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Related Concept Videos

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

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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Molecular Modeling of Protein-Peptide Complexes Mimicking Factor Xa-Prothrombin Using AlphaFold-Multimer and

Dejvid Veizaj1, David A Poole2, Ka Lei Cheung1

  • 1Department of Internal Medicine, Division of Thrombosis and Hemostasis, Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands.

Journal of Chemical Information and Modeling
|May 23, 2026
PubMed
Summary

Predicting how mutations affect protease activity is key for engineering proteins and understanding blood clotting. This study uses computational modeling and simulations to reveal how Factor Xa recognizes its substrate and how mutations impact this interaction.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Predicting protease activity changes due to mutations is challenging for protein engineering and coagulation disorders.
  • Factor Xa (FXa) is crucial for thrombin generation, but its direct enzyme-substrate recognition and the effects of pathogenic variants are not fully understood.

Purpose of the Study:

  • To investigate Factor Xa-substrate interactions using computational methods.
  • To understand the impact of mutations on FXa activity and substrate recognition.

Main Methods:

  • Combined AlphaFold-Multimer modeling with explicit-solvent molecular dynamics (MD) simulations.
  • Analyzed prothrombin-derived peptides representing FXa cleavage sites (Arg271 and Arg320).
  • Performed free energy analyses and experimental validation with recombinant FXa variants.

Main Results:

  • AlphaFold identified catalytically relevant peptide orientations.
  • MD simulations and free energy analyses revealed the importance of P1 and P4 residues for FXa binding.
  • Identified distinct stability and affinity differences between the two cleavage sites.
  • Showed that active site variants associated with factor X deficiency disrupt peptide engagement in a variant-specific manner.

Conclusions:

  • Established an integrative computational-experimental framework for studying protease-substrate recognition.
  • Rationalized the effects of disease-causing mutations on FXa activity.
  • Provided insights for designing therapeutic protease variants.