The incredible diversity of structures and functions of ABC transporters

Hamdoun, Amro; Hellmich, Ute A.; Kuchler, Karl

doi:10.1002/1873-3468.14061

Cited by 4 publications

(3 citation statements)

References 37 publications

(33 reference statements)

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“…ABC proteins play a role in important cellular processes in all types of organisms and most of them transport substrates through the cell membrane in an ATP-dependent manner [8][9][10]. ABCC7/CFTR is a special member, which is an ATP-gated chloride channel and includes a long intrinsically disordered regulatory R domain [11,12].…”

Section: Introductionmentioning

confidence: 99%

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Hegedüs

Geisler

Lukacs

et al. 2022

Cell. Mol. Life Sci.

103

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Transmembrane (TM) proteins are major drug targets, but their structure determination, a prerequisite for rational drug design, remains challenging. Recently, the DeepMind’s AlphaFold2 machine learning method greatly expanded the structural coverage of sequences with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the reliability of the generated TM structures should be assessed. Therefore, we quantitatively investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds and for specific membrane proteins (e.g. dimer modeling and stability in molecular dynamics simulations). We tested template-free structure prediction with a challenging TM CASP14 target and several TM protein structures published after AlphaFold2 training. Our results suggest that AlphaFold2 performs well in the case of TM proteins and its neural network is not overfitted. We conclude that cautious applications of AlphaFold2 structural models will advance TM protein-associated studies at an unexpected level.

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Section: Introductionmentioning

confidence: 99%

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Hegedüs

Geisler

Lukacs

et al. 2022

Cell. Mol. Life Sci.

103

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“…For example, Yap1, Yap2, and Yap8 transporters regulate the response to oxidative and heavy metal stress by inducing the expression of the yeast activator protein YCF1, the pleiotropic drug resistance elements PDR5, and the multidrug transporter involved in multidrug resistance and singlet oxygen species resistance (SNQ2) in response to a variety of toxic metals. Yeast ABC transporters play an important role in plasma membrane homeostasis, the site of action of antifungals, such as polyenes and azoles, and are involved in resistance to different drugs, which could explain the correlation between the presence of some heavy metals and the susceptibility to fluconazole of some yeasts isolated in this study [ 76 , 77 ]. In relation to the presence of heavy metals in soils, Suillus luteus uses different mechanisms, such as the exclusion and regulation of heavy metals through transporters located in the cell membrane and chelating agents, which are released to the outside of the cell wall, through storage, and the trapped ions are stored or transported in vesicles and expelled from the cells, in the same way that oxidizing agents relieve oxidative stress caused by reactive oxygen species through reducing agents such as cytochromes p450 (CYP450) [ 78 ].…”

Section: Discussionmentioning

confidence: 99%

Water Quality, Heavy Metals, and Antifungal Susceptibility to Fluconazole of Yeasts from Water Systems

Caicedo-Bejarano

Osorio-Vanegas

Castillo

et al. 2023

IJERPH

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Aquatic environments could be reservoirs of pathogenic yeasts with acquired antifungal resistance. The susceptibility to antifungal agents of yeasts present in the wastewater and natural waters of the city of Cali was evaluated. Samples were taken from two types of water: drinking water (Meléndez River, drinking water treatment plant “Puerto Mallarino” in the Cauca River) and wastewater (South Channel of the Cauca River, “Cañaveralejo-PTAR” wastewater treatment plant). Physico-chemical parameters, heavy metal concentration, and yeast levels were determined using standard procedures. Yeasts were identified using API 20 C AUX (BioMérieux) and sequence analysis of the ITS1-5.8S-ITS2 and D1/D2 regions of the large subunit of the ribosome. Susceptibility assays against fluconazole and amphotericin B using the minimum inhibitory concentration (MIC) test were determined using the microdilution method. The influence of physico-chemical parameters and heavy metals was established using principal component analysis (PCA). Yeast counts were higher at WWTP “PTAR” and lower at Melendez River, as expected. A total of 14 genera and 21 yeast species was identified, and the genus Candida was present at all locations. Susceptibility tests showed a 32.7% resistance profile to fluconazole in the order DWTP “Puerto Mallarino = WWTP “PTAR” > South Channel “Navarro”. There were significant differences (p < 0.05) in the physico-chemical parameters/concentration of heavy metals and yeast levels between the aquatic systems under study. A positive association was observed between yeast levels and total dissolved solids, nitrate levels, and Cr at the “PTAR” WWTP; conductivity, Zn, and Cu in the South Channel; and the presence of Pb in the “Puerto Mallarino” DWTP. Rhodotorula mucilaginosa, Candida albicans, and Candida sp. 1 were influenced by Cr and Cd, and Diutina catelunata was influenced by Fe (p < 0.05). The water systems explored in this study showed different yeast levels and susceptibility profiles, and, therefore, possible genetic differences among populations of the same species, and different physico-chemical and heavy metals concentrations, which were probably modulating the antifungal-resistant yeasts. All these aquatic systems discharge their content into the Cauca River. We highlight the importance to further investigate if these resistant communities continue to other locations in the second largest river of Colombia and to determine the risk posed to humans and animals.

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“…To demonstrate at a higher resolution that the predicted TM folds are native, we compared predicted structures of the ATP Binding Cassette (ABC) superfamily from the AF2-predicted 21 proteomes to existing experimental ABC folds. ABC proteins play a role in important cellular processes in all types of organisms and most of them transport substrates through the cell membrane in an ATP dependent manner [8][9][10] . ABCC7/CFTR is a special member, which is an ATP-gated chloride channel and includes a long intrinsically disordered regulatory R domain 11,12 .…”

Section: Introductionmentioning

confidence: 99%

AlphaFold2 transmembrane protein structure prediction shines

Hegedüs

Geisler

Lukacs

et al. 2021

Preprint

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Transmembrane (TM) proteins are major drug targets, indicated by the high percentage of prescription drugs acting on them. For a rational drug design and an understanding of mutational effects on protein function, structural data at atomic resolution are required. However, hydrophobic TM proteins often resist experimental structure determination and in spite of the increasing number of cryo-EM structures, the available TM folds are still limited in the Protein Data Bank. Recently, the DeepMind’s AlphaFold2 machine learning method greatly expanded the structural coverage of sequences, with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the validity of the generated TM structures should be assessed. Therefore, we investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds, and also in specific individual cases. We tested template-free structure prediction also with a new TM fold, dimer modeling, and stability in molecular dynamics simulations. Our results strongly suggest that AlphaFold2 performs astoundingly well in the case of TM proteins and that its neural network is not overfitted. We conclude that a careful application of its structural models will advance TM protein associated studies at an unexpected level.

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The incredible diversity of structures and functions of ABC transporters

Cited by 4 publications

References 37 publications

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Water Quality, Heavy Metals, and Antifungal Susceptibility to Fluconazole of Yeasts from Water Systems

AlphaFold2 transmembrane protein structure prediction shines

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