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Lysine 5,6-aminomutase is an adenosylcobalamin and pyridoxal-5 -phosphate-dependent enzyme that catalyzes a 1,2 rearrangement of the terminal amino group of DL-lysine and of L-␤-lysine. We have solved the x-ray structure of a substrate-free form of lysine-5,6-aminomutase from Clostridium sticklandii. In this structure, a Rossmann domain covalently binds pyridoxal-5 -phosphate by means of lysine 144 and positions it into the putative active site of a neighboring triosephosphate isomerase barrel domain, while simultaneously positioning the other cofactor, adenosylcobalamin, Ϸ25 Å from the active site. In this mode of pyridoxal-5 -phosphate binding, the cofactor acts as an anchor, tethering the separate polypeptide chain of the Rossmann domain to the triosephosphate isomerase barrel domain. Upon substrate binding and transaldimination of the lysine-144 linkage, the Rossmann domain would be free to rotate and bring adenosylcobalamin, pyridoxal-5 -phosphate, and substrate into proximity. Thus, the structure embodies a locking mechanism to keep the adenosylcobalamin out of the active site and prevent radical generation in the absence of substrate.A denosylcobalamin (AdoCbl; coenzyme B 12 ) is nature's biochemical radical reservoir, capable of catalyzing challenging chemical reactions by way of H atom abstraction and the generation of free-radical intermediates (1-3). AdoCbldependent isomerases catalyze 1,2 shifts between an H atom and a functional group such as -OH, -NH 3 ϩ , -(CO)S-coenzyme A, or other carbon-based groups. The catalytic power of AdoCbl lies in the homolytic cleavage of its weak (Ϸ30 kcal͞mol) organometallic C-Co bond, formed between an octahedral Co(III) center with five N coordinations and a 5Ј-deoxyadenosyl (Ado) axial ligand. C-Co bond homolysis results in the transient formation of cob(II)alamin and 5Ј-deoxyadenosyl radical (Ado • ). Ado • abstracts an H atom from the substrate, forming a substrate radical and 5Ј-deoxyadenosine (AdoH). To close the catalytic cycle, substrate reabstracts the H atom from AdoH, and recombination of cob(II)alamin and Ado • accompanies product formation. Amazingly, the enzymatic rate of C-Co bond homolysis is enhanced by a factor of Ϸ10 12 over nonenzymatic homolysis (4, 5). AdoCbl-dependent isomerases are often present in catabolic pathways and can serve to rearrange the substrate's carbon skeleton and͞or functional groups for further degradation. One such pathway that operates in several bacterial species is the fermentation of lysine to yield acetate. Interestingly, the lysine fermentation pathway contains two analogous enzymes: lysine 5,6-aminomutase (5,6-LAM), which is AdoCbl-dependent (6, 7), and lysine 2,3-aminomutase (2,3-LAM), which is an S-adenosylmethionine (AdoMet or SAM)-dependent ironsulfur enzyme (8-10). Both enzymes require pyridoxal 5Ј-phosphate (PLP) (8, 11) in addition to AdoCbl or AdoMet, and both catalyze a 1,2 amino group shift with concomitant H atom migration (Fig. 1A). In 5,6-LAM, AdoCbl is the source of the transient Ado • , whereas, in 2,3-L...

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R's popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.