- Email: [email protected]
Computing Substrate Selectivity in a Peptide Transporter
Please cite this article as: Tonnus et al., ‘‘Death is my Heir’’—Ferroptosis Connects Cancer Pharmacogenomics and Ischemia-Reperfusion Injury, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2016.02.001
Cell Chemical Biology
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non-replicating cultures and compared to the labeled proteins from rapidly replicating cells (Figure 1). Interestingly, despite the apparent metabolic quiescence of non-replicating cells, 34 serine hydrolases were active under both conditions, 41 were specific for rapid growth, and 3 were specifically labeled in the non-replicating cells. The panel of panactive hydrolases included MarP and the ClpP2 subunit of the housekeeping proteolytic complex. This study provides the first preview of enzymes with a serine nucleophile in the active site that are catalytically active during growth as opposed to non-replicating persistence. It is noteworthy that the activity-based serine hydrolase profiling distinguishes active from expressed yet inert proteins. Correlation of peptide fragment abundance from total protein extracts to fragments from activity-labeled samples showed poor correlation, pointing to the regulation
of serine hydrolase activity by post-translational regulation. The identification of a panel of in vitro pan-active serine hydrolases feeds the pipeline of targets that can be exploited for suicide inhibitor development. ACKNOWLEDGMENTS
Johnston, J.M., Jiang, M., Guo, Z., and Baker, E.N. (2010). Acta Crystallogr. D Biol. Crystallogr. 66, 909–917. Ortega, C., Liao, R., Anderson, L.N., Rustad, T., Ollodart, A.R., Wright, A.T., Sherman, D.R., and Grundner, C. (2014). PLoS Biol. 12, e1001746. Ortega, C., Anderson, L.N., Frando, A., Sadler, N.C., Brown, R.W., Smith, R.D., Wright, A.T., and Grundner, C. (2016). Cell. Chem. Biol. 23, this issue, 290–298.
This work was funded by the Intramural Research Program of NIAID.
Simon, G.M., and Cravatt, B.F. (2010). J. Biol. Chem. 285, 11051–11055.
REFERENCES
Tipper, D.J., and Strominger, J.L. (1965). Proc. Natl. Acad. Sci. USA 54, 1133–1141.
Ansong, C., Ortega, C., Payne, S.H., Haft, D.H., Chauvigne`-Hines, L.M., Lewis, M.P., Ollodart, A.R., Purvine, S.O., Shukla, A.K., Fortuin, S., et al. (2013). Chem. Biol. 20, 123–133.
Wiedner, S.D., Burnum, K.E., Pederson, L.M., Anderson, L.N., Fortuin, S., Chauvigne´-Hines, L.M., Shukla, A.K., Ansong, C., Panisko, E.A., Smith, R.D., and Wright, A.T. (2012). J. Biol. Chem. 287, 33447–33459.
Bachovchin, D.A., and Cravatt, B.F. (2012). Nat. Rev. Drug Discov. 11, 52–68.
World Health Organization. (2015). Global Tuberculosis Report 2015 (World Health Organization).
Gavrish, E., Sit, C.S., Cao, S., Kandror, O., Spoering, A., Peoples, A., Ling, L., Fetterman, A., Hughes, D., Bissell, A., et al. (2014). Chem. Biol. 21, 509–518.
Zhao, N., Darby, C.M., Small, J., Bachovchin, D.A., Jiang, X., Burns-Huang, K.E., Botella, H., Ehrt, S., Boger, D.L., Anderson, E.D., et al. (2015). ACS Chem. Biol. 10, 364–371.
Computing Substrate Selectivity in a Peptide Transporter Claire Colas,1 David E. Smith,3 and Avner Schlessinger2,* 1Department
of Pharmacology and Systems Therapeutics of Pharmacology and Systems Therapeutics, and Structural and Chemical Biology Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 3Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.chembiol.2016.02.001 2Departments
The human proton-coupled peptide transporter 1 (PepT1) is responsible for the absorption of di- and tri-peptides from the diet and peptide-like drugs. In this issue of Cell Chemical Biology, Samsudin et al. (2016) use an integrated computational and experimental approach to provide new insights into understanding substrate selectivity of PepTSt, a prokaryotic homolog of the human PepT1. In humans, there are presently 456 solute carrier (SLC) transporter genes from 52 families that modulate the import and efflux of a large variety of solutes across cell membranes, including amino acids, neurotransmitters, metabolites, ions, and drugs (Ce´sar-Razquin et al., 2015). PepT1 (SLC15A1) is a proton-coupled transporter of di- and tripeptides, primarily found in the small intestine, kidney, and pancreas, where it
absorbs a broad range of peptides from the diet and peptide-like drugs such as b-lactam antibiotics (Smith et al., 2013). PepT1 is also an emerging drug target. It is upregulated in the colons of patients with inflammatory bowel disease (IBD) where it transports bacterially derived products into colonic cytosol, thereby triggering an inflammatory response (Charrier and Merlin, 2006; Smith et al., 2013). Potential drugs tar-
geting PepT1 can be inhibitors that block the uptake of harmful peptides of bacterial origin or substrates that efficiently deliver anti-inflammatory prodrugs to the target site. The structure of the human PepT1 is unknown; however, structures of a prokaryotic homolog, PepTSt from Streptococcus thermophiles, have been determined in complex with di- and tri-peptides (Lyons et al., 2014). PepTSt has highly similar
Cell Chemical Biology 23, February 18, 2016 ª2016 Elsevier Ltd All rights reserved 211
Please cite this article as: Tonnus et al., ‘‘Death is my Heir’’—Ferroptosis Connects Cancer Pharmacogenomics and Ischemia-Reperfusion Injury, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2016.02.001
Cell Chemical Biology
Previews
substrate specificity to that Taken together, these data of PepT1 and a conserved suggested that longer di-pepbinding site, providing a tides may bind in a vertical useful tool for understanding orientation similar to that of PepT1 function. The PepTSt tri-peptides. structures revealed that Finally, the authors investidifferent substrates can have gated the utility of their different orientations within approach in predicting inhibithe binding site, where a dition for homology models. peptide binds ‘‘horizontally’’ They modeled PepTSt based on related structures with and a tri-peptide binds ‘‘vertivarying sequence similarity cally’’ (Figure 1). Figure 1. Binding Sites of PepTSt to PepTSt. The closer PepTSt Samsudin et al. (2016) first Binding site of PepTSt bound to the di-peptides AlaPhe (left; PDB: 4D2C) and was to the template strucevaluated the correlation of LysAla (right). LysAla binding pose was derived from the PepTSt tri-peptide ture, the higher the correlavarious binding prediction complex structure (PDB: 4D2D). The amino-acid determining di-peptides selectivity is colored in red. tion between predicted and methods with experimentally observed inhibition. Following determined IC50s of eight substrates (seven di-peptides a similar trend, the predicted and one tri-peptide). They assessed computed DDG between the DGs of inhibition for a human PepT1 model was methods with increasing computing time binding of AlaAla and AlaAla in which as accurate as random. This suggested and precision, including knowledge- the N- or C-terminal residues were that the accuracy of the computational based scoring function (AutoDock Vina), mutated to Phe, Asp, Glu, or Lys was approach highly depends on the quality ‘‘end-point’’ free-energy calculations higher for the N-terminal mutations. This of the protein structure or model. Although (e.g., the linear interaction energy [LIE]), suggested that the N-terminal residue the predicted affinity for the human PepT1 and a ‘‘theoretically exact’’ method (ther- determined the di-peptide specificity. did not correlate with the experimental modynamic integration [TI]). While the Models of PepTSt with various di-pep- data, the results presented in this study end-point methods were able to differen- tides indicated that the N-terminal resi- considerably improve our understanding tiate between peptides with low and due of the substrate forms hydrogen of the human PepT1 substrate specificity, high IC50 values, it was shown that TI re- bonds with polar residues previously based on its functional and structural simfines initial prediction to differentiate shown to be critical for binding and ilarity to PepTSt. Moreover, the results proamong substrates with similar IC50s for transport (e.g, Tyr30, Asn156, Asn328) vide potential explanation for how drugs four peptides. The authors concluded (Solcan et al., 2012) (Figure 1), whereas (and prodrugs) are recognized by PepT1. that end-point methods provide optimal the C-terminal side chain interacts with The PepT1 substrate valacyclovir is compromise between speed and preci- a hydrophobic pocket less critical for a structural analog of di-peptides: the sion to evaluate the interaction of PepTSt function. carrier group of the drug may act as and di-peptides. A key assumption that Next, to confirm the predicted signifi- a pseudo-N-terminal amino acid—it is was made was that all di- and tri-peptides cance of the N-terminal position, a pro- small, neutral, and predicted to form have similar horizontal and vertical con- ton-driven competition uptake assay was hydrogen bonds with key residues, and formations observed in the two struc- performed for four charged di-peptides the active part of the drug is equivalent to tures. Moreover, for the scoring function, (i.e., AlaAsp, AspAla, AlaLys, and LysAla). the C-terminal of di-peptide ligands—it is the flexibility of the binding site was not As expected, the experimental IC50s of bulky and may fit a large hydrophobic cavconsidered, which has been previously the acidic di-peptides correlated with ity. Still, one must appreciate that a drug shown to be critical for successful appli- the predicted affinities. Surprisingly, for affinity does not necessarily translate cations of related approaches (Colas basic di-peptides the opposite trend was to being a PepT1 substrate that gets et al., 2015; Schlessinger et al., 2011). observed, suggesting that some di-pep- transported. The authors then used the end-point tides’ binding mode is different. The auDesigning drugs targeting SLC transmethods to investigate the specificity of thors therefore hypothesized that di-pep- porters requires the description of their PepTSt in binding di-peptides. The esti- tides with a lysine, which consists of a structure and dynamics and the mode mated DG for 400 possible di-peptides long side chain, have a binding mode of interaction with their ligands, includindicated that PepTSt binds neutral similar to that of a tri-peptide (Figure 1). ing substrates and inhibitors. This di-peptides with the highest affinities. Indeed, modeling PepTSt with LysAla and study involves iterative application of Conversely, acidic di-peptides were pre- AlaLys based on the PepTSt structure in multiple computational and experimental dicted to have lower affinities for the complex with an AlaAlaAla tri-peptide sug- methods, to successfully explain subtransporter, which is in agreement with gested that LysAla forms hydrogen bonds strate specificity in an important transhigher IC50 values for acidic peptides with important polar residues of the binding port system. Due to rapid increase in (Solcan et al., 2012). The authors then site (Tyr68, Glu300, or Asn156), supporting the number of SLC structures and investigated the importance of the posi- this hypothesis (Figure 1). The DDG pre- improvement in computer power and tion of the amino acids in di-peptide dicted using TI based on this model was methods, future studies using related inligands using TI. Interestingly, the also in agreement with experimental data. tegrated approaches are expected to 212 Cell Chemical Biology 23, February 18, 2016 ª2016 Elsevier Ltd All rights reserved
Please cite this article as: Tonnus et al., ‘‘Death is my Heir’’—Ferroptosis Connects Cancer Pharmacogenomics and Ischemia-Reperfusion Injury, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2016.02.001
Cell Chemical Biology
Previews
be applied to other human SLC transporters. Furthermore, this study highlights the need for high quality structures and/or models of the human SLC members. For example, models, or even X-ray structures, can be optimized for protein ligand complementarity (Schlessinger et al., 2011). Finally, SLC transporters such as PepT1 are dynamic molecules that adopt multiple conformations during transport. Visualizing different conformations through computation or experiments can further describe the specificity of this transporter, and ultimately aid in design of unique PepT1 drugs.
ACKNOWLEDGMENTS
and Schlessinger, A. (2015). PLoS Comput. Biol. 11, e1004477.
The authors are supported by NIH grants R01 GM108911 (to A.S. and C.C.) and R01 GM115481 (to D.E.S.) and by DoD grant W81XWH-15-1-0539 (to A.S., C.C., and D.E.S.).
Lyons, J.A., Parker, J.L., Solcan, N., Brinth, A., Li, D., Shah, S.T., Caffrey, M., and Newstead, S. (2014). EMBO Rep. 15, 886–893.
REFERENCES Ce´sar-Razquin, A., Snijder, B., Frappier-Brinton, T., Isserlin, R., Gyimesi, G., Bai, X., Reithmeier, R.A., Hepworth, D., Hediger, M.A., Edwards, A.M., and Superti-Furga, G. (2015). Cell 162, 478–487. Charrier, L., and Merlin, D. (2006). Lab. Invest. 86, 538–546. Colas, C., Grewer, C., Otte, N.J., Gameiro, A., Albers, T., Singh, K., Shere, H., Bonomi, M., Holst, J.,
Samsudin, F., Parker, J.L., Sansom, M.S.P., Newstead, S., and Fowler, P.W. (2016). Cell Chem. Biol. 23, this issue, 299–309. Schlessinger, A., Geier, E., Fan, H., Irwin, J.J., Shoichet, B.K., Giacomini, K.M., and Sali, A. (2011). Proc. Natl. Acad. Sci. USA 108, 15810– 15815. Smith, D.E., Cle´menc¸on, B., and Hediger, M.A. (2013). Mol. Aspects Med. 34, 323–336. Solcan, N., Kwok, J., Fowler, P.W., Cameron, A.D., Drew, D., Iwata, S., and Newstead, S. (2012). EMBO J. 31, 3411–3421.
Cell Chemical Biology 23, February 18, 2016 ª2016 Elsevier Ltd All rights reserved 213
Cell Chemical Biology
Previews
non-replicating cultures and compared to the labeled proteins from rapidly replicating cells (Figure 1). Interestingly, despite the apparent metabolic quiescence of non-replicating cells, 34 serine hydrolases were active under both conditions, 41 were specific for rapid growth, and 3 were specifically labeled in the non-replicating cells. The panel of panactive hydrolases included MarP and the ClpP2 subunit of the housekeeping proteolytic complex. This study provides the first preview of enzymes with a serine nucleophile in the active site that are catalytically active during growth as opposed to non-replicating persistence. It is noteworthy that the activity-based serine hydrolase profiling distinguishes active from expressed yet inert proteins. Correlation of peptide fragment abundance from total protein extracts to fragments from activity-labeled samples showed poor correlation, pointing to the regulation
of serine hydrolase activity by post-translational regulation. The identification of a panel of in vitro pan-active serine hydrolases feeds the pipeline of targets that can be exploited for suicide inhibitor development. ACKNOWLEDGMENTS
Johnston, J.M., Jiang, M., Guo, Z., and Baker, E.N. (2010). Acta Crystallogr. D Biol. Crystallogr. 66, 909–917. Ortega, C., Liao, R., Anderson, L.N., Rustad, T., Ollodart, A.R., Wright, A.T., Sherman, D.R., and Grundner, C. (2014). PLoS Biol. 12, e1001746. Ortega, C., Anderson, L.N., Frando, A., Sadler, N.C., Brown, R.W., Smith, R.D., Wright, A.T., and Grundner, C. (2016). Cell. Chem. Biol. 23, this issue, 290–298.
This work was funded by the Intramural Research Program of NIAID.
Simon, G.M., and Cravatt, B.F. (2010). J. Biol. Chem. 285, 11051–11055.
REFERENCES
Tipper, D.J., and Strominger, J.L. (1965). Proc. Natl. Acad. Sci. USA 54, 1133–1141.
Ansong, C., Ortega, C., Payne, S.H., Haft, D.H., Chauvigne`-Hines, L.M., Lewis, M.P., Ollodart, A.R., Purvine, S.O., Shukla, A.K., Fortuin, S., et al. (2013). Chem. Biol. 20, 123–133.
Wiedner, S.D., Burnum, K.E., Pederson, L.M., Anderson, L.N., Fortuin, S., Chauvigne´-Hines, L.M., Shukla, A.K., Ansong, C., Panisko, E.A., Smith, R.D., and Wright, A.T. (2012). J. Biol. Chem. 287, 33447–33459.
Bachovchin, D.A., and Cravatt, B.F. (2012). Nat. Rev. Drug Discov. 11, 52–68.
World Health Organization. (2015). Global Tuberculosis Report 2015 (World Health Organization).
Gavrish, E., Sit, C.S., Cao, S., Kandror, O., Spoering, A., Peoples, A., Ling, L., Fetterman, A., Hughes, D., Bissell, A., et al. (2014). Chem. Biol. 21, 509–518.
Zhao, N., Darby, C.M., Small, J., Bachovchin, D.A., Jiang, X., Burns-Huang, K.E., Botella, H., Ehrt, S., Boger, D.L., Anderson, E.D., et al. (2015). ACS Chem. Biol. 10, 364–371.
Computing Substrate Selectivity in a Peptide Transporter Claire Colas,1 David E. Smith,3 and Avner Schlessinger2,* 1Department
of Pharmacology and Systems Therapeutics of Pharmacology and Systems Therapeutics, and Structural and Chemical Biology Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 3Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.chembiol.2016.02.001 2Departments
The human proton-coupled peptide transporter 1 (PepT1) is responsible for the absorption of di- and tri-peptides from the diet and peptide-like drugs. In this issue of Cell Chemical Biology, Samsudin et al. (2016) use an integrated computational and experimental approach to provide new insights into understanding substrate selectivity of PepTSt, a prokaryotic homolog of the human PepT1. In humans, there are presently 456 solute carrier (SLC) transporter genes from 52 families that modulate the import and efflux of a large variety of solutes across cell membranes, including amino acids, neurotransmitters, metabolites, ions, and drugs (Ce´sar-Razquin et al., 2015). PepT1 (SLC15A1) is a proton-coupled transporter of di- and tripeptides, primarily found in the small intestine, kidney, and pancreas, where it
absorbs a broad range of peptides from the diet and peptide-like drugs such as b-lactam antibiotics (Smith et al., 2013). PepT1 is also an emerging drug target. It is upregulated in the colons of patients with inflammatory bowel disease (IBD) where it transports bacterially derived products into colonic cytosol, thereby triggering an inflammatory response (Charrier and Merlin, 2006; Smith et al., 2013). Potential drugs tar-
geting PepT1 can be inhibitors that block the uptake of harmful peptides of bacterial origin or substrates that efficiently deliver anti-inflammatory prodrugs to the target site. The structure of the human PepT1 is unknown; however, structures of a prokaryotic homolog, PepTSt from Streptococcus thermophiles, have been determined in complex with di- and tri-peptides (Lyons et al., 2014). PepTSt has highly similar
Cell Chemical Biology 23, February 18, 2016 ª2016 Elsevier Ltd All rights reserved 211
Please cite this article as: Tonnus et al., ‘‘Death is my Heir’’—Ferroptosis Connects Cancer Pharmacogenomics and Ischemia-Reperfusion Injury, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2016.02.001
Cell Chemical Biology
Previews
substrate specificity to that Taken together, these data of PepT1 and a conserved suggested that longer di-pepbinding site, providing a tides may bind in a vertical useful tool for understanding orientation similar to that of PepT1 function. The PepTSt tri-peptides. structures revealed that Finally, the authors investidifferent substrates can have gated the utility of their different orientations within approach in predicting inhibithe binding site, where a dition for homology models. peptide binds ‘‘horizontally’’ They modeled PepTSt based on related structures with and a tri-peptide binds ‘‘vertivarying sequence similarity cally’’ (Figure 1). Figure 1. Binding Sites of PepTSt to PepTSt. The closer PepTSt Samsudin et al. (2016) first Binding site of PepTSt bound to the di-peptides AlaPhe (left; PDB: 4D2C) and was to the template strucevaluated the correlation of LysAla (right). LysAla binding pose was derived from the PepTSt tri-peptide ture, the higher the correlavarious binding prediction complex structure (PDB: 4D2D). The amino-acid determining di-peptides selectivity is colored in red. tion between predicted and methods with experimentally observed inhibition. Following determined IC50s of eight substrates (seven di-peptides a similar trend, the predicted and one tri-peptide). They assessed computed DDG between the DGs of inhibition for a human PepT1 model was methods with increasing computing time binding of AlaAla and AlaAla in which as accurate as random. This suggested and precision, including knowledge- the N- or C-terminal residues were that the accuracy of the computational based scoring function (AutoDock Vina), mutated to Phe, Asp, Glu, or Lys was approach highly depends on the quality ‘‘end-point’’ free-energy calculations higher for the N-terminal mutations. This of the protein structure or model. Although (e.g., the linear interaction energy [LIE]), suggested that the N-terminal residue the predicted affinity for the human PepT1 and a ‘‘theoretically exact’’ method (ther- determined the di-peptide specificity. did not correlate with the experimental modynamic integration [TI]). While the Models of PepTSt with various di-pep- data, the results presented in this study end-point methods were able to differen- tides indicated that the N-terminal resi- considerably improve our understanding tiate between peptides with low and due of the substrate forms hydrogen of the human PepT1 substrate specificity, high IC50 values, it was shown that TI re- bonds with polar residues previously based on its functional and structural simfines initial prediction to differentiate shown to be critical for binding and ilarity to PepTSt. Moreover, the results proamong substrates with similar IC50s for transport (e.g, Tyr30, Asn156, Asn328) vide potential explanation for how drugs four peptides. The authors concluded (Solcan et al., 2012) (Figure 1), whereas (and prodrugs) are recognized by PepT1. that end-point methods provide optimal the C-terminal side chain interacts with The PepT1 substrate valacyclovir is compromise between speed and preci- a hydrophobic pocket less critical for a structural analog of di-peptides: the sion to evaluate the interaction of PepTSt function. carrier group of the drug may act as and di-peptides. A key assumption that Next, to confirm the predicted signifi- a pseudo-N-terminal amino acid—it is was made was that all di- and tri-peptides cance of the N-terminal position, a pro- small, neutral, and predicted to form have similar horizontal and vertical con- ton-driven competition uptake assay was hydrogen bonds with key residues, and formations observed in the two struc- performed for four charged di-peptides the active part of the drug is equivalent to tures. Moreover, for the scoring function, (i.e., AlaAsp, AspAla, AlaLys, and LysAla). the C-terminal of di-peptide ligands—it is the flexibility of the binding site was not As expected, the experimental IC50s of bulky and may fit a large hydrophobic cavconsidered, which has been previously the acidic di-peptides correlated with ity. Still, one must appreciate that a drug shown to be critical for successful appli- the predicted affinities. Surprisingly, for affinity does not necessarily translate cations of related approaches (Colas basic di-peptides the opposite trend was to being a PepT1 substrate that gets et al., 2015; Schlessinger et al., 2011). observed, suggesting that some di-pep- transported. The authors then used the end-point tides’ binding mode is different. The auDesigning drugs targeting SLC transmethods to investigate the specificity of thors therefore hypothesized that di-pep- porters requires the description of their PepTSt in binding di-peptides. The esti- tides with a lysine, which consists of a structure and dynamics and the mode mated DG for 400 possible di-peptides long side chain, have a binding mode of interaction with their ligands, includindicated that PepTSt binds neutral similar to that of a tri-peptide (Figure 1). ing substrates and inhibitors. This di-peptides with the highest affinities. Indeed, modeling PepTSt with LysAla and study involves iterative application of Conversely, acidic di-peptides were pre- AlaLys based on the PepTSt structure in multiple computational and experimental dicted to have lower affinities for the complex with an AlaAlaAla tri-peptide sug- methods, to successfully explain subtransporter, which is in agreement with gested that LysAla forms hydrogen bonds strate specificity in an important transhigher IC50 values for acidic peptides with important polar residues of the binding port system. Due to rapid increase in (Solcan et al., 2012). The authors then site (Tyr68, Glu300, or Asn156), supporting the number of SLC structures and investigated the importance of the posi- this hypothesis (Figure 1). The DDG pre- improvement in computer power and tion of the amino acids in di-peptide dicted using TI based on this model was methods, future studies using related inligands using TI. Interestingly, the also in agreement with experimental data. tegrated approaches are expected to 212 Cell Chemical Biology 23, February 18, 2016 ª2016 Elsevier Ltd All rights reserved
Please cite this article as: Tonnus et al., ‘‘Death is my Heir’’—Ferroptosis Connects Cancer Pharmacogenomics and Ischemia-Reperfusion Injury, Cell Chemical Biology (2016), http://dx.doi.org/10.1016/j.chembiol.2016.02.001
Cell Chemical Biology
Previews
be applied to other human SLC transporters. Furthermore, this study highlights the need for high quality structures and/or models of the human SLC members. For example, models, or even X-ray structures, can be optimized for protein ligand complementarity (Schlessinger et al., 2011). Finally, SLC transporters such as PepT1 are dynamic molecules that adopt multiple conformations during transport. Visualizing different conformations through computation or experiments can further describe the specificity of this transporter, and ultimately aid in design of unique PepT1 drugs.
ACKNOWLEDGMENTS
and Schlessinger, A. (2015). PLoS Comput. Biol. 11, e1004477.
The authors are supported by NIH grants R01 GM108911 (to A.S. and C.C.) and R01 GM115481 (to D.E.S.) and by DoD grant W81XWH-15-1-0539 (to A.S., C.C., and D.E.S.).
Lyons, J.A., Parker, J.L., Solcan, N., Brinth, A., Li, D., Shah, S.T., Caffrey, M., and Newstead, S. (2014). EMBO Rep. 15, 886–893.
REFERENCES Ce´sar-Razquin, A., Snijder, B., Frappier-Brinton, T., Isserlin, R., Gyimesi, G., Bai, X., Reithmeier, R.A., Hepworth, D., Hediger, M.A., Edwards, A.M., and Superti-Furga, G. (2015). Cell 162, 478–487. Charrier, L., and Merlin, D. (2006). Lab. Invest. 86, 538–546. Colas, C., Grewer, C., Otte, N.J., Gameiro, A., Albers, T., Singh, K., Shere, H., Bonomi, M., Holst, J.,
Samsudin, F., Parker, J.L., Sansom, M.S.P., Newstead, S., and Fowler, P.W. (2016). Cell Chem. Biol. 23, this issue, 299–309. Schlessinger, A., Geier, E., Fan, H., Irwin, J.J., Shoichet, B.K., Giacomini, K.M., and Sali, A. (2011). Proc. Natl. Acad. Sci. USA 108, 15810– 15815. Smith, D.E., Cle´menc¸on, B., and Hediger, M.A. (2013). Mol. Aspects Med. 34, 323–336. Solcan, N., Kwok, J., Fowler, P.W., Cameron, A.D., Drew, D., Iwata, S., and Newstead, S. (2012). EMBO J. 31, 3411–3421.
Cell Chemical Biology 23, February 18, 2016 ª2016 Elsevier Ltd All rights reserved 213