Fascin − F-actin interaction studied by molecular dynamics simulation and protein network analysis

Abstract

Actin bundles are an important component of cellular cytoskeleton and participate in the movement of cells. The formation of actin bundles requires the participation of many actin binding proteins (ABPs). Fascin is a member of ABPs, which plays a key role in bundling filamentous actin (F-actin) to bundles. However, the detailed interactions between fascin and F-actin are unclear. In this study, we construct an atomic-level structure of fascin − F-actin complex based on a rather poor cryo-EM data with resolution of 20 nm. We first optimized the geometries of the complex by molecular dynamics (MD) simulation and analyzed the binding site and pose of fascin which bundles two F-actin chains. Next, binding free energy of fascin was calculated by MM/GBSA method. Finally, protein structure network analysis (PSNs) was performed to analyze the key residues for fascin binding. Our results show that residues of K22, E27, E29, K41, K43, R110, R149, K358, R408 and K471 on fascin are important for its bundling, which are in good agreement with the experimental data. On the other hand, the consistent results indicate that the atomic-level model of fascin − F-actin complex is reliable. In short, this model can be used to understand the detailed interactions between fascin and F-actin, and to develop novel potential drugs targeting fascin.

Communicated by Ramaswamy H. Sarma

Keywords:

• Fascin
• F-actin
• MD simulation
• binding free energy
• protein structure network

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

All data and software are available upon reasonable request to the corresponding author. Amber (https://ambermd.org/index.php) is a commercial software for all MD simulations. PyInteraph 2 (https://github.com/ELELAB/pyinteraph2) is an open-source and free program for protein structure network analysis.

Additional information

Funding

This investigation is supported by grants from the National Natural Science Foundation of China (21907063 and 81472613), Li Ka Shing Foundation (project LD0101) and 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (2020LKSFG07B), and Innovation Team Grant of Guangdong Department of Education (2021KCXTD005).