Memdock: an α-helical membrane protein docking algorithm

Hurwitz, Naama; Stroopinsky, Dina; Wolfson, Haim J.

doi:10.1093/bioinformatics/btw184

Cited by 45 publications

(49 citation statements)

References 40 publications

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“…TorC and TorE structures have been modelled with SWISS MODEL 63 – 67 based on NrfH an PilN crystallographic structures 68 , 71 . The presented modeled complex is composed of 4 TorE -TorC heterodimers predicted by Memdock 70 The TorC proteins are colored in gradient of grey and the TorE in a gradient of orange. Position of the membrane, determined by the PPM server 69 , was represented by dashed white lines.…”

Section: Discussionmentioning

confidence: 99%

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Small membranous proteins of the TorE/NapE family, crutches for cognate respiratory systems in Proteobacteria

Lemaire

Infossi

Chaouche

et al. 2018

Sci Rep

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In this report, we investigate small proteins involved in bacterial alternative respiratory systems that improve the enzymatic efficiency through better anchorage and multimerization of membrane components. Using the small protein TorE of the respiratory TMAO reductase system as a model, we discovered that TorE is part of a subfamily of small proteins that are present in proteobacteria in which they play a similar role for bacterial respiratory systems. We reveal by microscopy that, in Shewanella oneidensis MR1, alternative respiratory systems are evenly distributed in the membrane contrary to what has been described for Escherichia coli. Thus, the better efficiency of the respiratory systems observed in the presence of the small proteins is not due to a specific localization in the membrane, but rather to the formation of membranous complexes formed by TorE homologs with their c-type cytochrome partner protein. By an in vivo approach combining Clear Native electrophoresis and fluorescent translational fusions, we determined the 4:4 stoichiometry of the complexes. In addition, mild solubilization of the cytochrome indicates that the presence of the small protein reinforces its anchoring to the membrane. Therefore, assembly of the complex induced by this small protein improves the efficiency of the respiratory system.

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Section: Discussionmentioning

confidence: 99%

“…The membrane position was determined in TorC and TorE structure models using the PPM server using default parameters 69 . Membrane docking prediction was determined using the Memdock server, also using default parameters 70 . The model has been constructed using the PyMOL Molecular Graphics system.…”

Section: Methodsmentioning

confidence: 99%

Small membranous proteins of the TorE/NapE family, crutches for cognate respiratory systems in Proteobacteria

Lemaire

Infossi

Chaouche

et al. 2018

Sci Rep

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“…61 However, there have only been a few general efforts to dock membrane proteins. 14,15,48,62 Toward this goal, the final test is membrane protein-protein docking. We use RosettaM-PDock 48 to generate low-energy orientation of the protein partners by performing rigid-body rotations and translations with cycles of side-chain repacking and torsion minimization (see Methods).…”

Section: Test #12: Membrane Protein-protein Interactionsmentioning

confidence: 99%

“…14 The second dataset comprises 48 homo-and hetero-dimers formed by multi-pass α helical proteins. 15 The third dataset is a subset of the second consisting of nine targets with the starting backbone generated by homology modeling 66 to simulate an unbound docking scenario where the starting conformations of the partner are not pre-configured in the bound state. All calculations were performed in a DLPC bilayer.…”

Section: Datasetmentioning

confidence: 99%

“…This strategy has been applied to various membrane protein modeling tools including estimating the ∆∆G of mutation, 7 hydrophobic thickness, 8 native structure discrimination, 9,10 refinement, 11 protein design, 12 symmetry, 13 and protein-protein docking. 14,15 These tools enabled a decade of membrane protein modeling; however, their generalizability is unclear. Small quantities of data prevent cross-validation: a technique that ensures performance on targets that are different from the training set.…”

Section: Introductionmentioning

confidence: 99%

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Diverse scientific benchmarks for implicit membrane energy functions

Alford

Gray

2020

Preprint

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Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent datasets varying in size, diversity, and resolution. The tests probe an energy function's ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then present a vision for transforming these "small" datasets into "big data" that can be used for more sophisticated energy function optimization. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).

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SLC38A2 Overexpression Induces a Cancer‐like Metabolic Profile and Cooperates with SLC1A5 in Pan‐cancer Prognosis

Huang

Chang

Lin

et al. 2020

Chemistry An Asian Journal

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Cancer cells have dramatically increased demands for energy as well as biosynthetic precursors to fuel their restless growth. Enhanced glutaminolysis is a hallmark of cancer metabolism which fulfills these needs. Two glutamine transporters, SLC1A5 and SLC38A2, have been previously reported to promote glutaminolysis in cancer with controversial perspectives. In this study, we harnessed the proximity labeling reaction to map the protein interactome using mass spectrometry-based proteomics and discovered a potential protein-protein interaction between SLC1A5 and SLC38A2. The SLC1A5/SLC38A2 interaction was further confirmed by bimolecular fluorescence complementation assay. We further investigated the metabolic influence of SLC1A5 and SLC38A2 overexpression in human cells, respectively, and found that only SLC38A2, but not SLC1A5, resulted in a cancer-like metabolic profile, where the intracellular concentrations of essential amino acids and lactate were significantly increased as quantified by nuclear magnetic resonance spectroscopy. Finally, we analyzed the 5-year survival rates in a large pancancer cohort and found that the SLC1A5 hi /SLC38A2 lo group did not relate to a poor survival rate, whereas the SLC1A5 lo / SLC38A2 hi group significantly aggravated the lethality. Intriguingly, the SLC1A5 hi /SLC38A2 hi group resulted in an even worse prognosis, suggesting a cooperative effect between SLC1A5 and SCL38A2. Our data suggest that SLC38A2 plays a dominant role in reprogramming the cancer-like metabolism and promoting the cancer progression, whereas SLC1A5 may augment this effect when co-overexpressed with SLC38A2. We propose a model to explain the relationship between SLC1A5, SLC38A2 and SCL7A5, and discuss their impact on glutaminolysis and mTOR signaling.

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Memdock: an α-helical membrane protein docking algorithm

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 45 publications

References 40 publications

Small membranous proteins of the TorE/NapE family, crutches for cognate respiratory systems in Proteobacteria

Small membranous proteins of the TorE/NapE family, crutches for cognate respiratory systems in Proteobacteria

Diverse scientific benchmarks for implicit membrane energy functions

SLC38A2 Overexpression Induces a Cancer‐like Metabolic Profile and Cooperates with SLC1A5 in Pan‐cancer Prognosis

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