Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations

Ghanakota, Phani; DasGupta, Debarati; Carlson, Heather A.

doi:10.1021/acs.jcim.8b00925

Cited by 24 publications

(26 citation statements)

References 37 publications

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“…52 It uses a 3D grid overlapped on a region of interest in a protein surface to determine the location of putative pockets, and calculates a druggability score, i.e., a quantification of how adequate those pockets are to accommodate a small organic molecule. This druggability scoring was inspired by structural bioinformatics methodologies trained on data sets of X-ray crystallography-derived binding sites, 53,54 with the important difference that the druggability estimator was designed to be smooth and continuously differentiable with respect to protein atomic Cartesian coordinates. This enables the use of the JEDI score as a collective variable for biased MD simulations.…”

Section: Mechanisms Of Their Formationmentioning

confidence: 99%

Investigating Cryptic Binding Sites by Molecular Dynamics Simulations

et al. 2020

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Conspectus This Account highlights recent advances and discusses major challenges in investigations of cryptic (hidden) binding sites by molecular simulations. Cryptic binding sites are not visible in protein targets crystallized without a ligand and only become visible crystallographically upon binding events. These sites have been shown to be druggable and might provide a rare opportunity to target difficult proteins. However, due to their hidden nature, they are difficult to find through experimental screening. Computational methods based on atomistic molecular simulations remain one of the best approaches to identify and characterize cryptic binding sites. However, not all methods are equally efficient. Some are more apt at quickly probing protein dynamics but do not provide thermodynamic or druggability information, while others that are able to provide such data are demanding in terms of time and resources. Here, we review the recent contributions of mixed-solvent simulations, metadynamics, Markov state models, and other enhanced sampling methods to the field of cryptic site identification and characterization. We discuss how these methods were able to provide precious information on the nature of the site opening mechanisms, to predict previously unknown sites which were used to design new ligands, and to compute the free energy landscapes and kinetics associated with the opening of the sites and the binding of the ligands. We highlight the potential and the importance of such predictions in drug discovery, especially for difficult (“undruggable”) targets. We also discuss the major challenges in the field and their possible solutions.

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Section: Mechanisms Of Their Formationmentioning

confidence: 99%

Investigating Cryptic Binding Sites by Molecular Dynamics Simulations

et al. 2020

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“…The same region in ERβ was mapped by isopropanol, suggesting the placement of an amphipathic, as opposed to an aromatic, functional group in this isoform, to achieve selective ligand-protein interactions. The lack of probe density at the ERα BF-3 site points towards poor druggability of the site [47], which is indirectly supported by experimental results since inhibitors directed against the AR did not inhibit ERα [13]. The density maps of the BF-3 site of both TRs substantially differ from the ones of other receptors, which reduces the odds for the cross-binding of compounds harboring the proposed density-based pharmacophores.…”

Section: Distinct Pharmacophores Of the Allosteric Sitesmentioning

confidence: 68%

Allosteric Binding Sites On Nuclear Receptors: Focus On Drug Efficacy and Selectivity

Fischer

Smieško

2020

IJMS

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Nuclear receptors (NRs) are highly relevant drug targets in major indications such as oncologic, metabolic, reproductive, and immunologic diseases. However, currently, marketed drugs designed towards the orthosteric binding site of NRs often suffer from resistance mechanisms and poor selectivity. The identification of two superficial allosteric sites, activation function-2 (AF-2) and binding function-3 (BF-3), as novel drug targets sparked the development of inhibitors, while selectivity concerns due to a high conservation degree remained. To determine important pharmacophores and hydration sites among AF-2 and BF-3 of eight hormonal NRs, we systematically analyzed over 10 µs of molecular dynamics simulations including simulations in explicit water and solvent mixtures. In addition, a library of over 300 allosteric inhibitors was evaluated by molecular docking. Based on our results, we suggest the BF-3 site to offer a higher potential for drug selectivity as opposed to the AF-2 site that is more conserved among the selected receptors. Detected similarities among the AF-2 sites of various NRs urge for a broader selectivity assessment in future studies. In combination with the Supplementary Material, this work provides a foundation to improve both selectivity and potency of allosteric inhibitors in a rational manner and increase the therapeutic applicability of this promising compound class. , glucocorticoid receptor (GR), progesterone receptor (PR), and minearlocorticoid receptor (MR) share a common domain architecture as well as a similar fold regarding their ligand binding domain (LBD), the selectivity concern for allosteric NR inhibitors remains ( Figure 1B). For example, it has been reported that the AF-2 and BF-3 sites of AR and GR have a sequence identity of approximately 50% [9,28]. Further on, drug-like mimetics of coactivator peptides at the AF-2 site have the potential to disrupt protein-protein interactions for multiple NRs and, ultimately, promote off-target toxicity [16,29].While several structures with cocrystallized ligands have been determined for the AR, targeting superficial binding sites such as AF-2 and BF-3 of other receptors remains a challenge due to their comparably large size, shallowness, and high flexibility [30,31]. Knowledge regarding binding hotspots and distinct pharmacophores among structurally similar NRs is crucial for the design of effective and selective inhibitory compounds [31][32][33][34]. In this regard, cosolvent molecular dynamics (MD) simulations are a suitable computational tool to determine hotspots and assess their druggability, as well as to obtain detailed information on potentially useful pharmacophores to improve drug potency and selectivity for the site of interest. In this simulation protocol, which was inspired by crystallographic observations of small fragments binding to protein surfaces, organic solvent molecules mimicking drug fragments are added to the aqueous phase to monitor and quantify their interaction with a protein. Compared to similar methods for bi...

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“…We employed druggability simulations (mixed MD cosolvent simulations) to identity druggable sites in the screened/selected target protein TNFAIP3 as well as the three-dimensional (3D) pharmacophore (PH4) models. , Druggability simulations were performed using NAMD and a classical protocol of MD simulations . The DruGUI module of the ProDy application programming interface and Pharmmaker were used to prepare inputs of simulations and to analyze the results. , The generated 3D PH4 models and PDB structure of TNFAIP3 were submitted to the Pharmit server to search against the PubChem database which contains 450 million conformers of 93 million chemicals .…”

Section: Materials and Methodsmentioning

confidence: 99%

Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing “Omics Data” to Druggability Simulations

Yang

Wang

et al. 2021

ACS Chem. Neurosci.

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Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest pathbased algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein−protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing "omics data" and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.

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Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations

Cited by 24 publications

References 37 publications

Investigating Cryptic Binding Sites by Molecular Dynamics Simulations

Investigating Cryptic Binding Sites by Molecular Dynamics Simulations

Allosteric Binding Sites On Nuclear Receptors: Focus On Drug Efficacy and Selectivity

Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing “Omics Data” to Druggability Simulations

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