fpocket: online tools for protein ensemble pocket detection and tracking

Schmidtke, Peter; Guilloux, Vincent Le; Maupetit, Julien; Tufféry, Pierre

doi:10.1093/nar/gkq383

Cited by 256 publications

(212 citation statements)

References 33 publications

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“…To investigate how the protonation state of Asp79 2.50 affected the cavity volume, we analyzed both simulation trajectories using the mdpocket program. 74 The cavity volume for the simulation conducted with the ionized Asp79 2.50 (386 Å 3 ) was on average larger than in the simulation with the protonated form (304 Å 3 ). The difference in volume appeared to originate from multiple small changes in the cavity structure, e.g., side chain reorientations for Ser120 3.39 and Phe282 6.44 .…”

Section: ■ Resultsmentioning

confidence: 80%

“…Cavity volumes were calculated using the mdpocket suite of the fpocket program. 74 Pocket identification was performed using values slightly lower than the default parameters for mdpocket, with a minimum sphere size of 2.65 Å and a maximum of 5.5 Å. Calculations of cavity volume over all the snapshots were then performed using a minimum sphere size of 2.5 Å and a maximum of 5.5 Å, to obtain a better description of the cavity volume.…”

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confidence: 99%

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Insights into the Role of Asp79^2.50 in β₂ Adrenergic Receptor Activation from Molecular Dynamics Simulations

2014

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Achieving a molecular-level understanding of G-protein-coupled receptor (GPCR) activation has been a long-standing goal in biology and could be important for the development of novel drugs. Recent breakthroughs in structural biology have led to the determination of high-resolution crystal structures for the β2 adrenergic receptor (β2AR) in inactive and active states, which provided an unprecedented opportunity to understand receptor signaling at the atomic level. We used molecular dynamics (MD) simulations to explore the potential roles of ionizable residues in β2AR activation. One such residue is the strongly conserved Asp79(2.50), which is buried in a transmembrane cavity and becomes dehydrated upon β2AR activation. MD free energy calculations based on β2AR crystal structures suggested an increase in the population of the protonated state of Asp79(2.50) upon activation, which may contribute to the experimentally observed pH-dependent activation of this receptor. Analysis of MD simulations (in total > 100 μs) with two different protonation states further supported the conclusion that the protonated Asp79(2.50) shifts the conformation of the β2AR toward more active-like states. On the basis of our calculations and analysis of other GPCR crystal structures, we suggest that the protonation state of Asp(2.50) may act as a functionally important microswitch in the activation of the β2AR and other class A receptors.

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Section: ■ Resultsmentioning

confidence: 80%

mentioning

confidence: 99%

Insights into the Role of Asp79^2.50 in β₂ Adrenergic Receptor Activation from Molecular Dynamics Simulations

2014

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“…For substructure alignments we used Open3DAlign, 38 binding site volume was computed using mdpocket, 39 for docking and scoring we used smina with the Vina scoring function. Then, the methods tested consisted of the following:

Align-Close is a method that used Tanimoto score to identify the most similar ligand and its receptor among all cocrystals for each compound in the test sets.

…”

Section: Introductionmentioning

confidence: 99%

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

Baumgartner

Camacho

2015

J. Chem. Inf. Model.

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The 2013/2014 Community Structure–Activity Resource (CSAR) challenge was designed to prospectively validate advancement in the field of docking and scoring receptor–small molecule interactions. Purely computational methods have been found to be quite limiting. Thus, the challenges assessed methods that combined both experimental data and computational approaches. Here, we describe our contribution to solve three important challenges in rational drug discovery: rank-ordering protein primary sequences based on affinity to a compound, determining close-to-native bound conformations out of a set of decoy poses, and rank-ordering sets of congeneric compounds based on affinity to a given protein. We showed that the most significant contribution to a meaningful enrichment of native-like models was the identification of the best receptor structure for docking and scoring. Depending on the target, the optimal receptor for cross-docking and scoring was identified by a self-consistent docking approach that used the Vina scoring function, by aligning compounds to the closest cocrystal or by selecting the cocrystal receptor with the largest pocket. For tRNA (m1G37) methyltransferase (TRMD), ranking a set of 31 congeneric binding compounds cross-docked to the optimal receptor resulted in a R2 = 0.67; whereas, using any other of the 13 receptor structures led to almost no enrichment of native-like complex structures. Furthermore, although redocking predicted lower RMSDs relative to the bound structures, the ranking based on multiple receptor structures did not improve the correlation coefficient. Our predictions highlight the role of rational structure-based modeling in maximizing the outcome of virtual screening, as well as limitations scoring multiple receptors.

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“…The coenzyme A binding pocket included Val-812, Leu-813, Val-814, Gly-824, and Arg-825 residues, as detected by the fpocket2 program ( Fig. 3B) (34,35). WebLab Viewer Lite software was used to display the electrostatic surface of the PatZ catalytic domain (Fig.…”

Section: Patz Shows Positive Cooperativity In Response To Acetyl-coamentioning

confidence: 99%

The Protein Acetyltransferase PatZ from Escherichia coli Is Regulated by Autoacetylation-induced Oligomerization

Diego

Gallego‐Jara

Castaño-Cerezo

et al. 2015

Journal of Biological Chemistry

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fpocket: online tools for protein ensemble pocket detection and tracking

Cited by 256 publications

References 33 publications

Insights into the Role of Asp79^2.50 in β₂ Adrenergic Receptor Activation from Molecular Dynamics Simulations

Insights into the Role of Asp79^2.50 in β₂ Adrenergic Receptor Activation from Molecular Dynamics Simulations

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

The Protein Acetyltransferase PatZ from Escherichia coli Is Regulated by Autoacetylation-induced Oligomerization

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fpocket: online tools for protein ensemble pocket detection and tracking

Cited by 256 publications

References 33 publications

Insights into the Role of Asp792.50 in β2 Adrenergic Receptor Activation from Molecular Dynamics Simulations

Insights into the Role of Asp792.50 in β2 Adrenergic Receptor Activation from Molecular Dynamics Simulations

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

The Protein Acetyltransferase PatZ from Escherichia coli Is Regulated by Autoacetylation-induced Oligomerization

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Insights into the Role of Asp79^2.50 in β₂ Adrenergic Receptor Activation from Molecular Dynamics Simulations

Insights into the Role of Asp79^2.50 in β₂ Adrenergic Receptor Activation from Molecular Dynamics Simulations