Dissociation and isomerization of vibrationally excited species. II. Unimolecular reaction rate theory and its application

Data on quasi-unimolecular reactions have usually been compared with theoretical equations based on classical treatments, because the expressions are simpler than those obtained on the basis of a quantum model. The quantum reformulation of the RRK theory in Part I is used to compute the pressure dependence of the rate constants and the limiting low-pressure rates for a variety of unimolecular reactions without employing adjustable parameters. An asymptotic expansion of the integral for the limiting low-pressure second-order rate constant provides a very simple expression for this quantity.The errors inherent in corresponding classical calculations are estimated by comparing these results with those obtained from the theory in its classical limit. The error is temperature dependent and at low pressures increases from a factor of about three (under typical experimental conditions) for small reactants such as O3 and N2O to 105 or more for large molecules such as cyclopropane, C2H6, and N2O5. In most cases the rates calculated from the quantum form are in reasonable agreement with those obtained experimentally when all of the reactant oscillators are assumed effective in intramolecular energy transfer

Pharmacophore Models Derived From Molecular Dynamics Simulations of Protein-Ligand Complexes: A Case Study.

A single, merged pharmacophore hypothesis is derived combining 2000 pharmacophore models obtained during a 20 ns molecular dynamics simulation of a protein-ligand complex with one pharmacophore model derived from the initial PDB structure. This merged pharmacophore model contains all features that are present during the simulation and statistical information about the dynamics of the pharmacophore features. Based on the dynamics of the pharmacophore features we derive two distinctive feature patterns resulting in two different pharmacophore models for the analyzed system – the first model consists of features that are obtained from the PDB structure and the second uses two features that can only be derived from the molecular dynamics simulation. Both models can distinguish between active and decoy molecules in virtual screening. Our approach represents an objective way to add/remove features in pharmacophore models and can be of interest for the investigation of any naturally occurring system that relies on ligand-receptor interactions for its biological activity

Using Colistin as a Trojan Horse: Inactivation of Gram-Negative Bacteria with Chlorophyllin

Colistin (polymyxin E) is a membrane-destabilizing antibiotic used against Gram-negative bacteria. We have recently reported that the outer membrane prevents the uptake of antibacterial chlorophyllin into Gram-negative cells. In this study, we used sub-toxic concentrations of colistin to weaken this barrier for a combination treatment of Escherichia coli and Salmonella enterica serovar Typhimurium with chlorophyllin. In the presence of 0.25 µg/mL colistin, chlorophyllin was able to inactivate both bacteria strains at concentrations of 5–10 mg/L for E. coli and 0.5–1 mg/L for S. Typhimurium, which showed a higher overall susceptibility to chlorophyllin treatment. In accordance with a previous study, chlorophyllin has proven antibacterial activity both as a photosensitizer, illuminated with 12 mW/cm2, and in darkness. Our data clearly confirmed the relevance of the outer membrane in protection against xenobiotics. Combination treatment with colistin broadens chlorophyllin’s application spectrum against Gram-negatives and gives rise to the assumption that chlorophyllin together with cell membrane-destabilizing substances may become a promising approach in bacteria control. Furthermore, we demonstrated that colistin acts as a door opener even for the photodynamic inactivation of colistin-resistant (mcr-1-positive) E. coli cells by chlorophyllin, which could help us to overcome this antimicrobial resistance

Soil organic carbon models need independent time-series validation for reliable prediction

Numerical models are crucial to understand and/or predict past and future soil organic carbon dynamics. For those models aiming at prediction, validation is a critical step to gain confidence in projections. With a comprehensive review of ~250 models, we assess how models are validated depending on their objectives and features, discuss how validation of predictive models can be improved. We find a critical lack of independent validation using observed time series. Conducting such validations should be a priority to improve the model reliability. Approximately 60% of the models we analysed are not designed for predictions, but rather for conceptual understanding of soil processes. These models provide important insights by identifying key processes and alternative formalisms that can be relevant for predictive models. We argue that combining independent validation based on observed time series and improved information flow between predictive and conceptual models will increase reliability in predictions

Computational Identification of Novel Kir6 Channel Inhibitors

KATP channels consist of four Kir6.x pore–forming subunits and four regulatory sulfonylurea receptor (SUR) subunits. These channels couple the metabolic state of the cell to membrane excitability and play a key role in physiological processes such as insulin secretion in the pancreas, protection of cardiac muscle during ischemia and hypoxic vasodilation of arterial smooth muscle cells. Abnormal channel function resulting from inherited gain or loss-of-function mutations in either the Kir6.x and/or SUR subunits are associated with severe diseases such as neonatal diabetes, congenital hyperinsulinism, or Cantú syndrome (CS). CS is an ultra-rare genetic autosomal dominant disorder, caused by dominant gain-of-function mutations in SUR2A or Kir6.1 subunits. No specific pharmacotherapeutic treatment options are currently available for CS. Kir6 specific inhibitors could be beneficial for the development of novel drug therapies for CS, particular for mutations, which lack high affinity for sulfonylurea inhibitor glibenclamide. By applying a combination of computational methods including atomistic MD simulations, free energy calculations and pharmacophore modeling, we identified several novel Kir6.1 inhibitors, which might be possible candidates for drug repurposing. The in silico predictions were confirmed using inside/out patch-clamp analysis. Importantly, Cantú mutation C166S in Kir6.2 (equivalent to C176S in Kir6.1) and S1020P in SUR2A, retained high affinity toward the novel inhibitors. Summarizing, the inhibitors identified in this study might provide a starting point toward developing novel therapies for Cantú disease

Bacterial effector proteins in the evolution of pathogenic and symbiotic bacteria

Bakterielle Erkrankungen und Infektionen sind eine der häufigsten Todesursachen weltweit - ein tieferes Verständnis von Virulenzfaktoren ist unumgänglich für eine effektive Bekämp- fung von bakterielle Infektionen. In früheren Arbeiten wurde bereits gezeigt, dass Effektor- Proteine - welche als Haupt-Virulenzfaktoren anzusehen sind - sehr oft eine spezifische Domainsignatur besitzen (so genannte eukaryotic-like domains - ELDs). In dieser Arbeit wurde die Hypothese, dass Proteine mit eukaryotic-like domains das Resultat von hori- zontalen Gentransfer (HGT) mit Eukaryonten als Gen-Donatoren und Prokaryonten als Gen-Akzeptoren sind, untersucht. Weiters wurde das evolutionäre und phylogenetische Verhältnis zwischen Donor und Akzeptor des HGT analysiert. Methoden 70 Pathogene wurden randomisiert aus der effector.org Datenbank ausgewählt und ihr Proteom in Proteine mit ELDs (verwendete Definition von ELDs entspricht der effec- tor Datenbank Definition) und Proteine ohne ELDs unterteilt. Eine Softwarelösung wurde implementiert, welche folgende Schritte für die Menge aller Proteine der Proteome mit Hilfe vorhandener Software (verwendet wurden unter anderem phyloGenie, raxML, mtrees, blammer, phat und SIMAP) automatisierte: (1) Vorselektion des Proteoms an- hand von Sequenzähnlichkeit, (2) Erstellen eines multiplen alignments, (3) Konstruktion eines phylogenetischen Baumes und die (4) Analyse des Baumes (z.B. das Auffinden einer monophyletischen, prokaryontischen Gruppe mit eukaryontischen Proteinen im Ast) zum Auffinden eines HGT. Dieser Ansatz wurde sowohl auf Proteine als auch für Proteindo- mains angewandt. Anschließend an die Parametersuche für die einzelnen Programme wurde der methodische Ansatz angewendet, die phylogenetischen Bäume analysiert und nach positiven/negativen Signalen für HGTs mit Pathogene als Gen-Akzeptor und Eu- karyonten als Gen-Donor klassifiziert. Resultate Die Selektion der Proteine mittels Ähnlichkeitskriterien stellte sich als sehr effektiv heraus - es reduzierte die zu analysierenden Proteine um 40-50% und führte zu vernachlässig- barem Verlust an potentiellen HGT Kandidaten. Die Suche nach optimalen Parametern zur Konstruktion von phylogenetischen Bäume wurde mittels bekannten Beispielen aus der Literatur vollzogen - die Ergebnisse waren bis auf erklärbare Ausnahmen ident. Der Vergleich zwischen Maximum-likelihood und Neighbor-joining Methoden zur Konstruk- tion von phylogenetischen Bäumen zeigte am Beispiel eines Chlamydia Stammes keinen Unterschied im Ergebnis - was zum Verwenden einer Neighbor-joining Methode für die weitere Analyse führte. Angewendet auf Proteine zeigt die entwickelte Methode für14 Pathogene einen signifikanten (p < 0.05) Zusammenhang zwischen ELDs und HGTs. Die meisten dieser Pathogene sind hoch virulent und pathogen - und nach Analyse des Baums ist ersichtlich das nur wenige Gene-Donoren dem Phylum angehören welches das Pathogen infiziert. Wendet man die entwickelte Methode auf Proteindomains an (Do- mains sind evolutionär stabiler als komplette Proteinsequenzen) ergeben sich in 33 von 60 Fällen signifikante p-values und damit eine signifikante Korrelation zwischen ELDs und HGTs. Die Ergebnisse für Proteine und Proteindomains überschneiden sich in acht Pathogenen. Zusammenfassung Die Ergebnisse dieser Arbeit zeigen, dass man für eine bestimmte Gruppe an Bakterien einen signifikanten Zusammenhang zwischen HGTs und ELDs zeigen kann und dieser Zusammenhang bei Verwendung von Domaininsequenzen tendenziell stärker wird. Die acht Pathogene, welche sowohl auf Proteinsequenz- als auch auf Domainsequenzebene einen signifikanten Zusammenhang zwischen ELDs und HGTs zeigten, rekrutieren sich aus Bakterien, welche für ihre hohe Anzahl an HGTs aus der Literatur bekannt sind (Le- gionella pneumophila str. Lens, Legionella pneumophila str. Paris und Legionella pneu- mophila subsp. pneumophila str. Philadelphia 1), eine endosymbiontische Lebensweise besitzen (Wolbachia endosymbiont of Culex quinquefasciatus Pel) oder hoch virulent und pathogen sind (Coxiella burnetii RSA 493, Francisella tularensis subsp. tularensis NE061598, Leptospira interrogans serovar Copenhageni str. Fiocruz L1-130) mit der Ausnahme von Acidaminococcus intestini RyC-MR95.Bacterial diseases and infections are one of the major causes of morbidity and mortal- ity worldwide thus understanding key virulence factors is important to efficiently fight bacterial infections. It has been shown that effector proteins - as major virulence fac- tors - contain very often a specific domain signature (so called eukaryotic-like domains - ELDs). In this thesis the hypothesis if these effectors are the result of horizontal gene transfer events with eukaryots as donor and pathogens as acceptor organism was tested. Furthermore the evolutionary and phylogenetic relationship between the donor species and the acceptor species was analyzed. Methods 70 pathogens were chosen and their proteom was divided into ELD containing and non- ELD containing proteins based on the effector database classification. A computational method was implemented to use different available software solutions (e.g. phyloGenie, raxML, mtrees, blammer, phat, SIMAP) to automatize a pre-screening of the proteom based on similarity criteria (excluding e.g. housekeeping genes), calculating a multiple alignment, constructing a phylogenetic tree and analyzing the relationship of taxa in the tree (e.g. finding a monophyletic prokaryotic group with eukaryotic proteins) to identify horizontal gene transfer. This was done using full-length proteins as well as only domain sequences. The parameters used in these programs were tested and validated exhaustingly. Subsequently to the constructions of the trees was the analysis of the phylogenetic trees and the classification in HGT event with pathogen as gene acceptor and eukaryote as donor. Results The results indicated that pre-filtering with alien index was applicable - reducing the proteom by 40-50% yet conserving proteins with an ELD score and gaining a speed up factor of about 2 for the subsequent tree construction. Tree reconstruction parameters were tested by comparing positive signals for HGTs in Legionella pneumophila str. Paris with known positive signals in the literature - showing that the results are identical with some exceptions resulting from proteins with little homology. It was shown that the differences in the trees obtained from maximum-likelihood in comparison to neighbor- joining methods were negligible - thus the much faster neighbor-joining method could be used. Using full length proteins tree analysis showed for 14 bacteria a significant (p < 0.05) connection between ELDs and HGTs. Most of these bacteria are highly pathogenic and virulent - and most protein donors are not from the phyla these bacteria infects primarily. Utilizing domain sequences (domain structures are evolutionary more stable than proteins) for multiple alignment and tree construction resulted in 33 positive p-values out of 60 analyzed proteoms - with an overlap between positive signals from protein sequences an domain sequences of 8 bacteria. Summary The results of this thesis shows that for a certain set of organisms a significant con- nection between horizontal gene transfer events and eukaryotic-like domains can be stated and this link becomes even more established when using domain sequences. The 8 pathogens which showed a significant connection between ELDs and HGTs on pro- tein and on domain level were either pathogens which were known for their numerous HGT events (Legionella pneumophila str. Lens, Legionella pneumophila str. Paris, Legionella pneumophila subsp. pneumophila str. Philadelphia 1), their endosymbiotic life-style (Wolbachia endosymbiont of Culex quinquefasciatus Pel) or their high virulence and pathogenicity (Coxiella burnetii RSA 493, Francisella tularensis subsp. tularensis NE061598, Leptospira interrogans serovar Copenhageni str. Fiocruz L1-130) with the exception of Acidaminococcus intestini RyC-MR95 which colonize the human gut flora as commensal

Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using A Neural Network Potential

We present a comprehensive study investigating the potential gain in accuracy for calculating absolute solvation free energies (ASFE) using a neural network potential to describe the intramolecular energy of the solute. We calculated the ASFE for most compounds from the FreeSolv database using the Open Force Field (OpenFF) and compared them to earlier results obtained with the CHARMM General Force Field (CGenFF). By applying a nonequilibrium (NEQ) switching approach between the molecular mechanics (MM) description (either OpenFF or CGenFF) and the neural net potential (NNP)/MM level of theory (using ANI-2x as the NNP potential), we attempted to improve the accuracy of the calculated ASFEs. The predictive performance of the results did not change when applying this approach to all 589 small molecules in the FreeSolv database that ANI-2x can describe. When selecting a subset of 156 molecules, focusing on compounds where the force fields performed poorly, we saw a slight improvement in the root-mean-square error (RMSE) and mean absolute error (MAE). The majority of our calculations utilized unidirectional NEQ protocols based on Jarzynski\u27s equation. Additionally, we conducted bidirectional NEQ switching for the subset of 156 solutes. Notably, only a small fraction (10 out of 156) exhibited statistically significant discrepancies between unidirectional and bidirectional NEQ switching free energy estimates