Free Energies of Reaction for Aqueous Glycine Condensation Chemistry at Extreme Temperatures

Kroonblawd, Matthew P.; Goldman, Nir

doi:10.1002/9781119508229.ch23

Cited by 4 publications

(5 citation statements)

References 61 publications

(90 reference statements)

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“…2 kcal/mol lower than our calculated result with water assistance). This confirms the robustness and the accuracy of our simplified model to study the kinetics of this peptide bond formation [45].…”

Section: Discussionsupporting

confidence: 79%

Assisted dipeptide bond formation: glycine as a case study

Achour

Hosni

Darghouthi

et al. 2021

Heliyon

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Peptide bond formation is a crucial chemical process that dominates most biological mechanisms and is claimed to be a governing factor in the origin of life. Dipeptides made from glycine are studied computationally via Density Functional Theory (DFT) using two different basis sets. This reaction was investigated from both a thermodynamic and kinetic point of view. The effect of explicit assistance via the introduction of discrete solvent molecules was investigated. Water, methanol, and cyclohexane were all employed as solvent media in addition to gas to investigate their effects on the mechanism of peptide bond formation. This computational investigation revealed that methanol is slightly better than water to leverage peptide bond formation both kinetically and thermodynamically, while cyclohexane, a non-polar and non-protic solvent, is the least effective after gas as a medium of solvation. Energetic results in the gas environment are very close to those obtained in polar and protic solvents, suggesting that peptide bonds can be formed under interstellar conditions.

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Section: Discussionsupporting

confidence: 79%

Assisted dipeptide bond formation: glycine as a case study

Achour

Hosni

Darghouthi

et al. 2021

Heliyon

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“…Various forms of XL-BOMD have been used in a number of software packages, including density functional theory, semi-empirical electronic structure the-ory, polarizable force fields, excited state dynamics, and superfluidity [16,17,20,21,23,27,32,33,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. A few of those will be discussed below.…”

Section: Applicationsmentioning

confidence: 99%

“…XL-BOMD is based on an extended Lagrangian formulations, where the electronic degrees of freedom are propagated as extended dynamical variables in addition to the nuclear degrees of freedom along the molecular trajectories. Various formulations and techniques of XL-BOMD have been used in a number of software packages, including applications to density functional theory, semi-empirical electronic structure theory, polarizable force fields, excited state dynamics, and superfluidity [16,17,20,21,23,24,27,32,33,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. In this way, XL-BOMD is similar to Car-Parrinello molecular dynamics (CPMD) [18,[50][51][52][53][54][55], which also provides a general framework for QMD simulations based on an extended Lagrangian approach.…”

Section: Introductionmentioning

confidence: 99%

Extended Lagrangian Born–Oppenheimer molecular dynamics: from density functional theory to charge relaxation models

Niklasson

2021

Eur. Phys. J. B

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We present a review of extended Lagrangian Born-Oppenheimer molecular dynamics and its most recent development. The molecular dynamics framework is first derived for general Hohenberg-Kohn density functional theory and it is then presented in explicit forms for thermal Hartree-Fock theory using a density matrix formalism, for self-consistent charge density functional tight-binding theory, and for general non-linear charge relaxation models that can be designed and optimized using modern machine learning methods. Our intention is to give a self-contained but brief and hopefully pedagogical presentation.

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“…Density functional tight-binding , (DFTB) is a semiempirical approach derived from DFT that retains much of its accuracy while reducing the computational expense by orders of magnitude. With DFTB, it is practical to obtain many nanoseconds of QMD trajectory through ensemble sampling, which is critical for developing quantum-informed coarse-grained models through statistical sampling of microstates connected to macroscopic observables. ,− This makes the DFTB method a good choice for placing studies of radiation damage in reactive molecular materials on a firmer “quantum” basis.…”

Section: Introductionmentioning

confidence: 99%

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Kroonblawd

Goldman

Maiti

et al. 2020

J. Chem. Theory Comput.

Self Cite

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Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. These backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si−O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

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Free Energies of Reaction for Aqueous Glycine Condensation Chemistry at Extreme Temperatures

Cited by 4 publications

References 61 publications

Assisted dipeptide bond formation: glycine as a case study

Assisted dipeptide bond formation: glycine as a case study

Extended Lagrangian Born–Oppenheimer molecular dynamics: from density functional theory to charge relaxation models

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

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