The purification of ion channels from excitable cells

Conclusion It should be emphasized that the problem of isolating a gene for which the gene product has been defined solely by genetic criteria is not trivial, but this approach is potentially very powerful. Not only does it provide a means for determining the sequence and structure of ion channels for which there are no biochemical probes, but it also provides a system in which specific mutations in the channel gene can be correlated with changes in channel function. Although this approach is limited to organisms that are readily manipulated by genetic methods, the results of these studies should be widely applicable. The biophysical properties of ion channels are generally similar from one species to another, suggesting that the structures of the channels have been highly conserved. The combination of biochemical, biophysical and genetic techniques will undoubtedly be exploited more fully in future studies of ion channel structure and function.     

A novel cost function to estimate parameters of oscillatory biochemical systems

Oscillatory pathways are among the most important classes of biochemical systems with examples ranging from circadian rhythms and cell cycle maintenance. Mathematical modeling of these highly interconnected biochemical networks is needed to meet numerous objectives such as investigating, predicting and controlling the dynamics of these systems. Identifying the kinetic rate parameters is essential for fully modeling these and other biological processes. These kinetic parameters, however, are not usually available from measurements and most of them have to be estimated by parameter fitting techniques. One of the issues with estimating kinetic parameters in oscillatory systems is the irregularities in the least square (LS) cost function surface used to estimate these parameters, which is caused by the periodicity of the measurements. These irregularities result in numerous local minima, which limit the performance of even some of the most robust global optimization algorithms. We proposed a parameter estimation framework to address these issues that integrates temporal information with periodic information embedded in the measurements used to estimate these parameters. This periodic information is used to build a proposed cost function with better surface properties leading to fewer local minima and better performance of global optimization algorithms. We verified for three oscillatory biochemical systems that our proposed cost function results in an increased ability to estimate accurate kinetic parameters as compared to the traditional LS cost function. We combine this cost function with an improved noise removal approach that leverages periodic characteristics embedded in the measurements to effectively reduce noise. The results provide strong evidence on the efficacy of this noise removal approach over the previous commonly used wavelet hard-thresholding noise removal methods. This proposed optimization framework results in more accurate kinetic parameters that will eventually lead to biochemical models that are more precise, predictable, and controllable.     

Membrane Structure and Function

An understanding of the biochemical basis of membrane function is an important goal of present day biology. In this paper, a biochemical approach to the problem of the specific transport of sugars across the membrane of Escherichia coli is discussed. A new biochemical model for lactose transport system in this organism is presented, in which a specific membrane protein (M protein) plays the role of the sugar carrier. Experiments which have led to the discovery of such a protein, its specific labeling, and partial purification are briefly reviewed.     

Dynamics of granzyme B-induced apoptosis: mathematical modeling

Apoptosis is mediated by an intracellular biochemical system that mainly includes proteins (procaspases, caspases, inhibitors, Bcl-2 protein family as well as substances released from mitochondrial intermembrane space). The dynamics of caspase activation and target cleavage in apoptosis induced by granzyme B in a single K562 cell was studied using a mathematical model of the dynamics of granzyme B-induced apoptosis developed in this work. Also the first application of optimization approach to determination of unknown kinetic constants of biochemical apoptotic reactions was presented. The optimization approach involves solving of two problems: direct and inverse. Solving the direct optimization problem, we obtain the initial (baseline) concentrations of procaspases for known kinetic constants through conditional minimization of a cost function based on the principle of minimum protein consumption by the apoptosis system. The inverse optimization problem is aimed at determination of unknown kinetic constants of apoptotic biochemical reactions proceeding from the condition that the optimal concentrations of procaspases resulting from the solution of the direct optimization problem coincide with the observed ones, that is, those determined by biochemical methods. The Multidimensional Index Method was used to perform numerical solution of the inverse optimization problem.     

Biochemical detection of monovalent metal ion binding sites within RNA

Many RNAs, including the ribosome, RNase P, and the group II intron, explicitly require monovalent cations for activity in vitro. Although the necessity of monovalent cations for RNA function has been known for more than a quarter of a century, the characterization of specific monovalent metal sites within large RNAs has been elusive. Here we describe a biochemical approach to identify functionally important monovalent cations in nucleic acids. This method uses thallium (Tl+), a soft Lewis acid heavy metal cation with chemical properties similar to those of the physiological alkaline earth metal potassium (K+). Nucleotide analog interference mapping (NAIM) with the sulfur-substituted nucleotide 6-thioguanosine in combination with selective metal rescue of the interference with Tl+ provides a distinct biochemical signature for monovalent metal ion binding. This approach has identified a K+ binding site within the P4-P6 domain of the Tetrahymena group I intron that is also present within the X-ray crystal structure. The technique also predicted a similar binding site within the Azoarcus group I intron where the structure is not known. The approach is applicable to any RNA molecule that can be transcribed in vitro and whose function can be assayed.     

Biological network design strategies: discovery through dynamic optimization

An important challenge in systems biology is the inherent complexity of biological network models, which complicates the task of relating network structure to function and of understanding the conceptual design principles by which a given network operates. Here we investigate an approach to analyze the relationship between a network structure and its function using the framework of optimization. A common feature found in a variety of biochemical networks involves the opposition of a pair of enzymatic chemical modification reactions such as phosphorylation-dephosphorylation or methylation-demethylation. The modification pair frequently adjusts biochemical properties of its target, such as activating and deactivating function. We applied optimization methodology to study a reversible modification network unit commonly found in signal transduction systems, and we explored the use of this methodology to discover design principles. The results demonstrate that different sets of rate constants used to parameterize the same network topology represent different compromises made in the resulting network operating characteristics. Moreover, the same topology can be used to encode different strategies for achieving performance goals. The ability to adopt multiple strategies may lead to significantly improved performance across a range of conditions through rate modulation or evolutionary processes. The optimization framework explored here is a practical approach to support the discovery of design principles in biological networks.     

Thermodynamically consistent Bayesian analysis of closed biochemical reaction systems

Background  

Estimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable.     

Nuclear distribution and chromatin association of DNA polymerase α-primase is affected by TEV protease cleavage of Cdc23 (Mcm10) in fission yeast

Background  

Cdc23/Mcm10 is required for the initiation and elongation steps of DNA replication but its biochemical function is unclear. Here, we probe its function using a novel approach in fission yeast, involving Cdc23 cleavage by the TEV protease.     

Enzyme genomics: Application of general enzymatic screens to discover new enzymes

In all sequenced genomes, a large fraction of predicted genes encodes proteins of unknown biochemical function and up to 15% of the genes with "known" function are mis-annotated. Several global approaches are routinely employed to predict function, including sophisticated sequence analysis, gene expression, protein interaction, and protein structure. In the first coupling of genomics and enzymology, Phizicky and colleagues undertook a screen for specific enzymes using large pools of partially purified proteins and specific enzymatic assays. Here we present an overview of the further developments of this approach, which involve the use of general enzymatic assays to screen individually purified proteins for enzymatic activity. The assays have relaxed substrate specificity and are designed to identify the subclass or sub-subclasses of enzymes (phosphatase, phosphodiesterase/nuclease, protease, esterase, dehydrogenase, and oxidase) to which the unknown protein belongs. Further biochemical characterization of proteins can be facilitated by the application of secondary screens with natural substrates (substrate profiling). We demonstrate here the feasibility and merits of this approach for hydrolases and oxidoreductases, two very broad and important classes of enzymes. Application of general enzymatic screens and substrate profiling can greatly speed up the identification of biochemical function of unknown proteins and the experimental verification of functional predictions produced by other functional genomics approaches.     

Cementum is key to periodontal tissue regeneration: A review on apatite microstructures for creation of novel cementum-based dental implants

The cementum is the outermost layer of hard tissue covering the dentin within the root portion of the teeth. It is the only hard tissue with a specialized structure and function that forms a part of both the teeth and periodontal tissue. As such, cementum is believed to be critical for periodontal tissue regeneration. In this review, we discuss the function and histological structure of the cementum to promote crystal engineering with a biochemical approach in cementum regenerative medicine. We review the microstructure of enamel and bone while discussing the mechanism underlying apatite crystal formation to infer the morphology of cementum apatite crystals and their complex structure with collagen fibers. Finally, the limitations of the current dental implant treatments in clinical practice are explored from the perspective of periodontal tissue regeneration. We anticipate the possibility of advancing periodontal tissue regenerative medicine via cementum regeneration using a combination of material science and biochemical methods.     

Microbiome Structure and Function: A New Framework for Interpreting Data

A distinction between different notions of “structure” and “function” is suggested for interpreting the overwhelming amount of data on microbiome structure and function. Sequence data, biochemical agents, interaction networks, taxonomic communities, and their dynamics can be linked to potential or actual biochemical activities, causal roles, and selected effects, respectively. This conceptual clarification has important methodological consequences for how to interpret existing data and approach open questions in contemporary microbiome research practice. In particular, the field will have to start thinking about notions of function more directly. Also see the video abstract here https://youtu.be/j5pq5uGld1k .     

Biochemistry to the rescue: a ClC-2 auxiliary subunit provides a tangible link to leukodystrophy

ClC-2 is a broadly distributed chloride channel with an enigmatic neurophysiological function. In this issue of Neuron, Jeworutzki et al. (2012) use a biochemical approach to identify GlialCAM, a protein with a defined link to leukodystrophy, as a ClC-2 auxiliary subunit.     

Incorporating the amino acid properties to predict the significance of missense mutations

Determining if missense mutations are deleterious is critical for the analysis of genes implicated in disease. However, the mutational effects of many missense mutations in databases like the Breast Cancer Information Core are unclassified. Several approaches have emerged recently to determine such mutational effects but none have utilized amino acid property indices. We modified a previously described phylogenetic approach by first classifying benign substitutions based on the assumption that missense mutations that are maintained in orthologs are unlikely to affect function. A consensus conservation score based on 16 amino acid properties was used to characterize the remaining substitutions. This approach was evaluated with experimentally verified T4 lysozyme missnese mutations and is shown to be able to sieve out putative biochemical and structurally important residues. The use of amino acid properties can enhance the prediction of biochemical and structurally important residues and thus also predict the significance of missense mutations.     

Metabonomics and medicine: the Biochemical Oracle

Occasionally, a new idea emerges that has the potential to revolutionize an entire field of scientific endeavour. It is now within our grasp to be able to detect subtle perturbations within the phenomenally complex biochemical matrix of living organisms. The discipline of metabonomics promises an all-encompassing approach to understanding total, yet fundamental, changes occurring in disease processes, drug toxicity and cell function.     

Mathematical modelling the systemic regulation of blood glucose: 'a top-down' systems biology approach

The objective of this article is to review the mechanisms which the body uses to regulate its function. The author considers, in particular, the nature and structure of the physiological systems with a specific focus upon the systemic regulation of blood glucose and highlights an innovative technology, based upon the top-down cognitive approach, which incorporates a unique mathematical model of the physiological systems and autonomic nervous system. Most systems biology is a development of the prevailing reductionist biomedical paradigm. It adopts a bottom-up approach seeking systemic justification for biochemical and biophysical research findings. By contrast the 'top-down' approach considers the neural regulation of the physiological systems and the neurological, cognitive and biochemical consequences of systemic dysfunction i.e. the consequences of sensory input upon the neural regulation of the body's systems, organs, and its cellular and molecular biochemistry. In conclusion, the evidence suggests that the onset and progression of Diabetes Mellitus cannot be accurately assessed by individual biomedical indices but instead that the regulation of blood glucose is one of a number of inter-related physiological systems which act in a coordinated manner in order to maintain the body's physiological stability.     

Mapping protein–ligand interactions using whole genome phage display libraries

The function of many genes cannot be deduced from sequence similarity, and biochemical methods are usually required. Whole genome sequences can be thought of as not only a set of genes but also collections of functional domains. These domains can be studied by affinity methods whereby identification of the ligand can provide information on biochemical function. To take advantage of this method, one must express all functional domains in a form suitable for affinity studies. Phage display technology provides a means for accomplishing this. The pJuFo phage display system, based on the interaction between the leucine zippers Jun and Fos, has been modified and used to create a genomic phage display library from Escherichia coli MG1655. The system has been tested by using the library to map the dominant binding epitopes for an anti-RecA protein polyclonal antibody sera. This methodology provides a general biochemical approach to functional analysis of protein–ligand interactions on a genome-wide basis.     

Predicting and annotating catalytic residues: an information theoretic approach.

We introduce a computational method to predict and annotate the catalytic residues of a protein using only its sequence information, so that we describe both the residues' sequence locations (prediction) and their specific biochemical roles in the catalyzed reaction (annotation). While knowing the chemistry of an enzyme's catalytic residues is essential to understanding its function, the challenges of prediction and annotation have remained difficult, especially when only the enzyme's sequence and no homologous structures are available. Our sequence-based approach follows the guiding principle that catalytic residues performing the same biochemical function should have similar chemical environments; it detects specific conservation patterns near in sequence to known catalytic residues and accordingly constrains what combination of amino acids can be present near a predicted catalytic residue. We associate with each catalytic residue a short sequence profile and define a Kullback-Leibler (KL) distance measure between these profiles, which, as we show, effectively captures even subtle biochemical variations. We apply the method to the class of glycohydrolase enzymes. This class includes proteins from 96 families with very different sequences and folds, many of which perform important functions. In a cross-validation test, our approach correctly predicts the location of the enzymes' catalytic residues with a sensitivity of 80% at a specificity of 99.4%, and in a separate cross-validation we also correctly annotate the biochemical role of 80% of the catalytic residues. Our results compare favorably to existing methods. Moreover, our method is more broadly applicable because it relies on sequence and not structure information; it may, furthermore, be used in conjunction with structure-based methods.     

A systematic approach to biochemical profiling

Sequencing of the Arabidopsis thaliana genome is complete. The analytical tools for determining gene function by altering and monitoring gene expression are relatively well developed, and are generating large volumes of valuable data. Recent advances in techniques for the analysis of small molecules allow researchers to apply biochemical profiling as another powerful approach to functional genomics and metabolic research.