Equilibrium model selection: dTTP induced R1 dimerization

<p>Abstract</p> <p>Background</p> <p>Biochemical equilibria are usually modeled iteratively: given one or a few fitted models, if there is a lack of fit or over fitting, a new model with additional or fewer parameters is then fitted, and the process is repeated. The problem with this approach is that different analysts can propose and select different models and thus extract different binding parameter estimates from the same data. An alternative is to first generate a comprehensive standardized list of plausible models, and to then fit them exhaustively, or semi-exhaustively.</p> <p>Results</p> <p>A framework is presented in which equilibriums are modeled as pairs (<it>g</it>, <it>h</it>) where <it>g </it>= 0 maps total reactant concentrations (system inputs) into free reactant concentrations (system states) which <it>h </it>then maps into expected values of measurements (system outputs). By letting dissociation constants <it>K</it>_{<it>d </it>}be either freely estimated, infinity, zero, or equal to other <it>K</it>_<it>d</it>, and by letting undamaged protein fractions be either freely estimated or 1, many <it>g </it>models are formed. A standard space of <it>g </it>models for ligand-induced protein dimerization equilibria is given. Coupled to an <it>h </it>model, the resulting (<it>g</it>, <it>h</it>) were fitted to dTTP induced R1 dimerization data (R1 is the large subunit of ribonucleotide reductase). Models with the fewest parameters were fitted first. Thereafter, upon fitting a batch, the next batch of models (with one more parameter) was fitted only if the current batch yielded a model that was better (based on the Akaike Information Criterion) than the best model in the previous batch (with one less parameter). Within batches models were fitted in parallel. This semi-exhaustive approach yielded the same best models as an exhaustive model space fit, but in approximately one-fifth the time.</p> <p>Conclusion</p> <p>Comprehensive model space based biochemical equilibrium model selection methods are realizable. Their significance to systems biology as mappings of data into mathematical models warrants their development.</p

DNA building blocks: keeping control of manufacture

Ribonucleotide reductase (RNR) is the only source for de novo production of the four deoxyribonucleoside triphosphate (dNTP) building blocks needed for DNA synthesis and repair. It is crucial that these dNTP pools are carefully balanced, since mutation rates increase when dNTP levels are either unbalanced or elevated. RNR is the major player in this homeostasis, and with its four different substrates, four different allosteric effectors and two different effector binding sites, it has one of the most sophisticated allosteric regulations known today. In the past few years, the structures of RNRs from several bacteria, yeast and man have been determined in the presence of allosteric effectors and substrates, revealing new information about the mechanisms behind the allosteric regulation. A common theme for all studied RNRs is a flexible loop that mediates modulatory effects from the allosteric specificity site (s-site) to the catalytic site for discrimination between the four substrates. Much less is known about the allosteric activity site (a-site), which functions as an on-off switch for the enzyme's overall activity by binding ATP (activator) or dATP (inhibitor). The two nucleotides induce formation of different enzyme oligomers, and a recent structure of a dATP-inhibited α6β2 complex from yeast suggested how its subunits interacted non-productively. Interestingly, the oligomers formed and the details of their allosteric regulation differ between eukaryotes and Escherichia coli Nevertheless, these differences serve a common purpose in an essential enzyme whose allosteric regulation might date back to the era when the molecular mechanisms behind the central dogma evolved