New free-exchange model of EmrE transport

Robinson, A. R.; Thomas, Nathan E.; Morrison, Emma A.; B, Balthazor; Henzler-Wildman, Katherine A.

doi:10.1073/pnas.1708671114

Cited by 58 publications

(199 citation statements)

References 54 publications

(92 reference statements)

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“…Mechanistic models of transporters generally reflect these strict classifications and include only pathways that allow for stoichiometric coupled transport (Lolkema and Slotboom, 2019;Oh and Boudker, 2018;Stein, 1986). Recently, improved experimental methods applied to a broader set of transporters have revealed that some transporters cross these boundaries and do not fit cleanly within a single class (Bazzone et al, 2017;Bozzi et al, 2019;Dohán et al, 2007;Nguitragool and Miller, 2006;Robinson et al, 2017). In light of this, a new generalized model of transport is required that can simultaneously accommodate all transport modes.…”

Section: Discussionmentioning

confidence: 99%

“…Investigating influence of environmental pH on transport Our initial simulations all used an inverted-physiological pH parameter with lower pH on the inside of the membrane to mimic our liposomal flux assays (Robinson et al, 2017). However, to explore how pH affects transport under conditions more closely aligned to the environment of a transporter in the bacterial inner membrane, we performed additional simulations holding pH int = 7.4 while varying pH ext ±2 units (pH ext = 5.4-9.4).…”

Section: Modeling the Effect Of A Second Protonation Eventmentioning

confidence: 99%

“…This was tested in both the 8-state model and 10-state model to see if the extent of drug transport drug differed depending on the number of proton binding events. For the 8-state model, we used an antiport configuration with R AA = 10 and R off = 0.1 (Table 3, column 5); for the 10-state model, we used rates estimated for EmrE interacting with the substrate tetraphenyl phosphonium + (Table 3, column 6; Robinson et al, 2017).…”

Section: Modeling the Effect Of A Second Protonation Eventmentioning

confidence: 99%

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Highly coupled transport can be achieved in free-exchange transport models

Hussey¹,

Thomas

Henzler‐Wildman

2019

Journal of General Physiology

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Secondary active transporters couple the transport of an ion species down its concentration gradient to the uphill transport of another substrate. Despite the importance of secondary active transport to multidrug resistance, metabolite transport, and nutrient acquisition, among other biological processes, the microscopic steps of the coupling mechanism are not well understood. Often, transport models illustrate coupling mechanisms through a limited number of “major” conformations or states, yet recent studies have indicated that at least some transporters violate these models. The small multidrug resistance transporter EmrE has been shown to couple proton influx to multidrug efflux via a mechanism that incorporates both “major” and “minor” conformational states and transitions. The resulting free exchange transport model includes multiple leak pathways and theoretically allows for both exchange and cotransport of ion and substrate. To better understand how coupled transport can be achieved in such a model, we numerically simulate a free-exchange model of transport to determine the step-by-step requirements for coupled transport. We find that only moderate biasing of rate constants for key transitions produce highly efficient net transport approaching a perfectly coupled, stoichiometric model. We show how a free-exchange model can enable complex phenotypes, including switching transport direction with changing environmental conditions or substrates. This research has broad implications for synthetic biology, as it demonstrates the utility of free-exchange transport models and the fine tuning required for perfectly coupled transport.

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Section: Discussionmentioning

confidence: 99%

Section: Modeling the Effect Of A Second Protonation Eventmentioning

confidence: 99%

Section: Modeling the Effect Of A Second Protonation Eventmentioning

confidence: 99%

See 1 more Smart Citation

Highly coupled transport can be achieved in free-exchange transport models

Hussey¹,

Thomas

Henzler‐Wildman

2019

Journal of General Physiology

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“…For example, SGLT transporters are of biomedical interest due to the vital role that SGLT1 and SGLT2 play in the uptake of glucose in the small intestines and reabsorption in the kidneys, respectively (2), which in turn has prompted biophysical scrutiny of their mechanisms (3)(4)(5)(6)(7). Numerous other transporters have also been assayed on a quantitative basis (8)(9)(10)(11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

“…There is growing evidence that molecular machines may exhibit complexity beyond that embodied in typical 'textbook' cartoons models (8,9). Recent experimental studies have shown that certain traditional model assumptions such as fixed stoichiometry (10,11), homogeneous pathways (12), and unique binding sites (13) may be incorrect.…”

Section: Introductionmentioning

confidence: 99%

A systems-biology approach to molecular machines: Exploration of alternative transporter mechanisms

George

Bisignano

Grabe

et al. 2019

Preprint

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Motivated by growing evidence for pathway heterogeneity and alternative functions of molecular machines, we demonstrate a computational approach for investigating two questions: (1) Are there multiple mechanisms (state-space pathways) by which a machine can perform a given function, such as cotransport across a membrane? (2) How can additional functionality, such as proofreading/error-correction, be built into machine function using standard biochemical processes? Answers to these questions will aid both the understanding of molecular-scale cell biology and the design of synthetic machines. Focusing on transport in this initial study, we sample a variety of mechanisms by employing Metropolis Markov chain Monte Carlo. Trial moves adjust transition rates among an automatically generated set of conformational and binding states while maintaining fidelity to thermodynamic principles and a user-supplied fitness/functionality goal. Each accepted move generates a new model. The simulations yield both single and mixed reaction pathways for cotransport in a simple environment with a single substrate along with a driving ion. In a "competitive" environment including an additional decoy substrate, several qualitatively distinct reaction pathways are found which are capable of extremely high discrimination coupled to a leak of the driving ion, akin to proofreading. The array of functional models would be difficult to find by intuition alone in the complex state-spaces of interest. SIGNIFICANCE Molecular machines, which operate on the nanoscale, are proteins/complexes that perform remarkable tasks such as the selective absorption of nutrients into the cell by transporters. These complex machines are often described using a fairly simple set of states and transitions that may not account for the stochasticity and heterogeneity generally expected at the nanoscale at body temperature. New tools are needed to study the full array of possibilities. This study presents a novel in silico method to systematically generate testable molecular-machine kinetic models and explore alternative mechanisms, applied first to transporters. Our approach should aid the experimental study of physiological processes such as renal glucose re-absorption, and potentially the development of synthetic machines.As an example of mechanistic alternatives within a simple state-space, consider a hypothetical cotransporter motivated by the SGLT symporter which transports a single substrate and is driven by an ion gradient. The state-space is constructed using three state 'dimensions': conformational state, ion binding state, and substrate binding state. For this hypothetical transporter there are two conformations, each permitting four ion/substrate binding states: fully unbound, ion bound, substrate bound, and fully bound. Within this relatively simple state-space, we can construct four ideal kinetic pathways (Fig. 1) that connect the minimum number of states to produce a symport cycle (i.e., intracellular transport of the substrate coupled to ion flow). How...

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A mass spectrometry based transport assay for studying EmrE transport of unlabeled substrates

Robinson

Henderson

Henzler-Wildman

2018

Analytical Biochemistry

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Membrane transporters are an important class of proteins which remain challenging to study. Transport assays are crucial to developing our understanding of such proteins as they allow direct measurement of their transport activity. However, currently available methods for monitoring liposomal loading of organic substrates primarily rely on detection of radioactively or fluorescently labeled substrates. The requirement of a labeled substrate significantly restricts the systems and substrates that can be studied. Here we present a mass spectrometry based detection method for liposomal uptake assays that eliminates the need for labeled substrates. We demonstrate the efficacy of the assay with EmrE, a small multidrug resistance transporter found in E. coli that has become a model transport system for the study of secondary active transport. Furthermore, we develop a method for differentiation between bound and transported substrate, enhancing the information gained from the liposomal uptake assay. The transport assay presented here is readily applicable to other transport systems and substrates.

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New free-exchange model of EmrE transport

Cited by 58 publications

References 54 publications

Highly coupled transport can be achieved in free-exchange transport models

Highly coupled transport can be achieved in free-exchange transport models

A systems-biology approach to molecular machines: Exploration of alternative transporter mechanisms

A mass spectrometry based transport assay for studying EmrE transport of unlabeled substrates

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