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Related Concept Videos

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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Conservation of Protein Domains Over Different Proteins02:26

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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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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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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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Unsupervised multi-instance learning for protein structure determination.

Fardina Fathmiul Alam1, Amarda Shehu1

  • 1Department of Computer Science, George Mason University, Fairfax, Virginia 22030, USA.

Journal of Bioinformatics and Computational Biology
|February 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-instance learning approach for protein structure prediction, improving decoy selection. The method effectively identifies accurate protein models from computational decoys, advancing structural biology research.

Keywords:
Protein structure determinationdecoy qualitydecoy selectionmulti-instance learningunsupervised learning

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Machine Learning

Background:

  • Protein structure determination faces challenges in 'dark regions' inaccessible to current methods.
  • Decoy selection, discriminating correct structures from computational decoys, is a key challenge in *in silico* protein modeling.
  • Existing decoy selection methods, often based on clustering, lack clear strategies for utilizing identified decoy groups.

Purpose of the Study:

  • To formulate the protein decoy selection problem as an unsupervised multi-instance learning task.
  • To develop and evaluate novel algorithms for improved decoy selection in protein structure prediction.
  • To enhance the accuracy and efficiency of identifying relevant protein structures from computational decoys.

Main Methods:

  • Formulated decoy selection as unsupervised multi-instance learning, involving organizing decoys into bags.
  • Developed a three-stage approach: organizing decoys, identifying relevant bags, and selecting individual instances.
  • Proposed both non-parametric and parametric algorithms for instance selection from identified bags.

Main Results:

  • The proposed multi-instance learning approach demonstrated superior performance compared to state-of-the-art methods.
  • Evaluated on benchmark and CASP datasets, the method effectively improved decoy selection accuracy.
  • Comparative analysis confirmed the efficacy of the new approach in identifying relevant protein structures.

Conclusions:

  • Unsupervised multi-instance learning offers a promising framework for advancing protein decoy selection.
  • The developed algorithms provide a more effective strategy for utilizing clustered decoys in structure prediction.
  • Further research into multi-instance learning is warranted to address challenges in computational protein structure determination.