GaudiMM: A modular multi‐objective platform for molecular modeling

Pedregal, Jaime Rodríguez-Guerra; Sciortino, Giuseppe; Guasp, Jordi; Municoy, Martí; Maréchal, Jean‐Didier

doi:10.1002/jcc.24847

Cited by 38 publications

(42 citation statements)

References 47 publications

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“…2d) to embody the attributes of an intermediate enzyme-substrate complex, and the disposition of G2SG-OMe that of an incoming substrate. Hence, we used this complex for investigations of the hypothetical Glc displacement route via multi-scale molecular modelling employing docking 19 and MD simulations, followed by GaudiMM 20 and PELE 21 pathway calculations.…”

Section: Resultsmentioning

confidence: 99%

“…Initially, we used the GPathFinder extension of the GaudiMM platform 20 to reveal potential Glc exit routes from the active site into the bulk solvent, whereby we considered steric clashes. The initial set of simulations was performed with the HvExoI:Glc:G2OG ternary complex 1, based on the coordinates of HvExoI in complex with G2SG-OMe and Glc docked in the −1 subsite.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, we examine product and substrate pathways along the catalytic cycle of a plant exo-hydrolase HvExoI, and how this enzyme utilises its plasticity and that of glycoside substrates. We use the high-resolution X-ray crystallography supported with enzyme kinetics, mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, multi-scale molecular modelling employing docking 19 , Molecular dynamics (MD) simulations, Genetic Algorithms with Unrestricted Descriptors for Intuitive Molecular Modelling (GaudiMM) 20 and Protein Energy Landscape Exploration (PELE) 21 calculations, to reveal the Glc product displacement route, and to extract how each hydrolytic event including Glc release is precisely coordinated with the incoming substrate association and hydrolysis. This leads us to generate a variant enzyme and probe it for Glc entrapment and the S-glycoside analogue binding.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discovery of processive catalysis by an exo-hydrolase with a pocket-shaped active site

Streltsov¹,

Luang²,

Peisley³

et al. 2019

Nat Commun

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Substrates associate and products dissociate from enzyme catalytic sites rapidly, which hampers investigations of their trajectories. The high-resolution structure of the native Hordeum exo-hydrolase HvExoI isolated from seedlings reveals that non-covalently trapped glucose forms a stable enzyme-product complex. Here, we report that the alkyl β- d -glucoside and methyl 6-thio-β-gentiobioside substrate analogues perfused in crystalline HvExoI bind across the catalytic site after they displace glucose, while methyl 2-thio-β-sophoroside attaches nearby. Structural analyses and multi-scale molecular modelling of nanoscale reactant movements in HvExoI reveal that upon productive binding of incoming substrates, the glucose product modifies its binding patterns and evokes the formation of a transient lateral cavity, which serves as a conduit for glucose departure to allow for the next catalytic round. This path enables substrate-product assisted processive catalysis through multiple hydrolytic events without HvExoI losing contact with oligo- or polymeric substrates. We anticipate that such enzyme plasticity could be prevalent among exo-hydrolases.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discovery of processive catalysis by an exo-hydrolase with a pocket-shaped active site

Streltsov¹,

Luang²,

Peisley³

et al. 2019

Nat Commun

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“…The efficacy of the method can be evaluated on the site discrimination capability (see the animated video BlindDocking_2eb9.avi in the Supporting Information to illustrate the approach). The docking solutions were analyzed by means of GaudiView, an in‐house GUI (graphical user interface) tool built as an extension for UCSF Chimera.…”

Section: Methodsmentioning

confidence: 99%

Prediction of the interaction of metallic moieties with proteins: An update for protein‐ligand docking techniques

et al. 2017

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In this article, we present a new approach to expand the range of application of protein-ligand docking methods in the prediction of the interaction of coordination complexes (i.e., metallodrugs, natural and artificial cofactors, etc.) with proteins. To do so, we assume that, from a pure computational point of view, hydrogen bond functions could be an adequate model for the coordination bonds as both share directionality and polarity aspects. In this model, docking of metalloligands can be performed without using any geometrical constraints or energy restraints. The hard work consists in generating the convenient atom types and scoring functions. To test this approach, we applied our model to 39 high-quality X-ray structures with transition and main group metal complexes bound via a unique coordination bond to a protein. This concept was implemented in the protein-ligand docking program GOLD. The results are in very good agreement with the experimental structures: the percentage for which the RMSD of the simulated pose is smaller than the X-ray spectra resolution is 92.3% and the mean value of RMSD is < 1.0 Å . Such results also show the viability of the method to predict metal complexes-proteins interactions when the X-ray structure is not available. This work could be the first step for novel applicability of docking techniques in medicinal and bioinorganic chemistry and appears generalizable enough to be implemented in most protein-ligand docking programs nowadays available.

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“…Calculations were carried out with the protein-ligand docking software GOLD software [59][60][61] and repeated inside our GaudiMM platform (to provide with a freeware alternative). 32,62 Both calculations were performed (see Figure 9A).…”

Section: Case Study (3): Metalloenzyme Designmentioning

confidence: 99%

BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

Sánchez-Aparicio¹,

Tiessler-Sala²,

Velasco-Carneros³

et al. 2020

Preprint

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<div><div><div><p>With a large amount of research dedicated to decoding how metallic species bind to protein, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parametrization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 50 metal-binding X-Ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems which structures (either experimental or theoretical) are not optimal for metal binding sites. We report here its application on three different challenging cases i) the modulation of metal-binding sites during conformational transition in human serum albumin, ii) the identification of possible routes of metal migration in hemocyanins, and iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology, and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding.</p></div></div></div>

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GaudiMM: A modular multi‐objective platform for molecular modeling

Cited by 38 publications

References 47 publications

Discovery of processive catalysis by an exo-hydrolase with a pocket-shaped active site

Discovery of processive catalysis by an exo-hydrolase with a pocket-shaped active site

Prediction of the interaction of metallic moieties with proteins: An update for protein‐ligand docking techniques

BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

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