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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. 

Transcription fidelity is inherently coupled to its strength, and highly expressed genes often exhibit elevated error rates. Epigenetic and structural factors, including histone modifications, DNA methylation, and nucleoid-associated proteins, modulate transcriptional output and, consequently, fidelity. However, the mechanistic origin of this fidelity-strength relationship remains poorly understood. Here, we propose that repulsive interactions among cotranscribing RNA polymerases (RNAPs) might explain these couplings. We develop a stochastic kinetic model of transcription elongation that incorporates both kinetic proofreading and repulsive forces generated through collisions between the neighboring RNAPs. In this framework, it is found that the collision forces accelerate leading RNAPs' elongation speed and impede their kinetic proofreading; the opposite trends occur for the trailing enzymes. As a result, interactions among multiple RNAPs at high initiation rates substantially elevate transcriptional error relative to isolated enzymes, with the magnitude of this increase determined by the intrinsic proofreading rate. In contrast, the mechanical partitioning of force between forward translocation and backtracking pathways primarily modulates elongation speed without altering fidelity. Together, our study provides a quantitative and mechanistic framework that links the collective dynamics of RNAPs to transcriptional errors, offering new physical insights into how transcriptional strength intrinsically compromises fidelity. 

Understanding how specific molecular substructures control chemical behavior is central to rational molecular design and the development of new materials. However, most current predictive models offer limited mechanistic resolution at the fragmental level. We present a conceptually novel use of fragment-based structure–activity reasoning, based on systematically perturbing a parent molecule, to quantify fragment-level contributions to both a specific mechanistic action and a broader functional outcome. As a case study, we investigated local structural contributions to pyronaridine (PY), a clinically used antimalarial drug with a mechanistically distinctive mode of inhibition of hematin crystal growth via step-bunching. Chemically plausible PY molecular analogs have been computationally generated by selectively removing or substituting functional groups hypothesized to influence either step-bunching mechanisms or whole-parasite blood-stage activity. For each analog, we predicted the probability of four different crystal-growth inhibition mechanisms using a centroid-based similarity model based on a small dataset of experimentally verified crystal-growth inhibitors. The blood-stage antimalarial activity has also been estimated using the MAIP platform. A systematic comparison of molecular analogs revealed that step-bunching mechanisms depend primarily on two protonated pyrrolidines, with chlorobenzene as a strong secondary contributor. In contrast, antimalarial activity is more distributed, relying on coordinated interactions between aromatic–heteroatom scaffolds and an amine linker. The obtained results demonstrate that our approach can disentangle position-specific and cooperative fragmental effects, offering mechanistically interpretable guidance for the design of mechanism-optimized inhibitors. The framework might be broadly applicable across chemical and materials domains where linking local structure to specific mechanisms is essential. 

Sensitivity is a crucial feature for proper function and regulation of biological processes. Cooperative protein-ligand binding, which can be described as transitions in biological networks, is a fundamental mechanism for such sensitivity. The phenomenological sensitivity measure is the empirical Hill slope. However, a measure of sensitivity directly related to network dynamics at the cellular level is still lacking. In this study, using discrete-state stochastic analysis of seminal ligand-binding networks, we establish that the Fano factor is a crucial measure of sensitivity. Nonlinear variation of the Fano factor with fractional saturation of the network gives the criterion of cooperativity, with noncooperative binding producing a linear trend. The positive (negative) deviation of the Fano factor curve from linearity indicates positive (negative) cooperativity. Finally, the ratio of Fano factor values in cooperative and noncooperative binding becomes equal to the Hill slope, affirming its role as the essential measure of sensitivity in biological networks.