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Enregistrement W7066123912

Extension of Nonequilibrium Work Theorems with Applications to Diï¬usion and Permeation in Biological Systems

2012· dissertation· en· W7066123912 sur OpenAlexvenueno aff

Notice bibliographique

RevueLibrary and Archives Canada (Government of Canada) · 2012
Typedissertation
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueYeasts and Rust Fungi Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWork (physics)Noise (video)Term (time)Limit (mathematics)Feature (linguistics)Field (mathematics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Nonequilibrium work methods for determining potentials of mean force (PMF) w(z) have recently gained popularity as an alternative to standard equilibrium based methods. Introduced by Kosztin et al., the forward-reverse (FR) method is a bidirectional work method in that it requires the work to be sampled in both forward and reverse directions along the reaction coordinate z. This bidirectional sampling leads to much faster convergence than other nonequilibrium methods such as the Jarzynski equality, and the calculation itself is extremely simple, making the FR method an attractive way of determining the PMF. Presented here is an extension to the FR method that deals with sampling problems along essentially irreversible reaction coordinates. By oscillating a particle as it is steered along a reaction coordinate, both forward and reverse work samples are obtained as the particle progresses. Dubbed the oscillating forward-reverse (OFR) method, this new method overcomes the issue of irreversibility that is present in numerous soft-matter and biological systems, particularly in the stretching or unfolding of proteins. The data analysis of the OFR method is non-trivial however, and to this end a software package named the ‘OFR Analysis Tool’ has been created. This software performs all of the complicated analysis necessary, as well as a complete error analysis that considers correlations in the data, thus streamlining the use of the OFR method for potential end users. Another attractive feature of the FR method is that the dissipative work is collected at the same time as the free energy changes, making it possible to also calculate local diffusion coefficients, D(z), from the same simulation as the PMF through the Stokes-Nernst-Einstein relation Fdrag = −γv, with γ = kB T /D. While working with the OFR method, however, the D(z) results never matched known values or those obtained through other methods, including the mean square displacement (or Einstein) method. After a reformulation of the procedure to obtain D(z), i.e. by including the correct path length and particle speeds, results were obtained that were much closer to the correct values. The results however showed very little variation over the length of the reaction coordinate, even when D(z) was known to vary drastically. It seemed that the highly variable and noncontinuous velocity function of the particle being steered through the “stiff-spring” method was incompatible with the macroscopic definition of the drag coefficient, γ. The drag coefficient requires at most a slowly varying velocity so that the assumption of a linearly related dissipative work remains valid at all times. To address this, a new dynamic constraint steering protocol (DCP) was developed to replace the previously used “stiff-spring” method, now referred to as a dynamic restraint protocol (DRP). We present here the results for diffusion in bulk water, and both the PMF and diffusion results from the permeation of a water molecule through a DPPC membrane. We also consider the issue of ergodicity and sampling, and propose that to obtain an accurate w(z) (and D(z)) from even a moderately complex system, the final result should be a weighted average obtained from numerous pulls. An additional utility of the FR and OFR methods is that the permeability across lipid bilayers can be calculated from w(z) and D(z) using the inhomogeneous solubility-diffusion (ISD) model. As tests, the permeability was first calculated for H2O and O2 through DPPC. From the simulations, the permeability coefficients for H2O were found to be 0.129 ± 0.075 cm/s and 0.141 ± 0.043 cm/s, at 323 K and 350 K respectively, while the permeability coefficients for O2 were 114 ± 40 cm/s and 101 ± 27 cm/s, again at 323 K and 350 K respectively. As a final, more challenging system, the permeability of tyramine – a positively charged trace amine at physiological pH – was calculated. The final value of P = 0.89 ± 0.24 Ang/ns is over two orders of magnitude lower than that obtained from experiment (22 ± 4 Ang/ns), although it is clear that the permeability as calculated through the ISD is extremely sensitive to the PMF, as scaling the PMF by ∼ 20% allowed the simulation and experimental values to agree within uncertainty. With accurate predictions for free energies and permeabilities, the OFR method could potentially be used for many valuable endeavors such as rational drug design.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,755
Score d'incertitude au seuil0,461

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,164
Écart entre enseignants0,160 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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