Molecular modelling approaches for cystic fibrosis transmembrane conductance regulator studies

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Review

Molecular modelling approaches for cystic fibrosis transmembrane conductance regulator studies夽 Norbert Odolczyk a , Piotr Zielenkiewicz a,b,∗ a b

Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warszawa, Poland Faculty of Biology, Warsaw University, 02-106 Warszawa, Poland

a r t i c l e

i n f o

Article history: Received 18 February 2014 Received in revised form 1 April 2014 Accepted 4 April 2014 Available online xxx Keywords: Cystic fibrosis CFTR Molecular modelling In silico studies

a b s t r a c t Cystic fibrosis (CF) is one of the most common genetic disorders, caused by loss of function mutations in the gene encoding the CF transmembrane conductance regulator (CFTR) protein. CFTR is a member of ATP-binding cassette (ABC) transporters superfamily and functions as an ATP-gated anion channel. This review summarises the vast majority of the efforts which utilised molecular modelling approaches to gain insight into the various aspects of CFTR protein, related to its structure, dynamic properties, function and interactions with other protein partners, or drug-like compounds, with emphasis to its relation to CF disease. This article is part of a Directed Issue entitled: Cystic Fibrosis: From o-mics to cell biology, physiology, and therapeutic advances. © 2014 Elsevier Ltd. All rights reserved.

Contents 1. 2.

3.

4.

5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insights into CFTR structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Modelling of individual domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Assembly of domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1. NBDs heterodimer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2. Full-length CFTR models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insights into the motion of CFTR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Exploring the mechanism of action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Defining the defect caused by the F508 mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insights into CFTR interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. CFTR-protein interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. CFTR–drug interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Cystic fibrosis (CF) is one of the most common genetic disorders and is caused by loss-of-function mutations in the CFTR gene,

夽 This article is part of a Directed Issue entitled: Cystic Fibrosis: From o-mics to cell biology, physiology, and therapeutic advances. ∗ Corresponding author at: Institute of Biochemistry and Biophysics, Polish ´ 5a, 02-106 Warszawa, Poland. Academy of Sciences, Pawinskiego Tel.: +48 225922145; fax: +48 226584636. E-mail address: [email protected] (P. Zielenkiewicz).

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which encodes the CF transmembrane conductance regulator (CFTR) protein (Riordan et al., 1989). CFTR, a PKA-activated Cl− channel, is a rate-limiting factor for fluid absorption in numerous epithelia (Robert et al., 2008). Molecular modelling techniques have made significant advances in recent years due to several reasons: (1) increasing knowledge and better understanding of life phenomena in atomic scale; (2) development of more sophisticate and accurate algorithms with combined integrative strategies; (3) continuous increase of the computational power. All above aspects contributed to the fact, that molecular modelling tools are becoming essential

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components of biological research, and are widely used in the simulation of biomolecular systems. The many biophysical and biochemical processes that have been successfully investigated by molecular modelling tools include protein folding, protein-protein and protein-DNA recognition, conformational changes of macromolecules, and enzyme catalysis. In this review, we would like to summarise most of the efforts that have been using molecular modelling approaches to gain insight into the various structural aspects of the CFTR protein and its relationship to CF disease. 2. Insights into CFTR structure Protein structure is important due to its intimate connection with protein function (Laskowski et al., 2005). Thus, it is extremely difficult to fully understand the importance of a particular protein in biological phenomena without determining its three-dimensional structure. Since the first isolation, cloning and characterisation of the CFTR gene, there has been an incessant endeavour to characterise the structure of its protein product (Dalton et al., 2012). Experimental techniques, such as protein crystallography (Hess and Rupley, 1971) and NMR spectroscopy (Knowles, 1972), are invaluable for protein tertiary structure determination (Berman et al., 2000), but they also have limitations (Acharya and Lloyd, 2005, Marion, 2013), especially in the context of CFTR protein, which is high molecular mass membrane protein, difficult to express at high levels, purify, and reconstitute in a functional form. Despite significant progress in the X-ray crystallography of membrane proteins (Kang et al., 2013), there is still limited atomic-resolution information on the full-length CFTR channel. All current knowledge of the spatial organisation of CFTR has been derived from the following mixed approaches: X-ray crystallography of individual domains NBD1 (Lewis et al., 2004, 2005, 2010; Thibodeau et al., 2005; Atwell et al., 2010; Mendoza et al., 2012), NBD2 (Zhao et al., unpubl., PDB ID: 3GD7), and homodimer of NBD1s (Atwell et al., 2010); NMR studies on NBD1 with or without RD (Baker et al., 2007; Kanelis et al., 2010; Hudson et al., 2012); low-resolution experiments of full length CFTR structure (Awayn et al., 2005; Ford et al., 2011; Mio et al., 2008; Rosenberg et al., 2004; Rosenberg et al., 2011; Zhang et al., 2009, 2011), SAXS studies of NBDs hetero dimer (Galeno et al., 2011; Galfre et al., 2012) or RD (Marasini et al., 2013), as also various sequence analyses, and molecular modelling approaches. The latter techniques have been intensively developed in recent years to provide alternative methods to time-consuming experimental procedures. The theoretical methods for prediction of 3D-protein structures include first principle methods (de novo), fold recognition and homology (or comparative) modelling, which is currently the most accurate and, therefore, the most widely used approach for protein structure prediction (Dahl and Sylte, 2005). Homology modelling is based on the empirical observation that evolutionarily related proteins tend to have similar three-dimensional (3D) structures (Al-Lazikani et al., 2001). The first insights into the general topology of CFTR came from simple evolutionary sequence analysis of putative protein sequence, defining its similarities with protein products of homologous genes, and calculating hydropathy profile (Riordan et al., 1989). CFTR shares its evolved domain organisation with other members of the ATP-binding cassette (ABC) transporter protein superfamily (Hyde et al., 1990). Its 1480 amino acid long polypeptide chain consists of two nucleotide-binding domains (NBD1 and NBD2) and two transmembrane domains (TMD1 and TMD2), which are arranged alternately and are separated into two symmetrical fragments by a regulatory domain (RD) (Riordan, 2008) (Fig. 1). The RD is unique among the superfamily members and is responsible for the regulation of CFTR activity (Seibert et al., 1999; Ostedgaard et al., 2001).

2.1. Modelling of individual domains Because the crystal structures of human NBD1 and NBD2 domains are already known, this paragraph is presented here mostly for historical reasons, rather than knowledgeable aspects. The pioneering efforts to propose a 3D-structural model of the cytoplasmic domains were undertaken in the early 1990s. However, due to the lack of an appropriate template crystal structure, the models were constructed on non-homologous proteins using less reliable approaches based on fold recognition methods (Godzik, 2003; Peng and Xu, 2010). The prediction and analysis of secondary structure by different algorithms inclined two groups to select the structure of adenylate kinase as a template for NBD1 modelling (Hyde et al., 1990; Mimura et al., 1991), the threading approach resulted in constructing the NBD1 model based on aspartate aminotransferase (Hoedemaeker et al., 1998). According to the sequence similarity, others decided to used as a template bovine heart mitochondrial (Annereau et al., 1997) or rat liver mitochondrial F1-ATPase (Bianchet et al., 1997) structures. Bianchet et al. (1997) also proposed the structural model of the NBD2 domain as also the NBD1–NBD2 heterodimer. Unfortunately, all of the above template structures came from evolutionarily unrelated proteins, and the models had little or no information value. The only one study, which attempted to characterise the structural properties of the isolated R domain by in silico approaches, has employed discrete MD (DMD) simulations (Dokholyan et al., 1998, Dokholyan et al., 2000) and the all-atom force field Medusa (Ding and Dokholyan, 2006) to generate an ensemble of 3D-structures of the R domain at the atomic level (Hegedus et al., 2008). However the recently published experimental measurements contradicted the above theoretical studies (Marasini et al., 2013). 2.2. Assembly of domains Constructing models of a membrane integral protein composed of several domains is a challenging task and usually requires different approaches for cytosolic and membrane spanning fragments as well as different templates for each domain (Frishman, 2010). Moreover, the templates often come from crystals of individual domains in which the native domain-domain interaction interfaces are unsettled and at least require refinement of side chain conformations to correctly predict a relative domain position to each other (Fernandez-Fuentes et al., 2007; Wollacott et al., 2007). 2.2.1. NBDs heterodimer The first reliable homology model proposed for the NBD1–NBD2 dimer (Callebaut et al., 2004; Eudes et al., 2005) was constructed on a template of the experimentally resolved dimeric structure of the bacterial ABC transporter, MJ0796 (Smith et al., 2002). This model was supported by evolutionary information gained from other ABClike domains, such as BtuCD (Locher et al., 2002), HisP (Hung et al., 1998), MJ1267 (Yuan et al., 2003), TAP1 (Gaudet and Wiley, 2001) and MalK (Chen et al., 2003), as well as by hydrophobic cluster analysis (HCA) (Gaboriaud et al., 1987; Callebaut et al., 1997). The model provided the following important structural insights: (a) the heterodimer formation and its “head-to-tail” orientation; (b) the location of both nucleotide-binding sites on the dimer interface; (c) description of the important contribution of residues from both subunits into each active site; (d) and precisely identified the spatial organisation of functional sequence motifs with their characteristically asymmetric features. Indeed, the nucleotide-binding sites of NBD1 and NBD2 are differentiated by non-canonical residues in Walker-B (Ser instead of Glu) and by switch (Ser instead of His) motifs for NBD1 as well as by a signature sequence in NBD2 (LSHGH instead of LSGGQ) (Gadsby et al., 2006). Such asymmetry reflects on NBD1 catalytic activity, which, in contrast to NBD2, is not able to

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Fig. 1. General topology of CFTR protein, and structural models of full length CFTR prepared by Serohijos et al. (2008a).

hydrolyse ATP (Gadsby et al., 2006; Hwang and Kirk, 2013). The first model constructed on an evolutionarily related template led to the new and more accurate definition of NBD1 and NBD2 boundaries, namely 392–646 and 1210–1443, respectively, but these boundaries are even currently a matter of debate (Mendoza et al., 2012; Marasini et al., 2012). Release of the Mus musculus NBD1 (mNBD1) crystal structure, which shares 78% of the sequence similarity with the human NBD1 (hNBD1) without any indels, verified the previously proposed heterodimer model and improved its low homology fragments with MJ0796 (Lewis et al., 2004). However, the regulator extension (RE), present in the mNBD1 structures produce clashes when a dimer with NBD2 is attempted. This effect was overcome in studies of Callebaut et al. (2004). An entirely different concept based on the protein-protein docking approach instead of homology modelling has been used by Huang at al. to propose a new model of NBD1–NBD2 interactions (Huang et al., 2009). The rationale was to overcome the backbone clashing problem that resulted from the superposition methods used in previous homology modelling. The docking of NBD1 and NBD2 was performed using the ZDOCK program, which utilises the fast Fourier transform (FFT) algorithm (Mintseris et al., 2007). The proposed model has been verified by comparison with known crystal structures of other heterodimers in the ABC transporter protein superfamily. Although crystal data for the CFTR NBDs heterodimer has not been available until recently, the homodimeric structure of NBD1s (without RD) has been resolved (Atwell et al., 2010), and it has been strongly suggested that NBD1–NBD2 dimerisation occurs in the “head-to-tail” manner as proposed by homology models, what is also supported by biophysical studies (Galfre et al., 2012; Galeno et al., 2011). 2.2.2. Full-length CFTR models Resolving the full-length crystal structures of ABC transporter homologues, such as mammalian P-glycoprotein (Aller et al., 2009) or bacterial Sav1866 (Dawson and Locher, 2006), MsbA (Ward et al., 2007) and TM287/288 (Hohl et al., 2012) as well as human NBD1 and NBD2 CFTR domains (Lewis et al., 2004, 2005, 2010; Thibodeau

et al., 2005; Atwell et al., 2010), has provided the basis of our understanding of the structure and function of full-length CFTR (Patrick and Thomas, 2012; Hunt et al., 2013). 2.2.2.1. Template selection. Several homology models of the fulllength CFTR protein structure have been published (Table 1). Among full-length crystal structures of homologous ABC transporters available in the PDB, only few are suitable for modelling CFTR structure. A Sav1866 bacterial transporter crystal structure from Staphylococcus aureus (PDB: 2HYD, 2ONJ) (Dawson and Locher, 2006; Dawson and Locher, 2007) it reflects the openstate channel (outward-facing conformation) and has been used by Mornon et al. (2008) and Alexander et al. (2009) as the sole template for proposed models. Modelling of CFTR closed-state channel (inward-facing conformation) has been done with crystal structure of Vibrio cholerae MsbA transporter (Ward et al., 2007; Mornon et al., 2008), and M. musculus P-glycoprotein (Aller et al., 2009). The latter one was used to construct models for further molecular dynamics simulations (Furukawa-Hagiya et al., 2013; Rahman et al., 2013). Other groups have proposed multi-template models combining the available high-resolution crystal data for mice (Mendoza and Thomas, 2007) or human NBD1 domain(s) crystallised as a monomer (Serohijos et al., 2008a) or homodimer (Dalton et al., 2012) with the Sav1866-based fragments for transmembrane domains. 2.2.2.2. Sequence-to-structure alignments. The reliable sequence alignment between target and chosen structure template sequence(s) is the most critical step in homology modelling protocols because it projects the final model correctness. The difficulty of this step is conditioned on how both proteins are evolutionarily related. Among the entire ABC transporter superfamily, the conservation of cytoplasmic domains (excluding RD) is relatively strong, but the sequence and spatial organisation of TMDs is much more variable (Mendoza and Thomas, 2007). The sequence identities achieved in TMDs between CFTR and Sav1866 as well as between CFTR and MsbA are comparable and equal approximately 12–14%, which is far below the “twilight zone” (Rost, 1999). Such

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Table 1 Summary of the published CFTR full-length models. Models

Serohijos et al. (2008a)

Domains NBD1

NBD2

TMDs

RD

Homo sapiens NBD1 2BB0, 2.55 Å

Homology model according to (Callebaut et al., 2004)

Staphylococcus aureus Sav1866 transporter 2HYD, 3.00 Å

ab initio

Mornon et al. (2008)

Staphylococcus aureus Sav1866 transporter 2HYD, 3.00 Å

–

Mornon et al. (2009)

Vibrio cholerae MsbA transporter 3B5X, 5.50 Å

HCA

Alexander et al. (2009)

Sav1866 transporter Staphylococcus aureus 2HYD, 3.00 Å

–

Norimatsu et al. (2012)

Sav1866 transporter Staphylococcus aureus 2HYD, 3.00 Å

–

Dalton et al. (2012)

Furukawa-Hagiya et al. (2013)

Homo Sapiens Head-to-tail homodimer of NBD1 (del405-436) 2PZE, 1.70 Å Homo Sapiens NBD1 1XMI, 2.25 Å

Homo Sapiens NBD1 3GD7, 2.70 Å

Staphylococcus aureus Sav1866 transporter 2ONJ, 3.40 Å

–

Mus musculus P-glycoprotein 3G5U, 3.80 Å

–

Rahman et al. (2013)

Sav1866 transporter Staphylococcus aureus 2HYD, 3.00 Å

–

Rahman et al. (2013)

Mus musculus P-glycoprotein 3G5U, 3.80 Å

–

poor sequence similarity required the inclusion of additional data and more sophisticated algorithms to construct reliable alignments (Frishman, 2010). Serohijos et al. (2008a) used simple pairwise alignments that were done according to the positions of corresponding regions, such as membrane-embedded fragments and conserved intracellular coupling helices (ICL), which were previously identified by an experimental approach (Akabas et al., 1994) and HMMTOP method (Tusnady and Simon, 2001). Taking into consideration data from multiple sequence alignments of CFTR TMDs and others transporters from ABCC and ABCB subfamilies, including Sav1866 and MsbA, Mornon used hydrophobic cluster analysis (HCA) (Gaboriaud et al., 1987, Callebaut et al., 1997) to enhance detection of remote relationships (Mornon et al., 2008). The model created by Alexander et al., (2009) was based on the alignment of two fragments of CFTR sequence (TM1-NBD1 and TM2-NBD) separately aligned to Sav1866 sequence by the MUSCLE algorithm (Edgar, 2004). The most expanded and comprehensive bioinformatics analysis during alignment construction has been used by Dalton et al. (2012). These authors utilised different algorithms, such as TMHMM (Tusnady and Simon, 2001), HMMTOP (Tusnady and Simon, 2001), SPLIT4 (Juretic et al., 2002), and PSIPRED (Bryson et al., 2005), to approximate the location of TM helices in the CFTR sequence in addition to profile-to-profile fold recognition methods, such as HMAP (Petrey et al., 2003), HHPRED (Petrey et al., 2003), and FFAS03 (Jaroszewski et al., 2005), to obtain final sequence alignments between the TMDs of Sav1866 and CFTR. According to the results of the phylogenetic analysis of ABCC and ABCB, the final multiple sequence alignment (MSA) included 177 sequences from the ABCC subfamily and none from the ABCB. 2.2.2.3. Models differences. Because all published models are based on different templates and/or different alignments approaches,

they all significantly vary in some regions. Such discrepancies between models are also not surprising due to the following reasons: (1) different databases used to model low complexity regions; (2) different rotamers used for amino acid residues not adopted from template structure(s); (3) final refinement methods and (4) different experimental constrains applied during all steps of the modelling protocols. Here we would like to note the differences arising from the conceptual approach rather than from applied algorithms. The RD is approximately 200 residues in length, mostly unstructured, and it is unique for CFTR (Ostedgaard et al., 2001). Due to a lack of crystal structure for any homologous fragment, the RD was omitted in most of the published models (Mornon et al., 2008; Alexander et al., 2009; Dalton et al., 2012; Norimatsu et al., 2012). However, two other studies included the RD, constructed by ab initio folding simulations (Serohijos et al., 2008a) or HCA method (Mornon et al., 2009) and yielded different divergent molecular models of this domain. In two models of outward-facing conformation, released almost simultaneously, the main differences were localised mostly in the region of interfaces between MSDs and NBDs, including its important fragments of ICLs – which likely are involved in the stabilisation of inter-domain contacts and regulation of CFTR channel gating (Mornon et al., 2008; Serohijos et al., 2008a). The discrepancies were induced by the different modelling strategies used, because Mornon et al. (2008) resigned from incorporating the crystal structure of hNBD1 into the model and based only on the Sav1866 template, what resulted in better description of the hydrophobic cluster residues for the ICL4-NBD1 interface in the F508 region, which is believed to be disrupted in F508-CFTR. All models vary significantly in the regions of TMDs, mostly due to poor homology in this part between templates and CFTR,

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thus resulting in different alignments being applied by authors. However, the most recent molecular modelling studies were concentrated on optimisation of channel pore architecture of CFTR (Alexander et al., 2009; Norimatsu et al., 2012; Dalton et al., 2012), which was omitted by others (Serohijos et al., 2008a; Mornon et al., 2008, 2009). Alexander et al. and Norimatsu et al. proposed an interesting approach by combining the experimental studies together with homology modelling and MD simulations to precisely define the outer and inner channel residues reflecting significant improvement of the “channel like” conformation of TMD regions (Alexander et al., 2009; Norimatsu et al., 2012). Dalton et al. applied a unique modelling approach by insertion of a chloride ions column into the pore of the Sav1866 template structure, which prevented the collapse of the channel pore during further steps of the model refinement (Dalton et al., 2012). Thus, the proposed model possesses a continuous channel pore with the narrow region corresponding to the proposed ion selectivity filter (Alexander et al., 2009; Norimatsu et al., 2012; Linsdell, 2006; Smith et al., 2001) and inner vestibule sufficient to accommodate open channel blockers (Linsdell, 2005). 3. Insights into the motion of CFTR Molecular dynamics (MD) simulation is a one of the principal time-resolved approaches in the theoretical study of biological molecules, which can describe the individual and collective motion of atoms in detail within a probe molecular system (Leach, 2001). A fundamental appreciation for how proteins work requires an understanding not only of the three-dimensional structure but also of its dynamic behaviour, which is much more difficult to probe experimentally (Karplus and Kuriyan, 2005). MD simulation provides such links between structure and its motions by enabling the exploration of the conformational energy landscape accessible to protein molecules. MD supplies useful information about the timedependent behaviour of biomolecules and has been successfully applied to explore the abundant conformational space of protein structures, stability and folding.

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model (NBDs were adopted form crystal structures, and TMDs were modelled on the template of the M. musculus P-glycoprotein transporter (Aller et al., 2009)), authors have performed simulations as follows: first with NBDs occupied by ATPs; and second as a control for apo-structure. For the case of ATP-bound CFTR, the 100 ns simulation showed that the NBDs dimerise in a head-to-tail manner in the first 10 ns of simulations. However, the apo-structure formed only a partial contact between NBDs, which was not sufficient to form stable NBDs dimer. Despite the relatively long simulation time, the TMDs of ATP-bound CFTR did not achieve the open-channel conformation, and only significant movement and rearrangement of the parts anchored in the lipid bilayer were observed, which was interpreted as the formation of a chloride ion access path (Furukawa-Hagiya et al., 2013). It is worth noting that the results from the simulation studies were highly consistent with the previous experimental observations demonstrating that ATP binding to ABC proteins is crucial for the closure of the NBDs and for stabilising the dimerised state (Newstead et al., 2009; Zaitseva et al., 2005; Wen and Tajkhorshid, 2008). A slightly different approach utilising targeted MD simulations has been employed by Rahman et al. (2013), to describe the transition of the CFTR structure from the inward- to the outward-facing conformation. This method allows the induction of conformational changes in a known target structure at ordinary temperature by applying a time-dependent, purely geometrical constraint (Schlitter et al., 1994). Using this technique, the authors investigated the trajectory of a possible transition pathway between the two CFTR states (C0 and O). The targeted MD simulation was performed using two homology models of CFTR created for the open-channel state, which was based on Sav1866 (Dawson and Locher, 2006), and the closed-channel state, which was based on murine P-glycoprotein (Aller et al., 2009). However, due to the fact that targeted MD requires biasing force, which drives the system from one known state to another state (here C0 → O), the simulation provided insight into the nature of transition states C1 and C2 rather than the true transition pathway between C0 and O. 3.2. Defining the defect caused by the F508 mutation

3.1. Exploring the mechanism of action CFTR, as an ion channel during its gating cycle, passes through different conformational states that are strictly regulated and/or induced by PKA phosphorylation, ATP binding and its hydrolysis (Gadsby et al., 2006), but other proteins might also participate in this process (Bozoky et al., 2013; Venerando et al., 2013; Chappe et al., 2003). The transition between a closed state (inward-facing conformation) to an open and active state channel (outward-facing conformation) is not fully elucidated, but it is thought to proceed in at least four steps as follows: C0, the RD of closed state of apoCFTR is phosphorylated by PKA and dissociates from NBDs, thus opening the possibility to bind ATP resulting in NBDs being fully dissociated; C1, binding of ATP into one of the nucleotide-binding sites causing partial dimerisation of NBDs; C2, binding of the second ATP inducing a “head-to-tail” dimer formation, but the channel pore is still incapable of chloride ion conductance; and O, significant rearrangement of TMDs induced by NBD dimer formation opening the channel pore for chloride ions (for review see (Hwang and Kirk, 2013; Gadsby et al., 2006)). The uniqueness of CFTR manifests also at the gating mechanism. Whereas most of ABC transporters consume the power from ATP hydrolysis to translocate molecules across the membrane, the CFTR hydrolyses the ATP at the end of the gating cycle to mediate channel closure. By MD simulations, Furukawa-Hagiya et al. (2013) attempted to demonstrate the impact of ATP binding on spatial reorganisation and NBDs dimer formation at the atomic level. Starting from the inward-facing conformation of the full-length CFTR multitemplate

Applying the MD simulation to full-length homology models of CFTR protein has proven to be a highly efficient modelling tool to explore the details of the ion conductance mechanism and channel gating, or to improve the homology model structures. However, this technique has never been used to investigate the basic defect caused by CF-related mutations. Because the crystallographic solution of the NBD1 structure shows that the F508 mutation does not induce a significant difference in the domain fold but rather a small difference at the surface near the position of phenylalanine 508 (Lewis et al., 2005), few groups have used MD to test the impact of this change on the dynamic properties of NBD1 only (Warner et al., 2007; Serohijos et al., 2008b; Wieczorek and Zielenkiewicz, 2008; Bisignano and Moran, 2010). In the first such report, classical MD were used to show the dynamic fluctuations between mutated and non-mutated domains and to test the impact of a small molecule on those fluctuations (Warner et al., 2007). MD was performed on homology models of human wild type WT-NBD1 (apo form) and on F508-NBD1 (apo form and with a 8-cyclopentyl-1,3-dipropylxantine (CPX) docked in the ATP-binding pocket). The homology models of both domains were constructed using the mNBD1 crystal structure (Lewis et al., 2004). The simulation time for each system was 2 ns, and the results obtained were analysed using principle component analysis (PCA). Substantial differences in the dynamic properties between the two apo forms were observed, and the differences between the CPX-bound F508-NBD1 and apo WT-NBD1 were significantly smaller. Thus, the authors postulated that the CPX compound could

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restore the ‘wild type’ motion characteristics in the mutant protein after binding, thereby leading to its plasma membrane localisation (Warner et al., 2007). A slightly different approach was proposed by Wieczorek and Zielenkiewicz, who used MD simulations for comparative conformational analysis to more extensively evaluate the trajectories of ATP-bound WT- and F508-NBD1 (Wieczorek and Zielenkiewicz, 2008). These studies were performed on two crystal structures of hNBD1 with and without the F508 mutation (Lewis et al., 2005). The MD simulation time was 20 ns for each system. The results obtained were analysed by PCA to consider the most striking differences in the conformational space covered by the two structures, and the solvent-accessible surface was calculated for all conformational states. Thus, it was shown that WT- and F508-NBD1 exhibit different dynamic behaviours. The F508 mutation increases the conformational freedom of the NBD1 domain and causes the dissociation of a linker between the ABC␣ and ABC␤ subdomains in the NBD1 domain (residues 492–499, including the Q-loop). During more than 25% of the simulation time, F508 domain achieved a conformational state unobservable in the trajectory of the WT domain. Moreover, it was shown that both subdomains were allowed to bend and expose hydrophobic surfaces. The authors postulated that such hydrophobic areas on the protein’s surface might be responsible for the recognition of the F508-CFTR as improperly folded by housekeeping proteins. The results of this study were further used for the discovery of new small molecules that serve as highly effective correctors of the F508-CFTR protein (Odolczyk et al., 2013) and disrupt its interaction with keratin 8 (Colas et al., 2012). Others re-examined the MD studies of both domains and did not confirm that F508-NBD1 has much greater conformational freedom compared to WT (Bisignano and Moran, 2010). Bisignano and Moran performed MD simulations for each structure by running 20 replicas for 5 ns with a time step of 2 fs resulting in 100 ns of total simulation time for each structure, and the obtained trajectories were analysed. In contrast to Wieczorek and Zielenkiewicz (2008), the results presented by Bisignano and Moran (2010) did not provide evidence that the F508 mutation induces any significant difference between the WT- and F508-NBD1 structures. Instead, the authors noticed only small dynamic fluctuations near the position of the mutation. Due to fact that both groups used methods that differ (e.g., molecular force field), such discrepancy between results are not surprising. However, some experimental studies with substitution of proline residues in the Q-loop support observations done by Wieczorek and Zielenkiewicz (2008). Thus, the rigidifying effect of prolines in these locations might diminish the thermal instability of F508-NBD1 and restore channel function and thermostability of full-length F508-CFTR (Aleksandrov et al., 2012). Serohijos tested the impact of the F508 mutation on the stability, dynamics and kinetics of NBD1 by performing equilibrium dynamics and folding simulations (Serohijos et al., 2008b). The calculations were performed on simplified bead protein models of three NBD1 variants as follows: WT-NBD1, F508-NBD1 and F508A-NBD1. All bead models were prepared on crystal structures of hNBD1 (Lewis et al., 2005). Using DMD methods, simulations were performed at different temperatures to investigate the ¯ (Abe and equilibrium dynamics of NBD1-CFTR. The Go-model Go, 1981) was used to define the interactions among the beads. The folding simulations for each case were performed 300 times, and each simulation started from a fully unfolded chain followed by progressive reductions in the temperature of the system. The results showed different folding kinetic characteristics between F508- and WT-NBD1, and demonstrated that the non-mutated domain possessed a higher folding rate than the mutated one, but no major differences were found in the stability of WT and F508-NBD1. These simulation results were in agreement with

previously published experiments (Qu et al., 1997; Thibodeau et al., 2005; Lewis et al., 2005; Du et al., 2005). The authors also indicated the possibility of interactions, such as Q493/P574 and F575/F587, which would significantly affect the observed differences in folding kinetics (Serohijos et al., 2008b). 4. Insights into CFTR interactions 4.1. CFTR-protein interactions Siwiak et al. (2012) attempted to modelling the interactions of CFTR with two important kinases responsible for the regulation of its activity. Using homology modelling, in silico mutagenesis and experimental constraints, the authors proposed a model of CFTR interactions with cAMP-dependent protein kinase A (PKA) and 5 -AMP-activated protein kinase (AMPK). The proposed models hypothesise the availability of Ser813 of the CFTR protein for the phosphorylation of both kinases and highlight the key role of the structural flexibility of the serine-rich R-domain in CFTR for channel activity. 4.2. CFTR–drug interactions The first small molecules that could correct the function of the F508-CFTR protein were reported long ago (Becq et al., 2011), and from that time, the number of such molecules has consistently increased. Compounds that can overcome the trafficking defect are called correctors, and compounds that can increase CFTR activity are known as potentiators or activators. Some of those molecules might interact with the proteins responsible for F508-CFTR processing, and others are suspected of interacting directly with the CFTR protein (Riordan, 2008). Defining the binding sites for such molecules on the CFTR surface might substantially accelerate the efforts to discover and optimise new drugs against cystic fibrosis. The first report to use in silico methods to identify the binding sites for known potentiators on the surface of the NBD1–NBD2 heterodimer was published by Moran et al. (2005). The authors used a homology model of the NBD1–NBD2 heterodimer based on the mNBD1 crystal structure and the bacterial ABC transporter, MJ0796 (Smith et al., 2002). The prepared model was first used for the blind, rigid molecular docking of 18 known activators of CFTR, what resulted in the identification of three putative binding sites for the these compounds. Furthermore, the genetic docking algorithm implemented in the GOLD program was used for flexible docking, and the ChemScore (Eldridge et al., 1997) function was used to calculate the binding free energy (Gcalc ). Based on the correlations between the mean value of Gcalc from 100 docking runs for each ligand at each putative site and Gexp obtained from experimental data, the authors identified only one region as a putative binding site for such activators localised at the NBD1–NBD2 interface. The other two studies presented by He and colleagues (He et al., 2013) and Farinha and colleagues (Farinha et al., 2013) attempted to determine the binding modes of VX-809 (corrector of F508CFTR) on the Sav1866-based CFTR model. Both groups suggested that VX-809 should bind in the region of interaction between the ICL4 loop and F508 in the WT protein. This interaction is probably disrupted in F508-CFTR. Recently, in silico approaches based on virtual screening (VS) methods have also been used to discover new compounds with great potential as drugs for the treatment of cystic fibrosis (Kalid et al., 2010; Odolczyk et al., 2013). 5. Concluding remarks The gene related to CF disease has been discovered over two decades ago (Riordan et al., 1989), but due to the complexity of its

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Please cite this article in press as: Odolczyk N, Zielenkiewicz P. Molecular modelling approaches for cystic fibrosis transmembrane conductance regulator studies. Int J Biochem Cell Biol (2014), http://dx.doi.org/10.1016/j.biocel.2014.04.004