Computational Sampling of a Cryptic Drug Binding Site in a Protein Receptor: Explicit Solvent Molecular Dynamics and Inhibitor Docking to p38 MAP Kinase

Frembgen-Kesner, Tamara; Elcock, Adrian H.

doi:10.1016/j.jmb.2006.03.021

Cited by 94 publications

(86 citation statements)

References 61 publications

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“…For example, there are a variety of methods for understanding how information flows from one region of a protein to another (15)(16)(17)(18)(19), as well as for identifying potential binding pockets (20)(21)(22)(23)(24). More recent work has accounted for both of these ingredients (25,26).…”

mentioning

confidence: 99%

Discovery of multiple hidden allosteric sites by combining Markov state models and experiments

Bowman

Bolin

Hart

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

196

232

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The discovery of drug-like molecules that bind pockets in proteins that are not present in crystallographic structures yet exert allosteric control over activity has generated great interest in designing pharmaceuticals that exploit allosteric effects. However, there have only been a small number of successes, so the therapeutic potential of these pockets-called hidden allosteric sites-remains unclear. One challenge for assessing their utility is that rational drug design approaches require foreknowledge of the target site, but most hidden allosteric sites are only discovered when a small molecule is found to stabilize them. We present a means of decoupling the identification of hidden allosteric sites from the discovery of drugs that bind them by drawing on new developments in Markov state modeling that provide unprecedented access to microsecond-to millisecond-timescale fluctuations of a protein's structure. Visualizing these fluctuations allows us to identify potential hidden allosteric sites, which we then test via thiol labeling experiments. Application of these methods reveals multiple hidden allosteric sites in an important antibiotic target-TEM-1 β-lactamase. This result supports the hypothesis that there are many as yet undiscovered hidden allosteric sites and suggests our methodology can identify such sites, providing a starting point for future drug design efforts. More generally, our results demonstrate the power of using Markov state models to guide experiments.thiol labeling | antibiotic resistance | molecular dynamics

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mentioning

confidence: 99%

Discovery of multiple hidden allosteric sites by combining Markov state models and experiments

Bowman

Bolin

Hart

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

196

232

View full text Add to dashboard Cite

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“…Therefore, we can identify transient pockets, calculate how often they are open, and look for structural couplings between different regions of a protein with MSMs. Our approach builds on a number of existing methods for detecting cryptic pockets and allosteric sites (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). Important advances made here include (i) capturing events on much longer timescales (i.e., microseconds and beyond) and (ii) accounting for the requirement that residues surrounding a binding pocket are significantly (though perhaps indirectly) coupled to the active site.…”

mentioning

confidence: 99%

Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites

Bowman

Geissler²

2012

Proc. Natl. Acad. Sci. U.S.A.

249

357

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Cryptic allosteric sites-transient pockets in a folded protein that are invisible to conventional experiments but can alter enzymatic activity via allosteric communication with the active site-are a promising opportunity for facilitating drug design by greatly expanding the repertoire of available drug targets. Unfortunately, identifying these sites is difficult, typically requiring resourceintensive screening of large libraries of small molecules. Here, we demonstrate that Markov state models built from extensive computer simulations (totaling hundreds of microseconds of dynamics) can identify prospective cryptic sites from the equilibrium fluctuations of three medically relevant proteins-β-lactamase, interleukin-2, and RNase H-even in the absence of any ligand. As in previous studies, our methods reveal a surprising variety of conformations-including bound-like configurations-that implies a role for conformational selection in ligand binding. Moreover, our analyses lead to a number of unique insights. First, direct comparison of simulations with and without the ligand reveals that there is still an important role for an induced fit during ligand binding to cryptic sites and suggests new conformations for docking. Second, correlations between amino acid sidechains can convey allosteric signals even in the absence of substantial backbone motions. Most importantly, our extensive sampling reveals a multitude of potential cryptic sites-consisting of transient pockets coupled to the active site-even in a single protein. Based on these observations, we propose that cryptic allosteric sites may be even more ubiquitous than previously thought and that our methods should be a valuable means of guiding the search for such sites. molecular dynamics | native state dynamics T he degree to which standard, rational drug design protocols ignore protein-conformational changes is a potentially major flaw. Most assume that proteins are frozen in their crystallographic structures because we do not understand the intrinsic dynamics of proteins well enough to include them. Once this assumption is made, the only way to manipulate a protein's activity is with inhibitors that bind more tightly to the protein's active site than the molecule's natural ligand. Unfortunately, only approximately 15% of proteins have a sufficiently deep pocket coinciding with their active site to make this strategy feasible (1).Accounting for conformational heterogeneity could greatly expand the repertoire of available drug targets by revealing cryptic sites-transient pockets that are not readily visible in experimental structures, yet can inhibit enzymatic activity via allostery or block protein-protein interactions (2-5). For example, they may provide previously undescribed druggable pockets on proteins that are already considered viable targets, or even make it possible to target proteins that are currently considered undruggable. Targeting these sites may be easier than targeting the active site if there is no natural ligand to outcompete. Finally, crypt...

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“…Elcock and colleagues showed that in some 60 nanosecond explicit-solvent molecular dynamics simulations the DFG-in configuration transformed to DFG-out spontaneously [66]. This observation is consistent with an NMR study [67] that suggests the DFG-out configuration exists intrinsically, although rarely.…”

Section: Conformational Dynamics Of Map Kinasesmentioning

confidence: 55%

“…In addition to the diaryl urea containing BIRB-796, imatinib, sorafenib and doramapimod have been shown to stabilize the DFG-out conformation of various protein kinases [60][61][62][63][64]. Inhibitors that stabilize DFG-out conformations are sought after, because they are generally more selective and display slower dissociation kinetics relative to inhibitors that stabilize the DFG-in conformation [65,66].…”

Section: Conformational Dynamics Of Map Kinasesmentioning

confidence: 99%

Computational Insights for the Discovery of Non-ATP Competitive Inhibitors of MAP Kinases

Schnieders¹,

Kaoud²,

Cheng³

et al. 2012

curr drug metab

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Due to their role in cellular signaling mitogen activated protein (MAP) kinases represent targets of pharmaceutical interest. However, the majority of known MAP kinase inhibitors compete with cellular ATP and target an ATP binding pocket that is highly conserved in the 500 plus representatives of the human protein kinase family. Here we review progress toward the development of non-ATP competitive MAP kinase inhibitors for the extracellular signal regulated kinases (ERK1/2), the c-jun N-terminal kinases (JNK1/2/3) and the p38 MAPKs (α, β, γ, and δ). Special emphasis is placed on the role of computational methods in the drug discovery process for MAP kinases. Topics include recent advances in X-ray crystallography theory that improve the MAP kinase structures essential to structure-based drug discovery, the use of molecular dynamics to understand the conformational heterogeneity of the activation loop and inhibitors discovered by virtual screening. The impact of an advanced polarizable force field such as AMOEBA used in conjunction with sophisticated kinetic and thermodynamic simulation methods is also discussed.

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Computational Sampling of a Cryptic Drug Binding Site in a Protein Receptor: Explicit Solvent Molecular Dynamics and Inhibitor Docking to p38 MAP Kinase

Cited by 94 publications

References 61 publications

Discovery of multiple hidden allosteric sites by combining Markov state models and experiments

Discovery of multiple hidden allosteric sites by combining Markov state models and experiments

Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites

Computational Insights for the Discovery of Non-ATP Competitive Inhibitors of MAP Kinases

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