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Protein Families02:47

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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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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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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Structure-guided sequence representation learning for generalizable protein function prediction.

SeokJun On1, Yujin Jeong1, Eun-Sol Kim1,2

  • 1Department of Artificial Intelligence, Hanyang University, Seoul 04763, Republic of Korea.

Bioinformatics (Oxford, England)
|September 19, 2025
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Summary
This summary is machine-generated.

This study introduces Structure-guided Sequence Representation Learning (S2RL), a novel framework that integrates protein structure into sequence-based models for more accurate function prediction. S2RL effectively captures multiscale features, improving predictions for protein functions and expression sites.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Predicting protein function from sequence is crucial but challenging.
  • Existing methods struggle with variable protein sizes when using structural data.
  • Representing proteins as graphs of residues faces scalability issues due to size variations.

Purpose of the Study:

  • To develop a novel framework for protein function prediction that integrates structural information into sequence-based learning.
  • To address the challenge of variable protein sizes in graph-based representations.
  • To improve the accuracy and efficiency of predicting protein functions, functional sites, and their structure-sequence relationships.

Main Methods:

  • Proposed Structure-guided Sequence Representation Learning (S2RL) framework.
  • Incorporated structural knowledge into a sequence-based learning paradigm.
  • Developed a generalizable multitask learning architecture.

Main Results:

  • The attention pooling method effectively integrates global structural and local chemical properties.
  • Improved performance in predicting protein functions, functional expression sites, and structure-sequence relationships.
  • Achieved efficiency by enabling simultaneous prediction of multiple protein functions.

Conclusions:

  • S2RL framework enhances protein function prediction by leveraging structural information within sequence-based models.
  • The method effectively handles variable protein lengths and extracts multiscale features.
  • The developed architecture offers improved performance and flexibility for multitask protein function inference.