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Identification of Allosteric Effects in Proteins by Elastic Network Models

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Allostery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2253))

Abstract

Allostery is a fundamental regulatory mechanism in the majority of biological processes of molecular machines. Allostery is well-known as a dynamic-driven process, and thus, the molecular mechanism of allosteric signal transmission needs to be established. Elastic network models (ENMs) provide efficient methods for investigating the intrinsic dynamics and allosteric communication pathways in proteins. In this chapter, two ENM methods including Gaussian network model (GNM) coupled with Markovian stochastic model, as well as the anisotropic network model (ANM), were introduced to identify allosteric effects in hemoglobins. Techniques on model parameters, scripting and calculation, analysis, and visualization are shown step by step.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (31872723) and a Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.

The author thanks Prof. Ivet Bahar for giving the opportunity to study Elastic network models and ProDy in her lab. The author also thanks Drs. Hongchun Li and Chakra Chennubhotla for providing programming codes.

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Authors and Affiliations

  1. Center for Systems Biology, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China

    Guang Hu

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  1. Guang Hu
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Correspondence to Guang Hu .

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Editors and Affiliations

  1. Unit of Chemical-Physics Fundamentals in Chemical Engineering Department of Engineering, Universitá Campus Bio-Medico di Rome, Rome, Italy

    Luisa Di Paola

  2. Environment and Health Department, Instituto Superiore di Sanità, Rome, Italy

    Alessandro Giuliani

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Hu, G. (2021). Identification of Allosteric Effects in Proteins by Elastic Network Models. In: Di Paola, L., Giuliani, A. (eds) Allostery. Methods in Molecular Biology, vol 2253. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1154-8_3

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  • DOI: https://doi.org/10.1007/978-1-0716-1154-8_3

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1153-1

  • Online ISBN: 978-1-0716-1154-8

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