RecA tests homology at both pairing and strand exchange

RecA is a 38-kDa protein from Escherichia coli that polymerizes on single-stranded DNA, forming a nucleoprotein filament that pairs with homologous duplex DNA and carries out strand exchange in vitro. To observe the effects of mismatches on the kinetics of the RecA-catalyzed recombination reaction, we used assays based upon fluorescence energy transfer that can differentiate between the pairing and strand displacement phases. Oligonucleotide sequences that produced 2–14% mismatches in the heteroduplex product of strand exchange were tested, as well as completely homologous and heterologous sequences. The equilibrium constant for pairing decreased as the number of mismatches increased, which appeared to result from both a decrease in the rate of formation and an increase in the rate of dissociation of the intermediates. In addition, the rate of strand displacement decreased with increasing numbers of mismatches, roughly in proportion to the number of mismatches. The equilibrium constant for pairing and the rate constant for strand displacement both decreased 6-fold as the heterology increased to 14%. These results suggest that discrimination of homology from heterology occurs during both pairing and strand exchange.

Computation, prediction, and experimental tests of fitness for bacteriophage T7 mutants with permuted genomes

We created a simulation based on experimental data from bacteriophage T7 that computes the developmental cycle of the wild-type phage and also of mutants that have an altered genome order. We used the simulation to compute the fitness of more than 105 mutants. We tested these computations by constructing and experimentally characterizing T7 mutants in which we repositioned gene 1, coding for T7 RNA polymerase. Computed protein synthesis rates for ectopic gene 1 strains were in moderate agreement with observed rates. Computed phage-doubling rates were close to observations for two of four strains, but significantly overestimated those of the other two. Computations indicate that the genome organization of wild-type T7 is nearly optimal for growth: only 2.8% of random genome permutations were computed to grow faster, the highest 31% faster, than wild type. Specific discrepancies between computations and observations suggest that a better understanding of the translation efficiency of individual mRNAs and the functions of qualitatively “nonessential” genes will be needed to improve the T7 simulation. In silico representations of biological systems can serve to assess and advance our understanding of the underlying biology. Iteration between computation, prediction, and observation should increase the rate at which biological hypotheses are formulated and tested.