Enhanced Sampling Aided Design of Molecular Photoswitches

Raucci, Umberto; Sanchez, David M.; Martı́nez, Todd J.; Parrinello, Michele

doi:10.1021/jacs.2c04419

Cited by 14 publications

(15 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An unweighted average of Gaussian kernels is used to estimate on-the-fly the well-tempered distribution , whose explorative power is regulated by γ E . The resulting bias potential V E ( s ) is more coarse than its corresponding OPES one and does not converge as rapidly to a quasi-static one, allowing an augmented exploration 26,55,59 . As we will see below, a gradual introduction of OPES Explore in the replicas is beneficial to the exchange rate between replicas, therefore we progressively increase the intensity of the OPES explore bias through the replicas, making sure that it always remains a secondary one and does not overcome the maximum value of the dominant bias V O ( s ).…”

Section: Methodsmentioning

confidence: 99%

OneOPES, a combined enhanced sampling method to rule them all

Rizzi

Aureli

Ansari

et al. 2023

Preprint

View full text Add to dashboard Cite

Enhanced sampling techniques have revolutionised molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a system's dynamics and recover the original system's statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems. Another category of techniques, such as parallel tempering, simulates multiple replicas of the system in parallel, without requiring CVs. However, these methods may explore less relevant high-energy portions of the phase space and become computationally expensive for large systems. To overcome the limitations of both approaches, we propose a replica exchange method called OneOPES that combines the power of multi-replica simulations and CV-based enhanced sampling. This method efficiently accelerates the crossing of both enthalpic and entropic barriers without the need for optimal CVs definition, parameters fine tuning nor the use of a large number of replicas, as demonstrated by its successful applications to protein-ligand binding and protein folding benchmark systems. Our approach shows promise as a new direction in the development of enhanced sampling techniques for molecular dynamics simulations, providing an efficient and robust framework for the study of complex and unexplored problems.

show abstract

Section: Methodsmentioning

confidence: 99%

OneOPES, a combined enhanced sampling method to rule them all

Rizzi

Aureli

Ansari

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The determination of CVs appropriate for use in the present context is the second set of tools. To promote enzymatic reactions in a blind way, we used a CV derived from spectral graph theory. , In particular, we represent a molecule as a graph whose vertices and edges are its atoms and chemical bonds, respectively, and the CV is the maximum eigenvalue (λ max ) of the symmetric adjacency matrix associated with the graph. , When used in the OPES context, this CV has proved capable of both reproducing all known reaction pathways in challenging photomechanical switches and atmospheric oxidation reactions and discovering several completely novel and relevant pathways . To promote the sampling of reactive conformations and compute the associated energetics, we used machine learning-based CVs also trained to account for the water arrangement inside the enzymatic cavity.…”

Section: Introductionmentioning

confidence: 99%

“…22,23 When used in the OPES context, 24 this CV has proved capable of both reproducing all known reaction pathways in challenging photomechanical switches and atmospheric oxidation reactions and discovering several completely novel and relevant pathways. 25 To promote the sampling of reactive conformations and compute the associated energetics, we used machine learning-based CVs also trained to account for the water arrangement inside the enzymatic cavity. These CVs combined with the OPES method have been successfully applied to evaluate complex free energy landscapes.…”

Section: ■ Introductionmentioning

confidence: 99%

How and When Does an Enzyme React? Unraveling α-Amylase Catalytic Activity with Enhanced Sampling Techniques

et al. 2023

Self Cite

View full text Add to dashboard Cite

Enzymatic catalysis is a complex process that can involve multiple conformations of the enzyme:substrate complex and several competitive reaction pathways, resulting in a multi-dimensional free energy landscape. The study of enzymatic activity often requires deep knowledge of the system to establish the catalytic mechanism and identify the possible reactive conformations of the complex. Here, we present an enhanced sampling and machine learning-based approach to explore the catalytic reaction space and characterize the transformation from reactive to non-reactive conformations with minimal a priori knowledge of the system. We applied this approach to study the rate-determining step of the glycolysis reaction of maltopentose catalyzed by human pancreatic α-amylase, an important enzyme in glucose production as well as a major drug target for the treatment of type-II diabetes. We unravel the complexity of the enzymatic reaction, reveal three binding modes of the substrate within the active site, and highlight the role of water in the catalytic process and in the stepwise conversion of reactionready to non-reactive conformations. Overall, these insights offer atomistic details on the catalytic mechanism and dynamics of the active site, allowing one to shed light on two fundamental questions in enzymatic catalysis, that is how and when does an enzyme react?

show abstract

“…Quantifying the standard binding free energies of protein–ligand and protein–protein complexes by molecular dynamics (MD) simulations has been attracting great interest in rational drug design. − Although alchemical transformations, employing, for instance, free-energy perturbation (FEP), , have successfully predicted the binding affinities of small molecules toward proteins, they suffer from poor convergence in the event of large perturbations, precluding their use for protein–protein complexes.…”

Section: Introductionmentioning

confidence: 99%

Improving Speed and Affordability without Compromising Accuracy: Standard Binding Free-Energy Calculations Using an Enhanced Sampling Algorithm, Multiple-Time Stepping, and Hydrogen Mass Repartitioning

Blazhynska

Lacam

Chen

et al. 2023

J. Chem. Theory Comput.

View full text Add to dashboard Cite

Accurate evaluation of protein−ligand binding free energies in silico is of paramount importance for understanding the mechanisms of biological regulation and providing a theoretical basis for drug design and discovery. Based on a series of atomistic molecular dynamics simulations in an explicit solvent, using well-tempered metadynamics extended adaptive biasing force (WTM-eABF) as an enhanced sampling algorithm, the so-called "geometrical route" offers a rigorous theoretical framework for binding affinity calculations that match experimental values. However, although robust, this strategy remains expensive, requiring substantial computational time to achieve convergence of the simulations. Improving the efficiency of the geometrical route, while preserving its reliability through improved ergodic sampling, is, therefore, highly desirable. In this contribution, having identified the computational bottleneck of the geometrical route, to accelerate the calculations we combine (i) a longer time step for the integration of the equations of motion with hydrogen-mass repartitioning (HMR), and (ii) multiple time-stepping (MTS) for collective-variable and biasing-force evaluation. Altogether, we performed 50 independent WTM-eABF simulations in triplicate for the "physical" separation of the Abl kinase-SH3 domain:p41 complex, following different HMR and MTS schemes, while tuning, in distinct protocols, the parameters of the enhanced-sampling algorithm. To demonstrate the consistency and reliability of the results obtained with the best-performing setups, we carried out quintuple simulations. Furthermore, we demonstrated the transferability of our method to other complexes by triplicating a 200 ns separation simulation of nine chosen protocols for the MDM2-p53:NVP-CGM097 complex. [Holzer et al. J. Med. Chem. 2015, 58, 6348-6358.] Our results, based on an aggregate simulation time of 14.4 μs, allowed an optimal set of parameters to be identified, able to accelerate convergence by a factor of three without any noticeable loss of accuracy.

show abstract

Enhanced Sampling Aided Design of Molecular Photoswitches

Cited by 14 publications

References 53 publications

OneOPES, a combined enhanced sampling method to rule them all

OneOPES, a combined enhanced sampling method to rule them all

How and When Does an Enzyme React? Unraveling α-Amylase Catalytic Activity with Enhanced Sampling Techniques

Improving Speed and Affordability without Compromising Accuracy: Standard Binding Free-Energy Calculations Using an Enhanced Sampling Algorithm, Multiple-Time Stepping, and Hydrogen Mass Repartitioning

Contact Info

Product

Resources

About