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Conserved Binding Sites

Conserved Binding Sites

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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Ligand Binding Sites

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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.
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Protein-protein Interfaces

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: Jul 30, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

An updated dataset and a structure-based prediction model for protein-RNA binding affinity.

Xu Hong¹, Xiaoxue Tong¹, Juan Xie¹

¹School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.

|May 15, 2023

Summary

This study developed a computational model to predict protein-RNA binding affinity, essential for understanding cellular regulation. The model accurately predicts binding affinities, offering a faster alternative to experimental methods.

Keywords:

binding affinity feature selection protein-RNA interaction regression model structural features

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Structural biology
Biophysics
Computational biology

Background:

Protein-RNA interactions are crucial for cellular regulatory networks.
Experimental determination of binding affinity is time-consuming and labor-intensive.
Accurate binding affinity data is vital for understanding protein-RNA interaction mechanisms.

Purpose of the Study:

To develop a computational model for predicting protein-RNA binding affinity.
To provide a faster and more efficient alternative to experimental methods.
To enhance the understanding of protein-RNA interaction mechanisms.

Main Methods:

Updated the protein-RNA binding affinity benchmark (PRBAB) dataset to 145 complexes.
Extracted and selected representative structural features from complex structures.
Trained a regression model using selected features and experimental binding affinity data.

Main Results:

The model achieved a Pearson correlation coefficient (r) of 0.57 and RMSE of 2.51 kcal/mol on the PDBbind dataset.
Performance improved to r=0.7 upon excluding complexes with modified residues/nucleotides and metal ions.
On the ProNAB dataset, the model achieved r=0.61 and RMSE of 1.56 kcal/mol.

Conclusions:

The developed computational model shows promise for predicting protein-RNA binding affinity.
The model offers a valuable tool for structural biology and understanding regulatory mechanisms.
Further refinement, particularly regarding modified residues and metal ions, can enhance predictive accuracy.