Kinetic Models of Cyclosporin A in Polar and Apolar Environments Reveal Multiple Congruent Conformational States

Witek, Jagna; Keller, Bettina G.; Blatter, Marie-Claude; Meißner, Axel; Wagner, Trixie; Riniker, Sereina

doi:10.1021/acs.jcim.6b00251

Cited by 102 publications

(187 citation statements)

References 68 publications

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“…The method also provided a qualitative but, in terms of drug discovery, useful analysis of the IMHB patterns in the two environments. Witek and co‐workers applied a molecular dynamics (MD) approach to obtain information about the permeability of Cyclosporin A. In this case, MD simulations of Cyclosporin A in water and in chloroform were performed and used to characterize the metastable conformational states by means of the Markov state model theory.…”

Section: Which Tactics To Use When Including Imhb In Drug Design?mentioning

confidence: 99%

Intramolecular hydrogen bonding: An opportunity for improved design in medicinal chemistry

Caron

Kihlberg

2019

Medicinal Research Reviews

103

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Recent literature shows that intramolecular hydrogen bond (IMHB) formation can positively impact upon the triad of permeability, solubility, and potency of drugs and candidates. IMHB modulation can be applied to compounds in any chemical space as a means for discovering drug candidates with both acceptable potency and absorption, distribution, metabolism, and excretion‐Tox profiles. Integrating IMHB formation in design of drugs is, therefore, an exciting and timely challenge for modern medicinal chemistry. In this review, we first provide some background about IMHBs from the medicinal chemist's point of view and highlight some IMHB‐associated misconceptions. Second, we propose a classification of IMHBs for drug discovery purposes, review the most common in silico tactics to include IMHBs in lead optimization and list some experimental physicochemical descriptors, which quantify the propensity of compounds to form IMHBs. By focusing on the compounds size and the number of IMHBs that can potentially be formed, we also outline the major difficulties encountered when designing compounds based on the inclusion of IMHBs. Finally, we discuss recent case studies illustrating the application of IMHB to optimize cell permeability and physicochemical properties of small molecules, cyclic peptides and macrocycles.

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Section: Which Tactics To Use When Including Imhb In Drug Design?mentioning

confidence: 99%

Intramolecular hydrogen bonding: An opportunity for improved design in medicinal chemistry

Caron

Kihlberg

2019

Medicinal Research Reviews

103

View full text Add to dashboard Cite

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“…In this case, the gap between the short loop and the long loop can open and close (Figure 3b), and the conformations are distinct states along this reaction coordinate (Figure 3c). Finally, changes in the environment or interactions with other molecules, for example, ligands, can induce shifts in the Boltzmann distribution of a molecule, which are critical to its functions [33][34][35]. For the analysis of simulations and the elucidation of MD, it is therefore of supreme importance that we can accurately identify the regions of high probability density (core sets).…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Common Nearest Neighbor Clustering—A Benchmark

Lemke

Keller

2018

Algorithms

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Cluster analyses are often conducted with the goal to characterize an underlying probability density, for which the data-point density serves as an estimate for this probability density. We here test and benchmark the common nearest neighbor (CNN) cluster algorithm. This algorithm assigns a spherical neighborhood R to each data point and estimates the data-point density between two data points as the number of data points N in the overlapping region of their neighborhoods (step 1). The main principle in the CNN cluster algorithm is cluster growing. This grows the clusters by sequentially adding data points and thereby effectively positions the border of the clusters along an iso-surface of the underlying probability density. This yields a strict partitioning with outliers, for which the cluster represents peaks in the underlying probability density-termed core sets (step 2). The removal of the outliers on the basis of a threshold criterion is optional (step 3). The benchmark datasets address a series of typical challenges, including datasets with a very high dimensional state space and datasets in which the cluster centroids are aligned along an underlying structure (Birch sets). The performance of the CNN algorithm is evaluated with respect to these challenges. The results indicate that the CNN cluster algorithm can be useful in a wide range of settings. Cluster algorithms are particularly important for the analysis of molecular dynamics (MD) simulations. We demonstrate how the CNN cluster results can be used as a discretization of the molecular state space for the construction of a core-set model of the MD improving the accuracy compared to conventional full-partitioning models. The software for the CNN clustering is available on GitHub.

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“…MSMs have become an indispensable tool for the elucidation of complex molecular dynamics and have highlighted the importance of dynamic effects in understanding the function and macroscopic properties of molecular systems. [21][22][23][24][25][26] When constructing MSMs, one faces a similar sampling problem as for the estimation of phase-space ensemble averages. However until a few years ago, MSMs could not be estimated from enhanced sampling simulations because methods to reweight the transition probability density…”

Section: Introductionmentioning

confidence: 99%

Girsanov reweighting for metadynamics simulations

Donati

Keller

2018

The Journal of Chemical Physics

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Metadynamics is a computational method to explore the phase space of a molecular system. Gaussian functions are added along relevant coordinates on the fly during a molecular-dynamics simulation to force the system to escape from minima in the potential energy function. The dynamics in the resulting trajectory are however unphysical and cannot be used directly to estimate dynamical properties of the system. Girsanov reweighting is a recent method used to construct the Markov State Model (MSM) of a system subjected to an external perturbation. With the combination of these two techniques-metadynamics/Girsanov-reweighting-the unphysical dynamics in a metadynamics simulation can be reweighted to obtain the MSM of the unbiased system. We demonstrate the method on a one-dimensional diffusion process, alanine dipeptide, and the hexapeptide Val-Gly-Val-Ala-Pro-Gly (VGVAPG). The results are in excellent agreement with the MSMs obtained from direct unbiased simulations of these systems. We also apply metadynamics/Girsanov-reweighting to a -hairpin peptide, whose dynamics is too slow to efficiently explore its phase space by direct simulation.

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Kinetic Models of Cyclosporin A in Polar and Apolar Environments Reveal Multiple Congruent Conformational States

Cited by 102 publications

References 68 publications

Intramolecular hydrogen bonding: An opportunity for improved design in medicinal chemistry

Intramolecular hydrogen bonding: An opportunity for improved design in medicinal chemistry

Common Nearest Neighbor Clustering—A Benchmark

Girsanov reweighting for metadynamics simulations

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