Quantitative methods for developing Fc mutants with extended half-lives

Fc mutants with increased binding affinity for the neonatal receptor, FcRn, exhibit increased half-lives in vivo, and represent an attractive means for extending the half-lives of therapeutic antibodies. The half-lives of other therapeutic molecules (e.g., proteins) may also be extended by conjugating them to Fc fragments, thus decreasing the frequency of patient injections and allowing the administration of low and potentially nontoxic concentrations of the therapeutics. To investigate the possibility for further increasing the half-life of Fc, a pair of quantitative methods is presented to complement combinatorial screening and in vivo testing. Specifically, a simple molecular modeling procedure was developed to predict relative Gibbs free energies of binding values (DeltaDeltaGbind) between Fc and FcRn across different mutants and species. This procedure was found to reasonably reproduce experimental DeltaDeltaGbind values from our experiments and the literature, and may be used as an initial screen to explore Fc sequence space more fully prior to experimental testing. In addition, a mathematical model of Fc trafficking was formulated and combined with a cell-level pulse-chase assay to obtain a quantitative recycling parameter in human T84 cells. This Fc recycling parameter was found to be correlated with binding affinity, but captures the pH dependent nature of the interaction between Fc and FcRn and may serve as an additional screen following combinatorial experiments.     

Computational methods for diffusion-influenced biochemical reactions

MOTIVATION: We compare stochastic computational methods accounting for space and discrete nature of reactants in biochemical systems. Implementations based on Brownian dynamics (BD) and the reaction-diffusion master equation are applied to a simplified gene expression model and to a signal transduction pathway in Escherichia coli. RESULTS: In the regime where the number of molecules is small and reactions are diffusion-limited predicted fluctuations in the product number vary between the methods, while the average is the same. Computational approaches at the level of the reaction-diffusion master equation compute the same fluctuations as the reference result obtained from the particle-based method if the size of the sub-volumes is comparable to the diameter of reactants. Using numerical simulations of reversible binding of a pair of molecules we argue that the disagreement in predicted fluctuations is due to different modeling of inter-arrival times between reaction events. Simulations for a more complex biological study show that the different approaches lead to different results due to modeling issues. Finally, we present the physical assumptions behind the mesoscopic models for the reaction-diffusion systems. AVAILABILITY: Input files for the simulations and the source code of GMP can be found under the following address: http://www.cwi.nl/projects/sic/bioinformatics2007/     

Kinetics analysis methods for approximate folding landscapes

MOTIVATION: Protein motions play an essential role in many biochemical processes. Lab studies often quantify these motions in terms of their kinetics such as the speed at which a protein folds or the population of certain interesting states like the native state. Kinetic metrics give quantifiable measurements of the folding process that can be compared across a group of proteins such as a wild-type protein and its mutants. RESULTS: We present two new techniques, map-based master equation solution and map-based Monte Carlo simulation, to study protein kinetics through folding rates and population kinetics from approximate folding landscapes, models called maps. From these two new techniques, interesting metrics that describe the folding process, such as reaction coordinates, can also be studied. In this article we focus on two metrics, formation of helices and structure formation around tryptophan residues. These two metrics are often studied in the lab through circular dichroism (CD) spectra analysis and tryptophan fluorescence experiments, respectively. The approximated landscape models we use here are the maps of protein conformations and their associated transitions that we have presented and validated previously. In contrast to other methods such as the traditional master equation and Monte Carlo simulation, our techniques are both fast and can easily be computed for full-length detailed protein models. We validate our map-based kinetics techniques by comparing folding rates to known experimental results. We also look in depth at the population kinetics, helix formation and structure near tryptophan residues for a variety of proteins. AVAILABILITY: We invite the community to help us enrich our publicly available database of motions and kinetics analysis by submitting to our server: http://parasol.tamu.edu/foldingserver/.     

Global parameter estimation methods for stochastic biochemical systems

Background  

The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data.     

Novel methods for assessing fish blood biochemical data

The purpose of this study was the identification and quantification of biochemical parameters over a 1‐year cycle and to provide a detailed picture of seasonal changes in plasma metabolites and enzymes. Using the novel methods of machine learning techniques, the authors created and generated for the first time comprehensible classification models for exploring the importance of blood chemistry parameters, strength, mutual interactions or dependencies, and reliability of particular parameters within the seasonal groups.     

Simulation of biochemical systems

A computer program has been developed which solves sets of first-order non-linear differential equations which are typical of those encountered in biochemistry. The program includes its own algebraic compiler, which writes the subroutines needed by the integration routine, which is a fourth-order Runge-Kutta process incorporating variable step-length. The routine is simple to use and very fast in operation.     

Simulation methods for protein structure fluctuations

Three numerical techniques for generating thermally accessible configurations of globular proteins are considered; these techniques are the molecular dynamics method, the Metropolis Monte Carlo method, and a modified Monte Carlo method which takes account of the forces acting on the protein atoms. The molecular dynamics method is shown to be more efficient than either of the Monte Carlo methods. Because it may be necessary to use Monte Carlo methods in certain important types of sampling problems, the behavior of these methods is examined in some detail. It is found that an acceptance ratio close to 1/6 yields optimum efficiency for the Metropolis method, in contrast to what is often assumed. This result, together with the overall inefficiency of the Monte Carlo methods, appears to arise from the anisotropic forces acting on the protein atoms due to their covalent bonding. Possible ways of improving the Monte Carlo methods are suggested.     

A comparison of results obtained with two methods for assessing yield stability

Summary A major objective of the CIMMYT Maize Program is to develop open-pollinated varieties of maize (Zea mays L.) that are well adapted to a wide range of environments. To achieve this breeding goal, it is essential that the program use a stability technique that will identify high-yielding, stable genotypes accurately in international trials conducted under different environmental conditions. The objective of this study was to compare a spatial method with a modified conventional regression analysis method to determine the yield stability of 27 CIMMYT maize varieties evaluated at 37 locations. The methods also were compared on the basis of their consistency in assessing the stability of varieties when certain locations were omitted, and when subsets of varieties were analyzed. The varieties found to be stable by the spatial method with all sites included in the analysis were also stable (1) when the lowest and highest yielding sites were excluded from the analyses, and (2) when the varieties were considered, along with others, as a subset of the original group of materials. Stability parameters determined by regression analysis, however, varied for some varieties when (1) extreme sites were excluded, and (2) a subset of entries was considered in isolation. Because the spatial method was more consistent in identifying high-yielding stable varieties, it was considered the more useful of the two methods.     

Least-squares methods for identifying biochemical regulatory networks from noisy measurements

Background  

We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS) estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS). The Total Least Squares (TLS) technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks.     

Application of rapid biochemical methods for detecting avocado sunblotch disease

The method of Palukaitis & Symons (1980) for extracting low molecular weight ribonucleic acids from plant tissue was improved by CF-11-cellulose chromatography and further simplified for use in routine biochemical indexing for avocado sunblotch viroid (ASBV). Extracts were prepared routinely at 10 g dry weight equivalents (DWE) of tissue per ml; more concentrated than previously possible with many avocado cultivars. Conditions for assaying extracts of ASBV were standardised and the lower limits for detection determined as 80–230 ng ASBV/g DWE of tissue for polyacrylamide gel electrophoresis (PAGE) and 1 ng/g DWE of tissue for complementary DNA (cDNA) probe assays. The concentration of ASBV in a single infected tree varied from 5 to 5000 ng/g DWE between branches, but only to a minor degree between mature leaves and young blossoms within branches. Six independent sources of sunblotch disease were examined and all proved positive for ASBV by PAGE. The ASBV extracted from five of these sources hybridised with cDNA prepared from the sixth or standard source (Hass/SB-1), with hybridisation values ranging from 43% to 89%. In a survey of 76 trees intended for propagation in Australia, all of 17 trees previously accepted as healthy on the basis of graft transmission tests were negative for ASBV by PAGE and had cDNA hybridisation values ranging from 1.8% to 12.1%. Amongst 59 trees apparently free of sunblotch symptoms but not previously indexed, only one tree was positive for ASBV by both PAGE and cDNA probe assay. The other 58 trees were negative by PAGE but had hybridisation values ranging from 1.0% to 42.8%. Forty-nine trees had values consistent with known healthy trees (<12% hybridisation), while the results of the remaining nine trees will require confirmation by additional tests before a conclusion about ASBV is made. The cDNA probe assay successfully detected ASBV in avocado seedlings graft inoculated with Hass/SB-1, 1–2 months before symptoms were displayed but not until 6 months after inoculation. Methods for improving the cDNA probe assay still further are discussed.     

Axonal tubulin and axonal microtubules: biochemical evidence for cold stability

Nerve extracts containing tubulin labeled by axonal transport were analyzed by electrophoresis and differential extraction. We found that a substantial fraction of the tubulin in the axons of the retinal ganglion cell of guinea pigs is not solubilized by conventional methods for preparation of microtubules from whole brain. In two-dimensional polyacrylamide gel electrophoresis this cold-insoluble tubulin was biochemically distinct from tubulin obtained from whole brain microtubules prepared by cold cycling. Cleveland peptide maps also indicated some differences between the cold-extractable and cold- insoluble tubulins. The demonstration of cold-insoluble tubulin that is specifically axonal in origin permits consideration of the physiological role of cold-insoluble tubulin in a specific cellular structure. It appears likely that much of this material is in the form of cold-stable microtubules. We propose that the physiological role of cold-insoluble tubulin in the axon may be associated with the regulation of the axonal microtubule complexes in neurons.     

Automated sensitivity analysis of stiff biochemical systems using a fourth-order adaptive step size Rosenbrock integration method

Sensitivity analysis is one of the most effective approaches for studying mathematical models of biochemical systems. A stiff Rosenbrock integrator has been developed for sensitivity analysis using a direct sensitivity approach. Automated sparse Jacobian and Hessian calculations of the coupled system (the original model equations and the sensitivity equations) have been implemented in the freely available software package CellSim. The accuracy and efficiency of the integrator are tested extensively on the complex mitogen-activated protein kinase (MAPK) pathway model of Bhalla and Iyengar. Both time-dependent concentration and parameter-based sensitivity coefficients are measured using several integration schemes. The method is shown to perform sensitivity analysis in a manner that is cost effective with moderate accuracy. The error control strategy between the decoupled direct method and the Rosenbrock with direct method is discussed and their computational accuracies are compared. The method is used to analyse the positive feedback loop within the MAPK signal transduction pathway.     

Perylene attached to DNA through stiff or flexible linker: duplex stability and FRET

Two fluorescent nucleosides, 5-(perylen-3-ylethynyl)-2'-deoxyuridine and 5-[(perylen-3-yl)methoxypropyn-1-yl]-2'-deoxyuridine, were incorporated into synthetic oligodeoxyribonucleotides and spectral properties of the conjugates and their duplexes were studied.     

Simulation of Cfu-S Kinetics After Irradiation

Abstract. A simulation model of the CFU-s population is used to interpret data from experimental studies of bone marrow recovery after irradiation. the model includes an original hypothesis that the proliferation rate in the CFU-s population depends on the number of DNA-synthesizing CFU-s. It is assumed that the DNA-synthesizing CFU-s produce a factor in the presence of which CFU-s enter the resting state G_o after mitosis and remain there for prolonged periods of time. the model can adequately reproduce complex CFU-s kinetics observed after severe damage caused by irradiation with a unique set of parameters.     

Kinetics of adsorption of extended ligands on DNA at small fillings

In the present work, the adsorption kinetics of extended ligands on DNA duplexes at small fillings when molecules of DNA duplexes are on the underlayer within diffusion layer has been investigated. Both diffusion of ligands in solution (diffusion stage) and adsorption of ligands (kinetic stage) are taken into consideration at adsorption of ligands on DNA duplexes. Nonlinear system of differential equations describing adsorption of ligands where not only diffusion stage but also kinetic stage is taken into account, is obtained, moreover the equations allow localizing duplexes in arbitrary place within diffusion layer. Numeric solution of the equations makes possible to investigate the filling kinetics of DNA duplexes by ligands depending on parameters controlling adsorption process. It has been shown that depending on relation between adsorption parameters different kinetic regimes of adsorption – kinetic, complex, and diffusion regimes may be realized.     

Application of chemiluminescent methods in biochemical investigations

The review of modern luminescent methods of research, their prospects and potentialities of practical application in biological researches is presented. Special attention is given to the study of dynamics of the processes of peroxide oxidation and to activity of the enzymes of antioxidant protection in biological systems using the chemiluminescent methods, and also to their use in experimental oncology and radiobiology.