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(a) Phylogenetic tree of mammalian ABC subfamily G proteins. Tree based on 174 protein sequences, aligned with multiple alignment fast Fourier transform (MAFFT). Names of taxa have been removed for clarity. (b) Pie chart showing proportions of conservation and divergence. In the 594 columns showing conservation in at least one protein in the G subfamily, 61 are totally conserved (grey); 52 show simple type I divergence (where one set has conservation, and the others do not) (green); 193 show type II divergence (where each set is conserved, but with a different residue) (cyan); and the remaining 288 have some mixture of divergence (e.g., column 891 is a conserved cysteine in ABCG2, and a conserved leucine in ABCG1 and ABCG4, but is not conserved in other groups. Thus it has neither purely type I nor type II divergence) (red).
Source publication
The five members of the mammalian G subfamily of ATP-binding cassette transporters differ greatly in their substrate specificity. Four members of the subfamily are important in lipid transport and the wide substrate specificity of one of the members, ABCG2, is of significance due to its role in multidrug resistance. To explore the origin of substra...
Contexts in source publication
Context 1
... were grouped according to the protein they represent, and their conservation calculated as described in methods. A tree constructed from these sequences showing the relationship between the ABCG proteins is shown in Figure 1a. The alignment had a length of 1269 positions (henceforth "columns"). ...
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... alignment had a length of 1269 positions (henceforth "columns"). Of these, 674 columns had gaps in either >10% of all sequences or >30% of sequences for one of the proteins (see Supplementary Figure S1a). Of the remaining 595 columns, 594 met the entropy cutoff for conservation in at least one protein. ...
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... the remaining 595 columns, 594 met the entropy cutoff for conservation in at least one protein. A total of 61 of these columns were conserved across the ABCG family, and the remaining 533 had some type of divergence, as summarised in Figure 1b. In the 594 columns showing conservation in at least one protein in the G subfamily, 61 are totally conserved (grey); 52 show simple type I divergence (where one set has conservation, and the others do not) (green); 193 show type II divergence (where each set is conserved, but with a different residue) (cyan); and the remaining 288 have some mixture of divergence (e.g., column 891 is a conserved cysteine in ABCG2, and a conserved leucine in ABCG1 and ABCG4, but is not conserved in other groups. ...
Context 4
... were grouped according to the protein they represent, and their conservation calculated as described in methods. A tree constructed from these sequences showing the relationship between the ABCG proteins is shown in Figure 1a. The alignment had a length of 1269 positions (henceforth "columns"). ...
Context 5
... alignment had a length of 1269 positions (henceforth "columns"). Of these, 674 columns had gaps in either >10% of all sequences or >30% of sequences for one of the proteins (see Supplementary Figure S1a). Of the remaining 595 columns, 594 met the entropy cutoff for conservation in at least one protein. ...
Context 6
... the remaining 595 columns, 594 met the entropy cutoff for conservation in at least one protein. A total of 61 of these columns were conserved across the ABCG family, and the remaining 533 had some type of divergence, as summarised in Figure 1b. In the 594 columns showing conservation in at least one protein in the G subfamily, 61 are totally conserved (grey); 52 show simple type I divergence (where one set has conservation, and the others do not) (green); 193 show type II divergence (where each set is conserved, but with a different residue) (cyan); and the remaining 288 have some mixture of divergence (e.g., column 891 is a conserved cysteine in ABCG2, and a conserved leucine in ABCG1 and ABCG4, but is not conserved in other groups. ...
Citations
... Similarity-based searches have high specificity and accuracy, making it useful for detecting conserved regions of related sequences, single nucleotide polymorphisms, and indels. Disadvantages are their heavy computational demand, it may not work well with highly divergent sequences (Mitchell-White et al., 2021;Hubley et al., 2022;Hénault et al., 2023), can generate false positives when working with repetitive sequences as TEs, due to incomplete sequences, sequencing errors, permissive parameters not suited for repetitive sequences, and databases with low quality or redundant sequences resulting in spurious alignments (Markova-Raina and Petrov, 2011;Fujimoto et al., 2016;Choe et al., 2023), and are limited to known sequences, i.e., do not allow the discovery of completely new TEs with no similarity to know TEs from databases. ...
Transposable elements are repetitive and mobile DNA segments that can be found in virtually all organisms investigated to date. Their complex structure and variable nature are particularly challenging from the genomic annotation point of view. Many softwares have been developed to automate and facilitate TEs annotation at the genomic level, but they are highly heterogeneous regarding documentation, usability and methods. In this review, we revisited the existing software for TE genomic annotation, concentrating on the most often used ones, the methodologies they apply, and usability. Building on the state of the art of TE annotation software we propose best practices and highlight the strengths and weaknesses from the available solutions.
... Most of these works found a common dynamic behavior in protein families. However, it is also known that functional divergence during evolution is almost necessarily sustained by dynamical and conformational changes (Glembo et al., 2012;Liu & Bahar, 2012;Mitchell-White et al., 2021;Narayanan et al., 2017). Furthermore, a more detailed analysis of backbone flexibility, inter-domain movements, and ligand-binding motions revealed a poor conservation of the dynamic behavior in homologous families (Marino-Buslje et al., 2019). ...
CoDNaS ( http://ufq.unq.edu.ar/codnas/ ) and CoDNaS‐Q ( http://ufq.unq.edu.ar/codnasq ) are repositories of proteins with different degrees of conformational diversity. Following the ensemble nature of the native state, conformational diversity represents the structural differences between the conformers in the ensemble. Each entry in CoDNaS and CoDNaS‐Q contains a redundant collection of experimentally determined conformers obtained under different conditions. These conformers represent snapshots of the protein dynamism. While CoDNaS contains examples of conformational diversity at the tertiary level, a recent development, CoDNaS‐Q, contains examples at the quaternary level. In the emerging age of accurate protein structure prediction by machine learning approaches, many questions remain open regarding the characterization of protein dynamism. In this context, most bioinformatics resources take advantage of distinct features derived from protein alignments, however, the complexity and heterogeneity of information makes it difficult to recover reliable biological signatures. Here we present five protocols to explore tertiary and quaternary conformational diversity at the individual protein level as well as for the characterization of the distribution of conformational diversity at the protein family level in a phylogenetic context. These protocols can provide curated protein families with experimentally known conformational diversity, facilitating the exploration of sequence determinants of protein dynamism. © 2023 Wiley Periodicals LLC.
Basic Protocol 1 : Assessing conformational diversity with CoDNaS
Alternate Protocol 1 : Assessing conformational diversity at the quaternary level with CoDNaS‐Q
Basic Protocol 2 : Exploring conformational diversity in a protein family
Alternate Protocol 2 : Exploring quaternary conformational diversity in a protein family
Basic Protocol 3 : Representing conformational diversity in a phylogenetic context
... On this topic, Eckenstaler and Benndorf provided a complete review of the literature on the role of ABCG2 in urate homeostasis and its genetic variants involved in the pathogenesis of gout and hyperuricemia [16], while László Homolya proposed a classification of the genetic variants of ABCG2 based on their expression, traffic or function defects, and discussed their implication in human diseases, including cancer [17]. Using phylogenetic, sequence alignment, and structural analyses between members of the G subfamily of human ABC transporters, Mitchell-White et al. identified a conservation pattern that is different in ABCG2, which could confer greater flexibility to this transporter and thus explain its broader range of substrates [18]. Finally, based on already resolved 3D structures of ABCG2 and ABCG5/G8, and in the framework of providing a comprehensive review of the literature and comparative structural analysis, Khunweeraphong and Kuchler proposed a homology model of fungi pleiotropic drug-resistance (PDR) transporters, paving the path to a better understanding of infectious diseases due to pathogenic fungi, thus offering new therapeutic perspectives [19]. ...
The goal of this Special Issue on “ABC Transporters in Human Diseases”, for which I was invited as a Guest Editor, was to provide an overview of the state-of-the-art research, understandings, and advances made in recent years on human diseases implicating ATP-binding cassette (ABC) transporters [...]
... The most extensively studied ABC transporters are the ABCB1 (permeability glycoprotein/MDR1), ABCC1 (multidrug resistance-associated protein-1, MRP1), and ABCG2 (breast cancer resistance protein (BCRP)) [32]. Their main ability is to recognize chemotherapeutic agents after their internalization within the plasma membrane and use the energy produced via ATP hydrolysis in order to expel drug molecules out of the cells, thereby decreasing bioavailability and increasing drug resistance [29,33]. Transportation of drugs across cells occurs through multiple processes, such as passive diffusion, facilitated or active transport, or pinocytosis. ...
Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers worldwide. It is usually diagnosed in an advanced stage and is characterized by a high intrinsic drug resistance, leading to limited chemotherapeutic efficacy and relapse after treatment. There is therefore a vast need for understanding underlying mechanisms that contribute to drug resistance and for developing therapeutic strategies that would overcome this. The rapid proliferation of tumor cells, in combination with a highly inflammatory microenvironment, causes a chronic increase of protein synthesis in different hepatic cell populations. This leads to an intensified demand of protein folding, which inevitably causes an accumulation of misfolded or unfolded proteins in the lumen of the endoplasmic reticulum (ER). This process is called ER stress and triggers the unfolded protein response (UPR) in order to restore protein synthesis or—in the case of severe or prolonged ER stress—to induce cell death. Interestingly, the three different arms of the ER stress signaling pathways have been shown to drive chemoresistance in several tumors and could therefore form a promising therapeutic target. This review provides an overview of how ER stress and activation of the UPR contributes to drug resistance in HCC.
... Recently ABCG2 inhibitors of diverse chemical classes, including repurposed known drugs, have been reported for potential use as anti-cancer agents [24,25]. To understand the mechanism of the transport function of ABCG2, it is important to understand interactions between the drug-binding site of ABCG2 and drug substrates [26,27]. We previously found that A 3 adenosine derivatives interact with the drug-binding site of P-gp and can be important modulators of P-gp [15]. ...
Various adenosine receptor nucleoside-like ligands were found to modulate ATP hydrolysis by the multidrug transporter ABCG2. Both ribose-containing and rigidified (N)-methanocarba nucleosides (C2-, N⁶- and 5′-modified), as well as adenines (C2-, N⁶-, and deaza modified), were included. 57 compounds out of 63 tested either stimulated (50) or inhibited (7) basal ATPase activity. Structure-activity analysis showed a separation of adenosine receptor and ABCG2 activities. The 7-deaza modification had favorable effects in both (N)-methanocarba nucleosides and adenines. Adenine 37c (MRS7608) and (N)-methanocarba 7-deaza-5′-ethyl ester 60 (MRS7343) were found to be potent stimulators of ABCG2 ATPase activity with EC50 values of 13.2 ± 1.7 and 13.2 ± 2.2 nM, respectively. Both had affinity in the micromolar range for A3 adenosine receptor and lacked the 5′-amide agonist-enabling group (37c was reported as a weak A3 antagonist, Ki 6.82 μM). Compound 60 significantly inhibited ABCG2 substrate transport (IC50 0.44 μM). Docking simulations predicted the interaction of 60 with 21 residues in the drug-binding pocket of ABCG2.
