Quantitative delineation of how breathing motions open ligand migration channels in myoglobin and its mutants

Ligand migration and binding are central to the biological functions of many proteins such as myoglobin (Mb) and it is widely thought that protein breathing motions open up ligand channels dynamically. However, how a protein exerts its control over the opening and closing of these channels through its intrinsic dynamics is not fully understood. Specifically, a quantitative delineation of the breathing motions that are needed to open ligand channels is lacking. In this work, we present and apply a novel normal mode‐based method to quantitatively delineate what and how breathing motions open ligand migration channels in Mb and its mutants. The motivation behind this work springs from the observation that normal mode motions are closely linked to the breathing motions that are thought to open ligand migration channels. In addition, the method provides a direct and detailed depiction of the motions of each and every residue that lines a channel and can identify key residues that play a dominating role in regulating the channel. The all‐atom model and the full force‐field employed in the method provide a realistic energetics on the work cost required to open a channel, and as a result, the method can be used to efficiently study the effects of mutations on ligand migration channels and on ligand entry rates. Our results on Mb and its mutants are in excellent agreement with MD simulation results and experimentally determined ligand entry rates. Proteins 2015; 83:757–770. © 2015 Wiley Periodicals, Inc.     

Vibrational entropy differences between mesophile and thermophile proteins and their use in protein engineering

We recently introduced ENCoM, an elastic network atomic contact model, as the first coarse-grained normal mode analysis method that accounts for the nature of amino acids and can predict the effect of mutations on thermostability based on changes vibrational entropy. In this proof-of-concept article, we use pairs of mesophile and thermophile homolog proteins with identical structures to determine if a measure of vibrational entropy based on normal mode analysis can discriminate thermophile from mesophile proteins. We observe that in around 60% of cases, thermophile proteins are more rigid at equivalent temperatures than their mesophile counterpart and this difference can guide the design of proteins to increase their thermostability through series of mutations. We observe that mutations separating thermophile proteins from their mesophile orthologs contribute independently to a decrease in vibrational entropy and discuss the application and implications of this methodology to protein engineering.     

Structural and Functional Analysis of BipA,a Regulator of Virulence in Enteropathogenic Escherichia coli

The translational GTPase BipA regulates the expression of virulence and pathogenicity factors in several eubacteria. BipA-dependent expression of virulence factors occurs under starvation conditions, such as encountered during infection of a host. Under these conditions, BipA associates with the small ribosomal subunit. BipA also has a second function to promote the efficiency of late steps in biogenesis of large ribosomal subunits at low temperatures, presumably while bound to the ribosome. During starvation, the cellular concentration of stress alarmone guanosine-3′, 5′-bis pyrophosphate (ppGpp) is increased. This increase allows ppGpp to bind to BipA and switch its binding specificity from ribosomes to small ribosomal subunits. A conformational change of BipA upon ppGpp binding could explain the ppGpp regulation of the binding specificity of BipA. Here, we present the structures of the full-length BipA from Escherichia coli in apo, GDP-, and ppGpp-bound forms. The crystal structure and small-angle x-ray scattering data of the protein with bound nucleotides, together with a thermodynamic analysis of the binding of GDP and of ppGpp to BipA, indicate that the ppGpp-bound form of BipA adopts the structure of the GDP form. This suggests furthermore, that the switch in binding preference only occurs when both ppGpp and the small ribosomal subunit are present. This molecular mechanism would allow BipA to interact with both the ribosome and the small ribosomal subunit during stress response.     

New insights into the interplay between codon bias determinants in plants

Codon bias is the non-random use of synonymous codons, a phenomenon that has been observed in species as diverse as bacteria, plants and mammals. The preferential use of particular synonymous codons may reflect neutral mechanisms (e.g. mutational bias, G|C-biased gene conversion, genetic drift) and/or selection for mRNA stability, translational efficiency and accuracy. The extent to which these different factors influence codon usage is unknown, so we dissected the contribution of mutational bias and selection towards codon bias in genes from 15 eudicots, 4 monocots and 2 mosses. We analysed the frequency of mononucleotides, dinucleotides and trinucleotides and investigated whether the compositional genomic background could account for the observed codon usage profiles. Neutral forces such as mutational pressure and G|C-biased gene conversion appeared to underlie most of the observed codon bias, although there was also evidence for the selection of optimal translational efficiency and mRNA folding. Our data confirmed the compositional differences between monocots and dicots, with the former featuring in general a lower background compositional bias but a higher overall codon bias.     

Development of M2BPGi: a novel fibrosis serum glyco-biomarker for chronic hepatitis/cirrhosis diagnostics

Many proteins in the living body are glycoproteins, which present glycans linked on their surface. Glycan structures reflect the degree of cell differentiation or canceration and are cell specific. These characteristics are advantageous in the development of various disease biomarkers. Glycoprotein-based biomarkers (glyco-biomarkers) are developed by utilizing the specific changes in the glycan structure on a glycoprotein secreted from the diseased cells of interest. Therefore, quantification of the altered glycan structures is the key to developing a new glyco-biomarker. Glycoscience is a relatively new area of molecular science, and recent advancement of glycotechnologies is remarkable. In the author’s institute, new glycoscience technologies have been designed to be efficiently utilized for the development of new diagnostic agents. This paper introduces a strategy for glyco-biomarker development, which was successfully applied in the development of Wisteria floribunda agglutinin-positive Mac-2 binding protein M2BPGi, a liver fibrosis marker now commercially available for clinical use.     

Survey of the year 2009: applications of isothermal titration calorimetry

Isothermal titration calorimetry (ITC) is now an established and invaluable method for determining the thermodynamic constants, association constant and stoichiometry of molecular interactions in aqueous solutions. The technique has become widely used by biochemists to study protein interaction with other proteins, small molecules, metal ions, lipids, nucleic acids and carbohydrates; and nucleic acid interaction with small molecules. The drug discovery industry has utilized this approach to measure protein (or nucleic acid) interaction with drug candidates. ITC has been used to screen candidates, guide the design of potential drugs and validate the modelling used in structure-based drug design. Emerging disciplines including nanotechnology and drug delivery could benefit greatly from ITC in enhancing their understanding and control of nano-particle assembly, and drug binding and controlled release.     

Virtual screening using molecular simulations

Effective virtual screening relies on our ability to make accurate prediction of protein-ligand binding, which remains a great challenge. In this work, utilizing the molecular-mechanics Poisson-Boltzmann (or Generalized Born) surface area approach, we have evaluated the binding affinity of a set of 156 ligands to seven families of proteins, trypsin β, thrombin α, cyclin-dependent kinase (CDK), cAMP-dependent kinase (PKA), urokinase-type plasminogen activator, β-glucosidase A, and coagulation factor Xa. The effect of protein dielectric constant in the implicit-solvent model on the binding free energy calculation is shown to be important. The statistical correlations between the binding energy calculated from the implicit-solvent approach and experimental free energy are in the range of 0.56-0.79 across all the families. This performance is better than that of typical docking programs especially given that the latter is directly trained using known binding data whereas the molecular mechanics is based on general physical parameters. Estimation of entropic contribution remains the barrier to accurate free energy calculation. We show that the traditional rigid rotor harmonic oscillator approximation is unable to improve the binding free energy prediction. Inclusion of conformational restriction seems to be promising but requires further investigation. On the other hand, our preliminary study suggests that implicit-solvent based alchemical perturbation, which offers explicit sampling of configuration entropy, can be a viable approach to significantly improve the prediction of binding free energy. Overall, the molecular mechanics approach has the potential for medium to high-throughput computational drug discovery.     

Quantifying Histological Features of Cancer Biospecimens for Biobanking Quality Assurance Using Automated Morphometric Pattern Recognition Image Analysis Algorithms

Biorepository-supported translational research depends on high-quality, well-annotated specimens. Histopathology assessment contributes insight into how representative lesions are for research objectives. Feasibility of documenting histological proportions of tumor and stroma was studied in an effort to enhance information regarding biorepository tissue heterogeneity. Using commercially available software, unique spatial-spectral algorithms were developed for applying automated pattern recognition morphometric image analysis to quantify histologic tumor and nontumor tissue areas in biospecimen tissue sections. Measurements were acquired successfully for 75/75 (100%) lymphomas, 76/77 (98.7%) osteosarcomas, and 60/70 (85.7%) melanomas. The percentage of tissue area occupied by tumor varied among patients and tumor types and was distributed around medians of 94% [interquartile range (IQR)=14%] for lymphomas, 84% for melanomas (IQR=24%), and 39% for osteosarcomas (IQR=44%). Within-patient comparisons from a subset, including multiple individual patient specimens, revealed ≤12% median coefficient of variation (CV) for lymphomas and melanomas. Phenotypic heterogeneity of osteosarcomas resulted in 33% median CV. Uniformly applied, tumor-specific pattern recognition software permits automated tissue-feature quantification. Furthermore, dispersion analyses of area measurements across collections, as well as of multiple specimens from individual patients, support using limited tissue slices to gauge features for some tumor types. Quantitative image analysis automation is anticipated to minimize variability associated with routine biorepository pathologic evaluations and enhance biomarker discovery by helping to guide the selection of study-appropriate specimens.