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Related Experiment Video

Updated: Dec 27, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Sequence-Based Prediction of Fuzzy Protein Interactions.

Marton Miskei¹, Attila Horvath², Michele Vendruscolo³

¹MTA-DE Laboratory of Protein Dynamics, Department of Biochemistry and Molecular Biology, University of Debrecen, Hungary.

Journal of Molecular Biology

|March 1, 2020

Summary

Disordered proteins can bind in multiple ways, remaining disordered or folding upon interaction. New methods predict these fuzzy protein interactions based on amino acid sequences.

Keywords:

disordered proteins folding upon binding fuzzy complexes fuzzy interactions protein binding

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Last Updated: Dec 27, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Published on: January 26, 2024

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Published on: July 25, 2013

Area of Science:

Biochemistry and Molecular Biology
Structural Biology
Computational Biology

Background:

Intrinsically disordered proteins (IDPs) are recognized for their dynamic nature and ability to adopt diverse binding modes.
Beyond folding-upon-binding (disorder-to-order), IDPs can remain disordered (disorder-to-disorder) or exhibit context-dependent binding with partners.
Understanding these varied interaction mechanisms is crucial for deciphering protein function.

Purpose of the Study:

To investigate the sequence determinants underlying different disordered protein binding modes.
To develop a predictive method for disordered protein interaction types based solely on amino acid sequences.

Main Methods:

Assembly of three datasets covering disorder-to-order, context-dependent, and disorder-to-disorder transitions.
Analysis of 828 protein regions within 2157 protein complexes.
Development of the FuzPred computational method for predicting binding modes.

Main Results:

Fuzzy interactions are linked to local amino acid sequence compositions that favor structural diversity.
The FuzPred method accurately predicts binding modes of disordered proteins from their sequences.
Protein sequences encode the capacity for various conformational changes upon binding.

Conclusions:

Local sequence composition is a key determinant of disordered protein binding modes.
FuzPred offers a novel, partner-independent approach to predict disordered protein interactions.
This work highlights the sequence-encoded plasticity of disordered proteins in mediating diverse functional interactions.