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Protein-protein Interfaces

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...

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

Ligand Binding Sites

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

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Related Experiment Video

Updated: Jul 8, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning.

Zicong Zhang¹, Jacob Verburgt², Yuki Kagaya²

¹Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907, USA.

Biorxiv : the Preprint Server for Biology

|December 18, 2023

Summary

This summary is machine-generated.

DistPepFold enhances protein-peptide complex modeling by using knowledge distillation. This new method, built on AlphaFold-Multimer, outperforms its predecessor in predicting complex structures.

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Last Updated: Jul 8, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Area of Science:

Structural biology
Computational biology
Biochemistry

Background:

Protein-peptide interactions are crucial for biological processes.
Accurate modeling of receptor-peptide complexes aids in understanding and manipulating biological functions.
Deep learning methods, like AlphaFold and AlphaFold-Multimer, have advanced protein structure prediction.

Purpose of the Study:

To improve computational methods for protein-peptide complex docking.
To develop a novel deep learning architecture, DistPepFold, for enhanced prediction of protein-peptide interactions.

Main Methods:

Utilizing an AlphaFold-Multimer-based architecture.
Implementing a privileged knowledge distillation approach with a teacher-student model.
Training the teacher model with native interaction information.

Main Results:

DistPepFold demonstrated superior docking performance compared to AlphaFold-Multimer on two benchmark datasets.
The student model successfully learned from the teacher model, refining AlphaFold-Multimer predictions.
The knowledge distillation process led to improved structural accuracy in protein-peptide complex modeling.

Conclusions:

DistPepFold represents a significant advancement in modeling protein-peptide interactions.
Knowledge distillation is an effective strategy for enhancing deep learning models in structural biology.
The developed method offers a more accurate approach to predicting the structures of receptor-peptide complexes.