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Protein-Drug Binding: Mechanism and Kinetics

Protein-Drug Binding: Mechanism and Kinetics

Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...

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...

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...

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...

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within 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:

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DeepKinome: predicción cuantitativa de la afinidad de unión de una quinasa por un compuesto utilizando un modelo de

Yeeun Lee¹, Jisu Eun², Jinhyuk Lee^2,3

¹Department of Genome Medicine and Science, Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.

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|December 19, 2025

Resumen

DeepKinome, un modelo de aprendizaje profundo, predice con precisión la afinidad de unión de las quinasas. Este avance ayuda a comprender la inhibición de las quinasas y a desarrollar nuevos fármacos mediante el análisis de complejas interacciones compuesto-proteína.

Palabras clave:

aprendizaje profundo inteligencia artificial explicable actividad de quinasa predicción de inhibición de quinasa moléculas pequeñas

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Área de la Ciencia:

Bioquímica
Biología Computacional
Descubrimiento de Fármacos

Sus antecedentes:

Las quinasas son cruciales para los procesos celulares y son objetivos clave en el desarrollo de fármacos.
La predicción de la afinidad de unión entre moléculas pequeñas y quinasas es compleja debido a datos intrincados.

Objetivo del estudio:

Desarrollar un modelo de aprendizaje profundo, DeepKinome, para predecir la afinidad cuantitativa de unión quinasa-compuesto.
Evaluar el rendimiento de DeepKinome frente a los modelos existentes de aprendizaje automático y aprendizaje profundo.

Principales métodos:

Se desarrolló un modelo de regresión de aprendizaje profundo basado en una red neuronal convolucional de 20 capas (DeepKinome).
El modelo se entrenó con datos de 234 quinasas y 163 compuestos de la base de datos L1000.
El rendimiento se evaluó utilizando el error cuadrático medio (RMSE), R-cuadrado (R2), el coeficiente de correlación de Pearson (PCC) y la relación del intervalo de aceptación (AIR).

Principales resultados:

DeepKinome demostró un rendimiento superior en comparación con cinco modelos de aprendizaje profundo y cuatro modelos de aprendizaje automático.
Se logró un RMSE de 1.157, R2 de 0.535, PCC de 0.743 y AIR de 0.570.
La IA explicable identificó secuencias clave de aminoácidos que influyen en las predicciones, correlacionándose con sitios conocidos de fosforilación de quinasas.

Conclusiones:

DeepKinome presenta un enfoque robusto para predecir la afinidad de unión de las quinasas.
El modelo mejora la comprensión de los mecanismos de inhibición de las quinasas y ayuda en el desarrollo de nuevas terapias.