Implementation of force distribution analysis for molecular dynamics simulations

<p>Abstract</p> <p>Background</p> <p>The way mechanical stress is distributed inside and propagated by proteins and other biopolymers largely defines their function. Yet, determining the network of interactions propagating internal strain remains a challenge for both, experiment and theory. Based on molecular dynamics simulations, we developed force distribution analysis (FDA), a method that allows visualizing strain propagation in macromolecules.</p> <p>Results</p> <p>To be immediately applicable to a wide range of systems, FDA was implemented as an extension to Gromacs, a commonly used package for molecular simulations. The FDA code comes with an easy-to-use command line interface and can directly be applied to every system built using Gromacs. We provide an additional R-package providing functions for advanced statistical analysis and presentation of the FDA data.</p> <p>Conclusions</p> <p>Using FDA, we were able to explain the origin of mechanical robustness in immunoglobulin domains and silk fibers. By elucidating propagation of internal strain upon ligand binding, we previously also successfully revealed the functionality of a stiff allosteric protein. FDA thus has the potential to be a valuable tool in the investigation and rational design of mechanical properties in proteins and nano-materials.</p

Force Distribution in Macromolecules

All living organisms utilize thousands of molecular building blocks to perform mechanical tasks. These building blocks are mostly proteins, and their mechanical properties define the way they can be utilized by the cell. The spectrum ranges from rope like structures that give hold and stability to our bodies to microscopic engines helping us to perform or sense mechanical work. An increasing number of biological processes are revealed to be driven by force and well-directed distribution of strain is the very base of many of these mechanisms. We need to be able to observe the distribution of strain within bio-molecules if we want to gain detailed insight into the function of these highly complex nano-machines. Only by theoretical understanding and prediction of mechanical processes on the molecular level will we be able to rationally tailor proteins to mimic specific biological functions. This thesis aims at understanding the molecular mechanics of a wide range of biological molecules, such as the muscle protein titin or silk fibers. We introduce Force Distribution Analysis (FDA), a new approach to directly study the forces driving molecular processes, instead of indirectly observing them by means of coordinate changes

pcaMethods - a bioconductor package providing PCA methods for incomplete data

pcaMethods is a Bioconductor compliant library for computing principal component analysis (PCA) on incomplete data sets. The results can be analyzed directly or used to estimate missing values to enable the use of missing value sensitive statistical methods. The package was mainly developed with microarray and metabolite data sets in mind, but can be applied to any other incomplete data set as well

Influence of missing values substitutes on multivariate analysis of metabolomics data

Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%–20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the most well known substitute for missing values is a mean imputation. In fact, some researchers consider this aspect of data analysis in their metabolomics pipeline as so routine that they do not even mention using this replacement approach. However, this may have a significant influence on the data analysis output(s) and might be highly sensitive to the distribution of samples between different classes. Therefore, in this study we have analysed different substitutes of missing values namely: zero, mean, median, k-nearest neighbours (kNN) and random forest (RF) imputation, in terms of their influence on unsupervised and supervised learning and, thus, their impact on the final output(s) in terms of biological interpretation. These comparisons have been demonstrated both visually and computationally (classification rate) to support our findings. The results show that the selection of the replacement methods to impute missing values may have a considerable effect on the classification accuracy, if performed incorrectly this may negatively influence the biomarkers selected for an early disease diagnosis or identification of cancer related metabolites. In the case of GC-MS metabolomics data studied here our findings recommend that RF should be favored as an imputation of missing value over the other tested methods. This approach displayed excellent results in terms of classification rate for both supervised methods namely: principal components-linear discriminant analysis (PC-LDA) (98.02%) and partial least squares-discriminant analysis (PLS-DA) (97.96%) outperforming other imputation methods

Detailed phytochemical analysis of high- and low artemisinin-producing chemotypes of Artemisia annua

Chemical derivatives of artemisinin, a sesquiterpene lactone produced by Artemisia annua, are the active ingredient in the most effective treatment for malaria. Comprehensive phytochemical analysis of two contrasting chemotypes of A. annua resulted in the characterization of over 80 natural products by NMR, more than 20 of which are novel and described here for the first time. Analysis of high- and low-artemisinin producing (HAP and LAP) chemotypes of A. annua confirmed the latter to have a low level of DBR2 (artemisinic aldehyde Δ11(13) reductase) gene expression. Here we show that the LAP chemotype accumulates high levels of artemisinic acid, arteannuin B, epi-deoxyarteannuin B and other amorpha-4,11-diene derived sesquiterpenes which are unsaturated at the 11,13-position. By contrast, the HAP chemotype is rich in sesquiterpenes saturated at the 11,13-position (dihydroartemisinic acid, artemisinin and dihydro-epi-deoxyarteannunin B), which is consistent with higher expression levels of DBR2, and also with the presence of a HAP-chemotype version of CYP71AV1 (amorpha-4,11-diene C-12 oxidase). Our results indicate that the conversion steps from artemisinic acid to arteannuin B, epi-deoxyarteannuin B and artemisitene in the LAP chemotype are non-enzymatic and parallel the non-enzymatic conversion of DHAA to artemisinin and dihyro-epi-deoxyarteannuin B in the HAP chemotype. Interestingly, artemisinic acid in the LAP chemotype preferentially converts to arteannuin B rather than the endoperoxide bridge containing artemisitene. In contrast, in the HAP chemotype, DHAA preferentially converts to artemisinin. Broader metabolomic and transcriptomic profiling revealed significantly different terpenoid profiles and related terpenoid gene expression in these two morphologically distinct chemotypes

Urinary MicroRNA Profiling in the Nephropathy of Type 1 Diabetes

Background: Patients with Type 1 Diabetes (T1D) are particularly vulnerable to development of Diabetic nephropathy (DN) leading to End Stage Renal Disease. Hence a better understanding of the factors affecting kidney disease progression in T1D is urgently needed. In recent years microRNAs have emerged as important post-transcriptional regulators of gene expression in many different health conditions. We hypothesized that urinary microRNA profile of patients will differ in the different stages of diabetic renal disease. Methods and Findings: We studied urine microRNA profiles with qPCR in 40 T1D with >20 year follow up 10 who never developed renal disease (N) matched against 10 patients who went on to develop overt nephropathy (DN), 10 patients with intermittent microalbuminuria (IMA) matched against 10 patients with persistent (PMA) microalbuminuria. A Bayesian procedure was used to normalize and convert raw signals to expression ratios. We applied formal statistical techniques to translate fold changes to profiles of microRNA targets which were then used to make inferences about biological pathways in the Gene Ontology and REACTOME structured vocabularies. A total of 27 microRNAs were found to be present at significantly different levels in different stages of untreated nephropathy. These microRNAs mapped to overlapping pathways pertaining to growth factor signaling and renal fibrosis known to be targeted in diabetic kidney disease. Conclusions: Urinary microRNA profiles differ across the different stages of diabetic nephropathy. Previous work using experimental, clinical chemistry or biopsy samples has demonstrated differential expression of many of these microRNAs in a variety of chronic renal conditions and diabetes. Combining expression ratios of microRNAs with formal inferences about their predicted mRNA targets and associated biological pathways may yield useful markers for early diagnosis and risk stratification of DN in T1D by inferring the alteration of renal molecular processes. © 2013 Argyropoulos et al

Shear-Induced Unfolding Activates von Willebrand Factor A2 Domain for Proteolysis

To avoid pathological platelet aggregation by von Willebrand factor (VWF), VWF multimers are regulated in size and reactivity for adhesion by ADAMTS13-mediated proteolysis in a shear flow dependent manner. We examined if tensile stress in VWF under shear flow activates the VWF A2 domain for cleavage by ADAMTS13 using molecular dynamics simulations. We indeed observed stepwise unfolding of A2 and exposure of its deeply buried ADAMTS13 cleavage site. Interestingly, disulfide bonds in the adjacent and highly homologous VWF A1 and A3 domains obstruct their mechanical unfolding. We generated a full length mutant VWF featuring a homologous disulfide bond in A2 (N1493C and C1670S), in an attempt to lock A2 against unfolding. We find this mutant to feature ADAMTS13-resistant behavior in vitro. Our results yield molecular-detail evidence for the force-sensoring function of VWF A2, by revealing how tension in VWF due to shear flow selectively exposes the A2 proteolysis site to ADAMTS13 for cleavage while keeping the folded remainder of A2 intact and functional. We find the unconventional knotted Rossman fold of A2 to be the key to this mechanical response, tailored for regulating VWF size and activity. Based on our model we can explain the pathomechanism of some natural mutations in the VWF A2 domain that significantly increase the cleavage by ADAMTS13 without shearing or chemical denaturation, and provide with the cleavage-activated A2 conformation a structural basis for the design of inhibitors for VWF type 2 diseases

Dynamic Allostery in the Methionine Repressor Revealed by Force Distribution Analysis

Many fundamental cellular processes such as gene expression are tightly regulated by protein allostery. Allosteric signal propagation from the regulatory to the active site requires long-range communication, the molecular mechanism of which remains a matter of debate. A classical example for long-range allostery is the activation of the methionine repressor MetJ, a transcription factor. Binding of its co-repressor SAM increases its affinity for DNA several-fold, but has no visible conformational effect on its DNA binding interface. Our molecular dynamics simulations indicate correlated domain motions within MetJ, and quenching of these dynamics upon SAM binding entropically favors DNA binding. From monitoring conformational fluctuations alone, it is not obvious how the presence of SAM is communicated through the largely rigid core of MetJ and how SAM thereby is able to regulate MetJ dynamics. We here directly monitored the propagation of internal forces through the MetJ structure, instead of relying on conformational changes as conventionally done. Our force distribution analysis successfully revealed the molecular network for strain propagation, which connects collective domain motions through the protein core. Parts of the network are directly affected by SAM binding, giving rise to the observed quenching of fluctuations. Our results are in good agreement with experimental data. The force distribution analysis suggests itself as a valuable tool to gain insight into the molecular function of a whole class of allosteric proteins

Predicting Arabidopsis Freezing Tolerance and Heterosis in Freezing Tolerance from Metabolite Composition

Heterosis, or hybrid vigor, is one of the most important tools in plant breeding and has previously been demonstrated for plant freezing tolerance. Freezing tolerance is an important trait because it can limit the geographical distribution of plants and their agricultural yield. Plants from temperate climates increase in freezing tolerance during exposure to low, non-freezing temperatures in a process termed ‘cold acclimation’. Metabolite profiling has indicated a major reprogramming of plant metabolism in the cold, but it has remained unclear in previous studies which of these changes are related to freezing tolerance. In the present study, we have used metabolic profiling to discover combinations of metabolites that predict freezing tolerance and its heterosis in Arabidopsis thaliana. We identified compatible solutes and, in particular, the pathway leading to raffinose as crucial statistical predictors for freezing tolerance and its heterosis, while some TCA cycle intermediates contribute only to predicting the heterotic phenotype. This indicates coordinate links between heterosis and metabolic pathways, suggesting that a limited number of regulatory genes may determine the extent of heterosis in this complex trait. In addition, several unidentified metabolites strongly contributed to the prediction of both freezing tolerance and its heterosis and we present an exemplary analysis of one of these, identifying it as a hexose conjugate

Metabolomic Response of Calotropis procera Growing in the Desert to Changes in Water Availability

Water availability is a major limitation for agricultural productivity. Plants growing in severe arid climates such as deserts provide tools for studying plant growth and performance under extreme drought conditions. The perennial species Calotropis procera used in this study is a shrub growing in many arid areas which has an exceptional ability to adapt and be productive in severe arid conditions. We describe the results of studying the metabolomic response of wild C procera plants growing in the desert to a one time water supply. Leaves of C. procera plants were taken at three time points before and 1 hour, 6 hours and 12 hours after watering and subjected to a metabolomics and lipidomics analysis. Analysis of the data reveals that within one hour after watering C. procera has already responded on the metabolic level to the sudden water availability as evidenced by major changes such as increased levels of most amino acids, a decrease in sucrose, raffinose and maltitol, a decrease in storage lipids (triacylglycerols) and an increase in membrane lipids including photosynthetic membranes. These changes still prevail at the 6 hour time point after watering however 12 hours after watering the metabolomics data are essentially indistinguishable from the prewatering state thus demonstrating not only a rapid response to water availability but also a rapid response to loss of water. Taken together these data suggest that the ability of C. procera to survive under the very harsh drought conditions prevailing in the desert might be associated with its rapid adjustments to water availability and losses