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Related Concept Videos

Protein Organization01:24

Protein Organization

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.
Protein Organization01:13

Protein Organization

Overview
Protein and Protein Structure02:15

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme can...
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...
Conserved Binding Sites01:49

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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Related Experiment Video

Updated: Jul 16, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Protein structure prediction: combining de novo modeling with sparse experimental data.

Dorota Latek1, Dariusz Ekonomiuk, Andrzej Kolinski

  • 1Faculty of Chemistry, Warsaw University, Pateura 1, 02-093 Warsaw, Poland. pledor@chem.uw.edu.pl

Journal of Computational Chemistry
|March 8, 2007
PubMed
Summary

Predicting protein structures is difficult. This study combines NMR data with AI to improve protein structure modeling accuracy for drug interactions and complex analysis.

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Last Updated: Jul 16, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Protein structure prediction remains a significant challenge in computational biology.
  • Accurate protein models are crucial for understanding drug interactions and protein complexes.
  • Existing methods often require extensive experimental data, limiting their routine application.

Purpose of the Study:

  • To develop and validate a comprehensive protein structure modeling approach.
  • To enhance the accuracy of molecular modeling using sparse experimental data.
  • To integrate artificial intelligence with traditional methods for improved protein structure prediction.

Main Methods:

  • Utilized chemical shift-based restraints from Nuclear Magnetic Resonance (NMR) data.
  • Integrated artificial intelligence-based secondary structure prediction with NMR data.
  • Employed the CABS (reduced representation) modeling software for structure prediction.
  • Tested the approach on globular proteins, including CASP6 targets with existing NMR data.

Main Results:

  • Demonstrated significant enhancement in restraint accuracy for molecular modeling.
  • Showcased the effectiveness of combining AI-predicted secondary structures with NMR data.
  • Validated the method's performance on diverse protein structures.

Conclusions:

  • The proposed comprehensive approach improves protein structure modeling accuracy.
  • Combining AI with NMR data offers a powerful strategy for structure prediction.
  • The semi-automated pipeline is suitable for large-scale structural annotation of genomic data.