Morphoscanner2.0

Morphoscanner2.0 analyzes structural and temporal features of atomistic and coarse-grained molecular dynamics (MD) simulations to detect and track secondary-structure domains such as β-structured regions and α-helices in biologically inspired systems.

Key Features:

• Object-Oriented Design: Structured as an object-oriented Python library to support modular and reusable analysis workflows.
• Multi-Scale MD Support: Handles both atomistic and coarse-grained (CG-MD) molecular dynamics simulations.
• MDAnalysis Integration: Parses and manipulates simulation trajectories and topologies using MDAnalysis.
• PyTorch-Based Pattern Recognition: Uses PyTorch for pattern-recognition tasks to identify secondary-structure patterns.
• Network Analysis with NetworkX: Performs network-based structural analyses using NetworkX to explore structural relationships.
• Data Interoperability: Integrates results with Pandas, Numpy, and Matplotlib for data manipulation, computation, and visualization.
• Simulation Package Compatibility: Reads file formats produced by NAMD, Gromacs, and OpenMM via MDAnalysis.
• Specialized Secondary-Structure Routines: Includes routines for tracking emergence of β-structured domains in self-assembling peptide systems and for monitoring α-helix domain formation.
• Structural and Temporal Analysis: Performs combined structural and temporal analysis to follow secondary-structure evolution during simulations.

Scientific Applications:

• β-Structure Emergence Tracking: Tracking emergence and evolution of β-structured domains in self-assembling peptide systems.
• α-Helix Monitoring: Monitoring formation and dynamics of α-helix domains in protein systems.
• Protein Folding and Conformational Studies: Investigating protein folding, self-assembly, and conformational transitions in MD simulations.
• Multi-Scale Structural Dynamics: Analyzing structural relationships and dynamic behavior across atomistic and coarse-grained scales in biologically inspired systems.

Methodology:

Parses trajectories and topologies with MDAnalysis; applies PyTorch-based pattern recognition to identify secondary-structure patterns; conducts network-based analyses with NetworkX; and integrates results using Pandas, Numpy, and Matplotlib within an object-oriented Python framework.