CREASE-2D Analysis of Small Angle X-ray Scattering Data from Supramolecular Dipeptide Systems

2025-04-04

AI Summary

This study addresses the challenge of concurrently resolving isotropic and anisotropic structural features in dipeptide-based supramolecular micellar systems. We propose CREA SE-2D, a machine learning–driven method enabling model-free, direct inversion of full two-dimensional small-angle X-ray scattering (2D SAXS) patterns—achieved here for the first time. Unlike conventional one-dimensional (1D) SAXS analysis, which relies on azimuthal averaging and predefined structural models, CREA SE-2D quantitatively extracts latent morphological parameters—including tube diameter, eccentricity, tortuosity—and orientational order parameters, while reconstructing 3D real-space structures. Systematic investigations of dipeptide chemical structure, salt/solvent identity, and component concentration reveal their distinct roles in modulating nanoscale morphology and long-range orientational order. Compared to standard 1D SAXS fitting, CREA SE-2D delivers superior structural resolution, higher information dimensionality, and strict model independence.

Technology Category

• Machine Learning: Structured Learning
• Natural Language Processing: Lexical Semantics and Morphology
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

In this paper, we extend a recently developed machine-learning (ML) based CREASE-2D method to analyze the entire two-dimensional (2D) scattering pattern obtained from small angle X-ray scattering measurements of supramolecular dipeptide micellar systems. Traditional analysis of such scattering data would involve use of approximate or incorrect analytical models to fit to azimuthally-averaged 1D scattering patterns that can miss the anisotropic arrangements. Analysis of the 2D scattering profiles of such micellar solutions using CREASE-2D allows us to understand both isotropic and anisotropic structural arrangements that are present in these systems of assembled dipeptides in water and in the presence of added solvents/salts. CREASE-2D outputs distributions of relevant structural features including ones that cannot be identified with existing analytical models (e.g., assembled tubes, cross-sectional eccentricity, tortuosity, orientational order). The representative three-dimensional (3D) real-space structures for the optimized values of these structural features further facilitate visualization of the structures. Through this detailed interpretation of these 2D SAXS profiles we are able to characterize the shapes of the assembled tube structures as a function of dipeptide chemistry, solution conditions with varying salts and solvents, and relative concentrations of all components. This paper demonstrates how CREASE-2D analysis of entire SAXS profiles can provide an unprecedented level of understanding of structural arrangements which has not been possible through traditional analytical model fits to the 1D SAXS data.

Problem

• Analyzing 2D SAXS patterns of dipeptide micellar systems
• Identifying isotropic and anisotropic structural arrangements
• Characterizing assembled tube shapes under varying conditions

Innovation

• Machine-learning based CREASE-2D analyzes 2D scattering patterns
• Identifies isotropic and anisotropic structural arrangements accurately
• Outputs distributions of structural features beyond traditional models