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Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

期刊

CHEMICAL REVIEWS
卷 121, 期 13, 页码 8095-8160

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.chemrev.1c00044

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资金

  1. European Union's Horizon 2020 research and innovation programme [GrapheneCore3 881603]
  2. ETH Zurich

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The differentiation between specific and nonspecific binding in molecular interactions has been achieved in nature through precise control of biomolecules. However, artificial biosensors have struggled to overcome this issue. Through theoretical models and classification frameworks, it is possible to gain a better understanding and optimize the performance of biosensors, while exploring ways to address the challenge of nonspecific binding.
Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.

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