Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods

Wang, Debby D.; Ou-Yang, Le; Xie, Haoran; Zhu, Mengxu; Yan, Hong

doi:10.1016/j.csbj.2020.02.007

Cited by 48 publications

(30 citation statements)

References 53 publications

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“…To gain a realistic model of protein-ligand interactions in solution, MD simulations can be used to gain multiple data points from a single simulation, for example, protein-ligand interactions (number of contacts, different interaction-energies), surface area, orientation, and distances. 108 These can thereafter be used to develop a ML model. On top of improving the performance of ML, extracting features from MD simulations might be useful to shed light on the factors which are affected to a higher degree by resistance mutations, providing a structural and physical understanding of the reasons behind the emergence of resistance.…”

Section: Machine Learningmentioning

confidence: 99%

Computational studies of protein–drug binding affinity changes upon mutations in the drug target

Friedman

2021

WIREs Comput Mol Sci

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Mutations that lead to drug resistance limit the efficacy of antibiotics, antiviral drugs, targeted cancer therapies, and other treatments. Accurately calculating protein-drug binding affinity changes upon mutations in the drug target is of high interest as this can yield a better understanding into how such mutations drive drug-resistance, especially when the mutation in question does not directly interfere with binding of the drug. The main aim of this article is to provide an up-to-date reference on the computational tools that are available for the calculation of Gibbs energy (free energy) changes upon mutation, their strengths, and limitations. The methods that are discussed include free energy calculations (free energy perturbation, thermodynamic integration, multistate Bennett acceptance ratio), analysis of molecular dynamics simulations (linear interaction energy, molecular mechanics [MM]/Poisson-Boltzmann solvated area, and MM/generalized Born solvated area), and methods that involve quantum mechanical calculations (including QM/MM). The possibility to use machine learning is also introduced. Given that the benefit of accurately calculating binding affinity changes upon mutation depends on comparing calculated values with experimental measurements, a brief survey on experimental methods and observables is provided. Examples of computational studies that go beyond calculating the Gibbs energy changes are given. Factors that need to be addressed by the computational chemist and potential pitfalls are discussed at length.

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Section: Machine Learningmentioning

confidence: 99%

Computational studies of protein–drug binding affinity changes upon mutations in the drug target

Friedman

2021

WIREs Comput Mol Sci

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“…Na þ and Clcounterions were added to neutralise system charges and then to the concentration of 0.154 molÁL À1 to simulate the physiological environment. Second, for each system, two rounds of energy minimizations were carried out for structural optimizations [29][30][31][32][33][34]. All atoms of RET protein and the inhibitor were restrained in the first round of minimisation while were without any constraints in the second round.…”

Section: All-atom MD Simulationsmentioning

confidence: 99%

Deciphering the resistance mechanism of RET kinase mutant against vandetanib and nintedanib using molecular dynamics simulations

Zheng

Liu

et al. 2021

Journal of Experimental Nanoscience

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The RET protein is a transmembrane receptor tyrosine kinase (RTK) whose oncogenic mutations or fusions are closely related to human cancers such as thyroid and non-small cell lung cancer. Vandetanib as a clinical-approved protein-tyrosine kinase inhibitor (TKI) exhibits anti-cancer efficacy by blocking the RET ATP-binding site, but drug resistance was observed for the RET G810A mutant. Recent studies have identified another TKI nintedanib as an effective molecule to inhibit vandetanib-resistant RET G810A . However, there is no clear evidence of why nintedanib and vandetanib displayed different inhibitory activities towards RET G810A . Here, we exploited molecular dynamic (MD) simulations to compare the interactions of the RET G810A mutant with nintedanib and vandetanib. A higher structural flexibility of the activation loop was observed in the nintedanib-bound RET G810A , which may result in discrepant autophosphorylation activity in the nintedanib-and vandetanib-bound RET kinase, causing differentiated pharmacological effects of the two compounds. Molecular mechanics/Poisson-Bolzmann surface area method suggested that nintedanib had a higher affinity towards RET G810A over vandetanib, accounting for its better inhibitory effect as an ATP-competitive compound. These results depicted the underlying mechanism for the different inhibitory efficacy of nintedanib and vandetanib on RET G810A from both conformational and energetic aspects. Furthermore, we also found that both compounds maintained the 'DFG-in, aC-helix-in, and activation loopopen' conformation of RET G810A , which is the characteristic of the active state. Together, our results provide comprehensive mechanistic insights into nintedanib's capability in inhibiting vandetanibresistant RET mutant and enlighten future structural-based optimisation of RET TKIs to overcome drug resistance.

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“…Although most approved kinase inhibitors are ATP-competitive, they can be sensitive to different changes of the residue environment brought by different mutations, as each of them can have distinctive interactions with the kinase based on their favourable binding poses: (1) type I inhibitors bind to the active kinase (DFG-in), mainly occupying the hinge region where the adenine ring of the ATP binds; (2) type II inhibitors bind to the inactive kinase (DFG-out), extending to a hydrophobic back pocket while maintaining interactions with the ATP binding site. Apart from altering the drug affinity directly through the local atomic changes, mutations can also impact protein stability as well as dynamics, which may trigger conformational changes and impact drug recognition and interactions [7] . Moreover, these unknown structural changes may even cause favorable interactions with the endogenous ATP rather than the drugs, which could lead to the loss of drug competency and off-target effects.…”

Section: Introductionmentioning

confidence: 99%

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase

Zhou

Portelli

Pat

et al. 2021

Computational and Structural Biotechnology Journal

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Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods

Cited by 48 publications

References 53 publications

Computational studies of protein–drug binding affinity changes upon mutations in the drug target

Computational studies of protein–drug binding affinity changes upon mutations in the drug target

Deciphering the resistance mechanism of RET kinase mutant against vandetanib and nintedanib using molecular dynamics simulations

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase

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