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Related Concept Videos

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Updated: Sep 15, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Interpretable protein-DNA interactions captured by structure-sequence optimization.

Yafan Zhang1, Irene Silvernail2, Zhuyang Lin1

  • 1Bioinformatics Research Center, North Carolina State University, Raleigh, United States.

Elife
|July 17, 2025
PubMed
Summary
This summary is machine-generated.

A new computational model, Interpretable protein-DNA Energy Associative (IDEA), accurately predicts DNA recognition sites and binding affinities for proteins. This tool aids in understanding gene regulation and reduces experimental effort.

Keywords:
data-driven modelinggenomic binding sites predictionsmolecular biophysicsnoneprotein-DNA binding affinity predictionsequence-specific simulationstructural biologystructure-sequence integration

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Area of Science:

  • Biophysics
  • Computational Biology
  • Genomics

Background:

  • Sequence-specific DNA recognition is crucial for gene regulation.
  • Current methods lack simultaneous prediction of DNA binding sites and affinities.

Purpose of the Study:

  • To develop a residue-level, interpretable biophysical model for predicting protein-DNA binding sites and affinities.
  • To enable direct interpretation of physicochemical interactions in protein-DNA complexes.

Main Methods:

  • Developed the Interpretable protein-DNA Energy Associative (IDEA) model by fusing protein-DNA complex structures and sequences.
  • Integrated the IDEA model into a coarse-grained simulation framework.

Main Results:

  • The IDEA model accurately predicts DNA recognition sites and binding strengths across diverse protein families.
  • The model quantitatively captures absolute protein-DNA binding free energies.
  • Demonstrated interpretability of physicochemical interactions at the amino acid and nucleotide level.

Conclusions:

  • The IDEA model offers an integrated computational platform for assessing DNA recognition.
  • This approach alleviates experimental costs and biases.
  • Facilitates mechanistic studies of various DNA-recognition processes.