Dataset Information

Prediction of Drug-Target Binding Kinetics by Comparative Binding Energy Analysis.

ABSTRACT: A growing consensus is emerging that optimizing the drug-target affinity alone under equilibrium conditions does not necessarily translate into higher potency in vivo and that instead binding kinetic parameters should be optimized to ensure better efficacy. Therefore, in silico methods are needed to predict the kinetic parameters and the mechanistic determinants of drug-protein binding. Here we demonstrate the application of COMparative BINding Energy (COMBINE) analysis to derive quantitative structure-kinetics relationships (QSKRs) for the dissociation rate constants (k off) of inhibitors of heat shock protein 90 (HSP90) and HIV-1 protease. We derived protein-specific scoring functions by correlating k off rate constants with a subset of weighted interaction energy components determined from the energy-minimized structures of drug-protein complexes. As the QSKRs derived for these sets of chemically diverse compounds have good predictive ability and provide insights into important drug-protein interactions for optimizing k off, COMBINE analysis offers a promising approach for binding kinetics-guided lead optimization.

SUBMITTER: Ganotra GK

PROVIDER: S-EPMC6231175 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Prediction of Drug-Target Binding Kinetics by Comparative Binding Energy Analysis.

Ganotra Gaurav K GK Wade Rebecca C RC

ACS medicinal chemistry letters 20181004 11

A growing consensus is emerging that optimizing the drug-target affinity alone under equilibrium conditions does not necessarily translate into higher potency in vivo and that instead binding kinetic parameters should be optimized to ensure better efficacy. Therefore, in silico methods are needed to predict the kinetic parameters and the mechanistic determinants of drug-protein binding. Here we demonstrate the application of COMparative BINding Energy (COMBINE) analysis to derive quantitative st ...[more]

PMID: 30429958

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Project description:Background Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug–target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing explanations to the decision-making process of deep learning architectures. In this research study, we explore the reliability of convolutional neural networks (CNNs) at identifying relevant regions for binding, specifically binding sites and motifs, and the significance of the deep representations extracted by providing explanations to the model’s decisions based on the identification of the input regions that contributed the most to the prediction. We make use of an end-to-end deep learning architecture to predict binding affinity, where CNNs are exploited in their capacity to automatically identify and extract discriminating deep representations from 1D sequential and structural data. Results The results demonstrate the effectiveness of the deep representations extracted from CNNs in the prediction of drug–target interactions. CNNs were found to identify and extract features from regions relevant for the interaction, where the weight associated with these spots was in the range of those with the highest positive influence given by the CNNs in the prediction. The end-to-end deep learning model achieved the highest performance both in the prediction of the binding affinity and on the ability to correctly distinguish the interaction strength rank order when compared to baseline approaches. Conclusions This research study validates the potential applicability of an end-to-end deep learning architecture in the context of drug discovery beyond the confined space of proteins and ligands with determined 3D structure. Furthermore, it shows the reliability of the deep representations extracted from the CNNs by providing explainability to the decision-making process. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04767-y.

Dataset Information

Prediction of Drug-Target Binding Kinetics by Comparative Binding Energy Analysis.

Publications

Prediction of Drug-Target Binding Kinetics by Comparative Binding Energy Analysis.

Similar Datasets

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