dataesr/scientific-paragraphs-categorization huggingface dataset:
16  this work, we applied the method (and implementation) of molecular docking, which is wellestablished in protein-ligand binding, to the field of MOF-guest complexes. The software AutoDock v4. 2. 6, originally designed for protein-ligand binding, is used in the initial screening of various adsorption sites of guest molecules inside MOF pores. We employ a genetic sampling algorithm in combination with additional force field parameters, needed to describe MOFs (see Methods). Subsequently, the so-obtained structures are used as inputs for DFT-based geometry optimization. With these structures, in turn, host-guest interaction energies are computed. This new approach is validated by comparing the structures, obtained here via multilevel simulations, to five experimentally determined MOF-guest structures deposited in the Cambridge Structural Database (CSD).
Materials We have selected five MOF-guest systems from the Cambridge Structural Database, as examples for which the structures of the framework and the guest have been determined. Additionally, new MOF with two guests was synthesized and characterized experimentally and computationally. The six investigated systems are (see Figure  1 ): System 1. MOF [Zn 2 (bdc) 2 (bimx)] (H 2 bdc = 1,4-benzenedicarboxylic acid, bimx = 1,4bis(imidazol-1-ylmethyl)-2,3,5,6-tetramethylbenzene) with guest 1,4-benzenedicarboxylic acid (CSD refcode: DOGZIJ). 17 The guest molecules reside in channels along the a-axis.
0 mL) was carefully layered on top of the previous solution. Lastly, a solution of cadmium(II) iodide (99%, 7. 2 mg, 0. 02 mmol, 2. 0 equiv) in N,N-dimethyl acetamide (1. 0 mL) was added on top. The tube was then sealed with a septum, wrapped in aluminium foil, and allowed to sit undisturbed for six weeks at ambient temperature. The content of the tube was poured into a petri dish, and the dark-red prism crystals suitable for XRD analysis were removed from the solution shortly before measurement. The incorporation of chloride is most likely due to the Finkelstein reaction between TCE and The data collection strategy consisted of a ω-scan (-70° ≤ ω ≤ +70°), with 0. 5° step width and 0. 5 s exposure. The structure was solved with the dual-space algorithm, as implemented in SHELXT. All non-hydrogen atoms were refined anisotropically using the full-matrix, least-squares method on F 2 by the SHELXL software.
All hydrogen atoms were refined using the riding model. Isotropic displacement factors of hydrogen atoms were equal to 1. 2 times the value of an equivalent displacement factor of the parent atoms. Measurement details are presented in Table  S1  of the SI. CCDC 2157164 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from the Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/structures.
83 Å in the experimental crystal structure, and 1. 60 Å -in the optimized structure), while several longer contacts (2. 5-3. 0 Å) are established between the C=O groups of the guest and the hydrogen atoms of the framework. The geometry optimization did not substantially alter the position and geometry of the guest molecule (Figure  2 ). The interaction energy computed with DFT is -259. 87 kJ mol -1. We also performed full periodic geometry relaxation of system 1 with and without the guest molecule at the B3LYP-D3/pob_TZVP_rev2 level of theory using the software Crystal17. 25 The structures of the guest-occluded and guest-free MOF (Figure  3 ) are very similar, with only small deviations in atomic coordinates and unit cell parameters (see Table  S2  in SI). The docking with 20 GA runs resulted in only one cluster of conformations, since the RMSD for the ethanol docked at the same functional group is low regardless of the orientation.
This high conformational flexibility is further complicated by the residual electron density in the pores in the experimental structure. Moreover, the computed interaction energy is only insignificantly affected by the orientation of the ethanol molecule (see Figure  S5  in the SI), hampering a distinction between different conformations based on energy alone. Nevertheless, with molecular docking, a hydrogen bond between the guest and the sulfonate group of the linker was identified correctly. Our findings for system 2 resemble those of Korb et al. 11  Using the software GOLD,  37  which, like AutoDock, is optimized for protein-ligand binding, the authors also located a hydrogen bond between ethanol and the framework's sulfonate group, but could not correctly predict the XRD position. System 4 is illustrative of an alternative, yet equally challenging scenario: the 1,2-bis(4pyridyl)ethane guest occupies a large portion of the pore space.
They largely exhibit activities towards short aliphatic aldehydes, particularly MATOUAmDH2, which displays the highest specific activities with up to 11 U mg  -1  for isobutyraldehyde at 50 °C. [12] 6d, 12b, 13] Up to now, only few protein engineering experiments were performed on this nat-AmDH family. 12a] In the present work, with the aim at gaining activity towards larger aliphatic aldehydes and ketones by enlargement of the active site volumes, we performed rational substitutions on ten nat-AmDHs from a structure-based strategy using two structures of nat-AmDHs (CfusAmDH, PDB: 6IAU and MsmeAmDH, PDB: 6IAQ), (Scheme 1). X-ray crystallographic structures and Molecular Dynamics (MD) studies are described for one of the most promising mutants, to understand the effects of the mutation and to gain insights into the conformational dynamics in nat-AmDHs.
In silico analysis of biodiversity and determination of the mutated sites The structural resolution of AmDH4 in complex with NAD + (Uniprot: A8BHL2, PDB: 6G1M) previously enabled the identification of the residue positions defining the active site, named P1-P20. 12b, 13c] Based on the structure of CfusAmDH, MsmeAmDH and homologs, the list of residues constituting the active site was reconsidered by adding a new position between P4 and P5. Unlike in AmDH4, this position is occupied by bulky amino acids. The active sites are now defined by residues P1-P21. In contrast to many studies that generally base their protein engineering strategy on only a few structures or models, we decided to exploit the substantial resource of data from biodiversity to target and mutate candidate residues. 13c] The key hypothesis to be made was that the restricted substrate scope of nat-AmDHs may be due to a very small active site, mainly formed by bulky aromatic amino acids located on the first layer, including Y or F at P5, P12 and P17, Y, F or W at P10 and W at P8 (Figure  S1-S3 ).
The effect of a small residue at P8 for broader substrate scope was supported by activity results in presence of bulkier substrates such as benzaldehyde or norcamphor and structural analysis of the structure of MATOUAmDH2 (PDB: 7R09, 7ZBO). [12] The RX structure of CfusAmDH (PDB: 6IAU), displaying F140 and W145 respectively at P5 and P8, was used as a reference enzyme for in silico mutations and subsequent analysis of the extended pocket size through docking experiments. As the native enzyme showed a preference for aldehydes over ketones  [13c]  , the analysis was carried out with the docking of n-terminal primary amine products, from pentan-1-amine (2a) to decan-1-amine (2f). As depicted in Figure  1 , the separate mutations F140A and W145A increased the active site volume from 957 Å 3 in wild-type (WT) CfusAmDH to 1194 Å 3 and 1176 Å 3 , respectively.
Structures of CfusAmDH-W145A with NAD + and NADP + In order to explore the reasons for the altered activity of the CfusAmDH-W145A mutant and its cofactor specificity, its structure was solved using X-ray crystallography. Two structures were determined; one in complex with NAD + (PDB: 7QZN) and the other with NADP + (PDB: 7QZL) and pentan-1-amine (2a), to resolutions of 1. 64 Å and 1. 50 Å, respectively (Table  S6 ). 13c] The structures verify that the W145A mutation has created the extra active site space for the accommodation of longer aliphatic aldehyde and ketone substrates as predicted. Notable differences were observed between the NADP + -and NAD + -mutant structures both in the active site and also in the cofactor phosphate binding site. In the NAD + complex, the most striking difference locates in the active site with the side-chain rotation of 110 -115° for Y202.
The closed-to-open event, previously observed in AmDH4 (PDB: 6G1M)  [13c]  , and more recently for MATOUAmDH2  [12a]  , was observed for all the systems (Figure  S18A ). The Root Mean Square Fluctuation (RMSF), measured in Å for each full length protein, identified segments N169-Q179 (  6 ), G183-S197 (  7 ) and T229-H247 (  8 ) as highly flexible (Figure  S19 ). Monitoring the distance between G14 and G174, which are facing each other and are located on each side of the aperture, reveals that all the systems converge towards a so-called "relaxed" conformation opened at 14 Å (Figure  S18B  and Figure  S20 ). Interestingly, in M systems, a higher velocity of opening is observed when the protein is complexed to NADP + as compared to NAD +. Therefore, in contrast to WT complexes, the nature of the cofactor could influence the velocity of the loop opening in M complexes and so the kcat of the system.
Docking analysis The mutants CfusAmDH-F140A and CfusAmDH-W145A were modeled from the solved RX structure of CfusAmDH (PDB: 6iau, chain B) using the Wizard mutagenesis tool on PyMOL and rotamer selection. The products pentan-1-amine (2a), hexan-1-amine (2b), heptan-1-amine (2c), octan-1amine (2d), nonan-1-amine (2e) and decan-1-amine (2f) were docked into the resolved crystal structures of CfusAmDH and into the models CfusAmDH-F140A and CfusAmDH-W145A. The ligand PDB files were generated using the CORINA Molecular Online Tool. With AutoDockTool  [24]  , the docking simulations were performed on rigid structures, with no flexibility given to any catalytic pocket residue. The number of Genetic Algorithm (GA) runs was fixed at 10 using the Lamarckian GA (4. 2). The 10 ligand conformations obtained were then analyzed in PyMOL.
Kinetic parameters of CfusAmDH-W145A Kinetic parameters of CfusAmDH-W145A for the carbonyl-containing compound, the cofactor and ammonia were determined by spectrophotometry through NAD(P)H-monitoring at 340 nm. All the reactions were performed in ammonium formate buffer pH 8. 5 in a final volume of 100 µL at 30 °C, in spectrophotometer cell with optical paths of 0. 6 cm and the procedure was as described in the Specific activities measurements paragraph. Initial rates of the reaction were measured with various concentrations of substrate and saturated concentrations of the other substrates. Data were fitted to the Michaelis and Menten equation or to the equation provided in case of inhibition by excess of substrate, using Sigma Plot software. The uncertainties are those generated out of two to three experiments (Figure  S9 -S12).
Data collection and processing, structure solution and refinement Datasets for crystals of CfusAmDH-W145A in complex with NAD + or NADP + and 2a were collected on beamline I03 at the Diamond Light Source Synchrotron in Oxford, U. K. Data, which were collected to 1. 64 and 1. 50 Å respectively, were processed and integrated using XDS  [27]  and scaled using SCALA  [28]  as part of the Xia2 processing system  [29]. Data collection statistics are given in Table  S6. The structures were solved with the program MOLREP  [30]  , using a monomer of the WT CfusAmDH (PDB code 6IAU  [13c]  ) as a model. Each solution contained two molecules in the asymmetric unit. The structures were built and refined using iterative cycles within the programs COOT  [31]  and REFMAC  [32]  , respectively. Following building of the protein and water molecules in the NADP + structure, clear density was observed adjacent to the nicotinamide ring of NADP +.
This was successfully modeled and refined as the added ligand 2a. The NAD + and NADP + complexes were refined to Rcryst/Rfree values of 17. 8/21. 4 and 16. 4/18. 8%, respectively. The structures were validated upon deposition within the Protein DataBank (PDB). Refinement statistics are presented in Table  S6. Coordinates for the CfusAmDH-W145A structures in complex with NAD + or NADP + and 2a have been deposited in the PDB with the accession codes 7QZN and 7QZL, respectively.
Molecular dynamics MD simulations were executed using the GROMACS software (version 2021. 3), with CHARMM36 force field, following the GROMACS tutorial on protein/ligand complex. [33] In order to generate MD simulations starting from fully equilibrated structures, we first considered every system composed of Enzyme-Cofactor-Ligand, where Enzyme is either CfusAmDH (WT) or mutant CfusAmDH-W145A (M), Cofactor is either NAD + (NAD) or NADP + (NADP) and eventually, Ligand is either pentan-1amine (2a) (PEN) or heptan-1-amine (2c) (HEP). Each system was placed in a dodecahedron box, filled with TIP3P waters to model water solvatation, then counter-ions were added to reach system neutralization. The borders of the simulation box were cut off at 10 Å from the surface of the protein. A first step of energy minimization was carried out through steepest descent algorithm, to relax the eventual steric constraints.
Acknowledgements We acknowledge  Jean-Louis Petit  for his overall help for the mutagenesis and production of proteins with the technical assistance of  Adrien Debard  and  Virginie Pellouin , and  Peggy Sirvain  for the large scale production and purification of proteins. We thank  David Vallenet  and  Claude Scarpelli  and the  Very Large Computing Center (TGCC) of CEA  for access to the compute clusters and GROMACS software,  O. Maciejak  ( University Evry Val-d'Essonne ) for NMR assistance and the  Region Ile de France  for financial support of the 600 MHz spectrometer. We thank  Pierre-Loïc Saaidi  and  Delphine Muselet  for access and help on the GC-FID method development and  Alain Perret  for fruitful discussions for the determination and analysis of kinetic parameters. We thank  Mr.
Raman Spectroscopy Raman spectra were recorded on a Renishaw inVia Raman microscope equipped with a CCD (chargecoupled device) detector. We used the 457 nm line of an argon laser focused on the sample solution with a 20X objective. Ten accumulations were averaged with an exposure time of 10 s for each sample. The collected data are smoothed with a 13-point Savitzky-Golay 2 nd order polynomial function.
DFT calculations All the calculations were carried out by means of the Gaussian16 software package  28  in the context of DFT and its TD-DFT extension. The hybrid meta functional used for geometry optimizations and frequency calculations is M05. 29 Such a functional was employed because it accurately models metallic interactions. 30 Within the frequency calculations, the number of imaginary frequencies, 0 or 1, has been taken into account to confirm the nature of minima and transition states. In the case of the transition states, intrinsic reaction coordinate calculations (IRC) were performed to verify that the imaginary frequency corresponds to the proper motion along the reaction coordinate. The standard 6-311G* basis set of Pople was used for Cu, C, N, O, H atoms and 6-311+G* basis set was used for S atoms.
3. 2. Differential Scanning Calorimetry. DSC measurements were carried out on approximately 10 mg of samples using a Q2000 TA instruments. A liquid nitrogen cooling system (LNCS) was used with a 25 mL/min helium flow rate. Measurements were performed by placing the samples into aluminum pans. Data were acquired during cooling at 3 K/min from 353 K to 173 K. Temperaturemodulated experiments (MDSC) were performed using a sinusoidal variation of 0. 5 K amplitude and 60 s period. 3. 3. Small-Angle Neutron Scattering. SANS experiments on the blends were performed on the instrument D22 at the Institute Laue-Lagevin (ILL) in Grenoble, France. 41 Using an incident wavelength λ = 6 Å and sample-detector distances (SSDs) of 17, 5. 6, and 1. 5 m, a Q-range between 0. 003 and 0. 58 Å -1 was covered. Here, the modulus of the scattering vector Q is defined as Q = 4πλ -1 sin(θ/2), with θ as the scattering angle.
The samples with a thickness of 1 mm were sandwiched between aluminum foils. Experiments were carried out in isothermal conditions at 265, 277, and 298 K. Equilibration times of about 45 min were employed at each temperature. The data were reduced using ILL in-house software, correcting measured intensities for the transmission, deadtime, sample background, and detector background (with B 4 C as a neutron absorber at the sample position). 3. 4. Broad-Band Dielectric Spectroscopy. BDS experiments were conducted by using an Alpha dielectric analyzer (Novocontrol) to determine the complex dielectric permittivity (ε* = ε′ -iε″) over the frequency range from 10 -2 to 10 7 Hz. Samples were placed between two flat gold-plated electrodes (30 and 20 mm diameter) forming a parallel plate capacitor with a 0.
The solid lines fitting the neat polymers data were obtained by using eq 8 with parameters given in Table  2. established; namely, the relaxation time measured at the calorimetric T g (taken as the inflection point) is on the order of 10 seconds. 45 aking this into consideration, the starting point of our approach is to first do a relatively simple full characterization of the homopolymers' DSC behavior, which would encode the unsolved intricate connection between the segmental dynamics and the glass formation process. After that, we will use this simple picture to establish the connection between the segmental dynamics and the DSC data of the blends following a scheme mirroring that used before for the BDS data description. The description of the DSC traces in the glass transition range for the neat polymers required quantifying the three main quantities for each component: a characteristic temperature, a measure of the width of the glass transition range, and the associated heat capacity jump.
This approach is in line with the BDS modeling where we considered that in each region in the blend only the relaxation time of the components is affected. The usual identification of the glass transition temperature with that where the characteristic time for polymer segmental motions takes a given value (commonly in the range 1-100 s) provides the way of connecting DSC and BDS modeling. Using the neat polymers DSC and BDS data allows us to connect the DSC T g * value and the dielectric relaxation time evaluated at this temperature τ(T g *) for the two components. From the analysis of the data of the pure polymers SBR and PS (see the SI) we found, respectively, that the relationship between the dielectric α-relaxation time and the calorimetric T g * is τ g SBR ≡ τ SBR (T g *) = 1. 68 s and τ g PS ≡ τ PS (T g *) = 11.
After establishing this connection between the local segmental dynamics of each component and the glass transition temperature, the DSC curves were evaluated using the model describing the BDS results (see the SI). For the DSC calculations the local composition was described in terms of the same values of the self-concentrations φ self SBR and φ self PS (Table  3 ) and the distributions of concentration by means of same Gaussian functions g(φ i ) with the σ-values obtained from the SANS results, i. e. , with 2R c ≡ 25Å: σ 0. 8 = 0. 09, σ 0. 6 = 0. 11, σ 0. 5 = 0. 115, and σ 0. 3 = 0. 10. Thus, the evaluation of the DSC curves for the blends can be made with no additional free parameters. The resulting curves are shown in Figure  8  in comparison with the experimental data for the different blends investigated, where an overall excellent agreement between the two sets of data can be observed.
The comparison of SANS and BDS results has allowed establishing the relevant length scale for the segmental relaxation in these blends, as monitored by dielectric spectroscopy. The excellent agreement also with the DSC traces shows that this would also be the relevant length scale for the glass-formation process. The value found�2R c ≈ 25Å�is in the range usually assumed for the α-relaxation in many glass-forming systems. 37 Thus, with this work we provide additional experimental support to the relevance of nanometric length scales in the vitrification phenomenon. We note that 2R c and ξ are completely independent magnitudes, the former related to the dynamics of the α-relaxation associated with the glass-forming character of the material, and the latter arising exclusively in mixtures. Taking into account the good quality of the description of the DSC experiments using this simplified approach, it would be eventually possible to extract the model parameters just by using the DSC data of the blends.
Pseudo-second-order (PSO) kinetic model The PSO kinetic model was used to describe the efficiency of the adsorption process. Zn(II) and Cr(III) adsorption onto SiO 2 -PYZ nanocomposite obeyed PSO kinetics in linear and non-linear forms. Moreover, PSO constant rates (k 2 ) were similar when comparing the linear and nonlinear models at the same pH, but upper initial adsorption rates (h) were recorded for the nonlinear model. Higher linear R 2 % 1. 0 and sharp comparison between experimental and calculated adsorption at equilibrium ( P v 2 < 0. 05) suggested a better fit with the linear than the nonlinear model ( P v 2 < 0. 50), but being nonetheless, the two values were comparatively lower than the Chi-square tabular value (9. 488). A very small Chi-square test indicates that the calculated data is well-matched with the experimental data.
The inhibitor was diluted in vials in a series of steps. Two series of serial dilutions were examined at different concentrations. The SiO 2 -PYZ test was done at two doses of 20 and 40 ppm. The antimicrobial activity of SiO 2 -PYZ against SRB bacteria has been encouraged (Fig. 15 ). Images were acquired for vial sets injected after a 28-day incubation period, and a blank is shown in Fig. 15. Compared to white vials, the number of black vials (infected vials) was reduced, indicating a biocidal effect. The data interpretation from the conducted test is shown in Table  8. Because they scored 10 2 cell/ml and 0 cell/ml in the 20 ppm and 40 ppm doses, respectively, it appears that SiO 2 -PYZ has exhibited the most vigorous action for inhibiting bacterial growth (up to 100%).
View Article Online This journal is © The  Royal of Chemistry 2022 Chem. Commun. , 2022, 58, 4016-4019 4019 This work was financially supported by  MICINN  ( PID2019-106832RB-I00 ,  PID2019-105622RB-I00  and the  Severo Ochoa Program for Centers of Excellence  for the FUNFUTURE  CEX2019-000917-S project ). S. S. acknowledges financial support from  DOC-FAM , the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  754397. Z. K. is grateful for the general support of the  Premium Postdoctoral Research Program 2019. We thank  Dr Claudio Roscini  from  ICN2  for his kind help with the solid state luminescence measurements. Sohini Sinha  is enrolled in the UAB PhD Program.
, 2006 Czarnota et al. , , 2008;; Jacques et al. , 2015). This key feature is not captured by standard damage models in which the porosity stands for the single damage parameter with no contribution of the void size. Czarnota et al. (2008)  and  Jacques et al. (2010)  implemented the constitutive framework of  Molinari and Mercier (2001) , coupled with a specific void nucleation model, into the finite element code ABAQUS/Explicit, and simulated the spall fracture observed in the plate impact experiments performed by  Roy (2003)  and  Lin et al. (2004). The models of  Czarnota et al. (2008)  and  Jacques et al. (2010)  provided accurate predictions for the velocity in the rear side of the impacted plate, and for the spatial and size distribution of the voids in the spall plane. The finite element analysis also brought out the stabilizing effect of microinertia, which controls the level of the maximum stress and the growth of porosity in the spall plane.