CREASE-2D Analysis of Small Angle X-ray Scattering Data from Supramolecular Dipeptide Systems
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917977383763968 |
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| author | Gupta, Nitant Akepati, Sri V. V. R. Bianco, Simona Shah, Jay Adams, Dave J. Jayaraman, Arthi |
| author_facet | Gupta, Nitant Akepati, Sri V. V. R. Bianco, Simona Shah, Jay Adams, Dave J. Jayaraman, Arthi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_03869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CREASE-2D Analysis of Small Angle X-ray Scattering Data from Supramolecular Dipeptide Systems Gupta, Nitant Akepati, Sri V. V. R. Bianco, Simona Shah, Jay Adams, Dave J. Jayaraman, Arthi Soft Condensed Matter Machine Learning 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. |
| title | CREASE-2D Analysis of Small Angle X-ray Scattering Data from Supramolecular Dipeptide Systems |
| topic | Soft Condensed Matter Machine Learning |
| url | https://arxiv.org/abs/2504.03869 |