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Related Concept Videos

Protein Organization01:13

Protein Organization

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Protein Organization01:24

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.
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Protein Folding

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Protein Folding01:22

Protein Folding

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Protein Folding01:25

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
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Protein and Protein Structure02:15

Protein and Protein Structure

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.
A protein's shape is critical to its function. For example, an enzyme can...

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Related Experiment Video

Updated: Jul 17, 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

Parallel protein secondary structure prediction based on neural networks.

Wei Zhong1, Gulsah Altun, Xinmin Tian

  • 1Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
Summary

This study introduces a Denoeux belief neural network (DBNN) for protein secondary structure prediction, achieving 87% accuracy using position-specific scoring matrices (PSSM). Parallelization techniques significantly accelerated the training process for this bioinformatics task.

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RNA Secondary Structure Prediction Using High-throughput SHAPE
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Last Updated: Jul 17, 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

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Protein secondary structure prediction is crucial for bioinformatics research.
  • Existing methods require efficient computational approaches.
  • Neural network architectures offer potential for improved prediction accuracy.

Purpose of the Study:

  • To implement binary and tertiary classifiers for protein secondary structure prediction using the Denoeux belief neural network (DBNN) architecture.
  • To evaluate different encoding schemes, including hydrophobicity matrix, orthogonal matrix, BLOSUM62, and position-specific scoring matrix (PSSM).
  • To parallelize the DBNN for faster training on Intel architecture using Pthread and OpenMP.

Main Methods:

  • Implemented DBNN with binary and tertiary classifiers.
  • Experimented with four encoding schemes: hydrophobicity matrix, orthogonal matrix, BLOSUM62, and PSSM.
  • Utilized Pthread and OpenMP for parallelizing DBNN on a hyperthreading-enabled Intel architecture.

Main Results:

  • A DBNN binary classifier achieved 87% prediction accuracy for Helix versus not Helix using PSSM.
  • DBNN performance was comparable to other leading prediction methods.
  • Achieved speedups of 4.9x with 16 Pthreads and 4x with 16 OpenMP threads, demonstrating efficient parallelization.

Conclusions:

  • The DBNN binary classifier with PSSM offers a promising new approach for neural network-based protein structure prediction.
  • Parallelization using Pthread and OpenMP significantly enhances the efficiency of DBNN training.
  • Intel's hyperthreading technology is effective for accelerating parallel biological algorithms.