Advances in computational methods for ligand binding kinetics

Sohraby, Farzin; Nunes-Alves, Ariane

doi:10.1016/j.tibs.2022.11.003

Cited by 26 publications

(25 citation statements)

References 75 publications

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“…For example, Li et al optimized the donepezil drug to compound 12 through adding two F atoms to decrease the dissociation rate from its target acetylcholinesterase, which demonstrated significantly improved efficacy and a lower effective dose than that of donepezil. With remarkable theoretical and technical developments, increasing numbers of experimental and computational methods are available for calculating the biomolecular binding kinetic rates. ,,− However, it remains challenging for both experimental and computational approaches to accurately predict biomolecular binding kinetic rates with high throughput.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Predicting Biomolecular Binding Kinetics: A Review

Wang

Hung

Koirala

et al. 2023

J. Chem. Theory Comput.

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“…However, we have observed marked sensitivity of the stability of open states to the water model used, making their choice important when ligand (un)binding kinetics is of interest, e.g., when estimating residence times of inhibitors within the drug discovery frameworks. [68][69][70] Given the overestimated diffusivity of bulk TIP3P and its unrealistic migration rates via aquaporin AQP1, we believe the OPC water model represents a better choice under these circumstances. Nonetheless, considering the aforementioned points, we suggest that the 3-point TIP3P model could still be of use for the investigation of network topology and geometry in scenarios where available computational resources may prohibit the use of the more costly 4-point OPC model.…”

Section: Discussionmentioning

confidence: 99%

Impact of water models on structure and dynamics of enzyme tunnels

Agrawal

Brezovský

2023

Preprint

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Protein hydration plays a vital role in many biological functions. Molecular simulations are frequently used to study the effect of hydration on proteins at the atomic level. However, the accuracy of these simulations has often been highly sensitive to the water model used, perhaps best known in the case of intrinsically disordered proteins. In the present study, we have investigated to what extent the choice of a water model alters the behavior of complex networks of transport tunnels, which are critical for function of many enzymes with buried active sites. By performing all-atom molecular dynamics simulations of the haloalkane dehalogenase LinBWT and its two variants, LinB32 and LinB86, with synthetically engineered tunnel networks in TIP3P and OPC water models, we investigated their effects on the overall tunnel topology, properties of the main tunnels such as their conformation, residue composition, and duration of their open states. Our data showed that while all three proteins exhibited similar conformational behavior in both water models, they differed in the duration of openings of their main tunnels and, in limited cases, also in the properties of their auxiliary tunnels. Interestingly, the results indicate that the stability of the open tunnels is sensitive to the water model, rendering the generally more accurate OPC water model a preferred choice here, particularly when the kinetics of the ligand transport process is under question. However, since the TIP3P model can provide comparable inference on the overall topology of the networks of primary tunnels and their geometry, it may still be a relevant option when computational resources are limited.

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“…This is a significant deviation compared to the chemically available variation in k within a compound library, which at best covers 4 orders of magnitude (see, e.g., the range of Hsp90-binding compounds available in refs , , , and ) and about 2 orders of magnitude for compounds with the same chemical scaffold for the A 2 adenosine receptor . A discrepancy between predicted and measured k by a factor of ∼10 can already be considered a success . Only a few methods, notably infrequent metadynamics, dcTMD, and knowledge-biased methods, currently come close to or within a desired factor of ∼1.5.…”

Section: Current Challenges In Rate Calculationsmentioning

confidence: 99%

“…Consequentially, a large number of rate-predicting methods have been developed within the past ∼10 years and have been applied to an equally large number of different systems. The progress on the field has been tracked in a series of excellent reviews, ,− and an application-oriented comparison of methods can be found in a respective chapter of ref . In the following I will outline a theoretical basis of rate prediction, assess the current state-of-the-art of the field in general, focus on the challenges our community faces, and report on how my co-workers and I try to contribute to both predicting protein–ligand binding and unbinding kinetics and to understanding their microscopic basis via dissipation-corrected targeted MD. , …”

Section: Introductionmentioning

confidence: 99%

Predicting Protein–Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges

Wolf

2023

J. Chem. Inf. Model.

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The prediction of drug−target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

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Advances in computational methods for ligand binding kinetics

Cited by 26 publications

References 75 publications

Predicting Biomolecular Binding Kinetics: A Review

Predicting Biomolecular Binding Kinetics: A Review

Impact of water models on structure and dynamics of enzyme tunnels

Predicting Protein–Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges

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