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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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 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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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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Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.

Wei Zheng1,2, Qiqige Wuyun3, Yang Li4

  • 1NITFID, School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin, China.

Nature Biotechnology
|May 23, 2025
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Summary

A new hybrid method, deep-learning-based iterative threading assembly refinement (D-I-TASSER), enhances protein structure prediction. D-I-TASSER integrates deep learning with traditional simulations, outperforming existing tools for both single and multidomain proteins.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Deep learning has dominated protein structure prediction, questioning traditional simulation methods.
  • Existing deep learning models show limitations in predicting complex protein structures.

Purpose of the Study:

  • To develop a hybrid approach combining deep learning and physics-based simulations for improved protein structure prediction.
  • To assess the performance of the new method against state-of-the-art tools like AlphaFold2 and AlphaFold3.

Main Methods:

  • Developed deep-learning-based iterative threading assembly refinement (D-I-TASSER).
  • Integrated multisource deep learning potentials with iterative threading fragment assembly.
  • Implemented a domain splitting and assembly protocol for large multidomain proteins.

Main Results:

  • D-I-TASSER outperformed AlphaFold2 and AlphaFold3 in benchmark tests for both single-domain and multidomain proteins.
  • Successfully folded 81% of human protein domains and 73% of full-chain sequences.
  • Results are complementary to existing models, offering high accuracy for genome-wide applications.

Conclusions:

  • The hybrid D-I-TASSER approach offers a novel integration of deep learning and classical simulations.
  • This method provides a promising avenue for high-accuracy protein structure and function prediction.
  • D-I-TASSER demonstrates significant potential for large-scale genomic applications.