dataesr/scientific-paragraphs-categorization huggingface dataset:
1 by superimposing the hP-gp models over the crystal structure of the mP-gp (PDB ID: 4M1M) (Figure  6 ). The main-chain root mean square deviation (RMSD) and the Number of Overlapping Residues are shown in Table  6. The RMSD values with respect to the side chain, alpha carbons, and the whole protein were also calculated and are shown in Table  7. In order to assess the overall folding of the models, a structural comparison of the hP-gp models developed was done in Discovery Studio 4. 1 by superimposing the hP-gp models over the crystal structure of the mP-gp (PDB ID: 4M1M) (Figure  6 ). The main-chain root mean square deviation (RMSD) and the Number of Overlapping Residues are shown in Table  6. The RMSD values with respect to the side chain, alpha carbons, and the whole protein were also calculated and are shown in Table  7.
In order to assess the overall folding of the models, a structural comparison of the hP-gp models developed was done in Discovery Studio 4. 1 by superimposing the hP-gp models over the crystal structure of the mP-gp (PDB ID: 4M1M) (Figure  6 ). The main-chain root mean square deviation (RMSD) and the Number of Overlapping Residues are shown in Table  6. The RMSD values with respect to the side chain, alpha carbons, and the whole protein were also calculated and are shown in Table  7. The results demonstrated a small Main-chain RMSD against the crystal structure of mP-gp (PDB ID: 4M1M) with values of 0. 24 Å and 0. 70 Å for SWISS-MODEL and I-TASSER models, respectively. The model generated with Discovery Studio 4. 1 Client/Modeler 9. 12 resulted in the largest RMSD value of 1. 83 Å. When a model is overlapped to the template, the generally accepted RMSD threshold is of 2.
0 Å; thus, the three hP-gp models assessed are within the accepted limit. The superimposition between the mP-gp (PDB ID: 4M1M) and hP-gp models revealed that the models developed exhibit significant 3D similarities and that the overall folding is correct. Additional analysis of the predicted hP-gp 3D structures was done using Verify 3D, ERRAT and PROVE structural assessment tools (Table  8 ). The Verify 3D tool, which provides an analysis of the compatibility of the 3D models with their amino acid sequence (1D), resulted in scores smaller than 80% for the three models assessed. This means that less than the 80% of the amino acids in the structures have a score ≥0. 2 in the 3D/1D profile. The best score was obtained for the I-TASSER model with 63. 41% of the residues with averaged 3D/1D score ≥0.
2; very close to the result obtained by the crystal structure of the mP-gp (PDB ID: 4M1M). SWISS-MODEL and Discovery Studio models resulted both with 45% of the residues with averaged 3D/1D score ≥0. 2. According to these results none of the predicted 3D models of hP-gp passed the assessment; however, the quality indicator performed poorly also on the crystallized structure of mP-gp with just 65. 20% of the residues within the scoring limit. The overall quality factor for non-bonded atomic interactions between different atom types in the modeled structures was assessed using the ERRAT program. The ERRAT score is expressed as the percentage of the protein for which the calculated error value falls below the 95% rejection limit. The ERRAT score should be greater than 50% for considering a model of good quality.
The overall quality factors were around 95% for SWISS-MODEL and I-TASSER models, being the I-TASSER model the one with the highest score of 96. 08%. The Discovery Studio model and the crystal structure of the mP-gp (PDB ID: 4M1M) resulted both with scores around 80% below the rejection limit. Based on the previous results, the current 3D hP-gp models assessed have good reliability. The volume-based structure validation of the hP-gp models was done utilizing the PROVE program. The crystal structure of the mP-gp (PDB ID: 4M1M), used as a reference for the validation of the models developed, resulted in less than 1% of buried outlier atoms, which is the threshold value for considering the test passed. Outliers are considered here as being those buried atoms for which the volume is more than 3.
0 standard deviations away from the expected volume. The results for the three models assessed indicated that there are some errors present in the structures as the percentage of buried outlier atoms was greater than 5% in all the cases. The structure quality assessment made using the online tools PROCHECK, Verify 3D, ERRAT, and PROVE suggested that the three hP-gp models developed are as good as the crystal structure of the mP-gp (PDB ID: 4M1M) used as a template. They are reliable and of suitable quality for further molecular docking simulations. Nonetheless, the I-TASSER hP-gp model had a better performance with respect to the other two models in a majority of the tests. In order to check any possibility of a bias in the quality of the selected hP-gp model (I-TASSER) that comprises NBDs (according to the full primary sequence of the hP-gp protein used), an additional quality assessment of its truncated counterpart (without NBDs) was performed (see Table  S4 , Figure  S6 ).
Docking into Homology Model Ligand docking is a commonly used approach to identify ligand-protein interactions. However, in the case of P-gp, this could be challenging due to the high degree of flexibility and the large binding cavity consisting of multiple binding sites  [19, 32]. In addition to this, P-gp can bind more than one ligand simultaneously  [33, 34]  and until 2019, when the cryoEM structure of human ABCB1 was resolved (PDB ID: 6QEX)  [12] , there was a lack of a high resolution crystal structure of hP-gp which rendered necessary the use of protein homology models, adding additional layers of uncertainty to the process. Nonetheless, a recent study has reported the use of homology models in virtual screening applications with a superior performance in comparison to crystal structures  [35].
This fact is explained by the conformational flexibility provided by homology models which allows a better accommodation of diverse ligands and therefore a better screening performance. Two docking runs were performed utilizing two different algorithms, CDOCKER  [36]  and GOLD  [37, 38]. In order to analyse the binding pocket of the hP-gp, the simulations started with the docking of thirteen compounds, among them eight well-known molecules which interact with P-gp as substrates, inhibitors or both: cyclosporin A (CsA), amiodarone (AM), doxorubicin (DOX), digoxine (DIG), loperamide (LPM), rifampin (RMP), verapamil (VER), carvedilol (CAR), and five non-interacting compounds with P-gp: valproic acid (VPA), busulfan (BU), gentamicin (GEN), pamidronate (APD), and paraquat (PQ). The large binding pocket, observed in the mP-gp crystal structure (PDB ID: 4M1M), binds the ligands at different sites with partially overlapping residues; some of them identical to those involved in rhodamine and verapamil binding  [39] [40] [41] [42].
Therefore, when defining the binding site for performing the docking simulations, the entire transmembrane (TM) region was considered. The binding region was delineated by those atoms within a radius of 15 Å and 24. 7 Å, for CDOCKER and GOLD calculations respectively, utilizing the experimental coordinates of the co-crystallized mP-gp ligand (PDB ID: 4XWK). The selection of the binding site and the settings for the docking simulations were validated via re-docking (ligand reproduction) procedure, obtaining heavy-atoms RMDS values of 1. 5697 Å, 1. 6021 Å, 1. 6427 Å for CDOCKER calculations and 0. 5527 Å, 0. 7988 Å, 0. 8498 Å for GOLD calculations (Table  S2 , and Figure  S4  in Supplementary Materials). The RMSD results are in agreement with the accepted threshold of 2 Å. The resulting docking poses were subsequently rescored with fourteen additional scoring functions implemented in Discovery Studio 4.
These four residues were also found to interact with the inhibitor PBDE (polybrominated diphenyl ether)-100 in the co-crystallized structure of mP-gp (PDB ID: 4XWK) demonstrating their relevance in ligand binding and the consistency between the amino acids predicted by molecular docking and the experimental data available. Some compounds, such as PQ and GEN might not interact with the P-gp as shown in our study, despite the calculated binding energies that reflect a certain stability in the binding site. Although this could be seen as evidence of the lack of predictability of this approach to identify compounds that interact with P-gp, it should also be noted that these results highlight the importance of the physicochemical properties of the compounds (particularly their lipophilicity) that can prevent them from reaching the binding pocket of P-gp.
These four residues were also found to interact with the inhibitor PBDE (polybrominated diphenyl ether)-100 in the co-crystallized structure of mP-gp (PDB ID: 4XWK) demonstrating their relevance in ligand binding and the consistency between the amino acids predicted by molecular docking and the experimental data available. Some compounds, such as PQ and GEN might not interact with the P-gp as shown in our study, despite the calculated binding energies that reflect a certain stability in the binding site. Although this could be seen as evidence of the lack of predictability of this approach to identify compounds that interact with P-gp, it should also be noted that these results highlight the importance of the    Interestingly, six of the eight known active compounds of P-gp in the docking set (CsA, AM, DIG, DOX, LPM and RMP) involved simultaneous interactions with residues Y307, Y310, F728, and F732 in the binding mode, indicating that these residues may play a crucial role in ligand recognition and binding.
These four residues were also found to interact with the inhibitor PBDE (polybrominated diphenyl ether)-100 in the co-crystallized structure of mP-gp (PDB ID: 4XWK) demonstrating their relevance in ligand binding and the consistency between the amino acids predicted by molecular docking and the experimental data available. Some compounds, such as PQ and GEN might not interact with the P-gp as shown in our study, despite the calculated binding energies that reflect a certain stability in the binding site. Although this could be seen as evidence of the lack of predictability of this approach to identify compounds that interact with P-gp, it should also be noted that these results highlight the importance of the.
Docking Into the hP-gp cryoEM Structure The set of thirteen compounds was also docked into the experimentally solved cryo-electron microscopy structure of hP-gp (PDB ID: 6QEX). The binding region was delineated by those atoms within a radius of 9. 5 Å for CDOCKER and GOLD calculations, utilizing the experimental coordinates of the cryoEM hP-gp ligand (PDB ID: 6QEX). The selection of the binding site and the settings for the docking calculations were validated via re-docking (ligand reproduction) procedure, obtaining heavy-atoms RMDS values of 1. 2723 Å, 1. 3208 Å, 1. 4630 Å for CDOCKER calculations and 1. 0283 Å, 1. 1974 Å, 1. 2669 Å for GOLD calculations. The RMSD results are in agreement with the accepted threshold of 2 Å (see Table  S3  and Figure  S5  in Supplementary Materials). The resulting poses are distributed within the TM regions of P-gp (Figure  S2 ) and show interactions with protein residues of several TM helices located throughout the binding region (3D diagrams of the obtained binding poses can be found in Supplementary Materials Figure  S3 ).
The calculated binding energy values are also comparable with respect to the binding stability of the complexes, although the absolute values are about half of the energy values reported in the homology model, e. g. , the most stable complex in the set of docked compounds is CsA both in the homology model and in the cryoEM structure of hP-gp; the less stable complexes are VPA and BU in both docking systems as well. Table  15. Ligand-P-gp interaction types and amino acid residues involved in the binding of some well-known substrates and inhibitors of P-gp using the experimentally solved cryoEM structure of hP-gp (PDB ID: 6QEX). The numbers in parenthesis indicate the number of interactions in which the residue is involved.
Protein Homology Modeling The 3D protein homology model of hP-gp was constructed using three different tools, SWISS-MODEL  [25] , I-TASSER  [22] [23] [24]  and Discovery Studio 4. 1/Modeler 9. 12  [20, 21]. The complete hP-gp protein sequence, which consists of 1280 amino acids, was retrieved from the UniProtKB database (accession number P08183).
Template Selection and Alignment The selection of the templates was based on sequence similarity with known protein structures (homologous) from the protein data bank (PDB) repository. SWISS-MODEL and Discovery Studio 4. 1 protocols identified suitable templates based on BLAST  [63]  while I-TASSER identified structure templates by using LOMETS  [64, 65]. The target and template sequences were aligned in order to analyse the sequence conservation. Insertions and deletions were done so that the best alignment could be obtained. Generally, it is preferable to include more than one template in the alignment because it could provide a better fitting of regions where the percentage of identity is very low with the use of a single template.
Model Generation The overall structure of the hP-gp (based on full-length sequence as retrieved from UniProtKB accession number P08183) was modeled including the nucleotide binding domains (NBDs) and the flexible linker region. Since the quality of the constructed hP-gp model is directly dependent on the quality of the template used, the full primary sequence and secondary structure informations related to the hP-gp were utilized, including NBDs. Moreover, it seems that the linker region is important for the stabilization of the NBDs, where it acts as a 'damper' reducing the movements of the cytoplasmic regions of P-gp  [66] ; therefore, it was included in the model as well. In general, the steps for generating a protein homology model involve the creation of the target backbone by copying the coordinates of the template-backbone to the target.
SWISS-MODEL This tool extracts the initial structural information from the template structure. Insertions and deletions are resolved looking for viable candidates in a structural database. Final candidates are selected using statistical potentials of mean force scoring methods. If no candidates can be found, then a conformational space search is performed using Monte Carlo conformational techniques. Non-conserved side-chains are modeled using an in-house backbone-dependent rotamer library. The optimal configuration of rotamers is estimated using the graph-based TreePack algorithm [67] by minimising the SCWRL4 energy function  [68]. As a final step, small structural distortions, unfavorable interactions or clashes introduced during the modeling process are resolved by energy minimisation. SWISS-MODEL uses CHARMM27 force field for parameterisation.
I-TASSER I-TASSER tool uses fragments excised from the PDB templates, reassembles them into full-length models by using Monte Carlo simulations, and builds the loops by ab initio modeling. The large ensemble of structural conformations, called decoys, is then clustered by SPICKER  [69]  program based on the pair-wise structure similarity. The final full-atomic models are obtained by REMO  [70] , which builds the atomic details from the selected I-TASSER decoys through the optimization of the hydrogen-bonding network and for this purpose, it utilizes the CHARMM22 force field parameters. Five models are reported, which correspond to the five largest structure clusters. For assessing the global accuracy of the model I-TASSER employs the C-Score, the TM-Score  [27] , and the RMSD. The C-Score is a confidence score calculated based on the significance of the threading templates alignments and the convergence parameters of the structure assembly simulations.
The local accuracy of the model can be visualized in the Estimated Local Accuracy Plot, which shows the distance deviation between the residue positions in the model and the estimated native structure. Discovery Studio 4. 1/Modeler 9. 12 Modeler uses restraints on the spatial structure of the amino acid sequence and ligands to be modeled. The output is a 3D structure that satisfies these restraints as much as possible. The program automatically derives the restraints only from the known related structures and their alignment with the target sequence. The restraints can be on distances, angles, dihedral angles, pairs of dihedral angles and some other spatial features. During the model refinement, conjugate gradient and simulated annealing molecular dynamics (MDs) are used to optimize the positions of heavy atoms.
Assessment of the Models The validation of the models developed was performed using the PROCHECK  [28] , VERIFY 3D  [29] , ERRAT  [30] , and PROVE  [31]  programs, which are available at the Structural Analysis and Verification Server (SAVES)  [72]. PROCHECK was used for assessing the stereochemical quality of the protein structure. It checks the protein backbone conformation by analysing the torsion angles phi (ϕ) and psi (ψ) of the amino acid residues in the modeled protein utilizing the Ramachandran plot. VERIFY 3D program analyses the compatibility of the assembled atomic model (3D) with its corresponding amino acid primary sequence (1D). It classifies each residue in the protein into one of the 18 classes according to the residue's structural environment in the input model. The propensity of each amino acid to exist in each structural environment class is calculated according to statistics collected from structures in the PDB repository.
The ligand-P-gp complexes were built by docking the ligand into the homology model of hP-gp and the cryo-EM structure of hP-gp (PDB ID: 6QEX) using the Dock Ligands protocol inside Discovery Studio 4. 1 Client  [20]. Two algorithms, CDOCKER  [36]  and GOLD  [37, 38] , where utilized to investigate the binding affinities and conformations on a set of thirteen compounds, including some well-known compounds that interact with P-gp as substrates, inhibitors, or both. The docking studies were performed by not taking into consideration the flexible linker region present in the hP-gp structure. This region is more than 30 Å away from the binding pocket, therefore it does not appear to be involved in drug binding. The selection of the binding site and the settings for the molecular docking simulations using the constructed homology model and the cryoEM structure of hP-gp were validated via the procedure called "self-docking" or "re-docking".
Ten conformations for each ligand were generated in the binding site of the hP-gp, which was created as a spherical region defined by those atoms within a radius of 15 Å from the co-crystallized ligand originating from mP-gp (PDB ID: 4XWK). The selection of the binding site was validated by the re-docking procedure. Random conformations were generated using specific molecular dynamics steps while the system is heated to 700 K in 2000 steps, then cooled to 300 K in 5000 steps. The final refinement step of minimization was performed using full potential. The minimized docking poses were then clustered, based on a heavy atom RMSD approach. The final ranking was based on the total docking energy, which is composed of the ligand's intramolecular energy and the ligand receptor interaction.
7 Å from the ligand co-crystallized in the structure of the mP-gp PDB ID: 4XWK. The radius of the sphere is significantly higher than in the case of CDOCKER, because some of the residues in the binding site are allowed to move. The selection of the binding site was validated by the re-docking procedure. Flexible docking was performed, meaning that the side chains of some amino acids in the binding site were able to rotate continuously about single bonds during the docking simulation. Ten residues in the binding pocket were selected to be flexible based on some drug binding residues reported experimentally in references  [19, 76]. The selected residues were F303, Y307, Y310, F314, Q725, F728, F732, F759, F983, and Q990 with hydrophobic, aromatic and polar characteristics.
Conclusions The quality assessment of the hP-gp models developed suggested that the overall folding of the 3D structure is as good as the available crystal structure of the mP-gp (PDB ID: 4M1M) and therefore, reliable and suitable for further in silico structure-based studies. The employed method was capable of generating an hP-gp model similar to the near-native mP-gp. The characterization of the binding pocket of our homology model resulted in a big hydrophobic surface area. These results are in agreement with biochemical investigations, which concluded that the drug-binding pocket is assembled mostly of hydrophobic residues, creating a lipophilic environment. In silico QSAR models, classifying between active and non-active compounds, generally include physicochemical descriptors measuring lipophilicity of the molecules such as logP or log D (pH 7.
4), which is in good correlation with our observations. The analysis of the binding poses suggests that the large number of π interactions together with the simultaneous presence of hydrogen bond interactions contribute to the stability of the ligand-protein complex in the binding site; the hydrogen bond interactions present are mostly of weak character (carbon hydrogen bonds type C-H. O) with a greater dispersive component. Identical interacting amino acid residues observed in the mP-gp crystal structure (PDB ID: 4XWK) and the hP-gp cryoEM structure (PDB ID: 6QEX) contribute to drug binding in the hP-gp homology model, e. g. , Q725, Y307, F983, which occur in both the mP-gp crystal structure and the hP-gp cryoEM structure. Some amino acid residues that contribute to drug binding in our hP-gp homology model are also present in one or the other of the two experimentally solved structures, e.
Red-green-blue (RGB) graphic profiles were created by analyzing the distribution and intensity of pixels of these colors along a chosen line using Image J software. All individual images were processed using Image J software and assembled for presentation using Photoshop and Illustrator software (Adobe). Quantification of burst tight junctions was performed by manually counting the number of total and burst tri-cellular junctions in 8-10 randomly chosen fields in each of three independent experiments. Results are expressed as the frequency of burst TJs, and a one-way ANOVA analysis was used to determine significance.
Immunoblotting For immunoblotting, protein samples were separated by SDS-PAGE and transferred to Polyvinylidene fluoride (PVDF) membranes (Millipore). The membranes were blocked with PBS supplemented with 5% nonfat milk (or with 5% BSA when probing for phosphorylated proteins) before incubation with primary antibodies. Infrared imaging with two-color detection and band densitometry quantifications were performed using a Li-Cor Odyssey imaging system exactly as performed previously  (Garcia-Marcos et al, 2010 , 2011a , 2011c , 2012; Ghosh et al, 2010). All Odyssey images were processed using ImageJ software (NIH) and assembled into figure panels using Photoshop and Illustrator software (Adobe).
Statistical analyses Data are expressed as the Mean ± SEM. Statistical significance was assessed with the t test. Statistical significance between datasets with three or more experimental groups was determined using one-way ANOVA including a Tukey's test for multiple comparisons. For all tests, a P-value of 0. 05 was used as the cutoff to determine significance (*P < 0. 05, **P < 0. 01, ***P < 0. 001, and ****P < 0. 0001). All experiments were repeated a least three times, and P-values are indicated in each figure. All statistical analyses were performed using GraphPad prism 6. 1.