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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Ligand Binding Sites02:40

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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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Ligand Binding and Linkage00:49

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Related Experiment Video

Updated: Nov 28, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.

Wenjun Zheng1

  • 1Department of Physics, University at Buffalo, Buffalo, New York, USA.

Proteins
|November 27, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a fast method to predict cryptic binding sites in proteins. The approach uses normal mode analysis to guide conformational sampling, enabling efficient identification of potential drug targets.

Keywords:
SARS-CoV-2area under the curveconformational samplingcryptic siteelastic network modelligand bindinglogistic regressionmachine learningneural netnormal mode analysisrandom forestreceiver operating characteristic curve

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Area of Science:

  • Structural bioinformatics
  • Computational drug discovery

Background:

  • Identifying cryptic binding sites is crucial for expanding the druggable genome.
  • Existing methods for predicting these sites can be computationally intensive.

Purpose of the Study:

  • To develop a fast and accurate method for predicting cryptic binding sites in proteins.
  • To enable high-throughput screening of protein structures for drug discovery.

Main Methods:

  • A conformational sampling scheme guided by normal modes from coarse-grained elastic models.
  • Atomistic backbone refinement and side-chain repacking.
  • Machine learning protocols to optimize pocket scores with dynamic and conservation scores.

Main Results:

  • Sampling along the lowest 30 normal modes effectively restructures cryptic sites for detection.
  • The method achieved high prediction accuracy (AUC >0.8) on training and test datasets.
  • Achieved performance comparable to the CryptoSite server.

Conclusions:

  • The developed method is significantly faster (1-2 hours per protein) and simpler than existing approaches.
  • Suitable for large-scale, genome-wide analysis of protein structures.
  • Facilitates the expansion of the druggable genome by identifying novel binding sites.