Publications

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1541 Publications visible to you, out of a total of 1541

Abstract (Expand)

The performance of metal–organic and covalent organic framework materials in sought-after applications—capture, storage, and delivery of gases and molecules, and separation of their mixtures—heavilyxtures—heavily depends on the host–guest interactions established inside the pores of these materials. Computational modeling provides information about the structures of these host–guest complexes and the strength and nature of the interactions present at a level of detail and precision that is often unobtainable from experiment. In this Review, we summarize the key simulation techniques spanning from molecular dynamics and Monte Carlo methods to correlate ab initio approaches and energy, density, and wavefunction partitioning schemes. We provide illustrative literature examples of their uses in analyzing and designing organic framework hosts. We also describe modern approaches to the high-throughput screening of thousands of existing and hypothetical metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) and emerging machine learning techniques for predicting their properties and performances. Finally, we discuss the key methodological challenges on the path toward computation-driven design and reliable prediction of high-performing MOF and COF adsorbents and catalysts and suggest possible solutions and future directions in this exciting field of computational materials science.

Authors: Michelle Ernst, Jack D. Evans, Ganna Gryn'ova

Date Published: 1st Dec 2023

Publication Type: Journal

Abstract

Not specified

Authors: J. Bodensteiner, H. Sana, P. L. Dufton, C. Wang, N. Langer, G. Banyard, L. Mahy, A. de Koter, S. E. de Mink, C. J. Evans, Y. Götberg, V. Hénault-Brunet, L. R. Patrick, F. R. N. Schneider

Date Published: 1st Dec 2023

Publication Type: Journal

Abstract (Expand)

Abstract The recognition of dominantly inherited micro-satellite instable (MSI) cancers caused by pathogenic variants in one of the four mismatch repair ( MMR ) genes MSH2, MLH1, MSH6 and PMS2 has MMR ) genes MSH2, MLH1, MSH6 and PMS2 has modified our understanding of carcinogenesis. Inherited loss of function variants in each of these MMR genes cause four dominantly inherited cancer syndromes with different penetrance and expressivities: the four Lynch syndromes. No person has an “average sex “or a pathogenic variant in an “average Lynch syndrome gene” and results that are not stratified by gene and sex will be valid for no one. Carcinogenesis may be a linear process from increased cellular division to localized cancer to metastasis. In addition, in the Lynch syndromes (LS) we now recognize a dynamic balance between two stochastic processes: MSI producing abnormal cells, and the host’s adaptive immune system’s ability to remove them. The latter may explain why colonoscopy surveillance does not reduce the incidence of colorectal cancer in LS, while it may improve the prognosis. Most early onset colon, endometrial and ovarian cancers in LS are now cured and most cancer related deaths are after subsequent cancers in other organs. Aspirin reduces the incidence of colorectal and other cancers in LS. Immunotherapy increases the host immune system’s capability to destroy MSI cancers. Colonoscopy surveillance, aspirin prevention and immunotherapy represent major steps forward in personalized precision medicine to prevent and cure inherited MSI cancer.

Authors: Pal Møller, Toni T. Seppälä, Aysel Ahadova, Emma J. Crosbie, Elke Holinski-Feder, Rodney Scott, Saskia Haupt, Gabriela Möslein, Ingrid Winship, Sanne W. Bajwa-ten Broeke, Kelly E. Kohut, Neil Ryan, Peter Bauerfeind, Laura E. Thomas, D. Gareth Evans, Stefan Aretz, Rolf H. Sijmons, Elizabeth Half, Karl Heinimann, Karoline Horisberger, Kevin Monahan, Christoph Engel, Giulia Martina Cavestro, Robert Fruscio, Naim Abu-Freha, Levi Zohar, Luigi Laghi, Lucio Bertario, Bernardo Bonanni, Maria Grazia Tibiletti, Leonardo S. Lino-Silva, Carlos Vaccaro, Adriana Della Valle, Benedito Mauro Rossi, Leandro Apolinário da Silva, Ivana Lucia de Oliveira Nascimento, Norma Teresa Rossi, Tadeusz Dębniak, Jukka-Pekka Mecklin, Inge Bernstein, Annika Lindblom, Lone Sunde, Sigve Nakken, Vincent Heuveline, John Burn, Eivind Hovig, Matthias Kloor, Julian R. Sampson, Mev Dominguez-Valentin

Date Published: 1st Dec 2023

Publication Type: Journal

Abstract (Expand)

tive reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.

Authors: Elisabeth K. Brockhaus, Daniel Wolffram, Tanja Stadler, Michael Osthege, Tanmay Mitra, Jonas M. Littek, Ekaterina Krymova, Anna J. Klesen, Jana S. Huisman, Stefan Heyder, Laura M. Helleckes, Matthias an der Heiden, Sebastian Funk, Sam Abbott, Johannes Bracher

Date Published: 27th Nov 2023

Publication Type: Journal

Abstract (Expand)

Zusammenfassung Angesichts der umwälzenden Auswirkungen, die künstliche Intelligenz (KI) auf Wissenschaft, Medizin und darüber hinaus hat, betrachten wir hier das Potenzial von KI für die Entdeckungenzial von KI für die Entdeckung neuer Medikamente gegen Herzkrankheiten. Wir definieren KI im weitesten Sinne als den Einsatz von maschinellem Lernen, einschließlich Statistik und Deep Learning, um Muster in Datensätzen zu erkennen, die für Vorhersagen genutzt werden können. Jüngste Durchbrüche in der Fähigkeit, sehr große Datenmengen zu berücksichtigen, haben einen Boom in der KI-gestützten Arzneimittelentdeckung sowohl in der Wissenschaft als auch in der Industrie ausgelöst. Viele neue Unternehmen verfügen bereits über Arzneimittel-Pipelines, die bis in die klinische Erprobung reichen, aber noch keine Medikamente gegen Herzkrankheiten enthalten. Wir beschreiben hier den Einsatz von KI für die Entdeckung von niedermolekularen Medikamenten und Biologika, einschließlich therapeutischer Peptide, sowie für die Vorhersage von Wirkungen wie Kardiotoxizität. Der konzertierte Einsatz von KI zusammen mit physikbasierten Simulationen und experimentellen Rückkopplungsschleifen wird notwendig sein, um das Potenzial der KI für die Arzneimittelentdeckung und die Entwicklung von Präzisionsarzneimitteln für Herzkrankheiten voll auszuschöpfen.

Authors: Manuel Glaser, Julia Ritterhof, Patrick Most, Rebecca C. Wade

Date Published: 20th Nov 2023

Publication Type: Journal

Abstract (Expand)

Protein-surface adsorption phenomena play a crucial role in a variety of fields, including medicine, molecular and cell biology, biotechnology, phar- maceutical sciences, and biophysics. It is therefore desirable to develop accu- rate models to describe them. Hen-Egg-White-Lysozyme (HEWL) adsorption to silica- and mica-like surfaces entails minimal conformational changes, rendering it an ideal system for rigid-body Brownian dynamics simulations. Experimen- tal studies revealed that the fluorescence attached to HEWL exhibits a sharp overshoot followed by a trough as a function of time. This work identifies two inconsistencies in the explanation previously used by the authors to interpret the experiment. A reorientation does not seem to be the main driving factor for the overshoot. Furthermore, I examine previously used electrostatic potential models based on the Poisson-Boltzmann equation (PBE) used by Romanowska et al. and Reinhardt et al.. It is found that even though different orientations of HEWL- FITC on the surface are possible, the required amount of adsorbed proteins and corresponding reorientation is attained only with the linearized PBE-potential. The study provides a possible explanation why reaching the critical overshoot value with non-linearized PBE-based models poses difficulties. Overcoming this problem is an indispensable step in developing Brownian-dynamics-based atomic- detail multi-molecule models for protein-surface adsorption.

Author: Jakob Nießner

Date Published: 14th Nov 2023

Publication Type: Master's Thesis

Abstract (Expand)

Broad-spectrum anti-infective chemotherapy agents with activity against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening program of the 456 compounds belonging to the Ty-Box, an in-house industry database. Compound characterization using machine learning approaches enabled the identification and synthesis of 44 compounds with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar activity against two parasites and low toxicity. Given the volume and complexity of data generated by the diverse high-throughput screening assays performed on the compounds of the Ty-Box library, the chemoinformatic and machine learning tools enabled the selection of compounds eligible for further evaluation of their biological and toxicological activities and aided in the decision-making process toward the design and optimization of the identified lead.

Authors: P. Linciano, A. Quotadamo, R. Luciani, M. Santucci, K. M. Zorn, D. H. Foil, T. R. Lane, A. Cordeiro da Silva, N. Santarem, C. B Moraes, L. Freitas-Junior, U. Wittig, W. Mueller, M. Tonelli, S. Ferrari, A. Venturelli, S. Gul, M. Kuzikov, B. Ellinger, J. Reinshagen, S. Ekins, M. P. Costi

Date Published: 3rd Nov 2023

Publication Type: Journal

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