Native-Based Dissipative Particle Dynamics Approach for α-Helical Folding.

Area of Science:

• Computational chemistry
• Biophysics
• Materials science

Background:

• Simulating protein folding and biomaterial self-assembly is computationally challenging.
• Existing methods often struggle with the time and length scales required for complex biological systems.

Purpose of the Study:

• To develop a computationally efficient native-based dissipative particle dynamics (DPD) approach for modeling polypeptide folding.
• To accurately capture the formation of stable helical conformations in polyalanine and other polypeptides.
• To extend the modeling capabilities for biomaterials with alpha-helical segments.

Main Methods:

• Developed a novel native-based DPD approach.
• Derived DPD parameters from contact maps generated by molecular dynamics (MD) simulations.
• Applied the method to polyalanine chains of various lengths and helical segments of lysozyme.

Main Results:

• Successfully reproduced the folding of polyalanine into stable helical conformations.
• Observed bundle formation for longer polypeptides, consistent with experimental observations.
• Captured the folding of helical segments within the lysozyme protein.
• Demonstrated the potential for simulating systems at scales larger than achievable with MD.

Conclusions:

• The developed native-based DPD approach provides an efficient and accurate method for simulating polypeptide folding.
• This method enables the study of biomaterials incorporating alpha-helical structures at extended time and length scales.
• Further development can significantly advance the design and modeling of novel biomaterials.