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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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Conserved Binding Sites

Conserved Binding Sites

Ligand Binding Sites

Ligand Binding Sites

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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

Ligand Binding Sites

Ligand Binding Sites

Ligand Binding and Linkage

Ligand Binding and Linkage

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...

Ligand Binding and Linkage

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Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a

Trinh-Trung-Duong Nguyen¹, Duc-Khanh Nguyen², Yu-Yen Ou³

¹Computer science department of Yuan Ze University, Taiwan.

Briefings in Bioinformatics

|July 29, 2021

Summary

This study introduces a novel deep learning method using variational autoencoders (VAEs) to address imbalanced data in protein-ligand binding site prediction. The approach enhances model performance by generating synthetic minority class samples, improving prediction accuracy.

Keywords:

convolutional neural network data imbalance handlin gelectron transport proteins protein–ligand binding site prediction variational autoencoder

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Deep learning methods show promise for protein-ligand binding site prediction.
Existing methods often overlook data imbalance issues inherent in binding site prediction.
Traditional data balancing techniques may not be optimal for deep neural networks.

Purpose of the Study:

To develop a novel technique for balancing imbalanced datasets in protein-ligand binding site prediction.
To improve the performance of deep learning models on prediction tasks with imbalanced data.
To generate synthetic minority class samples using a variational autoencoder (VAE).

Main Methods:

A deep neural network-based variational autoencoder (VAE) was developed to learn nonlinear attributes of minority classes.
The trained VAE generated synthetic minority class samples to create a balanced dataset.
A convolutional neural network (CNN) was employed for classification on the balanced dataset.

Main Results:

The proposed method significantly improved sensitivity while maintaining high accuracy and specificity for FAD/FMN binding site prediction.
The VAE-based data balancing technique outperformed traditional methods like SMOTE, ADASYN, and class weight adjustment.
The models achieved superior performance compared to existing predictors for the studied binding types.

Conclusions:

The novel VAE-based data balancing technique effectively addresses data imbalance in protein-ligand binding site prediction.
This generalizable method can be applied to various prediction problems with significant data imbalances.
The approach offers a robust solution for enhancing deep learning model performance in bioinformatics.