Toward a Multipathway Perspective: pH-Dependent Kinetic Selection of Competing Pathways and the Role of the Internal Glutamate in Cl^–/H⁺ Antiporters

Abstract

Canonical descriptions of multistep biomolecular transformations generally follow a single-pathway viewpoint, with a series of transitions through intermediates converting reactants to products or repeating a conformational cycle. However, mounting evidence suggests that more complexity and pathway heterogeneity are mechanistically relevant due to the statistical distribution of multiple interconnected rate processes. Making sense of such pathway complexity remains a significant challenge. To better understand the role and relevance of pathway heterogeneity, we herein probe the chemical reaction network of a Cl − /H + antiporter, ClC-ec1, and analyze reaction pathways using multiscale kinetic modeling (MKM). This approach allows us to describe the nature of the competing pathways and how they change as a function of pH. We reveal that although pH-dependent Cl − /H + transport rates are largely regulated by the charge state of amino acid E148, the charge state of E203 determines relative contributions from coexisting pathways and can shift the flux pH-dependence. The selection of pathways via E203 explains how ionizable mutations (D/H/K/R) would impact the ClC-ec1 bioactivity from a kinetic perspective and lends further support to the indispensability of an internal glutamate in ClC antiporters. Our results demonstrate how quantifying the kinetic selection of competing pathways under varying conditions leads to a deeper understanding of the Cl − /H + exchange mechanism and can suggest new approaches for mechanistic control.

Proton coupling and the multiscale kinetic mechanism of a peptide transporter

“…S7), implying that the closing of the gate is as slow as the intracellular gate, which may be the rate-limiting step of the entire functional cycle. This behavior is in contrast with a kinetic model (71,72) in which multiple stochastic paths are possible; in this case the closing of the gate may provide a kinetic funnel bottleneck through which all paths are focused.…”

“…8 must be considered to be probabilistic. Moreover, there may exist multiple possible transition pathways connecting the states as hypothesized for other transporters, such as ClC-ec1 (71,72) and PiPT (78). As such, it should also be the case in PepT Sh , and for example, in one pathway (Fig.…”

From Average Transient Transporter Currents to Microscopic Mechanism─A Bayesian Analysis

“…While our choice of uniform priors introduces very little bias into the model, it may scale poorly for large dimensions or lead to overfitting if the model structure is not known, in which case, more informative data or priors may be required. For more complex systems, such as with increased ion/substate stoichiometry, there may be a mixture of pathways. − The BI workflow can be adapted to model this in several ways, based on: one large network model containing all possible elementary reactions, a mixture model of separate pathways, or a simplified representation of the model . Unlike single-cycle models, more complex models may exhibit multimodal posterior distributions.…”

“…Transporters are a type of biological molecular machine that help regulate cellular homeostasis by pumping molecules across a membrane and maintaining ion gradients. , As such, transporters play an essential role in cellular processes such as the uptake of nutrients and expelling of waste. These protein systems operate in a stochastic molecular environment, which suggests that they could exhibit some degree of stochasticity in their mechanism, a concept recently emerging as “pathway heterogeneity”, − but which has been implicit in reports of noninteger stoichiometry of secondary active transport over many years. − In a recent example, analysis of the small Escherichia coli multidrug transporter points to utilization of different sequences of biochemical steps that enable a wide range of behaviors including 2:1 and 1:1 transport stoichiometry. , Despite significant study, the precise mechanisms of transport, i.e., the states visited, the allowed order(s) of transitions, and the associated rate constants, remain difficult to characterize.…”

From average transient transporter currents to microscopic mechanism – A Bayesian analysis

“…1,2 As such, transporters play an essential role in cellular processes, such as the uptake of nutrients and expelling of waste. These protein systems operate in a stochastic molecular environment which suggests they could exhibit some degree of stochasticity in their mechanism, a concept recently emerging as 'pathway heterogeneity', [3][4][5][6] but which has been implicit in reports of non-integer stoichiometry of secondary active transport over many years. [7][8][9][10][11] In a recent example, analysis of the small Escherichia coli multidrug transporter 12 points to utilization of different sequences of biochemical steps that enable a wide range of behaviors including 2:1 and 1:1 transport stoichiometry.…”