Treffer: SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles.

Title:
SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles.
Authors:
Ramirez C; Rutgers, The State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey., Di Mare E; Rutgers, The State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey., Byrnes J; Energy & Photon Sciences Directorate, National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, New York., Ahmed E; Rutgers, The State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey., Pineiro-Goncalves M; Rutgers, The State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey., Lopez C; Rutgers, The State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey., Murthy NS; Rutgers, The State University of New Jersey, Laboratory for Biomaterials Research, Department of Chemistry and Chemical Biology, Piscataway, New Jersey. Electronic address: nsmurthy@chem.rutgers.edu., Gormley AJ; Rutgers, The State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey. Electronic address: adam.gormley@rutgers.edu.
Source:
Biophysical journal [Biophys J] 2025 Nov 04; Vol. 124 (21), pp. 3772-3786. Date of Electronic Publication: 2025 Sep 24.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Cell Press Country of Publication: United States NLM ID: 0370626 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1542-0086 (Electronic) Linking ISSN: 00063495 NLM ISO Abbreviation: Biophys J Subsets: MEDLINE
Imprint Name(s):
Publication: Cambridge, MA : Cell Press
Original Publication: New York, Published by Rockefeller University Press [etc.] for the Biophysical Society.
MeSH Terms:
Scattering, Small Angle* , X-Ray Diffraction*/methods , Nanoparticles*/chemistry , Biological Products*/chemistry , Polymers*/chemistry, Automation ; Machine Learning
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Grant Information:
P30 GM133893 United States GM NIGMS NIH HHS; R35 GM138296 United States GM NIGMS NIH HHS; S10 OD012331 United States OD NIH HHS
Substance Nomenclature:
0 (Biological Products)
0 (Polymers)
Entry Date(s):
Date Created: 20250926 Date Completed: 20251105 Latest Revision: 20251207
Update Code:
20251207
PubMed Central ID:
PMC12677192
DOI:
10.1016/j.bpj.2025.09.034
PMID:
40999685
Database:
MEDLINE

Weitere Informationen

Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. We present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (R <subscript>g</subscript> ), pair distance distribution function (PDDF)-derived R <subscript>g</subscript> , maximum particle dimension (D <subscript>max</subscript> ), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF R <subscript>g</subscript> agreement, an important indicator of well-measured data sets. For assistance in D <subscript>max</subscript> estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R <sup>2</sup> = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess D <subscript>max</subscript> estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.
(Copyright © 2025 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Declaration of interests The authors declare no competing interests.