Publications

What is a Publication?
1687 Publications visible to you, out of a total of 1687

Abstract (Expand)

Semiempirical methods like density functional tight-binding (DFTB) allow extensive phase space sampling, making it possible to generate free energy surfaces of complex reactions in condensed-phase environments. Such a high efficiency often comes at the cost of reduced accuracy, which may be improved by developing a specific reaction parametrization (SRP) for the particular molecular system. Thiol-disulfide exchange is a nucleophilic substitution reaction that occurs in a large class of proteins. Its proper description requires a high-level ab initio method, while DFT-GAA and hybrid functionals were shown to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop an SRP for thiol-disulfide exchange based on an artificial neural network (ANN) implementation in the DFTB+ software and compare its performance to that of a standard SRP approach applied to DFTB. As an application, we use both new DFTB-SRP as components of a QM/MM scheme to investigate thiol-disulfide exchange in two molecular complexes: a solvated model system and a blood protein. Demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of DFTB with an ANN only adds a small computational overhead.

Authors: Claudia L Gómez-Flores, Denis Maag, Mayukh Kansari, Van-Quan Vuong, Stephan Irle, Frauke Gräter, Tomáš Kubař, Marcus Elstner

Date Published: 8th Feb 2022

Publication Type: Journal

Abstract (Expand)

Harmonization of data integration is the key to standardization efforts in personalised medicine, which would also facilitate cross-European studies. Standardization of the models themselves is less essential within a research context, where new models are created and tested in line with research progress, harmonization and/or standardization of input data is both feasible and necessary. We argue that model validation should receive more attention, and other measures should be implemented such that validation of models within personalised medicine becomes easier, also across borders. While this is an evident necessity within the context of models implemented as medical devices or decision tools, which are regulated by the European Medicines Agency and national competent authorities, we argue that model validation should be a higher priority at research level also, facilitating assessment by peers and by medical doctors – who themselves should receive better training in assessment of research using in silico models. This will also ease the implementation of translational research results in the clinic. Acceptance by doctors and the relevant medical specialties is a key hurdle for in silico models in personalised medicine. Any medical product - device, algorithm or drug - has to prove itself safe and effective to be licensed for use by regulators; however, it has also to be accepted by medical experts as being a good choice, and be recommended within clinical specialties. EU-STANDS4PM joined forces to examine to what extent existing standards or standards under development for both format and semantics can be used to link clinical and health as well as research data to computational models relevant for personalised medicine. As all requirements should be equally understood and fulfilled by users it is important to define them uniformly in an international context. To achieve this the conclusion of our work shall be also discussed in international standardization and technical committees, especially in the case of standards that are still being drawn up, and new standardization projects shall be initiated where necessary. We present a White Paper featuring recommendations for standardization of data integration as well as recommendations for standardization of model validation within a collaborative research context, such that health-related data can be optimally used for translational research and personalised medicine across Europe. As such the White Paper showcases the approach that takes big data in health through harmonized data integration to the most relevant predictive computational models for personalised medicine. As they are refined and validated these models can provide guidance not just how to use data, but also how to best cope with disease and preserve wellbeing in the daily lives of patients.

Authors: Kirstine Belling, Marina Caldara, Catherine Bjerre Collin, Tom Gebhardt, Martin Golebiewski, Tugce Karaderi, Faiz M. Khan, Marc Kirschner, Sylvia Krobitsch, Lars Küpfer, Heike Moser, Flora Musuamba Tschinanu, Mariam Nassar, Tito Poli, Philip Rosenstiel, Dagmar Waltemath, Olaf Wolkehnauer, EU-STANDS4PM consortium

Date Published: 7th Feb 2022

Publication Type: Misc

Abstract (Expand)

Cellular mechanosensing is pivotal for virtually all biological processes, and many molecular mechano-sensors and their way of function are being uncovered. In this work, we suggest that c-Src kinase acts as a direct mechano-sensor. c-Src is responsible for, among others, cell proliferation, and shows increased activity in stretched cells. In its native state, c-Src has little basal activity, because its kinase domain binds to an SH2 and SH3 domain. However, it is known that c-Src can bind to p130Cas, through which force can be transmitted to the membrane. Using molecular dynamics simulations, we show that force acting between the membrane-bound N-terminus of the SH3 domain and p130Cas induces partial SH3 unfolding, thereby impeding rebinding of the kinase domain onto SH2/SH3 and effectively enhancing kinase activity. Forces involved in this process are slightly lower or similar to the forces required to pull out c-Src from the membrane through the myristoyl linker, and key interactions involved in this anchoring are salt bridges between negative lipids and nearby basic residues in c-Src. Thus, c-Src appears to be a candidate for an intriguing mechanosensing mechanism of impaired kinase inhibition, which can be potentially tuned by membrane composition and other environmental factors.

Authors: Csaba Daday, Svenja de Buhr, Davide Mercadante, Frauke Gräter

Date Published: 2nd Feb 2022

Publication Type: Journal

Abstract

Not specified

Authors: Giulia Paiardi, Stefan Richter, Pasqua Oreste, Chiara Urbinati, Marco Rusnati, Rebecca C. Wade

Date Published: 1st Feb 2022

Publication Type: Journal

Abstract

Not specified

Authors: Theodoros Soultanis, Andreas Bauswein, Nikolaos Stergioulas

Date Published: 1st Feb 2022

Publication Type: Journal

Abstract

Not specified

Authors: F. Lach, F. P. Callan, D. Bubeck, F. K. Röpke, S. A. Sim, M. Schrauth, S. T. Ohlmann, M. Kromer

Date Published: 1st Feb 2022

Publication Type: Journal

Abstract

Not specified

Editor:

Date Published: 1st Feb 2022

Publication Type: Master's Thesis

Powered by
(v.1.16.0)
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH