Abstract

Dystrophin plays a key role in neuronal structure and synaptic signaling, yet its full range of binding partners in the nervous system remains incompletely understood. Here, we examined dystrophin–dystrobrevin interactions using a combined thermodynamic and bioinformatic approach. Analysis of published calorimetric data revealed that binding is driven primarily by favorable electrostatic and polar interactions and displays strong enthalpy–entropy compensation (EEC), with a notably low compensation temperature, suggesting that dystrophin may accommodate diverse partners through compensatory energetics. Bioinformatic profiling and principal component analysis (PCA) distinguished dystrobrevin isoforms and indicated that the C-terminal flanking region and overall charge (pI) are key determinants of binding affinity. Multiple linear regression further showed that G° can be predicted from sequence-derived variables, particularly pI and intrinsic disorder. Together, these findings provide a thermodynamic rationale for the broad interaction capacity of dystrophin and establish a sequence-based framework for identifying potential dystrophin-binding proteins in the nervous system. 

Keywords: dystrophin; dystrobrevin; enthalpy–entropy compensation; coiled-coil interactions; principal component analysis; multiple linear regression 

How to Cite this Article: Madison Fountain, Sarah Cuneio & Jonghoon Kang (2025). Thermodynamic compensation indicates binding promiscuity of dystrophin in the nervous system. Journal of Multiscale Neuroscience 4(4): 257-265. DOI: https://doi.org/10.56280/1723375325 

Authors Affiliation: 

• Madison E. Fountain, Valdosta State University, Department of Biology, 1500 N Patterson St., Valdosta, Georgia 31698, USA.

• Sarah Cuneio, Valdosta State University, Department of Biology, 1500 N Patterson St., Valdosta, Georgia 31698, USA.

• Jonghoon Kang Valdosta State University, Department of Biology, 1500 N Patterson St., Valdosta, Georgia 31698, USA 

Conflict of Interest: The authors declare no conflict of interest 

Copyright: © 2025 The Author(s). Published by Neural Press. This is an open access article distributed under the terms and conditions of the CC BY-NC-ND 4.0 license. 

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