Accelerating the Calculation of Protein–Ligand Binding Free Energy and Residence Times Using Dynamically Optimized Collective Variables

Brotzakis, Z. Faidon; Limongelli, Vittorio; Parrinello, Michele

doi:10.1021/acs.jctc.8b00934

Cited by 64 publications

(64 citation statements)

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“…In some cases, a few direct binding events of ligands can be simulated using brute force MD [11][12][13] , but typically sampling of the relevant degrees of freedom is a limiting factor. A variety of enhanced sampling methods, such as replica exchange, funnel-metadynamics, or adaptive sampling, exist to improve the sampling and to study the binding kinetics and pathways [14][15][16][17] . In addition, when the binding mode is known, calculations can be performed to obtain (relative) binding free energies [18][19][20][21] .…”

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confidence: 99%

Protein–ligand binding with the coarse-grained Martini model

et al. 2020

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The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein-ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the wellcharacterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

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Protein–ligand binding with the coarse-grained Martini model

et al. 2020

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“…Here, πR 2 cyl is the surface of the cylinder used as restraint potential in the unbound state, while the potential W(x) and its value in the unbound state, W ref , can be derived from the potential of mean force (pmf) obtained through FM calculations. The method has proven to be successful in reproducing binding processes in ligand/protein and ligand/DNA systems, predicting crystallographic binding modes and experimental binding free energies …”

Section: Physical Pathway Methodsmentioning

confidence: 99%

“…Recent methodological advance focusing on dimensionality reduction problems alleviates this issue. Relevant examples are the path CVs and a variational approach to conformational dynamics in metadynamics (VAC‐MetaD) . In the former, large scale protein motions are described using matrices defined in terms of distances (root mean square deviation) or list of contact atoms, while in the latter general descriptors of LPB are expressed as a weighted linear combination using a low number of CVs.…”

Section: Physical Pathway Methodsmentioning

confidence: 99%

Ligand binding free energy and kinetics calculation in 2020

Limongelli

2020

WIREs Comput Mol Sci

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Ligand/protein binding (LPB) is a major topic in medicine, chemistry and biology. Since the advent of computers, many scientists have put efforts in developing theoretical models that could decode the alphabet of the LPB interaction. The success of this task passes by the resolution of the molecular mechanism of LPB. In the past century, major attention was dedicated to the thermodynamics of LPB, while more recent studies have revealed that ligand (un)binding kinetics is at least as important as ligand binding thermodynamics in determining the drug in vivo efficacy. In the present review, we introduce the most widely used computational methods to study LPB thermodynamics and kinetics. It is important to say that no method outperforms another, they all have pros and cons and the choice of the user should take carefully into account the system under investigation, the available structural and experimental data, and the goal of the research. A perspective on future directions of method development and research on LPB concludes the discussion. This article is categorized under: Molecular and Statistical Mechanics > Free Energy Methods Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

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“…This can occur for different reasons, such as large conformational movements during binding, slow transitions between states, rare events, or high-energy barriers that must be overcome. In such cases, a set of different computational methods has been proposed—the free energy pathway methods such as transition path sampling, umbrella sampling, steered-MD, and funnel-metadynamics (55–59). Among the free energy pathway methods is a subgroup of alchemical methods represented by the thermal integration (60, 61) and free energy perturbation (62, 63) methods.…”

Section: Structure-based Methodsmentioning

confidence: 99%

In Silico Predictions of Endocrine Disruptors Properties

et al. 2019

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Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure–activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs’ targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.

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Accelerating the Calculation of Protein–Ligand Binding Free Energy and Residence Times Using Dynamically Optimized Collective Variables

Cited by 64 publications

References 60 publications

Protein–ligand binding with the coarse-grained Martini model

Protein–ligand binding with the coarse-grained Martini model

Ligand binding free energy and kinetics calculation in 2020

In Silico Predictions of Endocrine Disruptors Properties

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