Journal of Computer-Aided Molecular Design最新文献

{"title":"Computational investigation of functional water molecules in GPCRs bound to G protein or arrestin","authors":"Jiaqi Hu,&nbsp;Xianqiang Sun,&nbsp;Zhengzhong Kang,&nbsp;Jianxin Cheng","doi":"10.1007/s10822-022-00492-z","DOIUrl":"10.1007/s10822-022-00492-z","url":null,"abstract":"<div><p>G protein-coupled receptors (GPCRs) are membrane proteins constituting the largest family of drug targets. The activated GPCR binds either the heterotrimeric G proteins or arrestin through its activation cycle. Water molecules have been reported to play a role in GPCR activation. Nevertheless, reported studies are focused on the hydrophobic helical bundle region. How water molecules function in GPCR bound either G protein or arrestin is rarely studied. To address this issue, we carried out computational studies on water molecules in both GPCR/G protein complexes and GPCR/arrestin complexes. Using inhomogeneous fluid theory (IFT), we locate all possible hydration sites in GPCRs binding either to G protein or arrestin. We observe that the number of water molecules on the interaction surface between GPCRs and signal proteins are correlated with the insertion depths of the α5-helix from G-protein or “finger loop” from arrestin in GPCRs. In three out of the four simulation pairs, the interfaces of Rhodopsin, M₂R and NTSR1 in the G protein-associated systems show more water-mediated hydrogen-bond networks when compared to these in arrestin-associated systems. This reflects that more functionally relevant water molecules may probably be attracted in G protein-associated structures than that in arrestin-associated structures. Moreover, we find the water-mediated interaction networks throughout the NPxxY region and the orthosteric pocket, which may be a key for GPCR activation. Reported studies show that non-biased agonist, which can trigger both GPCR-G protein and GPCR-arrestin activation signal, can result in pharmacologically toxicities. Our comprehensive studies of the hydration sites in GPCR/G protein complexes and GPCR/arrestin complexes may provide important insights in the design of G-protein biased agonists.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 2","pages":"91 - 105"},"PeriodicalIF":3.5,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4074320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular and thermodynamic insights into interfacial interactions between collagen and cellulose investigated by molecular dynamics simulation and umbrella sampling","authors":"Huaiqin Ma,&nbsp;Qingwen Shi,&nbsp;Xuhua Li,&nbsp;Junli Ren,&nbsp;Yuhan Wang,&nbsp;Zhijian Li,&nbsp;Lulu Ning","doi":"10.1007/s10822-022-00489-8","DOIUrl":"10.1007/s10822-022-00489-8","url":null,"abstract":"<div><p>Cellulose/collagen composites have been widely used in biomedicine and tissue engineering. Interfacial interactions are crucial in determining the final properties of cellulose/collagen composite. Molecular dynamics simulations were carried out to gain insights into the interactions between cellulose and collagen. It has been found that the structure of collagen remained intact during adsorption. The results derived from umbrella sampling showed that (110) and (<span>(1bar{1}0)</span>) faces exhibited the strongest affinity with collagen (100) face came the second and (010) the last, which could be attributed to the surface roughness and hydrogen-bonding linkers involved water molecules. Cellulose planes with flat surfaces and the capability to form hydrogen-bonding linkers produce stronger affinity with collagen. The occupancy of hydrogen bonds formed between cellulose and collagen was low and not significantly contributive to the binding affinity. These findings provided insights into the interactions between cellulose and collagen at the molecular level, which may guide the design and fabrication of cellulose/collagen composites.</p><h3>Graphical abstract</h3>\u0000 <figure><div><div><div><picture><source><img></source></picture></div></div></div></figure>\u0000 </div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 1","pages":"39 - 51"},"PeriodicalIF":3.5,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5370638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of interactions between the hepatitis C virus NS3/4A and sulfonamidobenzamide based molecules using molecular docking, molecular dynamics simulations and binding free energy calculations","authors":"Jinhong Ren,&nbsp;Tasneem M. Vaid,&nbsp;Hyun Lee,&nbsp;Isabel Ojeda,&nbsp;Michael E. Johnson","doi":"10.1007/s10822-022-00490-1","DOIUrl":"10.1007/s10822-022-00490-1","url":null,"abstract":"<div><p>The Hepatitis C Virus (HCV) NS3/4A is an attractive target for the treatment of Hepatitis C infection. Herein, we present an investigation of HCV NS3/4A inhibitors based on a sulfonamidobenzamide scaffold. Inhibitor interactions with HCV NS3/4A were explored by molecular docking, molecular dynamics simulations, and MM/PBSA binding free energy calculations. All of the inhibitors adopt similar molecular docking poses in the catalytic site of the protease that are stabilized by hydrogen bond interactions with G137 and the catalytic S139, which are known to be important for potency and binding stability. The quantitative assessments of binding free energies from MM/PBSA correlate well with the experimental results, with a high coefficient of determination, R² of 0.92. Binding free energy decomposition analyses elucidate the different contributions of Q41, F43, H57, R109, K136, G137, S138, S139, A156, M485, and Q526 in binding different inhibitors. The importance of these sidechain contributions was further confirmed by computational alanine scanning mutagenesis. In addition, the sidechains of K136 and S139 show crucial but distinct contributions to inhibitor binding with HCV NS3/4A. The structural basis of the potency has been elucidated, demonstrating the importance of the R155 sidechain conformation. This extensive exploration of binding energies and interactions between these compounds and HCV NS3/4A at the atomic level should benefit future antiviral drug design.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 1","pages":"53 - 65"},"PeriodicalIF":3.5,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00490-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4980716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores","authors":"Christian Meyenburg,&nbsp;Uschi Dolfus,&nbsp;Hans Briem,&nbsp;Matthias Rarey","doi":"10.1007/s10822-022-00485-y","DOIUrl":"10.1007/s10822-022-00485-y","url":null,"abstract":"<div><p>Fragment spaces are an efficient way to model large chemical spaces using a handful of small fragments and a few connection rules. The development of Enamine’s REAL Space has shown that large spaces of readily available compounds may be created this way. These are several orders of magnitude larger than previous libraries. So far, searching and navigating these spaces is mostly limited to topological approaches. A way to overcome this limitation is optimization via metaheuristics which can be combined with arbitrary scoring functions. Here we present Galileo, a novel Genetic Algorithm to sample fragment spaces. We showcase Galileo in combination with a novel pharmacophore mapping approach, called Phariety, enabling 3D searches in fragment spaces. We estimate the effectiveness of the approach with a small fragment space. Furthermore, we apply Galileo to two pharmacophore searches in the REAL Space, detecting hundreds of compounds fulfilling a HSP90 and a FXIa pharmacophore.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 1","pages":"1 - 16"},"PeriodicalIF":3.5,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00485-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5302124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining unsupervised ensemble learning using spectroscopy data of organic compounds","authors":"Kedan He,&nbsp;Djenerly G. Massena","doi":"10.1007/s10822-022-00488-9","DOIUrl":"10.1007/s10822-022-00488-9","url":null,"abstract":"<div><p>One solution to the challenge of choosing an appropriate clustering algorithm is to combine different clusterings into a single consensus clustering result, known as cluster ensemble (CE). This ensemble learning strategy can provide more robust and stable solutions across different domains and datasets. Unfortunately, not all clusterings in the ensemble contribute to the final data partition. Cluster ensemble selection (CES) aims at selecting a subset from a large library of clustering solutions to form a smaller cluster ensemble that performs as well as or better than the set of all available clustering solutions. In this paper, we investigate four CES methods for the categorization of structurally distinct organic compounds using high-dimensional IR and Raman spectroscopy data. Single quality selection (SQI) forms a subset of the ensemble by selecting the highest quality ensemble members. The Single Quality Selection (SQI) method is used with various quality indices to select subsets by including the highest quality ensemble members. The Bagging method, usually applied in supervised learning, ranks ensemble members by calculating the normalized mutual information (NMI) between ensemble members and consensus solutions generated from a randomly sampled subset of the full ensemble. The hierarchical cluster and select method (HCAS-SQI) uses the diversity matrix of ensemble members to select a diverse set of ensemble members with the highest quality. Furthermore, a combining strategy can be used to combine subsets selected using multiple quality indices (HCAS-MQI) for the refinement of clustering solutions in the ensemble. The IR + Raman hybrid ensemble library is created by merging two complementary “views” of the organic compounds. This inherently more diverse library gives the best full ensemble consensus results. Overall, the Bagging method is recommended because it provides the most robust results that are better than or comparable to the full ensemble consensus solutions.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 1","pages":"17 - 37"},"PeriodicalIF":3.5,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4840927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host–guest binding: II. regression and dielectric constant","authors":"Xiao Liu,&nbsp;Lei Zheng,&nbsp;Yalong Cong,&nbsp;Zhihao Gong,&nbsp;Zhixiang Yin,&nbsp;John Z. H. Zhang,&nbsp;Zhirong Liu,&nbsp;Zhaoxi Sun","doi":"10.1007/s10822-022-00487-w","DOIUrl":"10.1007/s10822-022-00487-w","url":null,"abstract":"&lt;div&gt;&lt;p&gt;End-point free energy calculations as a powerful tool have been widely applied in protein–ligand and protein–protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g., alchemical transformation) and coarser molecular docking. However, it is observed that this intermediate level of accuracy does not hold in relatively simple and prototypical host–guest systems. Specifically, in our previous work investigating a set of carboxylated-pillar[6]arene host–guest complexes, end-point methods provide free energy estimates deviating significantly from the experimental reference, and the rank of binding affinities is also incorrectly computed. These observations suggest the unsuitability and inapplicability of standard end-point free energy techniques in host–guest systems, and alteration and development are required to make them practically usable. In this work, we consider two ways to improve the performance of end-point techniques. The first one is the PBSA_E regression that varies the weights of different free energy terms in the end-point calculation procedure, while the second one is considering the interior dielectric constant as an additional variable in the end-point equation. By detailed investigation of the calculation procedure and the simulation outcome, we prove that these two treatments (i.e., regression and dielectric constant) are manipulating the end-point equation in a somehow similar way, i.e., weakening the electrostatic contribution and strengthening the non-polar terms, although there are still many detailed differences between these two methods. With the trained end-point scheme, the RMSE of the computed affinities is improved from the standard ~ 12 kcal/mol to ~ 2.4 kcal/mol, which is comparable to another altered end-point method (ELIE) trained with system-specific data. By tuning PBSA_E weighting factors with the host-specific data, it is possible to further decrease the prediction error to ~ 2.1 kcal/mol. These observations along with the extremely efficient optimized-structure computation procedure suggest the regression (i.e., PBSA_E as well as its GBSA_E extension) as a practically applicable solution that brings end-point methods back into the library of usable tools for host–guest binding. However, the dielectric-constant-variable scheme cannot effectively minimize the experiment-calculation discrepancy for absolute binding affinities, but is able to improve the calculation of affinity ranks. This phenomenon is somehow different from the protein–ligand case and suggests the difference between host–guest and biomacromolecular (protein–ligand and protein–protein) systems. Therefore, the spectrum of tools usable for protein–ligand complexes could be unsuitable for host–guest binding, and numerical validations are necessary to screen out really workable solutions in these ‘pr","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"36 12","pages":"879 - 894"},"PeriodicalIF":3.5,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4702276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable gas-phase tautomer equilibria of drug-like molecule scaffolds and the issue of continuum solvation","authors":"Andreas H. Göller","doi":"10.1007/s10822-022-00480-3","DOIUrl":"10.1007/s10822-022-00480-3","url":null,"abstract":"<div><p>Accurate calculation of relative tautomer energies in different environments is a prerequisite to many parameters of relevance in drug discovery. This work provides a thorough benchmark of the semiempirical methods AM1, PM3 and GFN2-xTB, the force-field OPLS4, Hartree–Fock and HF-3c, the density functionals PBEh-3c, B97-3c, r2SCAN-3c, PBE, PBE0, TPSS, r2SCAN, ω-B97X-V, M06-2X, B3LYP, B2PLYP, and second-order perturbation theory MP2 versus the gold-standard coupled-cluster DLPNO-CCSD(T) using the def2-QZVPP basis set. The outperforming method identified is M06-2X, whereas r2SCAN-3c is the best-perfoming one in the set of cost-optimized methods. Application of the two methods on a challenging subset from the SAMPL2 challenge provides evidence that deviations from experiment are caused by deficiencies of current continuum solvation methods.\u0000</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"36 11","pages":"805 - 824"},"PeriodicalIF":3.5,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4099831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The FMO2 analysis of the ligand-receptor binding energy: the Biscarbene-Gold(I)/DNA G-Quadruplex case study","authors":"Roberto Paciotti,&nbsp;Cecilia Coletti,&nbsp;Alessandro Marrone,&nbsp;Nazzareno Re","doi":"10.1007/s10822-022-00484-z","DOIUrl":"10.1007/s10822-022-00484-z","url":null,"abstract":"<div><p>In this work, the ab initio fragment molecular orbital (FMO) method was applied to calculate and analyze the binding energy of two biscarbene-Au(I) derivatives, [Au(9-methylcaffein-8-ylidene)₂]⁺ and [Au(1,3-dimethylbenzimidazol-2-ylidene)₂]⁺, to the DNA G-Quadruplex structure. The FMO2 binding energy considers the ligand-receptor complex as well as the isolated forms of energy-minimum state of ligand and receptor, providing a better description of ligand-receptor affinity compared with simple pair interaction energies (PIE). Our results highlight important features of the binding process of biscarbene-Au(I) derivatives to DNA G-Quadruplex, indicating that the total deformation-polarization energy and desolvation penalty of the ligands are the main terms destabilizing the binding. The pair interaction energy decomposition analysis (PIEDA) between ligand and nucleobases suggest that the main interaction terms are electrostatic and charge-transfer energies supporting the hypothesis that Au(I) ion can be involved in π-cation interactions further stabilizing the ligand-receptor complex. Moreover, the presence of polar groups on the carbene ring, as C = O, can improve the charge-transfer interaction with K⁺ ion. These findings can be employed to design new powerful biscarbene-Au(I) DNA-G quadruplex binders as promising anticancer drugs. The procedure described in this work can be applied to investigate any ligand-receptor system and is particularly useful when the binding process is strongly characterized by polarization, charge-transfer and dispersion interactions, properly evaluated by ab initio methods.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"36 12","pages":"851 - 866"},"PeriodicalIF":3.5,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00484-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4051635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From oncoproteins to spike proteins: the evaluation of intramolecular stability using hydropathic force field","authors":"Federica Agosta,&nbsp;Glen E. Kellogg,&nbsp;Pietro Cozzini","doi":"10.1007/s10822-022-00477-y","DOIUrl":"10.1007/s10822-022-00477-y","url":null,"abstract":"<div><p>Evaluation of the intramolecular stability of proteins plays a key role in the comprehension of their biological behavior and mechanism of action. Small structural alterations such as mutations induced by single nucleotide polymorphism can impact biological activity and pharmacological modulation. Covid-19 mutations, that affect viral replication and the susceptibility to antibody neutralization, and the action of antiviral drugs, are just one example. In this work, the intramolecular stability of mutated proteins, like Spike glycoprotein and its complexes with the human target, is evaluated through hydropathic intramolecular energy scoring originally conceived by Abraham and Kellogg based on the “Extension of the fragment method to calculate amino acid zwitterion and side-chain partition coefficients” by Abraham and Leo in <i>Proteins</i>: <i>Struct. Funct. Genet.</i> 1987, 2:130 − 52. HINT is proposed as a fast and reliable tool for the stability evaluation of any mutated system. This work has been written in honor of Prof. Donald J. Abraham (1936–2021).</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"36 11","pages":"797 - 804"},"PeriodicalIF":3.5,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00477-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5188513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction","authors":"Kiril Lanevskij,&nbsp;Remigijus Didziapetris,&nbsp;Andrius Sazonovas","doi":"10.1007/s10822-022-00483-0","DOIUrl":"10.1007/s10822-022-00483-0","url":null,"abstract":"<div><p>In an earlier study (Didziapetris R &amp; Lanevskij K (2016). J Comput Aided Mol Des. 30:1175–1188) we collected a database of publicly available hERG inhibition data for almost 6700 drug-like molecules and built a probabilistic Gradient Boosting classifier with a minimal set of physicochemical descriptors (log <i>P</i>, p<i>K</i>_a, molecular size and topology parameters). This approach favored interpretability over statistical performance but still achieved an overall classification accuracy of 75%. In the current follow-up work we expanded the database (provided in Supplementary Information) to almost 9400 molecules and performed temporal validation of the model on a set of novel chemicals from recently published lead optimization projects. Validation results showed almost no performance degradation compared to the original study. Additionally, we rebuilt the model using AFT (Accelerated Failure Time) learning objective in XGBoost, which accepts both quantitative and censored data often reported in protein inhibition studies. The new model achieved a similar level of accuracy of discerning hERG blockers from non-blockers at 10 µM threshold, which can be conceived as close to the performance ceiling for methods aiming to describe only non-specific ligand interactions with hERG. Yet, this model outputs quantitative potency values (<i>IC</i>₅₀) and is not tied to a particular classification cut-off. p<i>IC</i>₅₀ from patch-clamp measurements can be predicted with R² ≈ 0.4 and MAE &lt; 0.5, which enables ligand ranking according to their expected potency levels. The employed approach can be valuable for quantitative modeling of various ADME and drug safety endpoints with a high prevalence of censored data.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"36 12","pages":"837 - 849"},"PeriodicalIF":3.5,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-022-00483-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5098910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}