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Related Concept Videos

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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Protein Families02:47

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Conservation of Protein Domains Over Different Proteins02:26

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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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Protein Organization01:24

Protein Organization

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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.
The primary structure of a protein is its amino acid sequence....
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Protein Organization01:13

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Conserved Binding Sites01:49

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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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From Signal to Symphony: Exploring 2D Sequence Representations for Protein Function Prediction.

Yiquan Wang1,2, Minnuo Cai1, Yuhua Dong3

  • 1Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi 830049 Xinjiang, China.

Journal of Chemical Information and Modeling
|November 17, 2025
PubMed
Summary
This summary is machine-generated.

Protein sonification, converting amino acid sequences into spectrograms, shows promise for predicting protein function. This novel representation, particularly its structure, significantly aids deep learning models in biological sequence analysis.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Predicting protein function from primary sequences is crucial but challenging.
  • Optimal representation of biological sequence data for deep learning is an open question.
  • Deep learning models have shown success but require effective data representation.

Purpose of the Study:

  • To explore protein sonification (amino acid sequences to 2D spectrograms) as a novel data representation for predicting protein function.
  • To develop and evaluate a benchmark dataset and models for assessing sonification's effectiveness.
  • To investigate the contribution of representational structure versus biophysical information.

Main Methods:

  • Developed a benchmark dataset of 18,000 protein sequences across 12 classes.
  • Implemented and evaluated deep learning models using protein sonification spectrograms.
  • Conducted ablation studies on visual, acoustic, and biophysically informed features.
  • Compared performance against standard transformer architectures (ESM-2, ProtBERT) and an external benchmark (CARE).
  • Utilized a diffusion model guided by sonification encoding for novel protein variant generation.

Main Results:

  • Protein sonification as a 2D spectrogram representation significantly improves predictive performance.
  • Ablation studies confirmed the importance of the spectrogram's structural transformation and features.
  • A model without explicit biophysical meaning achieved 81.08% accuracy; a biophysically informed model reached 84.00%.
  • The fusion model trained on sonification data performed comparably to or better than ESM-2 and ProtBERT.
  • Achieved 90.44% accuracy on the external CARE enzyme classification benchmark.
  • Demonstrated proof-of-concept for generating novel protein variants using sonification-guided diffusion models.

Conclusions:

  • The structural representation of protein sonification spectrograms is a key factor in enhancing predictive performance.
  • Protein sonification offers a data-efficient and effective approach for biological sequence analysis and feature engineering.
  • This method shows potential for generalization and guiding the design of novel protein variants.