Computational analysis for residue-specific CDK2-inhibitor bindings

Yang, Yun; Liu, He; Bao, Jing Xiao; Qi, Yi; Zhang, John Z. H.

doi:10.1063/1674-0068/cjcp1901012

Cited by 10 publications

(5 citation statements)

References 43 publications

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“…There are two types of computational methods that are widely used to evaluate protein–ligand binding affinity. One type is based on molecular dynamics simulation and statistical analysis of conformations sampled from MD simulation, and examples include MM-PB/GBSA, − FEP, − TI, − ASIE, − and so forth. These methods are computationally expensive, which is usually used in lead optimization and postdocking analysis.…”

Section: Introductionmentioning

confidence: 99%

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking

Pan

Wang

Zhang

et al. 2022

J. Chem. Inf. Model.

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The protein−ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein−ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, πstacking, π-cation, and metal−ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein−ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/ xundrug/AA-Score-Tool.

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Section: Introductionmentioning

confidence: 99%

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking

Pan

Wang

Zhang

et al. 2022

J. Chem. Inf. Model.

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“…Recently we developed a method called interaction entropy (IE) for practical and efficient calculation of entropy in protein-ligand and protein-protein binding (Duan et al, 2016). This method has been used in combination with alanine scanning (Massova and Kollman, 1999) (AS) and MM/GBSA to obtain the residue-specific contribution of each pocket residue (ASE method) (Yan et al, 2017;Liu et al, 2018;Qiu et al, 2018;Zhou et al, 2018;He et al, 2019;Yang et al, 2019;Wang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Computational Analysis of Residue-Specific Binding Free Energies of Androgen Receptor to Ligands

Shao

Bao

Pan

et al. 2021

Front. Mol. Biosci.

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Androgen receptor (AR) is an important therapeutic target for the treatment of diseases such as prostate cancer, hypogonadism, muscle wasting, etc. In this study, the complex structures of the AR ligand-binding domain (LBD) with fifteen ligands were analyzed by molecular dynamics simulations combined with the alanine-scanning-interaction-entropy method (ASIE). The quantitative free energy contributions of the pocket residues were obtained and hotspot residues are quantitatively identified. Our calculation shows that that these hotspot residues are predominantly hydrophobic and their interactions with binding ligands are mainly van der Waals interactions. The total binding free energies obtained by summing over binding contributions by individual residues are in good correlation with the experimental binding data. The current quantitative analysis of binding mechanism of AR to ligands provides important insight on the design of future inhibitors.

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“…Recently, we developed a new method named interaction entropy (IE) that can effectively and accurately obtain the entropy contribution of the binding free energy (Duan et al., 2016). In addition, we combined the alanine scanning (AS) and IE method to calculate the contribution of each pocket residue and identify the hotspot residues in protein–ligand binding (He et al., 2019; Liu, Peng, et al., 2018; Qiu et al., 2018; Wang et al., 2020; Yan et al., 2017; Yang et al., 2019; Zhou, Liu, et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the binding modes of the first‐ and second‐generation antiandrogens with respect to F876L mutation

et al. 2021

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Androgen receptor (AR) is an important target for the treatment of prostate cancer, and mutations in the AR have an important impact on the resistance of existing drugs. In this work, we performed molecular dynamics simulations of the existing marketed antiandrogens flutamide, nilutamide, bicalutamide, enzalutamide, apalutamide, darolutamide, and its main metabolite ORM15341 in complex with the wild‐type and F876L mutant AR. We calculated the residue‐specific binding free energy contribution of the wild‐type and mutant ARs with the AS‐IE method and analyzed the hotspot residues and the binding free energy contributions of specific residues before and after the mutation. In addition, we analyzed the total binding obtained by adding residue binding energy contributions and compared the results with experimental values. The obtained residue‐specific binding information should be very helpful in understanding the mechanism of drug resistance with respect to specific mutations and in the design of new generation drugs against possible new mutations.

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Computational analysis for residue-specific CDK2-inhibitor bindings

Cited by 10 publications

References 43 publications

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking

Computational Analysis of Residue-Specific Binding Free Energies of Androgen Receptor to Ligands

Analysis of the binding modes of the first‐ and second‐generation antiandrogens with respect to F876L mutation

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