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Protein Organization01:24

Protein Organization

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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.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Efficient Multicriteria Protein Structure Comparison on Modern Processor Architectures.

Anuj Sharma1, Elias S Manolakos1

  • 1Department of Informatics and Telecommunications, University of Athens, Athens, Greece.

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|November 26, 2015
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Summary
This summary is machine-generated.

This study introduces a parallel framework for protein structure comparison (PSC) using many-core processors, significantly boosting efficiency for large-scale structural proteomics. The developed software framework achieves high performance, outperforming existing methods.

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Area of Science:

  • Computational Biology
  • Structural Proteomics
  • Bioinformatics

Background:

  • Increasing computational demands in protein structure comparison (PSC) stem from expanding databases and complex algorithms.
  • The trend towards multiple criteria for protein structures comparison (MCPSC) further exacerbates computational challenges.

Purpose of the Study:

  • To develop an efficient parallel software framework for MCPSC on modern multicore and many-core CPUs.
  • To evaluate and compare the performance of MCPSC implementations on different processor architectures.

Main Methods:

  • Developed a software framework for parallel MCPSC utilizing TMalign, CE, and USM algorithms.
  • Evaluated performance on Intel's many-core Single-Chip Cloud Computer (SCC) and Core i7 multicore processor.
  • Utilized the rckskel algorithmic skeletons library for implementation.

Main Results:

  • The 48-core SCC demonstrated superior efficiency over the Core i7, achieving a speedup factor of 42.
  • MCPSC outperformed individual methods in grouping related protein domains, reaching an F-measure of 0.91 on the CK34 dataset.
  • Many-core processors show promise for large-scale structural proteomics.

Conclusions:

  • Parallel MCPSC on many-core processors offers significant advantages for computational structural proteomics.
  • The developed software framework and library are publicly available on GitHub.
  • Many-core architectures represent a promising future for handling large-scale biological data analysis.