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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The Phase Rule01:20

The Phase Rule

The phase rule describes the relationship between the variance (degrees of freedom), the number of components, and the number of phases in a system at equilibrium.Variance is a concept that denotes the number of independent intensive properties (properties are those that do not depend on the amount of material in the system), such as temperature, pressure, and composition, that can be altered without impacting the number of phases in equilibrium.In a single-component system, such as pure water,...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

You might also read

Related Articles

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

Sort by
Same author

Structural basis of anticancer drug recognition and amino acid transport by LAT1.

Nature communications·2025
Same author

Deep-learning map segmentation for protein X-ray crystallographic structure determination.

Acta crystallographica. Section D, Structural biology·2024
Same author

A redox switch allows binding of Fe(II) and Fe(III) ions in the cyanobacterial iron-binding protein FutA from <i>Prochlorococcus</i>.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

The CCP4 suite: integrative software for macromolecular crystallography.

Acta crystallographica. Section D, Structural biology·2023
Same author

Interdigitated immunoglobulin arrays form the hyperstable surface layer of the extremophilic bacterium <i>Deinococcus radiodurans</i>.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Multivariate estimation of substructure amplitudes for a single-wavelength anomalous diffraction experiment.

Acta crystallographica. Section D, Structural biology·2023
Same journal

Structural insights into the synthesis of FMN in prokaryotic organisms.

Acta crystallographica. Section D, Biological crystallography·2015
Same journal

Native sulfur/chlorine SAD phasing for serial femtosecond crystallography.

Acta crystallographica. Section D, Biological crystallography·2015
Same journal

Serial crystallographic analysis of protein isomorphous replacement data from a mixture of native and derivative microcrystals.

Acta crystallographica. Section D, Biological crystallography·2015
Same journal

The first crystal structure of the peptidase domain of the U32 peptidase family.

Acta crystallographica. Section D, Biological crystallography·2015
Same journal

Atomic resolution crystal structure of Sapp2p, a secreted aspartic protease from Candida parapsilosis.

Acta crystallographica. Section D, Biological crystallography·2015
Same journal

Structural characterization of a mitochondrial 3-ketoacyl-CoA (T1)-like thiolase from Mycobacterium smegmatis.

Acta crystallographica. Section D, Biological crystallography·2015
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A multivariate likelihood SIRAS function for phasing and model refinement.

Pavol Skubák1, Garib Murshudov, Navraj S Pannu

  • 1Biophysical Structural Chemistry, Leiden Institute of Chemistry, Gorlaeus Laboratories, Leiden University, 2300 RA Leiden, The Netherlands. p.skubak@chem.leidenuniv.nl

Acta Crystallographica. Section D, Biological Crystallography
|September 23, 2009
PubMed
Summary
This summary is machine-generated.

A new likelihood function improves macromolecular model refinement and phasing by analyzing all structure-factor amplitudes from single isomorphous replacement with anomalous scattering experiments.

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Related Experiment Videos

Last Updated: Jun 20, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Crystallography
  • Structural Biology
  • Biophysics

Background:

  • Macromolecular structure determination relies on accurate phasing and model refinement.
  • Single isomorphous replacement with anomalous scattering (SIRAS) is a key experimental technique.
  • Current methods may face limitations in fully utilizing all available experimental data.

Purpose of the Study:

  • To derive and implement a novel likelihood function for macromolecular crystallography.
  • To enhance substructure refinement, phasing, and macromolecular model refinement.
  • To improve automated model building in protein structure determination.

Main Methods:

  • Development of a likelihood function based on multivariate probability distributions.
  • Utilizing all observed structure-factor amplitudes from SIRAS experiments.
  • Employing approximations for efficient multidimensional integration in function evaluation.

Main Results:

  • The derived likelihood function was successfully implemented.
  • Efficient calculation of multidimensional integrals was achieved.
  • The function proved essential for successful automated model building in test cases.

Conclusions:

  • The new likelihood function offers a robust approach for macromolecular structure refinement and phasing.
  • This method enhances the accuracy and efficiency of protein model building.
  • The approach is vital for advancing automated structure determination techniques.