<i>In silico</i> screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force

Menichetti, Roberto; Kanekal, Kiran H.; Kremer, Kurt; Bereau, Tristan

doi:10.1063/1.4987012

Cited by 42 publications

(79 citation statements)

References 43 publications

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“…Menichetti et al recently demonstrated this by running Martini HTCG simulations to construct a structure-property relationship describing the thermodynamics of the insertion of a small organic molecule into a biological membrane across CCS. 28,29 In doing so, they discovered a linear relationship between the bulk partitioning behavior of the solute and its potential of mean force. They were then able to identify a structure-property hyper surface to obtain membrane permeabilities for these solute molecules.…”

Section: Introductionmentioning

confidence: 99%

Resolution limit of data-driven coarse-grained models spanning chemical space

Kanekal

Bereau

2019

The Journal of Chemical Physics

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Increasing the efficiency of materials design and discovery remains a significant challenge, especially given the prohibitively large size of chemical compound space. The use of a chemically transferable coarse-grained model enables different molecular fragments to map to the same bead type, while also reducing computational expense. These properties further increase screening efficiency, as many compounds are screened through the use of a single coarse-grained simulation, effectively reducing the size of chemical compound space. Here, we propose new criteria for the rational design of coarse-grained models that allows for the optimization of their chemical transferability and evaluate the Martini model within this framework. We further investigate the scope of this chemical transferability by parameterizing three Martini-like models, in which the number of bead types ranges from five to sixteen for the different force fields. We then implement a Bayesian approach to determining which chemical groups are more likely to be present on fragments corresponding to specific bead types for each model. We demonstrate that a level of performance and accuracy comparable to Martini can be obtained by using a force field with fewer bead types. However, the advantage of including more bead types is a reduction of uncertainty with respect to back-mapping these bead types to specific chemistries. Just as reducing the size of the coarse-grained particles leads to a finer mapping of conformational space, increasing the number of bead types yields a finer mapping of chemical compound space. Finally, we note that, due to the relatively large size of the chemical fragments that map to a single martini bead, a clear resolution limit arises when using ∆G W→Ol as the only descriptor when coarse-graining chemical compound space.

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Section: Introductionmentioning

confidence: 99%

Resolution limit of data-driven coarse-grained models spanning chemical space

Kanekal

Bereau

2019

The Journal of Chemical Physics

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“…We have shown in the previous section that the strength of phase separation, expressed by f mix , correlates well with the dimer's relative partitioning between different lipid environments, quantified by the transfer free energy, ∆∆G. As such, using PMFs in the three lipid environments representative of Lo-Ld equilibrium offers two advantages: (i) identify the preferred lipid environment and (ii) estimate the dimer-induced effects on phase separation, both at a reduce computational cost (29)(30)(31). Additionally, the PMF profile contains spatial information about the insertion, which we have observed to also play a role in determining the bilayer-modifying character of the solute.…”

Section: High-throughput Search For Phase-modifying Solutesmentioning

confidence: 91%

“…Rather than focusing on specific compounds, we considered all CG dimers by exhaustively enumerating all combinations of neutral Martini beads. This resulted in a total of 105 dimer solutes, covering a wide range of hydrophobicity (29,30). Hence, a US simulation was constructed for each combination of solute and membrane environment, using as reaction coordinate the distance along the bilayer normal, z, between the membrane midplane and the solute molecule.…”

Section: Umbrella Samplingmentioning

confidence: 99%

“…The potential of mean force (PMF), obtained herein using umbrella sampling (US), provides a robust observable to quantify the stability of the system. Our group previously demonstrated the benefits of calculating PMFs to study the translocation of small molecules in a one-component lipid membrane at high throughput, i.e., across the chemical space of small organic molecules (29)(30)(31)(32). PMF measures have otherwise been successfully employed to shed light on the thermodynamic origins of many biologically relevant processes, including protein dimerization (33)(34)(35)(36), preferential binding and association of peptides and proteins to different membrane environments (35,37,38), as well as to study the selectivity of antimicrobial peptides between bacterial and mammalian-like membranes (39,40).…”

Section: Introductionmentioning

confidence: 99%

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Inserting small molecules across membrane mixtures: Insight from the potential of mean force

Centi

Dutta

Parekh

et al. 2019

Preprint

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Small solutes have been shown to alter the lateral organization of cell membranes and reconstituted phospholipid bilayers; however, the mechanisms by which these changes happen are still largely unknown. Traditionally, both experiment and simulation studies have been restricted to testing only a few compounds at a time, failing to identify general molecular descriptors or chemical properties that would allow extrapolating beyond the subset of considered solutes. In this work, we probe the competing energetics of inserting a solute in different membrane environments by means of the potential of mean force. We show that these calculations can be used as a computationally-efficient proxy to establish whether a solute will stabilize or destabilize domain phase separation. Combined with umbrella sampling simulations and coarse-grained molecular dynamics simulations, we are able to screen solutes across a wide range of chemistries and polarities. Our results indicate that, for the system under consideration, preferential partitioning and therefore effectiveness in altering membrane phase separation are strictly linked to the location of insertion in the bilayer (i.e., midplane or interface). Our approach represents a fast and simple tool for obtaining structural and thermodynamic insight into the partitioning of small molecules between lipid domains and its relation to phase separation, ultimately providing a platform for identifying the key determinants of this process. SIGNIFICANCE In this work we explore the relationship between solute chemistry and the thermodynamics of insertion in a mixed lipid membrane. By combining a coarse-grained resolution and umbrella-sampling simulations we efficiently sample conformational space to study the thermodynamics of phase separation. We demonstrate that measures of the potential of mean force-a computationally-efficient quantity-between different lipid environments can serve as a proxy to predict a compound's ability to alter the thermodynamics of the lipid membrane. This efficiency allows us to set up a computational screening across many compound chemistries, thereby gaining insight beyond the study of a single or a handful of compounds.

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“…Similarly, the recently established PerMM database (10) covers only cellular permeability together with permeability prediction using an implicit membrane model with rigid compounds. Finally, molecular dynamics simulations are often used for predictions of membrane partitioning (11) or permeability even on a large scale (12,13). However, current theoretical predictions of molecule/membrane interactions vary by method as well as in comparison with data from experiments, lacking community benchmark comparison between individual methods.…”

Section: Introductionmentioning

confidence: 99%

MolMeDB: Molecules on Membranes Database

Juračka

Šrejber

Melíková

et al. 2018

Preprint

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Biological membranes act as barriers or reservoirs for many compounds within the human body. As such, they play an important role in pharmacokinetics and pharmacodynamics of drugs and other molecular species. Until now, most membrane/drug interactions have been inferred from simple partitioning between octanol and water phases. However, the observed variability in membrane composition and among compounds themselves stretches beyond such simplification as there are multiple drug-membrane interactions. Numerous experimental and theoretical approaches are used to determine the molecule-membrane interactions with variable accuracy, but there is no open resource for their critical comparison. For this reason, we have built Molecules on Membranes Database (MolMeDB), which gathers data about over 3600 compound-membrane interactions including partitioning, penetration, and positioning. The data have been collected from scientific articles published in peer-reviewed journals and complemented by inhouse calculations from high-throughput COSMOmic approach to set up a baseline for further comparison. The data in MolMeDB are fully searchable and browsable by means of name, SMILES, membrane, method, or dataset and we offer the collected data openly for further reuse and we are open to further additions. MolMeDB can be a powerful tool that could help researchers better understand the role of membranes and to compare individual approaches used for the study of molecule/membrane interactions.Database URL: http://molmedb.upol.cz.

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In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force

Cited by 42 publications

References 43 publications

Resolution limit of data-driven coarse-grained models spanning chemical space

Resolution limit of data-driven coarse-grained models spanning chemical space

Inserting small molecules across membrane mixtures: Insight from the potential of mean force

MolMeDB: Molecules on Membranes Database

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