but also for the engineering of improved protein and enzyme variants. One common technique for measuring protein-binding affinities uses flow cytometry to analyze ligand binding to proteins presented on the surface of a cell. However, cell-binding assays require specific considerations to accurately quantify the binding affinity of a protein-protein interaction. Here we will cover the basic assumptions in designing a cell-based binding assay, including the relevant equations and theory behind determining binding affinities. Further, two major considerations in measuring binding affinities-time to equilibrium and ligand depletion-will be discussed. As these conditions have the potential to greatly alter the Kd, methods through which to avoid or minimize them will be provided. We then outline detailed protocols for performing direct- and competitive-binding assays against proteins displayed on the surface of yeast or mammalian cells that can be used to derive accurate Kd values. Finally, a comparison of cell-based binding assays to other types of binding assays will be presented.

摘要

确定两种相互作用蛋白质的平衡结合亲和力(Kd)不仅对于蛋白质信号传导和功能的生化研究至关重要,而且对于改进蛋白质和酶变体的工程设计也至关重要。一种测量蛋白质结合亲和力的常用技术是使用流式细胞术来分析配体与呈现在细胞表面的蛋白质的结合。然而,细胞结合测定需要特定的考虑因素才能准确量化蛋白质-蛋白质相互作用的结合亲和力。在这里,我们将介绍设计基于细胞的结合测定的基本假设,包括确定结合亲和力背后的相关方程和理论。此外,还将讨论测量结合亲和力时的两个主要考虑因素——达到平衡的时间和配体消耗。由于这些条件有可能极大地改变Kd,因此将提供避免或最小化它们的方法。然后,我们概述了针对展示在酵母或哺乳动物细胞表面的蛋白质进行直接和竞争性结合测定的详细方案,这些方案可用于得出准确的Kd值。最后,将对基于细胞的结合测定与其他类型的结合测定进行比较。

相似文献

1
Cell-Binding Assays for Determining the Affinity of Protein-Protein Interactions: Technologies and Considerations.
Methods Enzymol. 2016;580:21-44. doi: 10.1016/bs.mie.2016.05.002. Epub 2016 Aug 8.
2
Ligand binding assays at equilibrium: validation and interpretation.
Br J Pharmacol. 2010 Nov;161(6):1219-37. doi: 10.1111/j.1476-5381.2009.00604.x.
3
Protein-Protein Interactions: Surface Plasmon Resonance.
Methods Mol Biol. 2017;1615:257-275. doi: 10.1007/978-1-4939-7033-9_21.
4
GPCR-radioligand binding assays.
Methods Cell Biol. 2016;132:191-215. doi: 10.1016/bs.mcb.2015.11.004. Epub 2016 Feb 10.
6
Kinetic exclusion assay technology: characterization of molecular interactions.
Assay Drug Dev Technol. 2004 Dec;2(6):647-57. doi: 10.1089/adt.2004.2.647.
7
Quantitative Yeast-Yeast Two Hybrid for the Discovery and Binding Affinity Estimation of Protein-Protein Interactions.
ACS Synth Biol. 2021 Mar 19;10(3):505-514. doi: 10.1021/acssynbio.0c00472. Epub 2021 Feb 15.
8
Surface plasmon resonance spectroscopy for characterisation of membrane protein-ligand interactions and its potential for drug discovery.
Biochim Biophys Acta. 2014 Jan;1838(1 Pt A):43-55. doi: 10.1016/j.bbamem.2013.04.028. Epub 2013 May 9.
10
Titratable Avidity Reduction Enhances Affinity Discrimination in Mammalian Cellular Selections of Yeast-Displayed Ligands.
ACS Comb Sci. 2017 May 8;19(5):315-323. doi: 10.1021/acscombsci.6b00191. Epub 2017 Mar 31.

引用本文的文献

3
Ligand preference of EphB2 receptor is selectively regulated by N-glycosylation.
iScience. 2025 Apr 8;28(5):112386. doi: 10.1016/j.isci.2025.112386. eCollection 2025 May 16.
4
High-Throughput Centrifuge Force Microscopy Reveals Dynamic Immune-Cell Avidity at the Single-Cell Level.
bioRxiv. 2025 Feb 27:2025.02.27.640408. doi: 10.1101/2025.02.27.640408.
6
Nanomechanical characterization of soft nanomaterial using atomic force microscopy.
Mater Today Bio. 2025 Jan 31;31:101506. doi: 10.1016/j.mtbio.2025.101506. eCollection 2025 Apr.
7
Quantifying antibody binding: techniques and therapeutic implications.
MAbs. 2025 Dec;17(1):2459795. doi: 10.1080/19420862.2025.2459795. Epub 2025 Feb 16.
9
Engineered CD47 protects T cells for enhanced antitumour immunity.
Nature. 2024 Jun;630(8016):457-465. doi: 10.1038/s41586-024-07443-8. Epub 2024 May 15.
10
Identification of a potent PCNA-p15-interaction inhibitor by autodisplay-based peptide library screening.
Microb Biotechnol. 2024 Apr;17(4):e14471. doi: 10.1111/1751-7915.14471.

本文引用的文献

1
The mass action equation in pharmacology.
Br J Clin Pharmacol. 2016 Jan;81(1):41-51. doi: 10.1111/bcp.12810. Epub 2015 Dec 21.
2
Applications of Yeast Surface Display for Protein Engineering.
Methods Mol Biol. 2015;1319:155-75. doi: 10.1007/978-1-4939-2748-7_8.
3
Surface plasmon resonance spectroscopy for characterisation of membrane protein-ligand interactions and its potential for drug discovery.
Biochim Biophys Acta. 2014 Jan;1838(1 Pt A):43-55. doi: 10.1016/j.bbamem.2013.04.028. Epub 2013 May 9.
4
Engineering fibronectin-based binding proteins by yeast surface display.
Methods Enzymol. 2013;523:303-26. doi: 10.1016/B978-0-12-394292-0.00014-X.
5
On the binding affinity of macromolecular interactions: daring to ask why proteins interact.
J R Soc Interface. 2012 Dec 12;10(79):20120835. doi: 10.1098/rsif.2012.0835. Print 2013 Feb.
6
Radioligand binding assays and their analysis.
Methods Mol Biol. 2012;897:31-77. doi: 10.1007/978-1-61779-909-9_3.
7
Practical aspects of radioligand binding.
Curr Protoc Pharmacol. 2006 Jul;Chapter 1:Unit1.3. doi: 10.1002/0471141755.ph0103s33.
8
Engineering knottins as novel binding agents.
Methods Enzymol. 2012;503:223-51. doi: 10.1016/B978-0-12-396962-0.00009-4.
9
Engineering hepatocyte growth factor fragments with high stability and activity as Met receptor agonists and antagonists.
Proc Natl Acad Sci U S A. 2011 Aug 9;108(32):13035-40. doi: 10.1073/pnas.1102561108. Epub 2011 Jul 25.
10
A structure-based benchmark for protein-protein binding affinity.
Protein Sci. 2011 Mar;20(3):482-91. doi: 10.1002/pro.580. Epub 2011 Feb 16.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验