Author(s): Cooper WG
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Abstract Evidence requiring transcriptase quantum processing is identified and elementary quantum methods are used to qualitatively describe origins and consequences of time-dependent coherent proton states populating informational DNA base pair sites in T4 phage, designated by G-C-->G'-C', G-C-->*G-*C and AT-->*A-*T. Coherent states at these 'point' DNA lesions are introduced as consequences of hydrogen bond arrangement, keto-amino-->enol-imine, where product protons are shared between two sets of indistinguishable electron lone-pairs, and thus, participate in coupled quantum oscillations at frequencies of approximately 10(13) s(-1). This quantum mixing of proton energy states introduces stability enhancements of approximately 0.25-7 kcal/mole. Transcriptase genetic specificity is determined by hydrogen bond components contributing to the formation of complementary interstrand hydrogen bonds which, in these cases, is variable due to coupled quantum oscillations of coherent enol-imine protons. The transcriptase deciphers and executes genetic specificity instructions by implementing measurements on superposition proton states at G'-C', *G-*C and *A-*T sites in an interval Deltat<<10(-13) s. After initiation of transcriptase measurement, model calculations indicate proton decoherence time, tau(D), satisfies the relation DeltatT, G'-->C, *C-->T and *G-->A. Measurements of 37 degrees C lifetimes of the keto-amino DNA hydrogen bond indicate a range of approximately 3200-68,000 yrs. Arguments are presented that quantum uncertainty limits on amino protons may drive the keto-amino-->enol-imine arrangement. Data imply that natural selection at the quantum level has generated effective schemes (a) for introducing superposition proton states--at rates appropriate for DNA evolution--in decoherence-free subspaces and (b) for creating entanglement states that augment (i) transcriptase quantum processing and (ii) efficient decoherence for accurate Topal-Fresco replication.
This article was published in Biosystems and referenced in Journal of Computer Science & Systems Biology