A new definition of cavities for the computation of solvation free energies by the polarizable continuum model

Barone, Vincenzo; Cossi, Maurizio; Tomasi, Jacopo

doi:10.1063/1.474671

Cited by 2,338 publications

(1,767 citation statements)

References 33 publications

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“…[76,77] We used a dielectric constant of ε = 4 to simulate the polarity of a typical enzymatic environment, and the shape of the cavity was determined by the United Atom Topological Model. [78] 2.6 Graphical representation All molecular representations were created using the UCSF Chimera package. [79,80] Diagrams were created using XMGrace [81] and Plot.…”

Section: Dft Calculationsmentioning

confidence: 99%

Computational design of a lipase for catalysis of the Diels-Alder reaction

et al. 2010

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Combined molecular docking, molecular dynamics (MD) and density functional theory (DFT) studies have been employed to study catalysis of the Diels-Alder reaction by a modified lipase. Six variants of the versatile enzyme {\em Candida Antarctica} lipase B (CALB) have been rationally engineered {\em in silico} based on the specific characteristics of the pericyclic addition. A kinetic analysis reveals that hydrogen bond stabilization of the transition state and substrate binding are key components of the catalytic process. In the case of substrate binding, which has the greater potential for optimization, both binding strength and positioning of the substrates are important for catalytic efficiency. The binding strength is determined by hydrophobic interactions and can be tuned by careful selection of solvent and substrates. The MD simulations show that substrate positioning is sensitive to cavity shape and size, and can be controlled by a few rational mutations. The well-documented S105A mutation is essential to enable sufficient space in the vicinity of the oxyanion hole. Moreover, bulky residues on the edge of the active site hinders the formation of a sandwich-like near-attack conformer (NAC), and the I189A mutation is needed to obtain enough space above the face of the $\alpha$,$\beta$-double bond on the dienophile. The double mutant S105A/I189A performs quite well for two of three dienophiles. Based on binding constants and NAC energies obtained from MD simulations combined with activation energies from DFT computations, relative catalytic rates ($v_{cat}/v_{uncat}$) of up to $10^3$ are predicted.Response to Reviewers: We are happy with the favorable comments by the reviewers. We have have corrected the manuscript as suggested by reviewer 1 1. A sentence explaining catalytic promiscuity has been added. 2. A paragraph discussing the relationship between the NAC concept and the Reaction Force has been added.We have shortened the manuscript as suggested by reviewer 2. In particular, we have moved information regarding the structural relaxation of protein structure in MD simulations to the supplementary material. Abstract Combined molecular docking, molecular dynamics (MD) and density functional theory (DFT) studies have been employed to study catalysis of the DielsAlder reaction by a modified lipase. Six variants of the versatile enzyme Candida Antarctica lipase B (CALB) have been rationally engineered in silico based on the specific characteristics of the pericyclic addition. A kinetic analysis reveals that hydrogen bond stabilization of the transition state and substrate binding are key components of the catalytic process. In the case of substrate binding, which has the greater potential for optimization, both binding strength and positioning of the substrates are important for catalytic efficiency. The binding strength is determined by hydrophobic interactions and can be tuned by careful selection of solvent and substrates. The MD simulations show that substrate positioning is sensitive to cavity s...

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Section: Dft Calculationsmentioning

confidence: 99%

Computational design of a lipase for catalysis of the Diels-Alder reaction

et al. 2010

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“…Solvation free energies were calculated using the refined polarizable continuum model (PCM) of Tomasi et al [14a] included in Gaussian 03. As noted in the program, this PCM model is different from that included in Gaussian 98 [15], and the free energies from these two models should not be mixed.…”

Section: Calculationsmentioning

confidence: 99%

“…Calculations of the solvation energies of protonated adenine tautomers, using the polarizable continuum model (PCM), explain well this selectivity. The freeenergy data in Table 1 indicate that position N-1 in adenine in water is 48 -130 times more basic than N-3 (depending on the PCM model used, [14,15], and 250 -319 times more basic than N-7. The N-10 amino group has a negligible basicity in water.…”

Section: Protonated Adenine Tautomers In Watermentioning

confidence: 99%

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Protonated adenine: Tautomers, solvated clusters, and dissociation mechanisms

Turecček

Chen

2005

J. Am. Soc. Mass Spectrom.

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Ab initio and density functional theory calculations at the B3-MP2 and CCSD(T)/6-311 ϩ G(3df,2p) levels of theory are reported that address the protonation of adenine in the gas phase, water clusters, and bulk aqueous solution. The calculations point to N-1-protonated adenine (1 ؉ ) as the thermodynamically most stable cationic tautomer in the gas phase, water clusters, and bulk solution. This strongly indicates that electrospray ionization of adenine solutions produces tautomer 1 ؉ with a specificity calculated as 97-90% in the 298 -473 K temperature range. The mechanisms for elimination of hydrogen atoms and ammonia from 1 ؉ have also been studied computationally. Ion 1 ؉ is calculated to undergo fast migrations of protons among positions N-1, C-2, N-3, N-10, N-7, and C-8 that result in an exchange of five hydrogens before loss of a hydrogen atom forming adenine cation radical at 415 kJ mol Ϫ1 dissociation threshold energy. The elimination of ammonia is found to be substantially endothermic requiring 376 -380 kJ mol Ϫ1 at the dissociation threshold and depending on the dissociation pathway. The overall dissociation is slowed by the involvement of ion-molecule complexes along the dissociation pathways. The competing isomerization of 1 ؉ proceeds by a sequence of ring opening, internal rotations, imine flipping, ring closures, and proton migrations to effectively exchange the N-1 and N-10 atoms in 1 ؉ , so that either can be eliminated as ammonia. This mechanism explains the previous N-1/N-10 exchange upon collision-induced dissociation of protonated adenine. (J Am Soc Mass Spectrom 2005, 16, 1713-1726

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“…The procedure requires the assessment of the charge distribution for the solute and of different parameters for describing the solute's cavity, the nonelectrostatic terms, and the permittivity inside and outside the cavity. In this framework, different models have been applied to the calculation of ∆ solv G°by Barone et al, 12 Klamt et al, 13 and the group of Cramer and Truhlar. 14 The discrete models use Monte Carlo (MC) statistical mechanics 17 or molecular dynamics simulations 18,19 to model the solute and solvent.…”

Section: Prediction Of the Solvation Free Energymentioning

confidence: 99%

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

Bernazzani

Duce

Micheli

et al. 2006

J. Chem. Inf. Model.

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In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/ quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, ∆ solv G°, has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the ∆ solv G°in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R ) 0.9985, standard deviation S ) 0.68 kJ mol -1 , and mean absolute error MAE ) 0.46 kJ mol -1 . The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.

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A new definition of cavities for the computation of solvation free energies by the polarizable continuum model

Cited by 2,338 publications

References 33 publications

Computational design of a lipase for catalysis of the Diels-Alder reaction

Computational design of a lipase for catalysis of the Diels-Alder reaction

Protonated adenine: Tautomers, solvated clusters, and dissociation mechanisms

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

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