Affordability Alone Is Not the Constraint: Evolutionary Accessibility and the No-Waiting Region in GPCR-Class Signalling

This revised theoretical and computational study examines whether energetic cost constitutes a fundamental constraint on the evolutionary accessibility of G protein-coupled receptor (GPCR)-class signalling in unicellular systems. Using explicit residue-based energetic accounting, a bacterial two-component signalling system as a comparator, population-genetic reasoning, and Monte Carlo exploration, the analysis distinguishes the energetic affordability of a completed signalling architecture from the evolutionary accessibility of the pathway leading to it. The corrected calculations indicate that the translational energetic burden of the modeled GPCR-class module is approximately 0.00495% of the reference ATP-equivalent cellular budget per cycle, compared with approximately 0.000795% for the two-component comparator. Thus, although the GPCR-class architecture is more expensive, translation cost alone does not make it intrinsically prohibitive under the examined conditions. The central constraint instead arises when costly molecular components are expressed before they are sufficiently coupled to a fitness-relevant output. This condition is formalized as a “no-waiting region”, in which the energetic cost of an expressed intermediate can exceed the selectable benefit generated by incomplete coupling. The framework is explicitly presented as a conditional fitness-valley constraint rather than a universal evolutionary law. Potential evolutionary routes around this region include co-option, duplication and buffering, promiscuous coupling, conditional expression, linkage, stochastic tunnelling, and constructive neutral evolution. The revised version incorporates corrected energetic calculations, updated Monte Carlo simulations, clearer distinction between energetic burden and fitness effect, updated discussion of unicellular GPCR diversity, explicit falsification criteria, and a reproducible supplementary simulation script.