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77 Publications visible to you, out of a total of 77
Breaking Strong Alkynyl-Phenyl Bonds: Poly(para-phenylene ethynylene)s under Mechanical Stress

Abstract

Not specified

Authors: Maximilian Elter, Matthias Brosz, Daniel Sucerquia, Andrei Kuzhelev, Denis C. Kiesewetter, Markus Kurth, Andreas Dreuw, Thomas F. Prisner, Jan Freudenberg, Uwe H. F. Bunz, Frauke Gräter

Date Published: 27th Sep 2024

Publication Type: Journal

O-glycans Expand Lubricin and Attenuate Its Viscosity and Shear Thinning

Abstract

Not specified

Authors: Saber Boushehri, Hannes Holey, Matthias Brosz, Peter Gumbsch, Lars Pastewka, Camilo Aponte-Santamaría, Frauke Gräter

Date Published: 30th May 2024

Publication Type: Journal

Grappa – A Machine Learned Molecular Mechanics Force Field

Abstract

Not specified

Authors: Leif Seute, Eric Hartmann, Jan Stühmer, Frauke Gräter

Date Published: 25th Mar 2024

Publication Type: Journal

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins

Abstract (Expand)

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. … Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Authors: Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn’ova, Pascal Friederich, Frauke Gräter

Date Published: 14th Feb 2024

Publication Type: Journal

Mechanical control of the adhesion of malaria-infected erythrocytes to the placenta

Abstract (Expand)

Pathogens use sophisticated adhesion mechanisms to remain attached to the host’s surfaces. A key example of this is the adhesion of erythrocytes infected with Plasmodium, the parasite which causes … malaria, to the microvasculature. Remarkably, in the case of pregnancy-associated malaria, the adherence of parasitized erythrocytes to the placenta is enhanced by the shear of the flowing blood, suggesting a catch-bond adhesion mechanism. The adhesion is mediated by a parasite protein called VAR2CSA which anchors such infected erythrocytes to the proteoglycan matrix of the placenta. In this work, by using extensive equilibrium and force-probe molecular dynamics simulations, we elucidate the—so far unknown—molecular mechanism governing the adhesive function of VAR2CSA. We demonstrate that the elongation tension that arises from the shear of the flowing blood opens VAR2CSA into two structurally-intact domains, thereby exposing cryptic sugar binding sites. The orientation of VAR2CSA with respect to the pulling direction as well as strong sugar-protein shearing interactions favor this mode of opening. Accordingly, as the basis for a catch bond, we propose that mechanical forces strengthen the adhesion of infected erythrocytes, by increasing the number of sugar binding sites in VAR2CSA and not the bond lifetime as it would be canonically thought for a catch bond. This constitutes a new intriguing hypothesis which is of high relevance for our understanding of malaria infection and for the design of vaccines. More generally, our results put forward force-induced multivalency of mechano-responsive proteins as a key new concept for pathogen-host interactions.

Authors: Rita Roessner, Nicholas Michelarakis, Frauke Gräter, Camilo Aponte-Santamaría

Date Published: 8th Feb 2024

Publication Type: Journal

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins

Abstract (Expand)

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. … Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Authors: Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn'ova, Pascal Friederich, Frauke Gräter

Date Published: 16th Jan 2024

Publication Type: Journal

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins

Abstract (Expand)

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. … Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Authors: Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn’ova, Pascal Friederich, Frauke Gräter

Date Published: 16th Jan 2024

Publication Type: Journal

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