Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

X-ray Crystallography02:18

X-ray Crystallography

26.1K
The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
26.1K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

529
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
529
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

733
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
733
Machines01:19

Machines

576
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
576
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.5K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.5K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

938
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
938

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

IHMValidation: Assessment of Integrative Structure Models Deposited to the Protein Data Bank.

Journal of molecular biology·2025
Same author

Studies of the structural properties and stability of the molybdenum transport protein ModA from Oleidesulfovibrio alaskensis G20 upon metal binding.

Archives of biochemistry and biophysics·2025
Same author

Multi-Stimulus Soft Actuators from Aerosol Jet Printed MXene-Cellulose Composite.

Nano letters·2025
Same author

Two-step digestion pathways of hydrogels from pea proteins.

Journal of colloid and interface science·2025
Same author

Compact polyethylenimine-complexed mRNA vaccines.

Nature nanotechnology·2025
Same author

AF4-to-SAXS: expanded characterization of nanoparticles and proteins at the P12 BioSAXS beamline.

Journal of synchrotron radiation·2025
Same journal

Quantifying the Peripheral Surface Information Entropy from Conformational Ensembles of Globular Protein-Peptide Complexes.

Biophysical journal·2026
Same journal

Anisotropic unbinding and location-dependent hovering of a kinesin motor head over microtubule.

Biophysical journal·2026
Same journal

Kinesin-5/Cut7 C-terminal tail phosphorylation influence on motor regulation through multi-scale molecular modeling.

Biophysical journal·2026
Same journal

Dynamic conformations of fluorophores on self-labeling protein tags.

Biophysical journal·2026
Same journal

Different actions of RyR2 open and closed channel block explained by a multiscale Ca<sup>2+</sup> release model.

Biophysical journal·2026
Same journal

Membrane Environment Sets the Functional pK<sub>a</sub> of Ionizable Lipids.

Biophysical journal·2026
See all related articles

Related Experiment Video

Updated: Jan 28, 2026

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K

Machine Learning Methods for X-Ray Scattering Data Analysis from Biomacromolecular Solutions.

Daniel Franke1, Cy M Jeffries1, Dmitri I Svergun1

  • 1European Molecular Biology Laboratory, Hamburg, Germany.

Biophysical Journal
|June 7, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to analyze small-angle X-ray scattering (SAXS) data, enabling rapid shape classification and accurate estimation of macromolecular parameters like maximal diameter and molecular mass directly from scattering patterns.

More Related Videos

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering
07:19

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

Published on: November 5, 2018

13.3K
Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae
09:15

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Published on: January 10, 2018

10.4K

Related Experiment Videos

Last Updated: Jan 28, 2026

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K
Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering
07:19

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

Published on: November 5, 2018

13.3K
Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae
09:15

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

Published on: January 10, 2018

10.4K

Area of Science:

  • Structural biology
  • Biophysics
  • Computational biology

Background:

  • Small-angle X-ray scattering (SAXS) is crucial for determining the low-resolution structure and overall shape of biological macromolecules in solution.
  • Analyzing SAXS data traditionally involves complex computational methods for extracting structural parameters.

Purpose of the Study:

  • To develop a novel, rapid method for analyzing experimental SAXS patterns.
  • To enable direct estimation of particle shape, maximal diameter (Dmax), and molecular mass from SAXS data.
  • To create a robust automated data analysis pipeline for structural biology.

Main Methods:

  • Transformation of experimental SAXS patterns into feature vectors.
  • Application of a k-nearest neighbor algorithm for shape classification and parameter estimation.
  • Mapping of Protein Data Bank structures into a classification space for training and validation.
  • Implementation of the DATCLASS tool within the ATSAS data analysis suite.

Main Results:

  • Successful retrieval of particle shape, Dmax, and molecular mass directly from SAXS data using feature vectors.
  • Development of a rapid multiclass shape-classification system (compact, extended, flat, hollow, random-chain).
  • Accurate estimation of structural parameters without the need for inverse Fourier transforms for Dmax.
  • Validation using Protein Data Bank structures demonstrates the method's efficacy.

Conclusions:

  • The developed feature vector approach and k-nearest neighbor algorithm offer a significant advancement in SAXS data analysis.
  • This method facilitates rapid, automated classification and accurate structural parameter determination for biological macromolecules.
  • The DATCLASS tool provides a user-friendly and accessible solution for the structural biology community.