Multisequence algorithm for coarse-grained biomolecular simulations: Exploring the sequence-structure relationship of...

Area of Science:

• Computational Biology
• Biophysics
• Molecular Dynamics

Background:

• Biomolecular simulations are crucial for understanding protein folding and stability.
• Current methods may face challenges in exploring diverse sequence spaces and thermodynamic landscapes.
• Efficiently sampling conformational and sequence space is key to accurate predictions.

Purpose of the Study:

• To develop and validate a generalized-ensemble algorithm for enhanced thermodynamic and conformational sampling in biomolecular simulations.
• To enable the determination of thermodynamic behavior for multiple sequences in a single simulation run.
• To analyze the structural and stability properties of a wide range of protein sequences.

Main Methods:

• A generalized-ensemble algorithm employing a random walk in sequence space was developed.
• The method was tested on an intermediate-resolution coarse-grained protein folding model.
• Simulations were performed on sets exceeding 1000 sequences to explore large-scale sequence space coverage.

Main Results:

• The algorithm successfully determined the thermodynamic behavior of multiple sequences concurrently.
• Enhanced conformational sampling and accelerated escape from local energy minima were observed.
• Analysis of thermodynamic data provided insights into structures and stability of sequences with varying secondary structures.

Conclusions:

• The generalized-ensemble algorithm offers a powerful approach for comprehensive biomolecular sequence space exploration.
• This method significantly improves the efficiency of predicting thermodynamic properties and conformational dynamics.
• The findings facilitate a deeper understanding of protein folding, stability, and sequence-structure relationships.