Discover, Sample, and Refine: Exploring Chemistry with Enhanced Sampling Techniques

Raucci, Umberto; Rizzi, Valerio; Parrinello, Michele

doi:10.1021/acs.jpclett.1c03993

Cited by 25 publications

(28 citation statements)

References 62 publications

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“…This last feature of OPES-explore has been recently leveraged by Raucci et al to systematically discover reaction pathways in chemical processes. 41 …”

Section: Discussionmentioning

confidence: 99%

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Exploration vs Convergence Speed in Adaptive-Bias Enhanced Sampling

Invernizzi

Parrinello

2022

J. Chem. Theory Comput.

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In adaptive-bias enhanced sampling methods, a bias potential is added to the system to drive transitions between metastable states. The bias potential is a function of a few collective variables and is gradually modified according to the underlying free energy surface. We show that when the collective variables are suboptimal, there is an exploration–convergence tradeoff, and one must choose between a quickly converging bias that will lead to fewer transitions or a slower to converge bias that can explore the phase space more efficiently but might require a much longer time to produce an accurate free energy estimate. The recently proposed on-the-fly probability enhanced sampling (OPES) method focuses on fast convergence, but there are cases where fast exploration is preferred instead. For this reason, we introduce a new variant of the OPES method that focuses on quickly escaping metastable states at the expense of convergence speed. We illustrate the benefits of this approach in prototypical systems and show that it outperforms the popular metadynamics method.

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“…This last feature of OPES-explore has been recently leveraged by Raucci et al to systematically discover reaction pathways in chemical processes. 41 …”

Section: Discussionmentioning

confidence: 99%

“…To obtain the same effect with MetaD, one typically has to define some ad hoc static bias walls by trial and error. This last feature of OPES-explore has been recently leveraged by Raucci et al to systematically discover reaction pathways in chemical processes …”

Section: Discussionmentioning

confidence: 99%

Exploration vs Convergence Speed in Adaptive-Bias Enhanced Sampling

Invernizzi

Parrinello

2022

J. Chem. Theory Comput.

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“…The determination of collective variables 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 22,23 . 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 (l max ) of the symmetric adjacency matrix associated to the graph 22,23 .…”

Section: Introductionmentioning

confidence: 99%

“…To promote enzymatic reactions in a blind way, we used a CV derived from spectral graph theory 22,23 . 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 (l max ) of the symmetric adjacency matrix associated to the graph 22,23 . When used in the OPES context 24 , this CV allows the discovery of new chemical pathways 25 .…”

Section: Introductionmentioning

confidence: 99%

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

Das

Raucci

Neves

et al. 2023

Preprint

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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 in the catalytic pocket, and highlight the role of water in the catalytic process and in the stepwise conversion of reaction-ready to non-reactive conformations. Overall, these insights offer atomistic details on the catalytic mechanism and dynamics of the active site, allowing to shed light on two fundamental questions in enzymatic catalysis, that is how and when does an enzyme react?

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“…One way to accelerate this sampling is by applying an external bias potential to a set of carefully selected collective variables (CVs) that encode the generally undersampled and slow degrees of freedom of a system. − When proper CVs are identified, this procedure allows for the efficient exploration of reaction space and subsequent discovery of novel chemical pathways otherwise inaccessible on the timescale of AIMD (Figure ). Recently, we proposed an automatic workflow for reaction discovery that relies on a generic CV derived from spectral graph theory and on the explore variant of the on-the-fly probability enhanced sampling method (OPES E ). , With our approach, a molecule is represented 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 to the graph . In the OPES E method, λ max fluctuations are enhanced by introducing a bias from an on-the-fly estimation of the CV probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sampling Aided Design of Molecular Photoswitches

et al. 2022

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Advances in the evolving field of atomistic simulations promise important insights for the design and fundamental understanding of novel molecular photoswitches. Here, we use state-of-the-art enhanced simulation techniques to unravel the complex, multistep chemistry of donor–acceptor Stenhouse adducts (DASAs). Our reaction discovery workflow consists of enhanced sampling for efficient chemical space exploration, refinement of newly observed pathways with more accurate ab initio electronic structure calculations, and structural modifications to introduce design principles within future generations of DASAs. We showcase our discovery workflow by not only recovering the full photoswitching mechanism of DASA but also predicting a plethora of new plausible thermal pathways and suggesting a way for their experimental validation. Furthermore, we illustrate the tunability of these newly discovered reactions, leading to a potential avenue for controlling DASA dynamics through multiple external stimuli. Overall, these insights could offer alternative routes to increase the efficiency and control of DASA’s photoswitching mechanism, providing new elements to design more complex light-responsive materials.

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Discover, Sample, and Refine: Exploring Chemistry with Enhanced Sampling Techniques

Cited by 25 publications

References 62 publications

Exploration vs Convergence Speed in Adaptive-Bias Enhanced Sampling

Exploration vs Convergence Speed in Adaptive-Bias Enhanced Sampling

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

Enhanced Sampling Aided Design of Molecular Photoswitches

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