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Secondary Structure Bead-Encoded Amphiphilicity Biases Peptide Self-Assembly Prediction in MARTINI Coarse-Grained

Marko Babić¹, Goran Mauša^1,2, Ivan R Sasselli³

¹University of Rijeka, Faculty of Engineering, Rijeka 51000, Croatia.

ACS Applied Materials & Interfaces

|February 19, 2026

Summary

Coarse-grained molecular dynamics (CG-MD) simulations for peptide self-assembly are sensitive to secondary structure encoding. Changing this input significantly alters predicted assembly, highlighting the need for more accurate backbone representations in simulations.

Keywords:

MARTINI coarse-grained model amphiphilicity bias coarse-grained molecular dynamics peptide self-assembly

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

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Published on: January 26, 2024

Area of Science:

Supramolecular Chemistry
Computational Biology
Materials Science

Background:

Peptide self-assembly forms ordered structures for nanotech applications.
Coarse-grained molecular dynamics (CG-MD) simulations guide peptide design.
The MARTINI model predicts assembly using secondary structure-specific beads.

Purpose of the Study:

Investigate the impact of secondary structure encoding on peptide self-assembly simulations.
Evaluate the reliability of extended beta-sheet encoding in CG-MD.
Determine how backbone representation affects simulation outcomes.

Main Methods:

Utilized MARTINI 2.2p for CG-MD simulations.
Simulated hexapeptides and decapeptides with varying secondary structure encodings.
Analyzed assembly prediction (AP) scores to quantify aggregation.

Main Results:

Secondary structure encoding significantly impacts predicted peptide self-assembly.
AP scores varied substantially, shifting from dissolved to aggregated states.
The influence of encoding is sequence-specific, depending on side-chain polarity and peptide length.

Conclusions:

Conventional extended beta-sheet encoding can introduce significant bias in peptide self-assembly simulations.
Rethinking standard encoding practices is crucial for accurate simulation.
Advocate for native-like backbone representations for improved predictive power.