EASAL

EASAL maps and searches molecular assembly landscapes using a geometric methodology to analyze free energy and kinetics driven by short-range pair-potentials in an implicit solvent.

Key Features:

• Atlas Generation: Creates a labeled partition (atlas) of the assembly landscape into maximal, contiguous, nearly-equipotential-energy conformational regions (macrostates) organized with their neighborhood relationships.
• Decoupled Roadmap Generation: Decouples roadmap generation from sampling to separate construction of the atlas from sampling procedures.
• Queryable Atlas: Represents local potential energy minima, basin structures, energy barriers, and neighboring basins in a queryable atlas.
• Pathway Analysis: Identifies paths between specified basin pairs as sequences of macrostates below an energy threshold and provides approximations of relative path lengths, basin volumes (configurational entropy), and path probabilities.
• High Computational Efficiency: Implements computationally efficient algorithms capable of atlasing several hundred thousand conformational regions or macrostates in minutes on a single core, with subsequent computations in seconds, and a parallelized version offering additional speedup.
• Algorithmic Guarantees: Provides formal guarantees on correctness, time complexity, and efficiency–accuracy trade-offs based on modern distance geometry, geometric constraint systems, and combinatorial rigidity.
• Intuitive Input-Output Relationship: Links the geometric shape of assembling units to a barcode-like representation of the atlas to enable reverse analysis and design control.
• Convex Cayley Parametrizations: Uses convex Cayley (distance-based) custom parametrizations specific to assembly processes to avoid gradient-descent search methods and reduce repeated or discarded samples, improving sampling efficiency.

Scientific Applications:

• Structural Biology: Maps assembly landscapes to study macromolecular complex formation and conformational ensembles.
• Biochemistry: Analyzes kinetics and thermodynamics of molecular assembly processes in biochemical systems.
• Materials Science: Investigates assembly pathways and energy landscapes relevant to nanoscale and materials design.
• Computational Integration: Complements and integrates with Molecular Dynamics, Monte Carlo, and Fast Fourier Transform-based methods for comparative or hybrid analyses.

Methodology:

Applies a geometric methodology that analyzes free energy and kinetics from short-range pair-potentials in an implicit solvent using convex Cayley (distance-based) parametrizations, decoupled roadmap generation, identification of local minima, basins and energy barriers, pathfinding between basins with approximations of basin volumes (configurational entropy) and path probabilities, and formal guarantees founded on modern distance geometry, geometric constraint systems, and combinatorial rigidity.