Public Data Set of Protein–Ligand Dissociation Kinetic Constants for Quantitative Structure–Kinetics Relationship Studies

Liu, Huisi; Su, Minyi; Lin, Haixia; Wang, Renxiao; Li, Yan

doi:10.1021/acsomega.2c02156

Cited by 11 publications

(23 citation statements)

References 41 publications

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“…A user can perform a direct search within the Anabel’s KOFFI database and evaluate the quality of their binding data. PDBbind was initially developed for collecting binding affinity data and complex structures for developing docking score. In 2022, it released a subdatabase ( k off set) containing 169 entries of protein-small molecule dissociation rates.…”

Section: Available Experimental Techniques To Measure Binding Kineticsmentioning

confidence: 99%

“…Many experimental binding kinetic rates have been collected in different publicly accessible databases. A number of databases as listed in Table 1 are useful for exploring biomolecular binding kinetics, including the kinetic data of biomolecular interactions (KDBI), 53 BindingDB, 54 kinetics of featured interactions (KOFFI), 55 PDBbind, 56 structural database of kinetics and energetics of mutant protein interactions (SKEMPI), 57 kinetic and thermodynamic database of mutant protein interactions (dbMPIKT), 58 and so on. 8,59 KDBI 53 is developed to provide experimentally verified binding kinetic rates for interactions involving proteins and nucleic acids (RNA and DNA).…”

Section: Available Experimental Techniques To Measure Binding Kineticsmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Biomolecular Binding Kinetics: A Review

Wang

Hung

Koirala

et al. 2023

J. Chem. Theory Comput.

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Biomolecular binding kinetics including the association (k on ) and dissociation (k of f ) rates are critical parameters for therapeutic design of smallmolecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

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Section: Available Experimental Techniques To Measure Binding Kineticsmentioning

confidence: 99%

Section: Available Experimental Techniques To Measure Binding Kineticsmentioning

confidence: 99%

Predicting Biomolecular Binding Kinetics: A Review

Wang

Hung

Koirala

et al. 2023

J. Chem. Theory Comput.

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“…The Fourier space part of the Ewald splitting was computed by using the particle mesh Ewald method, 79 with a grid length of 0.12 nm on the side and a cubic spline interpolation. The MD simulation package used is Gromacs 2019. supporting information of two literatures by Amangeldiuly et al 47 and Liu et al 54 The GEM, MPG, RDKit, and RPM features and the simulated retention time values in csv format can be downloaded from the Supporting Information.…”

Section: Ramd Simulations and Ifpsmentioning

confidence: 99%

“…Figure 2. −log(k off ) prediction on the new 38 inhibitors of N-HSP90 from the independent database (dataset B) 54. The Pearson correlation coefficient (r P ) is 0.73, and the mean square error (MSE) is 0.64.…”

mentioning

confidence: 99%

Structure–Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation

et al. 2023

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Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (k off) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein–ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the k off value. We observed a high correlation among the simulated, predicted, and experimental −log(k off) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our k off predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental k off values and are not in our ML training dataset. The predicted k off values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict k off of other proteins and will enhance the kinetics-based drug design endeavor.

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“…2 Models and methods 2.1 Systems setup DOT1L prepared models complexed with SAH, EPZ003696, SYC-522, FED2, EPZ004777, SGC0946, and SGC0947 were obtained from the recent Quantitative Structure−Kinetics Relationship (QSKR) study carried out by Li and coworkers (Liu et al, 2022). EPZ003647 was docked with LeDock (Wang et al, 2016) in the 4EKI prepared model using a 30 × 30 × 30 Å 3 grid box size centered in G163.…”

Section: Introductionmentioning

confidence: 99%

Determination of nucleoside DOT1L inhibitors’ residence times by τRAMD simulations

Flores-León

Colorado-Pablo²,

Santos-Contreras³

et al. 2023

Front. Drug. Discov.

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Human epigenetic enzyme disruptor of telomeric silencing 1-like (DOT1L) is a key drug target for treating acute myeloid leukemia. Several nucleoside and non-nucleoside DOT1L inhibitors have been developed to inhibit its histone methyltransferase activity. Non-mechanism-based nucleoside DOT1L inhibitors have shown good inhibitory activity and high on-target residence times. Previous computational studies have explored the dynamic behavior of this group of molecules on DOT1L to design compounds with enhanced binding affinities. Nevertheless, it is well known that drug-target kinetics also plays a crucial role in the discovery of new drugs. Therefore, we performed τ-Random Acceleration Molecular Dynamics (τRAMD) simulations to estimate the residence times of nucleoside DOT1L inhibitors. The high correlation between the calculated and experimental residence times suggested that the method can reliably estimate the residence time of nucleoside DOT1L inhibitors when modifications are made to those substituents that occupy the buried hydrophobic pocket of the active site, exhibit hydrophobic interactions with F245 or that form H-bonds with D161 and G163. Overall, this study will be a step toward understanding the binding kinetics of nucleoside DOT1L inhibitors for the treatment of acute myeloid leukemia.

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Public Data Set of Protein–Ligand Dissociation Kinetic Constants for Quantitative Structure–Kinetics Relationship Studies

Cited by 11 publications

References 41 publications

Predicting Biomolecular Binding Kinetics: A Review

Predicting Biomolecular Binding Kinetics: A Review

Structure–Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation

Determination of nucleoside DOT1L inhibitors’ residence times by τRAMD simulations

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