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Related Concept Videos

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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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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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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Protein Families

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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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Updated: Oct 5, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment

Lupeng Kong1,2,3, Fusong Ju1,2, Wei-Mou Zheng4

  • 1Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 24, 2022
PubMed
Summary

ProALIGN, a new deep learning method, significantly improves protein structure prediction by generating more accurate sequence-template alignments. This approach enhances template-based modeling (TBM) by learning context-specific alignment motifs.

Keywords:
deep learning and protein threadingprotein alignmentprotein structure prediction

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning in bioinformatics

Background:

  • Template-based modeling (TBM) is crucial for protein structure prediction.
  • Accurate sequence-template alignment is challenging, especially with distant templates.
  • Current methods rely on handcrafted scoring functions that can be inaccurate.

Purpose of the Study:

  • To develop a novel deep learning approach for predicting highly accurate sequence-template alignments.
  • To improve the quality of 3D protein structure models generated by TBM.
  • To overcome limitations of existing alignment scoring functions.

Main Methods:

  • Representing protein alignments as binary matrices.
  • Utilizing a deep convolutional neural network (CNN) to predict optimal alignments.
  • Encoding an implicit alignment scoring function within the CNN.
  • Inferring likelihoods of residue pair alignments considering residue correlations.

Main Results:

  • ProALIGN achieved significantly more accurate sequence-template alignments compared to existing methods.
  • The method produced superior 3D structure models on independent datasets and CASP13 targets.
  • Demonstrated improved performance over HHpred, CNFpred, CEthreader, and DeepThreader.

Conclusions:

  • Deep learning effectively exploits context-specific alignment motifs for improved protein threading.
  • ProALIGN offers a more accurate and reliable approach for template-based protein structure prediction.
  • The findings highlight the potential of deep learning in advancing structural bioinformatics.