Lithostratigraphy of the Grant Lake Limestone and Grant Lake Formation (Upper Ordovician) in Southwestern Ohio

Author Institution: Ohio Department of Natural Resources, Division of Geological SurveyThe Grant Lake Limestone, including, in ascending order, the Bellevue, the Corryville, and the Straight Creek Members, and the Grant Lake Formation, including, in ascending order, the Bellevue, the Corryville, and the Mount Auburn Members, are herein defined as lithostratigraphic units in southwestern Ohio. Regional bedrock mapping, shale-percentage and geophysical logs, and mean shale percentage of lithostratigraphic units demonstrate a progressive change from a limestone-dominant stratigraphic section in the Maysville, KY, region to a shale-dominant stratigraphic section in the Cincinnati, OH, region. The Grant Lake Limestone is redefined to account for the progressive decrease in limestone content observed northwestward away from Maysville, KY. The Grant Lake Formation is introduced to describe the shaledominant lateral equivalent of the Grant Lake Limestone in the Cincinnati, OH, region. The Bellevue Limestone, the Corryville Formation, and the Mount Auburn Formation are reduced to members because, in some cases, they are not mappable at 1:62,500 or smaller scales. The Straight Creek Member is introduced to describe the limestone-dominant lateral equivalent of the shale-dominant Mount Auburn Member. The limestone-dominant and shale-dominant lithologies of the Grant Lake Limestone and the Grant Lake Formation can be recognized in shale-percentage and geophysical logs. Correlation between logs led to recognition of these stratigraphic units in the subsurface of southwestern Ohio

Potential Sand and Gravel Resources of the Mansfield 30 x 60 minute quadrangle

The Ohio Department of Natural Resources (ODNR), Division of Geological Survey has completed a reconnaissance map showing areas of mineable sand and gravel resources in the Mansfield, Ohio, 30 x 60 minute (scale 1:100,000) quadrangle. The main purpose of this map was to create a reconnaissance-level map that would show the potential for mining sand and gravel in this quadrangle. The map shows areas of surficial materials in increments of 10 feet and then differentiates sand, sand and gravel, and ice-contact deposits from finer grained materials, such as glacial till, lacustrine clay and silt, and alluvial materials. The sand and sand-and-gravel units include both surficial and buried outwash and valley train deposits and ice-contact deposits, such as kames, kame terraces, and eskers. To determine if a sand-and-gravel deposit was economically viable, this map shows the total thickness or accumulation of sand and gravel in the Mansfield 30 x 60-minute quadrangle.United States Geological Survey: National Cooperative Geologic Mapping Program, Great Lakes Geologic Mapping Coalitio

Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a ‘one-stop’ analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes

Hydrophobicity and Charge Shape Cellular Metabolite Concentrations

What governs the concentrations of metabolites within living cells? Beyond specific metabolic and enzymatic considerations, are there global trends that affect their values? We hypothesize that the physico-chemical properties of metabolites considerably affect their in-vivo concentrations. The recently achieved experimental capability to measure the concentrations of many metabolites simultaneously has made the testing of this hypothesis possible. Here, we analyze such recently available data sets of metabolite concentrations within E. coli, S. cerevisiae, B. subtilis and human. Overall, these data sets encompass more than twenty conditions, each containing dozens (28-108) of simultaneously measured metabolites. We test for correlations with various physico-chemical properties and find that the number of charged atoms, non-polar surface area, lipophilicity and solubility consistently correlate with concentration. In most data sets, a change in one of these properties elicits a ∼100 fold increase in metabolite concentrations. We find that the non-polar surface area and number of charged atoms account for almost half of the variation in concentrations in the most reliable and comprehensive data set. Analyzing specific groups of metabolites, such as amino-acids or phosphorylated nucleotides, reveals even a higher dependence of concentration on hydrophobicity. We suggest that these findings can be explained by evolutionary constraints imposed on metabolite concentrations and discuss possible selective pressures that can account for them. These include the reduction of solute leakage through the lipid membrane, avoidance of deleterious aggregates and reduction of non-specific hydrophobic binding. By highlighting the global constraints imposed on metabolic pathways, future research could shed light onto aspects of biochemical evolution and the chemical constraints that bound metabolic engineering efforts

Scaling behaviour for the water transport in nanoconfined geometries

The transport of water in nanoconfined geometries is different from bulk phase and has tremendous implications in nanotechnology and biotechnology. Here molecular dynamics is used to compute the self-diffusion coefficient D of water within nanopores, around nanoparticles, carbon nanotubes and proteins. For almost 60 different cases, D is found to scale linearly with the sole parameter theta as D(theta)=DB[1+(DC/DB-1)theta], with DB and DC the bulk and totally confined diffusion of water, respectively. The parameter theta is primarily influenced by geometry and represents the ratio between the confined and total water volumes. The D(theta) relationship is interpreted within the thermodynamics of supercooled water. As an example, such relationship is shown to accurately predict the relaxometric response of contrast agents for magnetic resonance imaging. The D(theta) relationship can help in interpreting the transport of water molecules under nanoconfined conditions and tailoring nanostructures with precise modulation of water mobility

Recognition of methylated DNA through methyl-CpG binding domain proteins

DNA methylation is a key regulatory control route in epigenetics, involving gene silencing and chromosome inactivation. It has been recognized that methyl-CpG binding domain (MBD) proteins play an important role in interpreting the genetic information encoded by methylated DNA (mDNA). Although the function of MBD proteins has attracted considerable attention and is well characterized, the mechanism underlying mDNA recognition by MBD proteins is still poorly understood. In this article, we demonstrate that the methyl-CpG dinucleotides are recognized at the MBD–mDNA interface by two MBD arginines through an interplay of hydrogen bonding and cation-π interaction. Through molecular dynamics and quantum-chemistry calculations we investigate the methyl-cytosine recognition process and demonstrate that methylation enhances MBD–mDNA binding by increasing the hydrophobic interfacial area and by strengthening the interaction between mDNA and MBD proteins. Free-energy perturbation calculations also show that methylation yields favorable contribution to the binding free energy for MBD–mDNA complex

Investigating the Structural Impacts of I64T and P311S Mutations in APE1-DNA Complex: A Molecular Dynamics Approach

Elucidating the molecular dynamic behavior of Protein-DNA complex upon mutation is crucial in current genomics. Molecular dynamics approach reveals the changes on incorporation of variants that dictate the structure and function of Protein-DNA complexes. Deleterious mutations in APE1 protein modify the physicochemical property of amino acids that affect the protein stability and dynamic behavior. Further, these mutations disrupt the binding sites and prohibit the protein to form complexes with its interacting DNA.In this study, we developed a rapid and cost-effective method to analyze variants in APE1 gene that are associated with disease susceptibility and evaluated their impacts on APE1-DNA complex dynamic behavior. Initially, two different in silico approaches were used to identify deleterious variants in APE1 gene. Deleterious scores that overlap in these approaches were taken in concern and based on it, two nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were taken further for structural analysis.Different parameters such as RMSD, RMSF, salt bridge, H-bonds and SASA applied in Molecular dynamic study reveals that predicted deleterious variants I64T and P311S alters the structure as well as affect the stability of APE1-DNA interacting functions. This study addresses such new methods for validating functional polymorphisms of human APE1 which is critically involved in causing deficit in repair capacity, which in turn leads to genetic instability and carcinogenesis

Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only

Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis

Solvent accessible surface area approximations for rapid and accurate protein structure prediction

The burial of hydrophobic amino acids in the protein core is a driving force in protein folding. The extent to which an amino acid interacts with the solvent and the protein core is naturally proportional to the surface area exposed to these environments. However, an accurate calculation of the solvent-accessible surface area (SASA), a geometric measure of this exposure, is numerically demanding as it is not pair-wise decomposable. Furthermore, it depends on a full-atom representation of the molecule. This manuscript introduces a series of four SASA approximations of increasing computational complexity and accuracy as well as knowledge-based environment free energy potentials based on these SASA approximations. Their ability to distinguish correctly from incorrectly folded protein models is assessed to balance speed and accuracy for protein structure prediction. We find the newly developed “Neighbor Vector” algorithm provides the most optimal balance of accurate yet rapid exposure measures