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Affinity and Avidity

Affinity and Avidity

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...

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 analyses the...

The Equilibrium Binding Constant and Binding Strength

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:

Affinity Chromatography

Affinity Chromatography

Affinity chromatography is a powerful technique extensively utilized for separating and purifying specific biomolecules from complex mixtures. It capitalizes on the highly selective binding between an analyte and its counterpart, such as antibody-antigen interactions. The counterpart is immobilized on the stationary phase, forming an affinity column. The stationary phase typically consists of solid support, such as agarose or porous glass beads, immobilizing the affinity ligand. The mobile...

Protein-Drug Binding: Determination Methods

Protein-Drug Binding: Determination Methods

Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...

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

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Comparison of methods for chemical-compound affinity prediction.

¹Central Research Laboratory, Hitachi Ltd, 1-280 Higashi-koigakubo Kokubunj, Tokyo 185-8601, Japan. asako.koike.ea@hitachi.com

SAR and QSAR in Environmental Research

|October 5, 2006

Summary

This study compares feature selection and classification methods for predicting chemical compound-protein binding affinities. The genetic algorithm combined with Random Forests or ensemble methods proved superior for accurate virtual screening.

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Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Accurate prediction of chemical compound-protein binding affinities is crucial for effective virtual screening.
Selecting optimal molecular descriptors and classification algorithms significantly impacts prediction accuracy.

Purpose of the Study:

To compare the performance of various feature selection methods and classifiers for predicting compound-protein binding affinities.
To identify the most effective strategies for enhancing virtual screening accuracy.

Main Methods:

Evaluated multiple feature selection techniques, including the genetic algorithm.
Assessed various classification algorithms such as Random Forests, Adaboosts, Baggings, and support-vector machines.
Utilized six diverse compound series targeting different proteins (e.g., cytochrome P450 2C9, estrogen receptor).

Main Results:

The genetic algorithm outperformed other feature selection methods.
Combinations of the genetic algorithm with Random Forests, Adaboosts, or Baggings achieved performance comparable to support-vector machines and superior to other classifiers.
Selected descriptors were plausible and aided in model interpretation.

Conclusions:

The genetic algorithm is a highly effective feature selection method for predicting compound-protein binding.
Ensemble methods and Random Forests, when combined with the genetic algorithm, offer robust classification performance for virtual screening.
The study provides valuable insights into optimizing computational approaches for drug discovery.