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Protein Organization

Protein Organization

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.

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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Protein Organization

Protein Organization

No description available

Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....

Protein Organization

Protein Organization

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes.

T-L Zhang¹, Y-S Ding

¹College of Information Sciences and Technology, Donghua University, Shanghai, China.

|February 20, 2007

Summary

This summary is machine-generated.

This study introduces a novel 46-dimensional pseudo amino acid composition (PseAA) method for protein structural class prediction. The enhanced PseAA approach significantly improves prediction accuracy compared to traditional amino acid composition (AA).

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Area of Science:

* Bioinformatics
* Computational Biology
* Protein Science

Background:

* Conventional amino acid composition (AA) has limitations in capturing comprehensive protein sequence information.
* Pseudo amino acid composition (PseAA) offers a more informative representation for discrete modeling of protein attributes.
* Predicting protein structural class is crucial for understanding protein function and design.

Purpose of the Study:

* To formulate a 46-dimensional pseudo amino acid composition (PseAA) for protein representation.
* To develop a novel prediction approach using binary-tree support vector machines (BTSVMs) for protein structural class.
* To evaluate the performance of the new PseAA-based method against conventional AA methods.

Main Methods:

* Formulation of a 46-dimensional pseudo amino acid composition (PseAA) based on Chou's concept.
* Implementation of a binary-tree support vector machines (BTSVMs) algorithm for classification.
* Validation using 10-fold cross-validation and jackknife tests.

Main Results:

* The 46-dimensional PseAA significantly outperformed the conventional 20-dimensional AA in predicting protein structural class.
* The BTSVMs approach demonstrated effectiveness in handling multi-class classification problems.
* Predictive performance using the new PseAA method showed encouraging results.

Conclusions:

* The proposed 46-D PseAA is a powerful feature descriptor for protein sequence analysis.
* The BTSVMs algorithm offers a robust framework for protein structural class prediction.
* This PseAA-based approach can serve as a valuable complementary tool for various protein attribute predictions.