MolClustPy: a Python package to characterize multivalent biomolecular clusters

Area of Science:

• Computational Biology and Biophysics
• Biomolecular Interactions and Self-Assembly

Background:

• Low-affinity interactions between multivalent biomolecules can drive phase transitions.
• These transitions lead to the formation of large, supply-limited molecular clusters.
• Stochastic simulations are crucial for understanding the size and composition variability of these clusters.

Purpose of the Study:

• To introduce MolClustPy, a novel Python package for analyzing molecular clusters.
• To enable characterization and visualization of cluster size, composition, and bonding.
• To facilitate the analysis of stochastic simulation data from biomolecular complex formation.

Main Methods:

• Development of MolClustPy, a Python package utilizing the NFsim (Network-Free stochastic simulator).
• Performing multiple stochastic simulation runs to capture a wide range of outcomes.
• Characterizing and visualizing distributions of cluster size, molecular composition, and molecular bonds.

Main Results:

• MolClustPy successfully performs stochastic simulations and analyzes resulting molecular clusters.
• The package provides detailed characterization of cluster size, composition, and bonding distributions.
• Demonstrated applicability to NFsim, with potential for integration with other simulation tools.

Conclusions:

• MolClustPy offers a robust computational tool for studying biomolecular phase transitions and cluster formation.
• The software enhances the analysis of complex molecular systems through detailed statistical characterization.
• Its statistical analysis framework is adaptable to various stochastic simulation platforms like SpringSaLaD and ReaDDy.