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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein and Protein Structure

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Updated: Aug 23, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Robust Prediction and Protein Selection with Adaptive PENSE.

David Kepplinger1, Gabriela V Cohen Freue2

  • 1George Mason University, Fairfax, VA, USA. dkepplin@gmu.edu.

Methods in Molecular Biology (Clifton, N.J.)
|October 29, 2022
PubMed
Summary

Adaptive PENSE is a new method for building predictive models from proteomic data, even with limited samples or data quality issues. This approach reliably identifies key proteins and creates accurate clinical outcome prediction models.

Keywords:
High-dimensional dataLinear regressionPredictionProtein selectionRobust estimation

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Area of Science:

  • Biostatistics
  • Proteomics
  • Clinical Bioinformatics

Background:

  • Predictive modeling in clinical proteomics often faces challenges with small sample sizes and large numbers of candidate proteins.
  • Data quality issues, such as aberrant values in a fraction of samples, can compromise the reliability of predictive models.

Purpose of the Study:

  • To introduce Adaptive PENSE, a robust method for developing accurate clinical outcome prediction models from proteomic data.
  • To address common challenges in proteomic studies, including limited sample size and data quality variations.

Main Methods:

  • Adaptive PENSE utilizes a statistical approach to select relevant proteins for predictive modeling.
  • The method is implemented as an R package, automating model selection and providing diagnostic visualizations.
  • It is designed to be resilient to data quality issues in up to 50% of samples.

Main Results:

  • Adaptive PENSE reliably identifies proteins relevant for prediction even with a large number of candidate proteins and small sample sizes.
  • The method demonstrates resilience to data quality issues, maintaining model accuracy with up to 50% of samples having aberrant values.
  • Users can select predictive models with high accuracy and an appropriate number of proteins.

Conclusions:

  • Adaptive PENSE offers a reliable solution for building predictive clinical outcome models from proteomic data under challenging conditions.
  • The R package facilitates user-guided model selection, enhancing the practical application of the method.
  • This approach improves the accuracy and robustness of predictive modeling in clinical proteomics.