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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...
Internal Loadings in Structural Members: Problem Solving01:28

Internal Loadings in Structural Members: Problem Solving

When designing or analyzing a structural member, it is important to consider the internal loadings developed within the member. These internal loadings include normal force, shear force, and bending moment. Engineers can ensure that the structural member can support the applied external forces by calculating these internal loadings.
To illustrate this, let's consider a beam OC of 5 kN, inclined at an angle of 53.13° with the horizontal and supported at both ends. Determine the internal loadings...
Unsymmetric Loading of Thin-Walled Members01:23

Unsymmetric Loading of Thin-Walled Members

Thin-walled members with non-symmetrical cross-sections are vital to engineering structures, offering material efficiency and structural integrity. However, unsymmetrical loading on these members leads to complex stress distributions, resulting in simultaneous bending and twisting can cause deformation or structural failure. The interaction between bending and twisting requires detailed analysis to ensure structural resilience.
The concept of the shear center is crucial in countering the...

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Related Experiment Video

Updated: Jul 3, 2026

Multimodal Approach to Assess Bone Regeneration and Scaffold Performance
06:54

Multimodal Approach to Assess Bone Regeneration and Scaffold Performance

Published on: February 13, 2026

Local alignment refinement using structural assessment.

Pierre Chodanowski1, Aurélien Grosdidier, Ernest Feytmans

  • 1Swiss Institute of Bioinformatics, Bâtiment Génopode, Lausanne, Switzerland.

Plos One
|July 10, 2008
PubMed
Summary

This study introduces new predictors to assess protein model quality from sequence alignments. The best predictor, using ANOLEA force fields, accurately identifies correct structural alignments for 90% of test cases.

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Last Updated: Jul 3, 2026

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Modeling

Background:

  • Homology modeling is crucial for protein structure prediction.
  • Model quality depends heavily on accurate sequence alignment.
  • Assessing alignment quality is essential for reliable protein models.

Purpose of the Study:

  • To develop and evaluate predictors for assessing protein model quality based on sequence alignments.
  • To identify the most effective method for evaluating local alignment accuracy.

Main Methods:

  • Fifteen novel predictors were developed using energy values from CHARMM, ProsaII, and ANOLEA force fields.
  • Predictors focused on physico-chemical properties around misaligned regions.
  • Evaluated predictors on ten challenging test cases with generated ungapped alignments.

Main Results:

  • The best predictor, utilizing ANOLEA atomistic mean force potential around secondary structure elements, correctly identified the structural alignment in 9 out of 10 cases.
  • Other predictors showed significantly lower performance.
  • This highlights the importance of local structural assessment.

Conclusions:

  • Careful assessment of local structure in protein models can substantially improve local alignment accuracy.
  • The ANOLEA-based predictor shows promise for evaluating homology modeling alignments.