A Structure-Based View on ABC-Transporter Linked to Multidrug Resistance

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Abstract

The discovery of the first ATP-binding cassette (ABC) transporter, whose overexpression in cancer cells is responsible for exporting anticancer drugs out of tumor cells, initiated enormous efforts to overcome tumor cell multidrug resistance (MDR) by inhibition of ABC-transporter. Because of its many physiological functions, diverse studies have been conducted on the mechanism, function and regulation of this important group of transmembrane transport proteins. In this review, we will focus on the structural aspects of this transporter superfamily. Since the resolution revolution of electron microscope, experimentally solved structures increased rapidly. A summary of the structures available and an overview of recent structure-based studies are provided. More specifically, the artificial intelligence (AI)-based predictions from AlphaFold-2 will be discussed.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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Protein complex prediction with AlphaFold-Multimer

Richard R. Evans, Michael O’Neill, Alexander Pritzel … (2021)

While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] ≥ 0.49) on 14 targets and high accuracy (DockQ ≥ 0.8) on 6 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,433 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ ≥ 0.23) in 67% of cases, and produce high accuracy predictions (DockQ ≥ 0.8) in 23% of cases, an improvement of +25 and +11 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric interfaces we successfully predict the interface in 69% of cases, and produce high accuracy predictions in 34% of cases, an improvement of +5 percentage points in both instances.

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Distantly related sequences in the alpha- and beta-subunits of ATP synthase, myosin, kinases and other ATP-requiring enzymes and a common nucleotide binding fold.

J. Walker, M Saraste, M Runswick … (1982)

The alpha- and beta-subunits of membrane-bound ATP synthase complex bind ATP and ADP: beta contributes to catalytic sites, and alpha may be involved in regulation of ATP synthase activity. The sequences of beta-subunits are highly conserved in Escherichia coli and bovine mitochondria. Also alpha and beta are weakly homologous to each other throughout most of their amino acid sequences, suggesting that they have common functions in catalysis. Related sequences in both alpha and beta and in other enzymes that bind ATP or ADP in catalysis, notably myosin, phosphofructokinase, and adenylate kinase, help to identify regions contributing to an adenine nucleotide binding fold in both ATP synthase subunits.

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Gerhard F. Ecker: (View ORCID Profile)

Journal

Journal ID (publisher-id): MOLEFW

Title: Molecules

Abbreviated Title: Molecules

Publisher: MDPI AG

ISSN (Electronic): 1420-3049

Publication date Created: January 2023

Publication date (Electronic): January 04 2023

Volume: 28

Issue: 2

Page: 495

Article

DOI: 10.3390/molecules28020495

PubMed ID: 36677553

SO-VID: 7752b6e5-27e9-47df-9a76-42a5612c2f95

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https://creativecommons.org/licenses/by/4.0/

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A Structure-Based View on ABC-Transporter Linked to Multidrug Resistance

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Most cited references 78

Highly accurate protein structure prediction with AlphaFold

Protein complex prediction with AlphaFold-Multimer

Distantly related sequences in the alpha- and beta-subunits of ATP synthase, myosin, kinases and other ATP-requiring enzymes and a common nucleotide binding fold.

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