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Protein and Protein Structure02:15

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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 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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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.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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MULTICOM2 open-source protein structure prediction system powered by deep learning and distance prediction.

Tianqi Wu1, Jian Liu1, Zhiye Guo1

  • 1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.

Scientific Reports
|June 24, 2021
PubMed
Summary

MULTICOM2 is a new open-source system for protein structure prediction. It integrates template-based and template-free methods, achieving high accuracy in CASP14 and offering a unified approach for modeling any protein structure.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein structure prediction is a long-standing challenge in bioinformatics.
  • Existing open-source comprehensive packages are limited.
  • Accurate protein tertiary structure prediction is crucial for understanding biological function.

Purpose of the Study:

  • To introduce MULTICOM2, an advanced open-source protein tertiary structure prediction system.
  • To integrate template-based modeling (TBM) and template-free modeling (FM) for improved prediction accuracy.
  • To provide a comprehensive and accessible tool for the scientific community.

Main Methods:

  • Developed MULTICOM2, integrating TBM using deep multiple sequence alignments and FM utilizing DeepDist for inter-residue distance prediction.
  • Employed sequence alignment tools for template searching in TBM.
  • Reconstructed tertiary structures using predicted distances in FM without relying on known templates.

Main Results:

  • Achieved an average TM-score of 0.720 for TBM domains and 0.514 for FM/TBM domains in the CASP14 blind experiment.
  • Successfully predicted the correct fold for 95% of regular and 55% of hard domains with a single prediction.
  • Demonstrated that deep learning-based FM methods show comparable accuracy to TBM, suggesting a potential for a uniform modeling approach.

Conclusions:

  • MULTICOM2 demonstrates robust performance in protein tertiary structure prediction across various domain types.
  • The advanced template-free modeling approach shows promise in replacing traditional template-based methods.
  • The open-source availability of MULTICOM2 facilitates further research and application in structural bioinformatics.