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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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PepHarmony: a multi-view contrastive learning framework for integrated sequence and structure-based peptide

Ruochi Zhang1, Haoran Wu2, Chang Liu3

  • 1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; Syneron Technology, Guangzhou, 510700, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 7, 2025
PubMed
Summary
This summary is machine-generated.

PepHarmony, a new framework, improves peptide sequence representation by integrating sequence and structure data. This advance aids peptide drug discovery and engineering by better understanding complex peptide characteristics.

Keywords:
BioinformaticsContrastive learningMulti-View LearningPeptide sequence representationSequence-Based Encoding

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Protein language models have advanced peptide sequence representation.
  • Peptide-specific pre-trained models are lacking due to challenges in capturing complex peptide structures.

Purpose of the Study:

  • Introduce PepHarmony, a novel multi-view contrastive learning framework for peptide representation.
  • Integrate sequence and structure information for enhanced peptide sequence encoding.

Main Methods:

  • Developed a multi-view contrastive learning framework (PepHarmony).
  • Utilized datasets from Protein Data Bank and AlphaFold DB.
  • Combined sequence- and structure-level information via contrastive learning.

Main Results:

  • PepHarmony demonstrated superior capability in capturing peptide sequence-structure relationships compared to baseline and fine-tuned models.
  • Ablation studies confirmed model robustness and highlighted the importance of contrastive loss and data sorting.

Conclusions:

  • PepHarmony offers significant contributions to peptide representation learning.
  • The model and training strategies provide valuable insights for peptide drug discovery and engineering.