Unraveling discrimination strategies in biological error-correction networks

Nonequilibrium error-correction mechanisms, such as kinetic proofreading, enable biological systems to amplify the discrimination among cognate and non-cognate substrates beyond what is possible at equilibrium. However, it remains unclear how such discrimination should be distributed over the underlying network to achieve the full nonequilibrium advantage of error reduction. Using a discrete-state stochastic framework, we first show that the Hopfield network, the seminal model of proofreading, with discrimination concentrated in two dissociation steps, displays distinct regimes of error reduction depending on the relative magnitudes of the rate constants of various steps. One such regime is actually anti-proofreading, showing no improvement in accuracy with increasing discrimination. In contrast, a biologically realistic model of the tRNA selection network in protein translation by the E. coli ribosome exhibits distributed discrimination. We demonstrate that the spread of discrimination across the entire network enables the system to achieve the full nonequilibrium advantage, even in the kinetic regime where the Hopfield network lies in the anti-proofreading zone at low discrimination strength. Our results further indicate that excessively strong discrimination adversely affects the system, eliminating the nonequilibrium advantage of error reduction without providing any additional gain in the speed of translation.