Novel method to identify group-specific non-catalytic pockets of human kinome for drug design

Wang, Huiwen; Guan, Zeyu; Qiu, Jiadi; Jia, Ya; Zeng, Chen; Zhao, Yunjie

doi:10.1039/c9ra07471f

Cited by 8 publications

(4 citation statements)

References 62 publications

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“…The residues of proteins that interact with ligands are called binding sites or binding pockets [ 13 , 14 ]. As shown in Figure 1C , the binding pocket refers to a cavity or groove on the surface of the target protein, where the ligand binds and interacts with the protein.…”

Section: Introductionmentioning

confidence: 99%

Prediction of protein–ligand binding affinity via deep learning models

Wang

2024

Briefings in Bioinformatics

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Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein–ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein–ligand binding affinity. We start with a brief introduction to protein–ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein–ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein–ligand binding affinity via DL models.

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Section: Introductionmentioning

confidence: 99%

Prediction of protein–ligand binding affinity via deep learning models

Wang

2024

Briefings in Bioinformatics

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“…31 The protein pocket used in the Pafnucy model is the local structure of the protein targeted by a ligand. [32][33][34] Pafnucy thus is hard to learn ''knowledge'' about long-range indirect interactions between protein-ligand pairs. Topology-Net has represented a 3D protein-ligand structure as one dimensional (1D) topological fingerprints used as input features to predict the protein-ligand binding affinity.…”

Section: Introductionmentioning

confidence: 99%

DLSSAffinity: protein–ligand binding affinity prediction via a deep learning model

Wang

Liu

Ning

et al. 2022

Phys. Chem. Chem. Phys.

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“…For example, the SARS-CoV-2 S protein stabilizes the virus RNA and enhances virus translation by hijacking the host factor IGF2BP1 [8]. Thus, the RNA-protein structural pocket information helps understand biological processes and drug design [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

RPpocket: An RNA–Protein Intuitive Database with RNA Pocket Topology Resources

Rui

Liu

Yang

et al. 2022

IJMS

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RNA–protein complexes regulate a variety of biological functions. Thus, it is essential to explore and visualize RNA–protein structural interaction features, especially pocket interactions. In this work, we develop an easy-to-use bioinformatics resource: RPpocket. This database provides RNA–protein complex interactions based on sequence, secondary structure, and pocket topology analysis. We extracted 793 pockets from 74 non-redundant RNA–protein structures. Then, we calculated the binding- and non-binding pocket topological properties and analyzed the binding mechanism of the RNA–protein complex. The results showed that the binding pockets were more extended than the non-binding pockets. We also found that long-range forces were the main interaction for RNA–protein recognition, while short-range forces strengthened and optimized the binding. RPpocket could facilitate RNA–protein engineering for biological or medical applications.

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Novel method to identify group-specific non-catalytic pockets of human kinome for drug design

Abstract: Kinase proteins have been intensively investigated as drug targets for decades because of their crucial involvement in many biological pathways. We developed hybrid approach to identify non-catalytic pockets and will benefit the kinome drug design.

Cited by 8 publications

References 62 publications

Prediction of protein–ligand binding affinity via deep learning models

Prediction of protein–ligand binding affinity via deep learning models

DLSSAffinity: protein–ligand binding affinity prediction via a deep learning model

RPpocket: An RNA–Protein Intuitive Database with RNA Pocket Topology Resources

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