Current Tools and Methods in Molecular Dynamics (MD) Simulations for Drug Design

Hernández-Rodríguez, Maricarmen; Rosales‐Hernández, Martha Cecilia; Mendieta-Wejebe, Jessica Elena; Martínez‐Archundia, Marlet; Correa-Basurto, José

doi:10.2174/0929867323666160530144742

Cited by 79 publications

(39 citation statements)

References 80 publications

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“…Techniques such as SCA, molecular docking, molecular dynamics (MD) simulations and allosteric networks have emerged as a valuable complement to experimental methods for the study of allostery [16, 24, 47–49]. These approaches have also allowed the prediction of allosteric sites and the quantification of protein and/or ligand motions in full atomic detail, describing the molecules behavior at high resolution [28, 50–52].…”

Section: Introductionmentioning

confidence: 99%

Dissecting a novel allosteric mechanism of cruzain: A computer-aided approach

et al. 2019

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Trypanosoma cruzi is the causative agent of Chagas disease, a neglected infection affecting millions of people in tropical regions. There are several chemotherapeutic agents for the treatment of this disease, but most of them are highly toxic and generate resistance. Currently, the development of allosteric inhibitors constitutes a promising research field, since it can improve the accessibility to more selective and less toxic medicines. To date, the allosteric drugs prediction is a state-of-the-art topic in rational structure-based computational design. In this work, a simulation strategy was developed for computational discovery of allosteric inhibitors, and it was applied to cruzain, a promising target and the major cysteine protease of T. cruzi. Molecular dynamics simulations, binding free energy calculations and network-based modelling of residue interactions were combined to characterize and compare molecular distinctive features of the apo form and the cruzain-allosteric inhibitor complexes. By using geometry-based criteria on trajectory snapshots, we predicted two main allosteric sites suitable for drug targeting. The results suggest dissimilar mechanisms exerted by the same allosteric site when binding different potential allosteric inhibitors. Finally, we identified the residues involved in suboptimal paths linking the identified site and the orthosteric site. The present study constitutes the first approximation to the design of cruzain allosteric inhibitors and may serve for future pharmacological intervention. Here, no major effects on active site structure were observed due to compound binding (modification of distance and angles between catalytic residues), which indicates that allosteric regulation in cruzain might be mediated via alterations of its dynamical properties similarly to allosteric inhibition of human cathepsin K (HCatK). The current findings are particularly relevant for the design of allosteric modulators of papain-like cysteine proteases.

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Section: Introductionmentioning

confidence: 99%

Dissecting a novel allosteric mechanism of cruzain: A computer-aided approach

et al. 2019

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“…The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26].…”

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confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

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“…at the level of atoms, sidechains, loops, small molecules or interfaces, MD is commonly applied to refine docked complexes with the aim of improving their quality. (15)(16)(17)(18)(19)(20)(21) MD can be used at a more extensive level, where the docking process is simulated, however modelling spontaneous association and dissociation of proteins is very rare unless coarse-grained models or enhanced sampling methods are used. (22)(23)(24)(25) With regard to all-atom MD simulations, enhanced sampling techniques like Markov states models, (26)(27)(28)(29) umbrella sampling combined with replica exchange MD, (30)(31)(32)(33)(34)(35) elastic-network approaches (35), string method (36), metadynamics (37,38) and other methods have been used to sample conformational change prior to or during binding and to facilitate such binding events under the condition that the binding interface is known (39).…”

Section: Introductionmentioning

confidence: 99%

Native or non-native protein-protein docking models? Molecular dynamics to the rescue

Jandová

Vargiu

Bonvin

2021

Preprint

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Molecular docking excels at creating a plethora of potential models of protein-protein complexes. To correctly distinguish the favourable, native-like models from the remaining ones remains, however, a challenge. We assessed here if a protocol based on molecular dynamics (MD) simulations would allow to distinguish native from non-native models to complement scoring functions used in docking. To this end, first models for 25 protein-protein complexes were generated using HADDOCK. Next, MD simulations complemented with machine learning were used to discriminate between native and non-native complexes based on a combination of metrics reporting on the stability of the initial models. Native models showed higher stability in almost all measured properties, including the key ones used for scoring in the CAPRI competition, namely the positional root mean square deviations and fraction of native contacts from the initial docked model. A Random Forest classifier was trained, reaching 0.85 accuracy in correctly distinguishing native from non-native complexes. Reasonably modest simulation lengths in the order of 50 to 100 ns are already sufficient to reach this accuracy, which makes this approach applicable in practice.

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Current Tools and Methods in Molecular Dynamics (MD) Simulations for Drug Design

Cited by 79 publications

References 80 publications

Dissecting a novel allosteric mechanism of cruzain: A computer-aided approach

Dissecting a novel allosteric mechanism of cruzain: A computer-aided approach

A Review on Applications of Computational Methods in Drug Screening and Design

Native or non-native protein-protein docking models? Molecular dynamics to the rescue

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