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Related Concept Videos

Protein Folding01:22

Protein Folding

Overview
Protein Folding01:25

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
Protein Folding01:22

Protein Folding

Overview
Molecular Chaperones and Protein Folding03:00

Molecular Chaperones and Protein Folding

The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
The...
Molecular Chaperones and Protein Folding03:00

Molecular Chaperones and Protein Folding

The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
The...
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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Related Experiment Video

Updated: Jun 1, 2026

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One α-Synuclein Monomer at a Time
07:56

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One α-Synuclein Monomer at a Time

Published on: May 30, 2021

QSyncFold: quantum neural network for multidimensional sync-discovery in protein folding.

Jinjing Shi1, Peng Du1, Wenwu Zeng2

  • 1Department of Communication Engineering, School of Electronic Information, Central South University, 68 South Shaoshan Road, Tianxin District, Changsha 410083, Hunan, China.

Briefings in Bioinformatics
|May 30, 2026
PubMed
Summary

QSyncFold, a novel quantum-classical framework, enhances protein structure prediction (PSP) by efficiently encoding continuous coordinates. This method shows significant improvements over existing quantum approaches for short peptides.

Keywords:
protein structure predictionquantum learning algorithmsquantum machine learningquantum neural network

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Last Updated: Jun 1, 2026

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One α-Synuclein Monomer at a Time
07:56

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One α-Synuclein Monomer at a Time

Published on: May 30, 2021

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

Area of Science:

  • Quantum Computing
  • Computational Biology
  • Biomolecular Modeling

Background:

  • Protein structure prediction (PSP) is crucial for understanding protein function.
  • Existing quantum-PSP methods face challenges with resource scaling and continuous coordinate encoding.
  • Developing efficient quantum algorithms for PSP is an active research area.

Purpose of the Study:

  • To introduce QSyncFold, a hybrid quantum-classical neural network for protein structure prediction.
  • To address limitations of current quantum-PSP methods, particularly encoding continuous coordinates.
  • To demonstrate a resource-efficient quantum framework viable for NISQ (Noisy Intermediate-Scale Quantum) constraints.

Main Methods:

  • QSyncFold utilizes a hybrid quantum-classical neural network architecture.
  • ProtaQode enables reversible continuous-space encoding of residue coordinates and interaction modeling.
  • An Any-State RY (ASRY) operator encodes residue-pair interactions in superposition, reducing qubit requirements to $3+\lceil \log _{2} N \rceil $ per iteration.

Main Results:

  • QSyncFold achieved a 5.25-fold improvement in the lDDT metric for short peptide structure prediction compared to Variational Quantum Eigensolver (VQE).
  • The framework demonstrated a trade-off between qubit budget and convergence speed.
  • Performance approaches AlphaFold2 for short peptides, indicating competitive potential.

Conclusions:

  • QSyncFold presents a viable and experimentally practical approach for quantum computing in protein structure prediction.
  • The study advances quantum methodologies for biomolecular modeling, offering improved precision.
  • This work paves the way for future quantum algorithms in complex biological simulations.