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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Mutations01:39

Mutations

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Mutations01:35

Mutations

Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
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Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
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Related Experiment Video

Updated: Jul 16, 2026

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches
05:56

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Published on: October 13, 2022

Sequence analysis and rule development of predicting protein stability change upon mutation using decision tree

Liang-Tsung Huang1, M Michael Gromiha, Shinn-Ying Ho

  • 1Institute of Information Engineering and Computer Science, Feng-Chia University, Taichung, Taiwan.

Journal of Molecular Modeling
|March 31, 2007
PubMed
Summary

Predicting protein stability changes from mutations is crucial. A new interpretable prediction tree method (iPTREE-2) accurately forecasts these changes using sequence data and reveals mutation residue importance.

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Published on: July 14, 2015

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Protein stability changes due to mutations are complex and challenging to predict.
  • Accurate prediction requires insights from large biological databases.
  • Understanding these changes is vital for molecular biology research.

Purpose of the Study:

  • To develop an interpretable method for predicting protein stability changes caused by single point mutations.
  • To analyze sequence characteristics influencing protein stability.
  • To extract meaningful rules from mutation data for data mining.

Main Methods:

  • An interpretable prediction tree method (iPTREE-2) was developed.
  • The method utilizes sequence-based information, considering composition and order.
  • A regression tree algorithm was employed for prediction and rule discovery.

Main Results:

  • iPTREE-2 achieved a correlation coefficient of 0.70 between predicted and experimental values on a dataset of 1859 mutations.
  • Sequence analysis revealed compositional specificity of residues related to stability changes.
  • The study identified mutation residues in wild-type and mutant proteins as significant factors.

Conclusions:

  • The iPTREE-2 method provides accurate predictions of protein stability changes.
  • The approach offers understandable knowledge and insights into mutation effects.
  • This method aids in data mining and understanding sequence-based stability determinants.