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Related Concept Videos

Steps in the Modeling Process01:14

Steps in the Modeling Process

Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...

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Related Experiment Video

Updated: May 7, 2026

3D Printing of Preclinical X-ray Computed Tomographic Data Sets
11:06

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Published on: March 22, 2013

High-resolution comparative modeling with RosettaCM.

Yifan Song1, Frank DiMaio, Ray Yu-Ruei Wang

  • 1Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.

Structure (London, England : 1993)
|September 17, 2013
PubMed
Summary
This summary is machine-generated.

RosettaCM is an improved comparative modeling method that optimizes a realistic energy function for protein structures. It shows more accurate modeling of protein conformations compared to other methods when template sequence identity exceeds 15%.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modeling

Background:

  • Comparative modeling is crucial for predicting protein structures.
  • Existing methods face challenges in accuracy, especially with low sequence identity.
  • Accurate protein structure prediction is vital for understanding biological function.

Purpose of the Study:

  • To introduce RosettaCM, an enhanced method for protein comparative modeling.
  • To improve the accuracy of protein structure prediction using homologous templates.
  • To optimize the conformational space with a physically realistic all-atom energy function.

Main Methods:

  • RosettaCM assembles protein topologies by recombining aligned segments.
  • Unaligned regions are built de novo, with junctions regularized by loop closure and minimization.
  • All-atom refinement optimizes model energies, selecting the most representative low-energy model.

Main Results:

  • RosettaCM demonstrates improved accuracy in side-chain and backbone conformations.
  • The method's performance is superior when template sequence identity is greater than approximately 15%.
  • Comparison with other methods in the CASP10 experiment validated RosettaCM's effectiveness.

Conclusions:

  • RosettaCM offers a significant advancement in comparative protein modeling.
  • The method provides more accurate protein structure predictions, particularly with moderate sequence identity to templates.
  • RosettaCM is a valuable tool for structural bioinformatics and drug discovery efforts.