Alternative Conformational States Research Articles

Modeling Alternative Conformational States of Pseudo-Symmetric Solute Carrier Transporters using Methods from Deep Learning.

The Solute Carrier (SLC) superfamily of integral membrane proteins function to transport a wide array of small molecules across plasma and organelle membranes. SLC proteins also function as important drug transporters and as viral receptors. Despite being classified as a single superfamily, SLC proteins do not share a single common fold classification; however, most belong to multi-pass transmembrane helical protein fold families. SLC proteins populate different conformational states during the solute transport process, including outward-open, intermediate (occluded), and inward-open conformational states. For some SLC fold families this structural "flipping" corresponds to swapping between conformations of their N-terminal and C-terminal symmetry-related sub-structures. Conventional AlphaFold2, AlphaFold3, or Evolutionary Scale Modeling methods typically generate models for only one of these multiple conformational states of SLC proteins. Several modifications of these AI-based protocols for modeling multiple conformational states of proteins have been described recently. These methods are often impacted by "memorization" of one of the alternative conformational states, and do not always provide both the inward and outward facing conformations of SLC proteins. Here we describe a combined ESM - template-based-modeling process, based on a previously described template-based modeling method that relies on the internal pseudo-symmetry of many SLC proteins, to consistently model alternate conformational states of SLC proteins. We further demonstrate how the resulting multi-state models can be validated experimentally by comparison with sequence-based evolutionary co-variance data (ECs) that encode information about contacts present in the various conformational states adopted by the protein. This simple, rapid, and robust approach for modeling conformational landscapes of pseudo-symmetric SLC proteins is demonstrated for several integral membrane protein transporters, including SLC35F2 the receptor of a feline leukemia virus envelope protein required for viral entry into eukaryotic cells.

Comparison of two crystal polymorphs of NowGFP reveals a new conformational state trapped by crystal packing.

Crystal polymorphism serves as a strategy to study the conformational flexibility of proteins. However, the relationship between protein crystal packing and protein conformation often remains elusive. In this study, two distinct crystal forms of a green fluorescent protein variant, NowGFP, are compared: a previously identified monoclinic form (space group C2) and a newly discovered orthorhombic form (space group P212121). Comparative analysis reveals that both crystal forms exhibit nearly identical linear assemblies of NowGFP molecules interconnected through similar crystal contacts. However, a notable difference lies in the stacking of these assemblies: parallel in the monoclinic form and perpendicular in the orthorhombic form. This distinct mode of stacking leads to different crystal contacts and induces structural alteration in one of the two molecules within the asymmetric unit of the orthorhombic crystal form. This new conformational state captured by orthorhombic crystal packing exhibits two unique features: a conformational shift of the β-barrel scaffold and a restriction of pH-dependent shifts of the key residue Lys61, which is crucial for the pH-dependent spectral shift of this protein. These findings demonstrate a clear connection between crystal packing and alternative conformational states of proteins, providing insights into how structural variations influence the function of fluorescent proteins.

Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR.

Recent advances in molecular modeling using deep learning can revolutionize our understanding of dynamic protein structures. NMR is particularly well-suited for determining dynamic features of biomolecular structures. The conventional process for determining biomolecular structures from experimental NMR data involves its representation as conformation-dependent restraints, followed by generation of structural models guided by these spatial restraints. Here we describe an alternative approach: generating a distribution of realistic protein conformational models using artificial intelligence-(AI-) based methods and then selecting the sets of conformers that best explain the experimental data. We applied this conformational selection approach to redetermine the solution NMR structure of the enzyme Gaussia luciferase. First, we generated a diverse set of conformer models using AlphaFold2 (AF2) with an enhanced sampling protocol. The models that best-fit NOESY and chemical shift data were then selected with a Bayesian scoring metric. The resulting models include features of both the published NMR structure and the standard AF2 model generated without enhanced sampling. This "AlphaFold-NMR" protocol also generated an alternative "open" conformational state that fits nearly as well to the overall NMR data but accounts for some NOESY data that is not consistent with first "closed" conformational state; while other NOESY data consistent with this second state are not consistent with the first conformational state. The structure of this "open" structural state differs from that of the "closed" state primarily by the position of a thumb-shaped loop between α-helices H5 and H6, revealing a cryptic surface pocket. These alternative conformational states of Gluc are supported by "double recall" analysis of NOESY data and AF2 models. Additional structural states are also indicated by backbone chemical shift data indicating partially-disordered conformations for the C-terminal segment. Considered as a multistate ensemble, these multiple states of Gluc together fit the NOESY and chemical shift data better than the "restraint-based" NMR structure and provide novel insights into its structure-dynamic-function relationships. This study demonstrates the potential of AI-based modeling with enhanced sampling to generate conformational ensembles followed by conformer selection with experimental data as an alternative to conventional restraint satisfaction protocols for protein NMR structure determination.

THEORETICAL DESCRIPTION OF PUCKER STATES OF CYCLIC GROUPS OF CAROTENOIDS

Carotenoids are one of the largest classes among biological pigments. They are found both in free form (dissolved in hydrophobic environments), and bound in stoichiometric complexes with protein (carotenoproteins). The interaction between the carotenoid and the protein environment is determined by the molecular conformation and dynamic behavior of the cofactor. In this work, using molecular modeling methods, we analyzed the entire set of alternative conformational states of cyclic carotenoids. The result of the theoretical study is an energysorted catalog of alternative conformational states for biogenic cyclic carotenoids and their optical isomers. The conformational states characterized in this study can be used to correctly select initial conditions when solving problems of molecular modeling of carotenoproteins

THEORETICAL DESCRIPTION OF PUCKER STATES OF CYCLIC GROUPS OF CAROTENOIDS

Carotenoids are one of the largest classes among biological pigments. They are found both in free form (dissolved in hydrophobic environments), and bound in stoichiometric complexes with protein (carotenoproteins). The interaction between the carotenoid and the protein environment is determined by the molecular conformation and dynamic behavior of the cofactor. In this work, using molecular modeling methods, we analyzed the entire set of alternative conformational states of cyclic carotenoids. The result of the theoretical study is an energysorted catalog of alternative conformational states for biogenic cyclic carotenoids and their optical isomers. The conformational states characterized in this study can be used to correctly select initial conditions when solving problems of molecular modeling of carotenoproteins

Heat-induced structural and chemical changes to a computationally designed miniprotein.

The de novo design of miniprotein inhibitors has recently emerged as a new technology to create proteins that bind with high affinity to specific therapeutic targets. Their size, ease of expression, and apparent high stability makes them excellent candidates for a new class of protein drugs. However, beyond circular dichroism melts and hydrogen/deuterium exchange experiments, little is known about their dynamics, especially at the elevated temperatures they seemingly tolerate quite well. To address that and gain insight for future designs, we have focused on identifying unintended and previously overlooked heat-induced structural and chemical changes in a particularly stable model miniprotein, EHEE_rd2_0005. Nuclear magnetic resonance (NMR) studies suggest the presence of dynamics on multiple time and temperature scales. Transiently elevating the temperature results in spontaneous chemical deamidation visible in the NMR spectra, which we validate using both capillary electrophoresis and mass spectrometry (MS) experiments. High temperatures also result in greatly accelerated intrinsic rates of hydrogen exchange and signal loss in NMR heteronuclear single quantum coherence spectra from local unfolding. These losses are in excellent agreement with both room temperature hydrogen exchange experiments and hydrogen bond disruption in replica exchange molecular dynamics simulations. Our analysis reveals important principles for future miniprotein designs and the potential for high stability to result in long-lived alternate conformational states.

In Silico Exploration of Alternative Conformational States of VDAC.

VDAC (Voltage-Dependent Anion-selective Channel) is the primary metabolite pore in the mitochondrial outer membrane (OM). Atomic structures of VDAC, consistent with its physiological "open" state, are β-barrels formed by 19 transmembrane (TM) β-strands and an N-terminal segment (NTERM) that folds inside the pore lumen. However, structures are lacking for VDAC's partially "closed" states. To provide clues about possible VDAC conformers, we used the RoseTTAFold neural network to predict structures for human and fungal VDAC sequences modified to mimic removal from the pore wall or lumen of "cryptic" domains, i.e., segments buried in atomic models yet accessible to antibodies in OM-bound VDAC. Predicted in vacuo structures for full-length VDAC sequences are 19-strand β-barrels similar to atomic models, but with weaker H-bonding between TM strands and reduced interactions between NTERM and the pore wall. Excision of combinations of "cryptic" subregions yields β-barrels with smaller diameters, wide gaps between N- and C-terminal β-strands, and in some cases disruption of the β-sheet (associated with strained backbone H-bond registration). Tandem repeats of modified VDAC sequences also were explored, as was domain swapping in monomer constructs. Implications of the results for possible alternative conformational states of VDAC are discussed.

Alternate conformational states of the HIV-1 capsid protein: Atomistic structures and dynamics of interconversion.

The HIV-1 capsid protein assembles into a conical shell that encases the viral RNA genome. The capsid protein is a two-domain protein, with the C-terminal domain (CTD) forming a dimer. Depending on being in a solution environment or the crystalline state, different dimer arrangements have been observed for the full capsid protein, as well as a truncated construct consisting of only the CTD. To characterize the structures and dynamics of these alternate quaternary arrangements, we carried out atomistic simulations using the weighted ensemble enhanced sampling strategy. Our simulations generated continuous pathways for interconversion between the two states with rate constants that agree with those measured by 19F NMR exchange spectroscopy. Our results demonstrate the advantages of pairing atomistic simulations with 19F NMR and have implications for the structural polymorphism of the HIV-1 viral capsid assembly process.

Expanding the Accessible Chemical Space of SIRT2 Inhibitors through Exploration of Binding Pocket Dynamics.

Considerations of binding pocket dynamics are one of the crucial aspects of the rational design of binders. Identification of alternative conformational states or cryptic subpockets could lead to the discovery of completely novel groups of the ligands. However, experimental characterization of pocket dynamics, besides being expensive, may not be able to elucidate all of the conformational states relevant for drug discovery projects. In this study, we propose the protocol for computational simulations of sirtuin 2 (SIRT2) binding pocket dynamics and its integration into the structure-based virtual screening (SBVS) pipeline. Initially, unbiased molecular dynamics simulations of SIRT2:inhibitor complexes were performed using optimized force field parameters of SIRT2 inhibitors. Time-lagged independent component analysis (tICA) was used to design pocket-related collective variables (CVs) for enhanced sampling of SIRT2 pocket dynamics. Metadynamics simulations in the tICA eigenvector space revealed alternative conformational states of the SIRT2 binding pocket and the existence of a cryptic subpocket. Newly identified SIRT2 conformational states outperformed experimentally resolved states in retrospective SBVS validation. After performing prospective SBVS, compounds from the under-represented portions of the SIRT2 inhibitor chemical space were selected for in vitro evaluation. Two compounds, NDJ18 and NDJ85, were identified as potent and selective SIRT2 inhibitors, which validated the in silico protocol and opened up the possibility for generalization and broadening of its application. The anticancer effects of the most potent compound NDJ18 were examined on the triple-negative breast cancer cell line. Results indicated that NDJ18 represents a promising structure suitable for further evaluation.

Sampling alternative conformational states of transporters and receptors with AlphaFold2.

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

The Dynamic Ensemble of A:G and A:8-Oxoguanine Mismatches in Duplex DNA: Implications for Replication Errors and Damage Repair

Canonical Watson-Crick G-C and A-T base pairs as well as G•T mismatches have been shown to adopt multiple conformational states that can play important roles in DNA recognition and mutagenesis. Here, we implement NMR relaxation dispersion (RD) techniques and chemical modifications to characterize the dynamic ensemble of the A•G mismatch. The data establish a cis-Watson-Watson (cWW) ground state conformation in two different sequence contexts that exhibit a star-like equilibrium with two minor conformational states: cis-Hoogsteen-Watson (cHW) and cis-Watson-Hoogsteen (cWH) which form via anti-to-syn rotation of the purine base about the glycosidic bond. While both form with a syn flip, they exhibit distinct dynamics. The cHW conformation with population ∼6% forms with kex∼2000 s−1 while cWH with population ∼3% forms more slowly with kex ∼100 s−1. We also show that substitution of guanine with 8-oxoguanine, one of the most common lesions resulting from oxidative damage, redistributes the ensemble to favor the mutagenic cWH conformation as a ground state while the cWW and cHW conformations now form as minor species. We describe the potential role of these alternative conformational states in misincorporation by DNA polymerases as well as DNA mismatch and damage repair.

Fitness landscape of a dynamic RNA structure.

RNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to fitness, should capture mutational effects across multiple conformational states simultaneously. Here, we provide a proof-of-principle that this is indeed the case, using the self-splicing group I intron from Tetrahymena thermophila as a model system. We comprehensively mutagenized two 4-bp segments of the intron. These segments first come together to form the P1 extension (P1ex) helix at the 5' splice site. Following cleavage at the 5' splice site, the two halves of the helix dissociate to allow formation of an alternative helix (P10) at the 3' splice site. Using an in vivo reporter system that couples splicing activity to fitness in E. coli, we demonstrate that fitness is driven jointly by constraints on P1ex and P10 formation. We further show that patterns of epistasis can be used to infer the presence of intramolecular pleiotropy. Using a machine learning approach that allows quantification of mutational effects in a genotype-specific manner, we demonstrate that the fitness landscape can be deconvoluted to implicate P1ex or P10 as the effective genetic background in which molecular fitness is compromised or enhanced. Our results highlight deep mutational scanning as a tool to study alternative conformational states, with the capacity to provide critical insights into the structure, evolution and evolvability of RNAs as dynamic ensembles. Our findings also suggest that, in the future, deep mutational scanning approaches might help reverse-engineer multiple alternative or successive conformations from a single fitness landscape.

A Pipeline for Integrative Structural Modelling of Very Complex Molecular Complexes

Although electron microscopy (EM) allows determining the structure of macromolecular complexes at near-atomic resolution, high resolution is still difficult to achieve for complexes that are flexible, heterogenous, or imaged in cells with cryo-electron tomography. A state-of-the-art methodology to obtain structures of such complexes is integrative structural modelling, which combines data from complementary techniques, such as X-ray crystallography, EM, NMR, SAXS or cross-linking mass spectrometry. The current integrative modelling software, however, is either limited to complexes of simple architectures or requires custom modifications, and thus a major expertise, when applied to more complex architectures. Thus, we developed a versatile modelling pipeline for integrative modelling of protein complexes with very complex architecture in an accessible and efficient way. Our pipeline is implemented using Integrative Modeling Platform library, on top of which it implements custom algorithms for efficient sampling of the conformational space, versatile configuration language and graphical interface for input preparation, modelling and analysis, and additional restraints. Flexible and symmetrical modeling is also supported. The pipeline enables modeling of very complex architectures, for example, with multiple symmetries or with EM maps for alternative conformational states. I will present the pipeline and how we applied it to model complexes ranging from the 1 MDa Elongator to the 110 MDa human nuclear pore complex (NPC). For the Elongator complex, I will present how our published model agrees with the subsequent high-resolution cryo-EM structure. For the NPC, I will show our most recent results of how the pipeline allowed building structural models of NPCs from yeast S. cerevisiae and S. pombe, including a novel multi-state modelling protocol for modelling based on multiple EM maps simultaneously. The new NPC models reveal surprisingly different architectures and depict conformational changes of NPCs in response to cellular states.

Modeling of Hidden Structures Using Sparse Chemical Shift Data from NMR Relaxation Dispersion

NMR relaxation dispersion measurements report on conformational changes occurring on the μs-ms timescale. Chemical shift information derived from relaxation dispersion can be used to generate structural models of weakly populated alternative conformational states. Current methods to obtain such models rely on determining the signs of chemical shift changes between the conformational states, which are difficult to obtain in many situations. Here, we use a “sample and select” method to generate relevant structural models of alternative conformations of the C-terminal-associated region of Escherichia coli dihydrofolate reductase (DHFR), using only unsigned chemical shift changes for backbone amides and carbonyls (1H, 15N, and 13C′). We find that CS-Rosetta sampling with unsigned chemical shift changes generates a diversity of structures that are sufficient to characterize a minor conformational state of the C-terminal region of DHFR. The excited state differs from the ground state by a change in secondary structure, consistent with previous predictions from chemical shift hypersurfaces and validated by the x-ray structure of a partially humanized mutant of E. coli DHFR (N23PP/G51PEKN). The results demonstrate that the combination of fragment modeling with sparse chemical shift data can determine the structure of an alternative conformation of DHFR sampled on the μs-ms timescale. Such methods will be useful for characterizing alternative states, which can potentially be used for in silico drug screening, as well as contributing to understanding the role of minor states in biology and molecular evolution.

2'-O-Methylation can increase the abundance and lifetime of alternative RNA conformational states.

2′-O-Methyl (Nm) is a highly abundant post-transcriptional RNA modification that plays important biological roles through mechanisms that are not entirely understood. There is evidence that Nm can alter the biological activities of RNAs by biasing the ribose sugar pucker equilibrium toward the C3′-endo conformation formed in canonical duplexes. However, little is known about how Nm might more broadly alter the dynamic ensembles of flexible RNAs containing bulges and internal loops. Here, using NMR and the HIV-1 transactivation response (TAR) element as a model system, we show that Nm preferentially stabilizes alternative secondary structures in which the Nm-modified nucleotides are paired, increasing both the abundance and lifetime of low-populated short-lived excited states by up to 10-fold. The extent of stabilization increased with number of Nm modifications and was also dependent on Mg2+. Through phi-value analysis, the Nm modification also provided rare insights into the structure of the transition state for conformational exchange. Our results suggest that Nm could alter the biological activities of Nm-modified RNAs by modulating their secondary structural ensembles as well as establish the utility of Nm as a tool for the discovery and characterization of RNA excited state conformations.

Discriminating changes in protein structure using tyrosine conjugation.

Chemical modification of proteins has been crucial in engineering protein-based therapies, targeted biopharmaceutics, molecular probes, and biomaterials. Here, we explore the use of a conjugation-based approach to sense alternative conformational states in proteins. Tyrosine has both hydrophobic and hydrophilic qualities, thus allowing it to be positioned at protein surfaces, or binding interfaces, or to be buried within a protein. Tyrosine can be conjugated with 4-phenyl-3H-1,2,4-triazole-3,5(4H)-dione (PTAD). We hypothesized that individual protein conformations could be distinguished by labeling tyrosine residues in the protein with PTAD. We conjugated tyrosine residues in a well-folded protein, bovine serum albumin (BSA), and quantified labeled tyrosine with liquid chromatography with tandem mass spectrometry. We applied this approach to alternative conformations of BSA produced in the presence of urea. The amount of PTAD labeling was found to relate to the depth of each tyrosine relative to the protein surface. This study demonstrates a new use of tyrosine conjugation using PTAD as an analytic tool able to distinguish the conformational states of a protein.

Real-Time Analysis of Individual Ebola Virus Glycoproteins Reveals Pre-Fusion, Entry-Relevant Conformational Dynamics.

The Ebola virus (EBOV) envelope glycoprotein (GP) mediates the fusion of the virion membrane with the membrane of susceptible target cells during infection. While proteolytic cleavage of GP by endosomal cathepsins and binding of the cellular receptor Niemann-Pick C1 protein (NPC1) are essential steps for virus entry, the detailed mechanisms by which these events promote membrane fusion remain unknown. Here, we applied single-molecule Förster resonance energy transfer (smFRET) imaging to investigate the structural dynamics of the EBOV GP trimeric ectodomain, and the functional transmembrane protein on the surface of pseudovirions. We show that in both contexts, pre-fusion GP is dynamic and samples multiple conformations. Removal of the glycan cap and NPC1 binding shift the conformational equilibrium, suggesting stabilization of conformations relevant to viral fusion. Furthermore, several neutralizing antibodies enrich alternative conformational states. This suggests that these antibodies neutralize EBOV by restricting access to GP conformations relevant to fusion. This work demonstrates previously unobserved dynamics of pre-fusion EBOV GP and presents a platform with heightened sensitivity to conformational changes for the study of GP function and antibody-mediated neutralization.

Inward-facing conformation of a multidrug resistance MATE family transporter

Multidrug and toxic compound extrusion (MATE) transporters mediate excretion of xenobiotics and toxic metabolites, thereby conferring multidrug resistance in bacterial pathogens and cancer cells. Structural information on the alternate conformational states and knowledge of the detailed mechanism of MATE transport are of great importance for drug development. However, the structures of MATE transporters are only known in V-shaped outward-facing conformations. Here, we present the crystal structure of a MATE transporter from Pyrococcus furiosus (PfMATE) in the long-sought-after inward-facing state, which was obtained after crystallization in the presence of native lipids. Transition from the outward-facing state to the inward-facing state involves rigid body movements of transmembrane helices (TMs) 2-6 and 8-12 to form an inverted V, facilitated by a loose binding of TM1 and TM7 to their respective bundles and their conformational flexibility. The inward-facing structure of PfMATE in combination with the outward-facing one supports an alternating access mechanism for the MATE family transporters.

PH-Driven Conformational Reorganization of Proteins: NMR Spectroscopy Study with Buried Lys Residues

The pH sensitivity of proteins is essential for biological energy transduction and for cellular and physiological homeostasis. The thermodynamic basis of pH effects in proteins is well understood but detailed mechanistic understanding of how changes in pH drive conformational reorganization in proteins is not. This was addressed systematically using NMR spectroscopy to characterize pH-dependent conformational changes coupled to the ionization of residues buried in the hydrophobic core of a protein. The study used 25 variants of staphylococcal nuclease with buried Lys residues. The pKa values of 19 of 25 of these internal Lys residues are depressed relative to the normal pKa of Lys in water, some by as much as 5 pH units. This shift in pKa ensures that the ionizable moiety stays neutral at pH values below 10 when it is buried, consistent with what is expected from the unfavorable transfer of a charge from water to a hydrophobic environment. In most cases, ionization of the buried Lys shifts the conformation of the protein into a state where the charged Lys can contact water. This study contributes a detailed description of the location, amplitude and time scale of reorganization coupled to the ionization of buried Lys residues. The structural details of the conformational response were characterized with backbone chemical shift perturbation analysis, titrations of the buried Nζ atom, and in some cases even X-ray crystallography. At least in this protein, the anomalous pKa values of buried groups are governed by the propensity of the protein to reorganize. These results demonstrate that computational modeling of pH dependent processes in proteins will require prediction of alternative conformational states and accurate calculation of free energies; this remains a significant challenge even under the most favorable circumstances.

Inward- and outward-facing X-ray crystal structures of homodimeric P-glycoprotein CmABCB1

P-glycoprotein extrudes a large variety of xenobiotics from the cell, thereby protecting tissues from their toxic effects. The machinery underlying unidirectional multidrug pumping remains unknown, largely due to the lack of high-resolution structural information regarding the alternate conformational states of the molecule. Here we report a pair of structures of homodimeric P-glycoprotein: an outward-facing conformational state with bound nucleotide and an inward-facing apo state, at resolutions of 1.9 Å and 3.0 Å, respectively. Features that can be clearly visualized at this high resolution include ATP binding with octahedral coordination of Mg2+; an inner chamber that significantly changes in volume with the aid of tight connections among transmembrane helices (TM) 1, 3, and 6; a glutamate−arginine interaction that stabilizes the outward-facing conformation; and extensive interactions between TM1 and TM3, a property that distinguishes multidrug transporters from floppases. These structural elements are proposed to participate in the mechanism of the transporter.