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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: Jul 5, 2026

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition
07:32

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Published on: February 23, 2024

HingeMaster: normal mode hinge prediction approach and integration of complementary predictors.

Samuel C Flores1, Kevin S Keating, Jay Painter

  • 1Department of Physics, Yale University, New Haven, Connecticut 06520, USA. samuel.flores@aya.yale.edu

Proteins
|April 25, 2008
PubMed
Summary

Predicting protein hinges, crucial for function, is improved by combining normal-mode analysis with other methods. This approach enhances the accuracy of identifying protein domain movements.

Related Experiment Videos

Last Updated: Jul 5, 2026

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition
07:32

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Published on: February 23, 2024

Area of Science:

  • Structural biology
  • Computational biophysics
  • Protein dynamics

Background:

  • Protein motion is key to function, with hinge bending being a common mechanism.
  • Accurate prediction of hinge locations is essential for understanding protein dynamics.

Purpose of the Study:

  • To develop and evaluate methods for predicting protein hinge locations from structural data.
  • To compare a novel normal-mode based approach with existing structure- and sequence-based predictors.
  • To create an integrated predictor combining multiple methods for improved accuracy.

Main Methods:

  • Utilized normal-mode analysis to group residues with correlated motions for hinge prediction.
  • Benchmarked the normal-mode predictor against a gold standard dataset (Database of Macromolecular Motions).
  • Compared performance against TLSMD, StoneHinge, FlexOracle (structure-based), and HingeSeq (sequence-based).
  • Developed a combined predictor using a weighted voting scheme integrating all methods.

Main Results:

  • The normal-mode based predictor showed competitive performance.
  • Combining multiple prediction methods via weighted voting significantly improved hinge prediction accuracy.
  • A web tool was developed for users to submit protein structures and visualize predicted hinges.

Conclusions:

  • Integrating diverse prediction strategies, including normal-mode analysis, enhances the accuracy of identifying protein hinges.
  • The developed web tool provides a valuable resource for researchers studying protein motion and function.