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Related Concept Videos

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. 
The...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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,...
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

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Updated: Jun 6, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Improved predictions by Pcons.net using multiple templates.

Per Larsson1, Marcin J Skwark, Björn Wallner

  • 1Center for Biomembrane Research, Department of Biochemistry and Biophysics, Swedish e-Science Research Centre, Stockholm Bioinformatics Centre, SciLifeLab, Stockholm University SE-10691 Stockholm, Sweden.

Bioinformatics (Oxford, England)
|December 15, 2010
PubMed
Summary
This summary is machine-generated.

PconsM uses multiple templates to generate more accurate protein homology models than single-template methods. This automated protocol improves protein structure prediction quality, especially for targets with numerous high-identity templates.

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Last Updated: Jun 6, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

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Published on: July 22, 2025

Area of Science:

  • Computational biology
  • Structural bioinformatics

Background:

  • Homology modeling is crucial for protein structure prediction.
  • Single-template approaches can limit model accuracy.

Purpose of the Study:

  • Introduce PconsM, an automated protocol for protein homology modeling.
  • Enhance protein model accuracy using multiple templates.

Main Methods:

  • Developed PconsM, an automated multi-template homology modeling protocol.
  • Integrated PconsM into the Pcons.net protein structure prediction server.

Main Results:

  • PconsM ranked among top methods in recent CASP experiments.
  • PconsM consistently outperforms single-template models from Pcons.net.
  • Achieved notable quality improvements for targets with multiple high-identity templates.

Conclusions:

  • Multi-template homology modeling with PconsM offers superior protein structure prediction.
  • PconsM provides an accessible pipeline for improved protein model generation.