Research article

Rational polynomial representation of ribonucleotide reductase activity

Tomas Radivoyevitch¹ , Ossama B Kashlan² and Barry S Cooperman³

¹  Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA

²  Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA

³  Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA

author email corresponding author email

BMC Biochemistry 2005, 6:8doi:10.1186/1471-2091-6-8

Published:	6 May 2005

Abstract

Background

Recent data suggest that ribonucleotide reductase (RNR) exists not only as a heterodimer R1₂R2₂ of R1₂ and R2₂ homodimers, but also as tetramers R1₄R2₄ and hexamers R1₆R2₆. Recent data also suggest that ATP binds the R1 subunit at a previously undescribed hexamerization site, in addition to its binding to previously described dimerization and tetramerization sites. Thus, the current view is that R1 has four NDP substrate binding possibilities, four dimerization site binding possibilities (dATP, ATP, dGTP, or dTTP), two tetramerization site binding possibilities (dATP or ATP), and one hexamerization site binding possibility (ATP), in addition to possibilities of unbound site states. This large number of internal R1 states implies an even larger number of quaternary states. A mathematical model of RNR activity which explicitly represents the states of R1 currently exists, but it is complicated in several ways: (1) it includes up to six-fold nested sums; (2) it uses different mathematical structures under different substrate-modulator conditions; and (3) it requires root solutions of high order polynomials to determine R1 proportions in mono-, di-, tetra- and hexamer states and thus RNR activity as a function of modulator and total R1 concentrations.

Results

We present four (one for each NDP) rational polynomial models of RNR activity as a function of substrate and reaction rate modifier concentrations. The new models avoid the complications of the earlier model without compromising curve fits to recent data.

Conclusion

Compared to the earlier model of recent data, the new rational polynomial models are simpler, adequately fitting, and likely better suited for biochemical network simulations.