Hybrid characteristics of oligonucleotides consisting of isonucleoside 2′,5′-anhydro-3′-deoxy-3′-(thymin-1-yl)-d-mannitol with different linkage modes

Oligonucleotides consisting of the isonucleoside repeating unit 2′,5′-anhydro-3′-deoxy-3′-(thymin-1-yl)-d-mannitol (4) were synthesized with the monomeric unit 4 incorporated into oligonucleotides as 1′→4′ linkage 4a (oligomer I) or 6′→4′ linkage 4b (oligomer II). The hybrid properties of the two oligonucleotides I and II with their complementary strands were investigated by thermal denaturation and CD spectra. Oligonucleotide I (4a) formed a stable duplex with d(A)14 with a slightly reduced Tm value of 36.6°C, relative to 38.2°C for the control duplex d(T)14/d(A)14, but oligomer II (4b) failed to hybridize with a DNA complementary single strand. The spectrum of the duplex oligomer I/d(A)14 showed a positive CD band at 217 nm and a negative CD band at 248 nm attributable to a B-like conformation. Molecular modeling showed that in the case of oligomer I the C6′ hydroxy group of each unit could be located in the groove area when hybridized to the DNA single strand, which might contribute additional hydrogen bonding to the stability of duplex formation.

Modeling and optimization of populations subject to time-dependent mutation.

It has become clear that many organisms possess the ability to regulate their mutation rate in response to environmental conditions. So the question of finding an optimal mutation rate must be replaced by that of finding an optimal mutation schedule. We show that this task cannot be accomplished with standard population-dynamic models. We then develop a "hybrid" model for populations experiencing time-dependent mutation that treats population growth as deterministic but the time of first appearance of new variants as stochastic. We show that the hybrid model agrees well with a Monte Carlo simulation. From this model, we derive a deterministic approximation, a "threshold" model, that is similar to standard population dynamic models but differs in the initial rate of generation of new mutants. We use these techniques to model antibody affinity maturation by somatic hypermutation. We had previously shown that the optimal mutation schedule for the deterministic threshold model is phasic, with periods of mutation between intervals of mutation-free growth. To establish the validity of this schedule, we now show that the phasic schedule that optimizes the deterministic threshold model significantly improves upon the best constant-rate schedule for the hybrid and Monte Carlo models.