Cited by 20Drug Transport and Resistance MechanismsEnzyme Structure and FunctionAmino Acid Enzymes and Metabolism

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Plasticity of the binding pocket in peptide transporters underpins promiscuous substrate recognition

Open access

Published version of a preprint

Abstract

Proton-dependent oligopeptide transporters (POTs) are promiscuous transporters of the major facilitator superfamily that constitute the main route of entry for a wide range of dietary peptides and orally administrated peptidomimetic drugs. Given their clinical and pathophysiological relevance, several POT homologs have been studied extensively at the structural and molecular level. However, the molecular basis of recognition and transport of diverse peptide substrates has remained elusive. We present 14 X-ray structures of the bacterial POT DtpB in complex with chemically diverse di- and tripeptides, providing novel insights into the plasticity of the conserved central binding cavity. We analyzed binding affinities for more than 80 peptides and monitored uptake by a fluorescence-based transport assay. To probe whether all 8400 natural di- and tripeptides can bind to DtpB, we employed state-of-the-art molecular docking and machine learning and conclude that peptides with compact hydrophobic residues are the best DtpB binders.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.celrep.2023.112831

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 105

  1. Reitzer, 2003, Nitrogen assimilation and global regulation in Escherichia coli, Annu. Rev. Microbiol., 57, 155, 10.1146/annurev.micro.57.030502.090820 · DOI
  2. Daniel, 2006, From bacteria to man: archaic proton-dependent peptide transporters at work, Physiology, 21, 93, 10.1152/physiol.00054.2005 · DOI
  3. Doki, 2013, Structural basis for dynamic mechanism of proton-coupled symport by the peptide transporter POT, Proc. Natl. Acad. Sci. USA, 110, 11343, 10.1073/pnas.1301079110 · DOI
  4. Guettou, 2013, Structural insights into substrate recognition in proton-dependent oligopeptide transporters, EMBO Rep., 14, 804, 10.1038/embor.2013.107 · DOI
  5. Lyons, 2014, Structural basis for polyspecificity in the POT family of proton-coupled oligopeptide transporters, EMBO Rep., 15, 886, 10.15252/embr.201338403 · DOI
  6. Newstead, 2011, Crystal structure of a prokaryotic homologue of the mammalian oligopeptide-proton symporters, PepT1 and PepT2, EMBO J., 30, 417, 10.1038/emboj.2010.309 · DOI
  7. Ural-Blimke, 2019, Structure of Prototypic Peptide Transporter DtpA from E. coli in Complex with Valganciclovir Provides Insights into Drug Binding of Human PepT1, J. Am. Chem. Soc., 141, 2404, 10.1021/jacs.8b11343 · DOI
  8. Zhao, 2014, Crystal structure of the E. coli peptide transporter YbgH, Structure, 22, 1152, 10.1016/j.str.2014.06.008 · DOI
  9. Jardetzky, 1966, Simple allosteric model for membrane pumps, Nature, 211, 969, 10.1038/211969a0 · DOI
  10. Solcan, 2012, Alternating access mechanism in the POT family of oligopeptide transporters, EMBO J., 31, 3411, 10.1038/emboj.2012.157 · DOI
  11. Bartels, 2021, Single-Molecule FRET of Membrane Transport Proteins, Chembiochem, 22, 2657, 10.1002/cbic.202100106 · DOI
  12. Drew, 2021, Structures and General Transport Mechanisms by the Major Facilitator Superfamily (MFS), Chem. Rev., 121, 5289, 10.1021/acs.chemrev.0c00983 · DOI
  13. Quistgaard, 2016, Understanding transport by the major facilitator superfamily (MFS): structures pave the way, Nat. Rev. Mol. Cell Biol., 17, 123, 10.1038/nrm.2015.25 · DOI
  14. Newstead, 2017, Recent advances in understanding proton coupled peptide transport via the POT family, Curr. Opin. Struct. Biol., 45, 17, 10.1016/j.sbi.2016.10.018 · DOI
  15. Goh, 2004, The Escherichia coli tppB (ydgR) gene represents a new class of OmpR-regulated genes, J. Bacteriol., 186, 4019, 10.1128/JB.186.12.4019-4024.2004 · DOI
  16. Harder, 2008, DtpB (YhiP) and DtpA (TppB, YdgR) are prototypical proton-dependent peptide transporters of Escherichia coli, FEBS J., 275, 3290, 10.1111/j.1742-4658.2008.06477.x · DOI
  17. Meredith, 2000, Structure and function of eukaryotic peptide transporters, Cell. Mol. Life Sci., 57, 754, 10.1007/s000180050040 · DOI
  18. Killer, 2022, Cryo-EM Structure of an Atypical Proton-Coupled Peptide Transporter: Di- and Tripeptide Permease C, Front. Mol. Biosci., 9, 917725, 10.3389/fmolb.2022.917725 · DOI
  19. Chen, 2000, Functional roles of histidine and tyrosine residues in the H(+)-peptide transporter PepT1, Biochem. Biophys. Res. Commun., 272, 726, 10.1006/bbrc.2000.2851 · DOI
  20. Foley, 2010, Bioavailability through PepT1: the role of computer modelling in intelligent drug design, Curr. Comput. Aided Drug Des., 6, 68, 10.2174/157340910790980133 · DOI
  21. Prabhala, 2017, Several hPepT1-transported drugs are substrates of the Escherichia coli proton-coupled oligopeptide transporter YdgR, Res. Microbiol., 168, 443, 10.1016/j.resmic.2017.01.005 · DOI
  22. Prabhala, 2014, New insights into the substrate specificities of proton-coupled oligopeptide transporters from E. coli by a pH sensitive assay, FEBS Lett., 588, 560, 10.1016/j.febslet.2014.01.004 · DOI
  23. Smith, 1999, Substrate specificity of the periplasmic dipeptide-binding protein from Escherichia coli: experimental basis for the design of peptide prodrugs, Microbiology (Read.), 145, 2891, 10.1099/00221287-145-10-2891 · DOI
  24. Weitz, 2007, Functional and structural characterization of a prokaryotic peptide transporter with features similar to mammalian PEPT1, J. Biol. Chem., 282, 2832, 10.1074/jbc.M604866200 · DOI
  25. Aduri, 2015, Salt Bridge Swapping in the EXXERFXYY Motif of Proton-coupled Oligopeptide Transporters, J. Biol. Chem., 290, 29931, 10.1074/jbc.M115.675603 · DOI
  26. Ernst, 2009, Ligand binding analyses of the putative peptide transporter YjdL from E. coli display a significant selectivity towards dipeptides, Biochem. Biophys. Res. Commun., 389, 112, 10.1016/j.bbrc.2009.08.098 · DOI
  27. Jensen, 2014, Critical role of a conserved transmembrane lysine in substrate recognition by the proton-coupled oligopeptide transporter YjdL, Int. J. Biochem. Cell Biol., 55, 311, 10.1016/j.biocel.2014.09.016 · DOI
  28. Jensen, 2012, Functional investigation of conserved membrane-embedded glutamate residues in the proton-coupled peptide transporter YjdL, Protein Pept. Lett., 19, 282, 10.2174/092986612799363109 · DOI
  29. Jensen, 2012, Probing the putative active site of YjdL: an unusual proton-coupled oligopeptide transporter from E. coli, PLoS One, 7, e47780, 10.1371/journal.pone.0047780 · DOI
  30. Jensen, 2012, Biophysical characterization of the proton-coupled oligopeptide transporter YjdL, Peptides, 38, 89, 10.1016/j.peptides.2012.08.012 · DOI
  31. Killer, 2021, Structural snapshots of human PepT1 and PepT2 reveal mechanistic insights into substrate and drug transport across epithelial membranes, Sci. Adv., 7, eabk3259, 10.1126/sciadv.abk3259 · DOI · Paper page
  32. Martinez Molledo, 2018, Multispecific Substrate Recognition in a Proton-Dependent Oligopeptide Transporter, Structure, 26, 467, 10.1016/j.str.2018.01.005 · DOI
  33. Martinez Molledo, 2018, Tripeptide binding in a proton-dependent oligopeptide transporter, FEBS Lett., 592, 3239, 10.1002/1873-3468.13246 · DOI
  34. Minhas, 2018, Structural basis of malodour precursor transport in the human axilla, Elife, 7, e34995, 10.7554/eLife.34995 · DOI
  35. Nagamura, 2019, Structural basis for oligomerization of the prokaryotic peptide transporter PepT(So2), Acta Crystallogr. F Struct. Biol. Commun., 75, 348, 10.1107/S2053230X19003546 · DOI
  36. Parker, 2021, Cryo-EM structure of PepT2 reveals structural basis for proton-coupled peptide and prodrug transport in mammals, Sci. Adv., 7, eabh3355, 10.1126/sciadv.abh3355 · DOI
  37. Quistgaard, 2017, Structure determination of a major facilitator peptide transporter: Inward facing PepTSt from Streptococcus thermophilus crystallized in space group P3121, PLoS One, 12, e0173126, 10.1371/journal.pone.0173126 · DOI
  38. Shen, 2022, Extracellular domain of PepT1 interacts with TM1 to facilitate substrate transport, Structure, 30, 1035, 10.1016/j.str.2022.04.011 · DOI · Paper page
  39. Stauffer, 2022, Peptide transporter structure reveals binding and action mechanism of a potent PEPT1 and PEPT2 inhibitor, Commun. Chem., 5, 23, 10.1038/s42004-022-00636-0 · DOI
  40. Boggavarapu, 2015, Role of electrostatic interactions for ligand recognition and specificity of peptide transporters, BMC Biol., 13, 58, 10.1186/s12915-015-0167-8 · DOI
  41. Guettou, 2014, Selectivity mechanism of a bacterial homolog of the human drug-peptide transporters PepT1 and PepT2, Nat. Struct. Mol. Biol., 21, 728, 10.1038/nsmb.2860 · DOI
  42. Pardon, 2014, A general protocol for the generation of Nanobodies for structural biology, Nat. Protoc., 9, 674, 10.1038/nprot.2014.039 · DOI
  43. Cornaciu, 2021, The Automated Crystallography Pipelines at the EMBL HTX Facility in Grenoble, J. Vis. Exp., 10.3791/62491 · DOI
  44. Zander, 2016, Automated harvesting and processing of protein crystals through laser photoablation, Acta Crystallogr. D Struct. Biol., 72, 454, 10.1107/S2059798316000954 · DOI
  45. Biegel, 2006, Structural requirements for the substrates of the H+/peptide cotransporter PEPT2 determined by three-dimensional quantitative structure-activity relationship analysis, J. Med. Chem., 49, 4286, 10.1021/jm0601811 · DOI
  46. Brandsch, 2013, Drug transport via the intestinal peptide transporter PepT1, Curr. Opin. Pharmacol., 13, 881, 10.1016/j.coph.2013.08.004 · DOI
  47. Boivin, 2013, Optimization of protein purification and characterization using Thermofluor screens, Protein Expr. Purif., 91, 192, 10.1016/j.pep.2013.08.002 · DOI
  48. Chari, 2015, ProteoPlex: stability optimization of macromolecular complexes by sparse-matrix screening of chemical space, Nat. Methods, 12, 859, 10.1038/nmeth.3493 · DOI
  49. Pantoliano, 2001, High-density miniaturized thermal shift assays as a general strategy for drug discovery, J. Biomol. Screen, 6, 429, 10.1177/108705710100600609 · DOI
  50. Vedadi, 2010, Biophysical characterization of recombinant proteins: a key to higher structural genomics success, J. Struct. Biol., 172, 107, 10.1016/j.jsb.2010.05.005 · DOI
  51. Bai, 2019, Isothermal Analysis of ThermoFluor Data can readily provide Quantitative Binding Affinities, Sci. Rep., 9, 2650, 10.1038/s41598-018-37072-x · DOI
  52. Layton, 2010, Thermodynamic analysis of ligand-induced changes in protein thermal unfolding applied to high-throughput determination of ligand affinities with extrinsic fluorescent dyes, Biochemistry, 49, 10831, 10.1021/bi101414z · DOI
  53. Lepock, 1992, Influence of transition rates and scan rate on kinetic simulations of differential scanning calorimetry profiles of reversible and irreversible protein denaturation, Biochemistry, 31, 12706, 10.1021/bi00165a023 · DOI
  54. Sanchez-Ruiz, 1992, Theoretical analysis of Lumry-Eyring models in differential scanning calorimetry, Biophys. J., 61, 921, 10.1016/S0006-3495(92)81899-4 · DOI
  55. Hall, 2019, A simple model for determining affinity from irreversible thermal shifts, Protein Sci., 28, 1880, 10.1002/pro.3701 · DOI
  56. Jarmoskaite, 2020, How to measure and evaluate binding affinities, Elife, 9, e57264, 10.7554/eLife.57264 · DOI
  57. Fei, 1994, Expression cloning of a mammalian proton-coupled oligopeptide transporter, Nature, 368, 563, 10.1038/368563a0 · DOI
  58. Ganapathy, 1986, Carrier-mediated reabsorption of small peptides in renal proximal tubule, Am. J. Physiol., 251, F945
  59. Li, 2022, Proton coupling and the multiscale kinetic mechanism of a peptide transporter, Biophys. J., 121, 2266, 10.1016/j.bpj.2022.05.029 · DOI
  60. Kano, 1978, Pyranine as a sensitive pH probe for liposome interiors and surfaces. pH gradients across phospholipid vesicles, Biochim. Biophys. Acta, 509, 289, 10.1016/0005-2736(78)90048-2 · DOI
  61. Parker, 2014, Thermodynamic evidence for a dual transport mechanism in a POT peptide transporter, Elife, 3, e04273, 10.7554/eLife.04273 · DOI
  62. Seifert, 1993, Charge transport by ion translocating membrane proteins on solid supported membranes, Biophys. J., 64, 384, 10.1016/S0006-3495(93)81379-1 · DOI
  63. Kyte, 1982, A simple method for displaying the hydropathic character of a protein, J. Mol. Biol., 157, 105, 10.1016/0022-2836(82)90515-0 · DOI
  64. Raveh, 2010, Sub-angstrom modeling of complexes between flexible peptides and globular proteins, Proteins, 78, 2029, 10.1002/prot.22716 · DOI
  65. Mobley, 2009, Binding of small-molecule ligands to proteins: "what you see" is not always "what you get, Structure, 17, 489, 10.1016/j.str.2009.02.010 · DOI
  66. Klepsch, 2011, Escherichia coli peptide binding protein OppA has a preference for positively charged peptides, J. Mol. Biol., 414, 75, 10.1016/j.jmb.2011.09.043 · DOI
  67. Zainol, 2021, Thermodynamics of semi-specific ligand recognition: the binding of dipeptides to the E.coli dipeptide binding protein DppA, Eur. Biophys. J., 50, 1103, 10.1007/s00249-021-01572-y · DOI
  68. Ito, 2013, Analysing the substrate multispecificity of a proton-coupled oligopeptide transporter using a dipeptide library, Nat. Commun., 4, 2502, 10.1038/ncomms3502 · DOI
  69. Samsudin, 2016, Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter, Cell Chem. Biol., 23, 299, 10.1016/j.chembiol.2015.11.015 · DOI
  70. Majd, 2018, Screening of candidate substrates and coupling ions of transporters by thermostability shift assays, Elife, 7, e38821, 10.7554/eLife.38821 · DOI
  71. Park, 2021, Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein-Ligand Docking, J. Chem. Theor. Comput., 17, 2000, 10.1021/acs.jctc.0c01184 · DOI
  72. Dalmasso, 2008, PepT1-mediated tripeptide KPV uptake reduces intestinal inflammation, Gastroenterology, 134, 166, 10.1053/j.gastro.2007.10.026 · DOI
  73. Ingersoll, 2012, The role and pathophysiological relevance of membrane transporter PepT1 in intestinal inflammation and inflammatory bowel disease, Am. J. Physiol. Gastrointest. Liver Physiol., 302, G484, 10.1152/ajpgi.00477.2011 · DOI
  74. Kovacs-Nolan, 2012, The PepT1-transportable soy tripeptide VPY reduces intestinal inflammation, Biochim. Biophys. Acta, 1820, 1753, 10.1016/j.bbagen.2012.07.007 · DOI
  75. Miyake, 2017, Inhibitory Potency of Marketed Drugs for Ulcerative Colitis and Crohn's Disease on PEPT1, Biol. Pharm. Bull., 40, 1572, 10.1248/bpb.b17-00181 · DOI
  76. Viennois, 2018, Function, Regulation, and Pathophysiological Relevance of the POT Superfamily, Specifically PepT1 in Inflammatory Bowel Disease, Compr. Physiol., 8, 731, 10.1002/j.2040-4603.2018.tb00024.x · DOI
  77. Zhu, 2020, The Potential of Food Protein-Derived Bioactive Peptides against Chronic Intestinal Inflammation, Mediat. Inflamm., 2020, 6817156, 10.1155/2020/6817156 · DOI
  78. Yang, 2020, Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets, Front. Pharmacol., 11, 69, 10.3389/fphar.2020.00069 · DOI
  79. Kotov, 2021, In-depth interrogation of protein thermal unfolding data with MoltenProt, Protein Sci., 30, 201, 10.1002/pro.3986 · DOI
  80. Roelfs, 2020, tBuLi/Symfit, symfit
  81. Cock, 2009, Biopython: freely available Python tools for computational molecular biology and bioinformatics, Bioinformatics, 25, 1422, 10.1093/bioinformatics/btp163 · DOI · Paper page
  82. Hamelryck, 2003, PDB file parser and structure class implemented in Python, Bioinformatics, 19, 2308, 10.1093/bioinformatics/btg299 · DOI
  83. Nivón, 2013, A Pareto-optimal refinement method for protein design scaffolds, PLoS One, 8, e59004, 10.1371/journal.pone.0059004 · DOI
  84. Wojdyr, 2022, GEMMI: a library for structural biology, J. Open Source Softw., 7, 4200, 10.21105/joss.04200 · DOI
  85. Pedregosa, 2011, Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825
  86. McKinney, 2010, Data Structures for Statistical Computing in Phyton, 51, 10.25080/Majora-92bf1922-00a · DOI
  87. Kabsch, 2010, Xds, Acta Crystallogr. D Biol. Crystallogr., 66, 125, 10.1107/S0907444909047337 · DOI · Paper page
  88. Potterton, 2018, CCP4i2: the new graphical user interface to the CCP4 program suite, Acta Crystallogr. D Struct. Biol., 74, 68, 10.1107/S2059798317016035 · DOI
  89. Emsley, 2010, Features and development of Coot, Acta Crystallogr. D Biol. Crystallogr., 66, 486, 10.1107/S0907444910007493 · DOI · Paper page
  90. Adams, 2010, PHENIX: a comprehensive Python-based system for macromolecular structure solution, Acta Crystallogr. D Biol. Crystallogr., 66, 213, 10.1107/S0907444909052925 · DOI · Paper page
  91. Croll, 2018, ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps, Acta Crystallogr. D Struct. Biol., 74, 519, 10.1107/S2059798318002425 · DOI
  92. Goddard, 2018, UCSF ChimeraX: Meeting modern challenges in visualization and analysis, Protein Sci., 27, 14, 10.1002/pro.3235 · DOI · Paper page
  93. Crooks, 2004, WebLogo: a sequence logo generator, Genome Res., 14, 1188, 10.1101/gr.849004 · DOI
  94. Savitsky, 2010, High-throughput production of human proteins for crystallization: the SGC experience, J. Struct. Biol., 172, 3, 10.1016/j.jsb.2010.06.008 · DOI
  95. Löw, 2013, High-throughput analytical gel filtration screening of integral membrane proteins for structural studies, Biochim. Biophys. Acta, 1830, 3497, 10.1016/j.bbagen.2013.02.001 · DOI
  96. Löw, 2013, Nanobody mediated crystallization of an archeal mechanosensitive channel, PLoS One, 8, e77984, 10.1371/journal.pone.0077984 · DOI
  97. Dimasi, 2007, Expression, crystallization and X-ray data collection from microcrystals of the extracellular domain of the human inhibitory receptor expressed on myeloid cells IREM-1, Acta Crystallogr., Sect. F: Struct. Biol. Cryst. Commun., 63, 204, 10.1107/S1744309107004903 · DOI
  98. Dupeux, 2011, A thermal stability assay can help to estimate the crystallization likelihood of biological samples, Acta Crystallogr. D Biol. Crystallogr., 67, 915, 10.1107/S0907444911036225 · DOI
  99. Alexander, 2014, Novel microscale approaches for easy, rapid determination of protein stability in academic and commercial settings, Biochim. Biophys. Acta, 1844, 2241, 10.1016/j.bbapap.2014.09.016 · DOI
  100. Mazurenko, 2017, Exploration of Protein Unfolding by Modelling Calorimetry Data from Reheating, Sci. Rep., 7, 16321, 10.1038/s41598-017-16360-y · DOI
  101. Gao, 2012, Implementing the Nelder-Mead simplex algorithm with adaptive parameters, Comput. Optim. Appl., 51, 259, 10.1007/s10589-010-9329-3 · DOI
  102. Storn, 1997, Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11, 341, 10.1023/A:1008202821328 · DOI · Paper page
  103. Hunter, 2007, Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90, 10.1109/MCSE.2007.55 · DOI · Paper page
  104. Lemmon, 2012, Rosetta Ligand docking with flexible XML protocols, Methods Mol. Biol., 819, 143, 10.1007/978-1-61779-465-0_10 · DOI
  105. Sezonov, 2007, Escherichia coli physiology in Luria-Bertani broth, J. Bacteriol., 189, 8746, 10.1128/JB.01368-07 · DOI

Metadata

Authors
Kotov, V., Killer, M., Jungnickel, K. E. J., Lei, J., Finocchio, G., Steinke, J., Bartels, K., Strauss, J., Dupeux, F., Humm, A.-S., Cornaciu, I., Márquez, J. A., Pardon, E., Steyaert, J., & Löw, C.
Affiliations
  • European Molecular Biology Laboratory (DE)
  • Heidelberg University (DE)
  • Sichuan University (CN)
  • European Molecular Biology Laboratory (FR)
  • Vrije Universiteit Brussel (BE)
Corresponding author
Löw, Christian
Venue
Cell Reports
Publisher
Elsevier BV
Published
2023-08
Volume
42
Issue
8
Pages
112831
ISSN
2211-1247 (ISSN-L)
OpenAlex
W4384820608
Cited by
20
References
4
Citations by year
2026: 3 · 2025: 5 · 2024: 10 · 2023: 2
FWCI
2.43
Type
journal article
Language
en
Open access
Locations
  • Cell Reports (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • VUBIR (Vrije Universiteit Brussel) (submittedVersion)
  • Desy Publications Database (Deutsches Elektronen-Synchrotron DESY) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron)
Funders
  • European Commission (847543, 653706, HORIZON2020, 871037)
  • Fonds Wetenschappelijk Onderzoek
  • Deutsches Elektronen-Synchrotron
Topics
  • Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biochemistry › Amino Acid Enzymes and Metabolism
Keywords
peptide transporters, major facilitator superfamily, substrate recognition, peptide absorption, hydrophobic residues, molecular docking
MeSH terms
Models, Molecular, Peptides, Membrane Transport Proteins, Molecular Docking Simulation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review