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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

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Related Experiment Video

Updated: May 26, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

HKD-CPI: high-order knowledge distillation enhanced inductive compound-protein interaction prediction.

Zhongyu He1, Xiangrong Liu1, Yinghui Jiang1

  • 1School of Informatics, Xiamen University, Xiamen, 361005, China.

Bioinformatics (Oxford, England)
|May 25, 2026
PubMed
Summary

HKD-CPI enhances compound-protein interaction prediction by using high-order knowledge and large language models. This framework improves generalization to new drug targets, boosting discovery efficiency.

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Last Updated: May 26, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Published on: January 26, 2024

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09:39

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Computational Chemistry
  • Bioinformatics
  • Drug Discovery

Background:

  • Accurate compound-protein interaction (CPI) identification is crucial for drug discovery.
  • Current deep learning methods often neglect high-order interaction patterns in molecules.

Purpose of the Study:

  • To propose HKD-CPI, a novel framework for enhanced inductive compound-protein interaction prediction.
  • To improve generalization capabilities for predicting interactions with unseen compound-protein pairs.

Main Methods:

  • HKD-CPI integrates molecular graph features with large language model (LLM) embeddings via molecular graph tokenization.
  • A hypergraph representation models high-order relationships between feature-similar compound/protein groups.
  • Knowledge distillation transfers high-order interaction knowledge to a lightweight model for efficient prediction.

Main Results:

  • HKD-CPI demonstrates superior performance in inductive CPI prediction tasks compared to state-of-the-art methods.
  • Achieved an average improvement of 4.94% in AUROC and 4.90% in AUPRC across five benchmark datasets.
  • The framework effectively infuses sequence-derived semantics into structural representations.

Conclusions:

  • HKD-CPI offers a robust and efficient approach for predicting compound-protein interactions.
  • The proposed methods enhance the understanding and prediction of complex molecular interactions.
  • This framework holds significant potential for accelerating drug discovery pipelines.