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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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 polypeptide...
Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

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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

A classification scoring schema to validate protein interactors.

Prashanth Suravajhala, Vijayaraghava Seshadri Sundararajan

    Bioinformation
    |February 24, 2012
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a six-point system to validate hypothetical protein (HP) interactions in protein interaction networks (PINs). Machine learning models achieved 81.08% accuracy, highlighting subcellular location as key for HP function prediction.

    Keywords:
    hypothetical proteinsprotein interaction networkstotal reliability score

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Proteomics

    Background:

    • Hypothetical protein (HP) annotation is challenging, especially within protein interaction networks (PINs).
    • Existing PIN visualizers often lack support for HP annotation.
    • Accurate HP annotation is crucial for understanding protein functions and biological pathways.

    Purpose of the Study:

    • To develop a robust classification system for validating protein interactions involving hypothetical proteins.
    • To leverage machine learning for predicting functional interaction partners for unannotated proteins.
    • To identify key features that contribute to the accurate prediction of HP functions.

    Main Methods:

    • A six-point classification system was devised to assess protein interactions based on diverse features.
    • A hypothetical protein dataset was utilized for training machine learning models.
    • A Total Reliability Score (TRS) was calculated and evaluated using a multilayer perceptron neural network.
    • Feature selection algorithms and statistical analyses (variance, co-variance) were employed.

    Main Results:

    • The multilayer perceptron model achieved 81.08% accuracy in modeling the Total Reliability Score (TRS).
    • Feature selection confirmed the implementability of all proposed classification features.
    • Statistical analyses validated the utility of the developed classification metrics.
    • Subcellular location (sorting signals) was identified as the most impactful feature for predicting HP function.

    Conclusions:

    • The proposed six-point classification system effectively validates protein interactions for hypothetical proteins.
    • Machine learning, particularly neural networks, can accurately model protein interaction reliability.
    • Subcellular location is a critical determinant in predicting the functional roles of hypothetical proteins within biological systems.