PEARL – Peptide Engineering & Assembly via Residue-specific Landscapes

PEARL is a modular analysis framework designed for studying self-assembling

pentapeptides using molecular dynamics (MD) simulations. This repository

contains computational workflows for quantifying peptide aggregation,

clustering, π–π interactions, residue contacts, and solvent structure. These

tools support structure–function insights into peptide hydrogel design,

sequence dependency, and emergent assembly behavior.

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📁 Repository Structure

PEARL/

│── clustering_analysis/ # Cluster identification and time-series analysis

│── data/ # Trajectories, topology files, intermediate outputs

│── peptide_contact_analysis/ # Residue–specific contact calculations and plots

│── pi_pi_analysis_complete/ # Comprehensive π–π interaction detection pipeline

│── rdf_analysis/ # Radial distribution function workflows and outputs

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🔍 Module Descriptions

clustering_analysis/

Contains scripts and notebooks for determining peptide cluster formation

across MD trajectories. Includes algorithms based on inter-residue distance

cutoffs, block averaging, and sequence comparisons.

Key outputs:

• Cluster count vs. time

• Block-averaged cluster profiles

• Sequence-dependent aggregation behavior

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data/

Stores all necessary input and intermediate files for the analyses, including:

• Preprocessed csv files

• Contact matrices, block averages, RDF tables

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peptide_contact_analysis/

Implements residue–residue contact detection based on a Lennard–Jones–derived

cutoff (4 Å, heavy atoms only). Generates time-averaged contact maps and

quantifies positional dependency of interactions across sequences.

Key features:

• Contact matrices

• Heatmaps

• Sequence comparison

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pi_pi_analysis_complete/

Full pipeline for aromatic π–π interaction detection. Calculates the number and

identity of aromatic pairs (e.g., Phe–Tyr, Tyr–Tyr) that participate in stacking

interactions.

Outputs:

• Pair counts per frame

• Time-averaged π–π interaction statistics

• Bar plots with error bars

• Per-sequence aromatic interaction profiles

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rdf_analysis/

Tools for computing radial distribution functions for atom groups of interest,

Outputs:

• g(r) curves

• Residue group patterning comparison across sequences

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🚀 Getting Started

1. Clone the repository

git clone <your-github-url>

cd PEARL



📊 Outputs & Analysis Goals

This repository supports analysis toward designing effective peptide
hydrogels by quantifying sequence-dependent self-assembly through:

Aggregation & clustering profiles

Heavy-atom contacts

Aromatic π–π interactions

End-to-END interactions via RDF

These metrics help establish connections between peptide hydrophobicity,
aromaticity, positional context, and emergent assembly behavior.

👥 Authors & Acknowledgements

Shimanto Roy
Bilodeau Group & Lampe Group
University of Virginia

Special thanks to:

Dr. Camille Bilodeau

Dr. Kyle Lampe
for continuous support, feedback, and guidance.

📬 Contact

For questions or collaboration inquiries, please contact:

Shimanto Roy
Email: dtc9ry@virginia.edu