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Protein Families02:47

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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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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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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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DeepFold-PLM: accelerating protein structure prediction via efficient homology search using protein language models.

Minsoo Kim1, Hanjin Bae1, Gyeongpil Jo1

  • 1Department of Physics, Sungkyunkwan University, Suwon 16419, Korea.

Bioinformatics (Oxford, England)
|October 17, 2025
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Summary

DeepFold-PLM accelerates protein structure prediction by integrating protein language models and vector databases for ultra-fast multiple sequence alignment (MSA) construction. This novel framework achieves significant speedups while maintaining high prediction accuracy and enabling analysis of complex protein structures.

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Area of Science:

  • Computational structural biology
  • Artificial intelligence in bioinformatics
  • Protein structure prediction

Background:

  • AI methods like AlphaFold have advanced protein structure prediction.
  • A major limitation is the computational cost of multiple sequence alignments (MSA).

Purpose of the Study:

  • Introduce DeepFold-PLM, a novel framework to overcome MSA limitations.
  • Enhance MSA construction, remote homology detection, and protein structure prediction.

Main Methods:

  • Integrate advanced protein language models with vector embedding databases.
  • Utilize high-dimensional embeddings and contrastive learning for MSA generation.
  • Develop a scalable PyTorch-based implementation for large-scale predictions.

Main Results:

  • Achieve 47x faster MSA generation compared to standard methods.
  • Maintain protein structure prediction accuracy comparable to AlphaFold.
  • Increase sequence diversity (Neff = 8.65 vs 4.83), enriching coevolutionary information.
  • Extend modeling to multimeric protein complexes.

Conclusions:

  • DeepFold-PLM offers a versatile and practical resource for high-throughput computational structural biology.
  • The framework enables faster and more comprehensive protein structure prediction.