A restraint molecular dynamics and simulated annealing approach for protein homology modeling utilizing mean angles

BACKGROUND: We have developed the program PERMOL for semi-automated homology modeling of proteins. It is based on restrained molecular dynamics using a simulated annealing protocol in torsion angle space. As main restraints defining the optimal local geometry of the structure weighted mean dihedral angles and their standard deviations are used which are calculated with an algorithm described earlier by Döker et al. (1999, BBRC, 257, 348–350). The overall long-range contacts are established via a small number of distance restraints between atoms involved in hydrogen bonds and backbone atoms of conserved residues. Employing the restraints generated by PERMOL three-dimensional structures are obtained using standard molecular dynamics programs such as DYANA or CNS. RESULTS: To test this modeling approach it has been used for predicting the structure of the histidine-containing phosphocarrier protein HPr from E. coli and the structure of the human peroxisome proliferator activated receptor γ (Ppar γ). The divergence between the modeled HPr and the previously determined X-ray structure was comparable to the divergence between the X-ray structure and the published NMR structure. The modeled structure of Ppar γ was also very close to the previously solved X-ray structure with an RMSD of 0.262 nm for the backbone atoms. CONCLUSION: In summary, we present a new method for homology modeling capable of producing high-quality structure models. An advantage of the method is that it can be used in combination with incomplete NMR data to obtain reasonable structure models in accordance with the experimental data

An R-Package for the Deconvolution and Integration of 1D NMR Data: MetaboDecon1D

NMR spectroscopy is a widely used method for the detection and quantification of metabolites in complex biological fluids. However, the large number of metabolites present in a biological sample such as urine or plasma leads to considerable signal overlap in one-dimensional NMR spectra, which in turn hampers both signal identification and quantification. As a consequence, we have developed an easy to use R-package that allows the fully automated deconvolution of overlapping signals in the underlying Lorentzian line-shapes. We show that precise integral values are computed, which are required to obtain both relative and absolute quantitative information. The algorithm is independent of any knowledge of the corresponding metabolites, which also allows the quantitative description of features of yet unknown identity

Robust Metabolite Quantification from J-Compensated 2D 1H-13C-HSQC Experiments

The spectral resolution of 2D H-1-C-13 heteronuclear single quantum coherence (H-1-C-13-HSQC) nuclear magnetic resonance (NMR) spectra facilitates both metabolite identification and quantification in nuclear magnetic resonance-based metabolomics. However, quantification is complicated by variations in magnetization transfer, which among others originate mainly from scalar coupling differences. Methods that compensate for variation in scalar coupling include the generation of calibration factors for individual signals or the use of additional pulse sequence schemes such as quantitative HSQC (Q-HSQC) that suppress the J(CH)-dependence by modulating the polarization transfer delays of HSQC or, additionally, employ a pure-shift homodecoupling approach in the 1(H) dimension, such as Quantitative, Perfected and Pure Shifted HSQC (QUIPU-HSQC). To test the quantitative accuracy of these three methods, employing a 600 MHz NMR spectrometer equipped with a helium cooled cryoprobe, a Latin-square design that covered the physiological concentration ranges of 10 metabolites was used. The results show the suitability of all three methods for the quantification of highly abundant metabolites. However, the substantially increased residual water signal observed in QUIPU-HSQC spectra impeded the quantification of low abundant metabolites located near the residual water signal, thus limiting its utility in high-throughput metabolite fingerprinting studies

Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data

Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarker

Isotope Ratio Outlier Analysis (IROA) for HPLC–TOFMS-Based Metabolomics of Human Urine

Metabolic fingerprinting by mass spectrometry aims at the comprehensive, semiquantitative analysis of metabolites. Isotope dilution, if successfully implemented, may provide a more reliable, relative quantification. Therefore, the 13C labeled yeast extract of the IROA TruQuant kit was added as an internal standard (IS) to human urine samples measured in full-scan mode on a high-performance liquid chromatography-time-of-flight mass spectrometer (HPLC–TOFMS) system. The isotope ratio approach enabled the analysis of 112 metabolites. The correlation with reference data did not improve significantly using 12C/13C ratios compared to absolute 12C peak areas. Moreover, using an intricate 13C-labeled standard increased the complexity of the mass spectra, which made correct signal annotation more challenging. On the positive side, the ratio approach helps to reduce batch effects, but it does not perform better than computational methods such as the “removebatcheffect” function in the R package Limma

Empagliflozin Reduces Renal Hyperfiltration in Response to Uninephrectomy, but Is Not Nephroprotective in UNx/DOCA/Salt Mouse Models

Large-scale clinical outcome studies demonstrated the efficacy of SGLT2 inhibitors in patients with type II diabetes. Besides their therapeutic efficacy in diabetes, significant renoprotection was observed in non-diabetic patients with chronic kidney disease (CKD), suggesting the existence of glucose-independent beneficial effects of SGLT2 inhibitors. However, the relevant mechanisms by which SGLT2 inhibition delays the progression of renal injury are still largely unknown and speculative. Previous studies showed that SGLT2 inhibitors reduce diabetic hyperfiltration, which is likely a key element in renoprotection. In line with this hypothesis, this study aimed to investigate the nephroprotective effects of the SGLT2 inhibitor empagliflozin (EMPA) in different mouse models with non-diabetic hyperfiltration and progressing CKD to identify the underlying diabetes-independent cellular mechanisms. Non-diabetic hyperfiltration was induced by unilateral nephrectomy (UNx). Since UNx alone does not result in renal damage, renal disease models with varying degrees of glomerular damage and albuminuria were generated by combining UNx with high NaCl diets ± deoxycorticosterone acetate (DOCA) in different mouse strains with and without genetic predisposition for glomerular injury. Renal parameters (GFR, albuminuria, urine volume) were monitored for 4–6 weeks. Application of EMPA via the drinking water resulted in sufficient EMPA plasma concentration and caused glucosuria, diuresis and in some models renal hypertrophy. EMPA had no effect on GFR in untreated wildtype animals, but significantly reduced hyperfiltration after UNx by 36%. In contrast, EMPA did not reduce UNx induced hyperfiltration in any of our kidney disease models, regardless of their degree of glomerular damage caused by DOCA/salt treatment. Consistent with the lack of reduction in glomerular hyperfiltration, EMPA-treated animals developed albuminuria and renal fibrosis to a similar extent as H2O control animals. Taken together, the data clearly indicate that blockade of SGLT2 has the potential to reduce non-diabetic hyperfiltration in otherwise untreated mice. However, no effects on hyperfiltration or progression of renal injury were observed in hypervolemic kidney disease models, suggesting that high salt intake and extracellular volume might attenuate the protective effects of SGLT2 blockers

BITES: Balanced Individual Treatment Effect for Survival data

Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes). Several methods were suggested for this scenario based on observational data, i.e.~data where the intervention was not applied randomly, for both continuous and binary outcome variables. However, patient outcome is often recorded in terms of time-to-event data, comprising right-censored event times if an event does not occur within the observation period. Albeit their enormous importance, time-to-event data is rarely used for treatment optimization. We suggest an approach named BITES (Balanced Individual Treatment Effect for Survival data), which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network; i.e.~we regularize differences between treated and non-treated patients using Integral Probability Metrics (IPM). We show in simulation studies that this approach outperforms the state of the art. Further, we demonstrate in an application to a cohort of breast cancer patients that hormone treatment can be optimized based on six routine parameters. We successfully validated this finding in an independent cohort. BITES is provided as an easy-to-use python implementation

Cold Atmospheric Plasma Changes the Amino Acid Composition of Solutions and Influences the Anti-Tumor Effect on Melanoma Cells

Cold Atmospheric Plasma (CAP) is an ionized gas near room temperature. Its anti-tumor effect can be transmitted either by direct treatment or mediated by a plasma-treated solution (PTS), such as treated standard cell culture medium, which contains different amino acids, inorganic salts, vitamins and other substances. Despite extensive research, the active components in PTS and its molecular or cellular mechanisms are not yet fully understood. The purpose of this study was the measurement of the reactive species in PTS and their effect on tumor cells using different plasma modes and treatment durations. The PTS analysis yielded mode- and dose-dependent differences in the production of reactive oxygen and nitrogen species (RONS), and in the decomposition and modification of the amino acids Tyrosine (Tyr) and Tryptophan (Trp). The Trp metabolites Formylkynurenine (FKyn) and Kynurenine (Kyn) were produced in PTS with the 4 kHz (oxygen) mode, inducing apoptosis in Mel Im melanoma cells. Nitrated derivatives of Trp and Tyr were formed in the 8 kHz (nitrogen) mode, elevating the p16 mRNA expression and senescence-associated ß-Galactosidase staining. In conclusion, the plasma mode has a strong impact on the composition of the active components in PTS and affects its anti-tumor mechanism. These findings are of decisive importance for the development of plasma devices and the effectiveness of tumor treatment