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Related Concept Videos

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.
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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.
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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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Binding sites linkages can regulate a protein's function.  For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
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Conservation of Protein Domains Over Different Proteins02:26

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Related Experiment Video

Updated: Sep 9, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Evolutionary Constraints Guide AlphaFold2 in Predicting Alternative Conformations and Inform Rational Mutation

Valerio Piomponi1, Alberto Cazzaniga1, Francesca Cuturello1

  • 1Research and Technology Institute, Area Science Park, località Padriciano, 99, 34149 Trieste, Italy.

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Summary

This study enhances protein structure prediction by generating diverse conformational ensembles and identifying sequence patterns. The new method integrates protein language models and clustering for better interpretability and prediction of functional states.

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Understanding protein structural variability is crucial for elucidating biological functions.
  • AlphaFold2 excels at static structure prediction but misses dynamic functional states.
  • Existing methods for generating conformational ensembles lack interpretability and evolutionary insights.

Purpose of the Study:

  • To improve the generation of protein conformational ensembles.
  • To identify sequence patterns driving alternative protein fold predictions.
  • To integrate evolutionary signals into structural ensemble generation.

Main Methods:

  • Developed a refined clustering strategy combining protein language model representations with hierarchical clustering.
  • Applied the strategy to generate diverse sequence ensembles for protein families.
  • Utilized direct coupling analysis (DCA) on clustered alignments to identify coevolutionary signals.
  • Designed and validated stabilizing mutations using molecular dynamics and alchemical free energy calculations.

Main Results:

  • Successfully identified high-confidence alternative protein conformations.
  • Generated abundant sequence ensembles, enabling robust direct coupling analysis (DCA).
  • Uncovered key coevolutionary signals linked to specific protein folds.
  • Validated designed mutations for stabilizing distinct conformations.

Conclusions:

  • The refined clustering strategy enhances the interpretability of conformational ensembles.
  • The method effectively captures diverse protein conformational changes, including fold-switching.
  • Integrating evolutionary signals provides a powerful framework for understanding and manipulating protein dynamics.