Journal of Chemical Information and Modeling 最新文献

{"title":"Algorithm for Efficient Superposition and Clustering of Molecular Assemblies Using the Branch-and-Bound Method.","authors":"Yuki Yamamoto","doi":"10.1021/acs.jcim.4c02217","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02217","url":null,"abstract":"The root-mean-square deviation (RMSD) is one of the most common metrics for comparing the similarity of three-dimensional chemical structures. The chemical structure similarity plays an important role in data chemistry because it is closely related to chemical reactivity, physical properties, and bioactivity. Despite the wide applicability of the RMSD, the simultaneous determination of atom mapping and spatial superposition of RMSD remains a challenging problem to solve in polynomial time. We introduce an algorithm called mobbRMSD, which is formulated in molecular-oriented coordinates and uses the branch-and-bound method to obtain an exact solution for the RMSD. mobbRMSD can efficiently handle a wide range of chemical systems, such as molecular liquids, solute solvations, and self-assembly of large molecules, using chemical knowledge such as atom types, chemical bonding, and chirality. In benchmarks involving small molecular aggregates, mobbRMSD extends the limiting system size of existing exact solution methods by almost twice. Furthermore, mobbRMSD demonstrated the ability to analyze the structural similarity of large molecular micelles, which has been difficult with previous methods. We also propose a mobbRMSD-based structural clustering method designed for molecular dynamics trajectories, which improves the computational cost of branch-and-bound methods to asymptotically average the polynomial time as the number of data increases. Our algorithm is freely available at https://github.com/yymmt742/mobbrmsd.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"16 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting Drug-Disease Association Prediction for Drug Repositioning via Dual-Feature Extraction and Cross-Dual-Domain Decoding.","authors":"Enqiang Zhu,Xiang Li,Chanjuan Liu,Nikhil R Pal","doi":"10.1021/acs.jcim.5c00070","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00070","url":null,"abstract":"The extraction of biomedical data has significant academic and practical value in contemporary biomedical sciences. In recent years, drug repositioning, a cost-effective strategy for drug development by discovering new indications for approved drugs, has gained increasing attention. However, many existing drug repositioning methods focus on mining information from adjacent nodes in biomedical networks without considering the potential inter-relationships between the feature spaces of drugs and diseases. This can lead to inaccurate encoding, resulting in biased mined drug-disease association information. To address this limitation, we propose a new model called Dual-Feature Drug Repurposing Neural Network (DFDRNN). DFDRNN allows the mining of two features (similarity and association) from the drug-disease biomedical networks to encode drugs and diseases. A self-attention mechanism is utilized to extract neighbor feature information. It incorporates two dual-feature extraction modules: the single-domain dual-feature extraction (SDDFE) module for extracting features within a single domain (drugs or diseases) and the cross-domain dual-feature extraction (CDDFE) module for extracting features across domains. By utilizing these modules, we ensure more appropriate encoding of drugs and diseases. A cross-dual-domain decoder is also designed to predict drug-disease associations in both domains. Our proposed DFDRNN model outperforms six state-of-the-art methods on four benchmark data sets, achieving an average AUROC of 0.946 and an average AUPR of 0.597. Case studies on three diseases show that the proposed DFDRNN model can be applied in real-world scenarios, demonstrating its significant potential in drug repositioning.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"72 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined In Vitro and In Silico Workflow to Deliver Robust, Transparent, and Contextually Rigorous Models of Bioactivity.","authors":"Nathaniel Charest,Gabriel Sinclair,Stephanie A Eytcheson,Daniel T Chang,Todd M Martin,Charles N Lowe,Katie Paul Friedman,Antony J Williams","doi":"10.1021/acs.jcim.5c00713","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00713","url":null,"abstract":"New approach methodologies (NAMs) are an increasing priority in the field of toxicology to fill data gaps and reduce time and resources in chemical safety assessment. We describe an NAMs workflow that integrates an in vitro high-throughput bioassay with an in silico computational model. In defining this workflow, we propose, as a crucial step of in silico development, the identification of explicit \"purpose contexts\": a priori definitions of the scope and intent of an in silico solution, which provide natural targets for the mechanistic interpretation, validation, and output design of the model. By inspecting data from an in vitro assay measuring the displacement of fluorescent probe 8-anilino-1-naphthalenesulfonic acid (ANSA) from the serum transport protein transthyretin (TTR) as a proxy for potential disruption of thyroxine (T4) binding, in collaboration with the experimenters, we developed three relevant purpose contexts for this in silico modeling effort: (1) examination and confirmation of the in vitro assay principle via orthogonal information, (2) immediate integration with the in vitro experimental cycle to reduce costs and enhance hit rates, and (3) ultimate replacement of the use of single-concentration screening as a prioritization strategy for bioactivity testing of bulk chemical libraries. From these purpose contexts, we derived the foundations of a robust and transparent quantitative structure-activity relationship (QSAR) model that is constructively fit for purpose, characterized by first-principles mechanistic analysis, strict data quality evaluation, contextually rigorous performance testing and, finally, delivery of a quantitative recommendation schedule to simultaneously improve in vitro hit rates and in silico model learning potential.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"16 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Point Mutations on the Thermal Stability of the NBD1 Domain of hCFTR.","authors":"Lior Lublin,Hanoch Senderowitz","doi":"10.1021/acs.jcim.4c01932","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01932","url":null,"abstract":"Cystic fibrosis (CF) is an autosomal recessive genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel. The first nucleotide-binding domain (NBD1) of the CFTR is considered to be a hotspot for CF-causing mutations, and some of these mutations compromise the domain's thermal stability as well as its interactions with other domains. The mechanisms by which such mutations exert their deleterious effects are important in the basic research of this complex disease as well as for the development of mutation-specific therapies. With this in mind, we studied two class-II, severe, CF-causing mutations, L467P and A559T, known to destabilize the domain by 19.3 and 10.7 °C, respectively, and to lead to a misfolded, nonfunctioning CFTR, by conducting microsecond-long molecular dynamics (MD) simulations at an elevated temperature of 410 K on L467P-NBD1 and A559T-NBD1 constructs. For comparison, similar simulations were also performed on the wild-type (WT) construct and on the 6SS-NBD1 and 2PT/M470V-NBD1 constructs, both bearing sets of stabilizing mutations that stabilize the domain by 17.5 and 8.2 °C, respectively. The resulting trajectories were analyzed using multiple metrics, leading to a good correlation between the experimental ΔTm values and the results of the simulations, as well as multiple experimental observations and results of previous modeling efforts. Specifically, our analyses point to specific regions within NBD1 that are substantially affected by the L467P and A559T mutations and, therefore, may play some role in their pathogenesis. Many of these regions are also known to be important for the proper folding and function of the full-length CFTR. Using time-dependent assignment of DSSP elements, we also found that the two mutants follow different disintegration pathways, that of L467P-NBD1 starting in region 464-471 which resides within the F1-like ATP-binding core subdomain and continues in regions 550-562 and 514-523 within the ABCα subdomain whereas that of A559T-NBD1 simultaneously starting at the 550-562 and 514-523 regions. We propose that the analyses presented in this work may pave the way toward the development of L467P and A559T-specific CF therapies and by extension to other mutation-specific therapies for CF and for other diseases involving mutations in NBDs of other proteins.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"24 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aliphatic Polyester Recognition and Reactivity at the Active Cleft of a Fungal Cutinase.","authors":"Pietro Vidossich,Madushanka Manathunga,Andreas W Götz,Kenneth M Merz,Marco De Vivo","doi":"10.1021/acs.jcim.5c00739","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00739","url":null,"abstract":"Protein engineering of cutinases is a promising strategy for the biocatalytic degradation of non-natural polyesters. We report a mechanistic study addressing the hydrolysis of the aliphatic polyester poly(butylene succinate, or PBS) by the fungal Apergillus oryzae cutinase enzyme. Through atomistic molecular dynamics simulations and advanced alchemical transformations, we reveal how three units of a model PBS substrate fit the active site cleft of the enzyme, interacting with hydrophobic side chains. The substrate ester moiety approaches the Asp-His-Ser catalytic triad, displaying catalytically competent conformations. Acylation and deacylation hydrolytic reactions were modeled according to a canonical esterase mechanism using umbrella sampling simulations at the quantum mechanical/molecular mechanical DFT(B3LYP)/6-31G**/AMBERff level. The free energy profiles of both steps show a high-energy tetrahedral intermediate resulting from the nucleophilic attack on the ester's carboxylic carbon. The free energy barrier of the acylation step is higher (20.2 ± 0.6 kcal mol-1) than that of the deacylation step (13.6 ± 0.6 kcal mol-1). This is likely due to the interaction of the ester's carboxylic oxygen with the oxyanion hole in the reactive conformation of the deacylation step. In contrast, these interactions form as the reaction proceeds during the acylation step. The formation of an additional hydrogen bond interaction with the side chain of Ser48 is crucial to stabilizing the developing charge at the carboxylic oxygen, thus lowering the activation free energy barrier. These mechanistic insights will inform the design of enzyme variants with improved activity for plastic degradation.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"8 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KNDM: A Knowledge Graph Transformer and Node Category Sensitive Contrastive Learning Model for Drug and Microbe Association Prediction.","authors":"Dongliang Chen,Tiangang Zhang,Hui Cui,Jing Gu,Ping Xuan","doi":"10.1021/acs.jcim.5c00186","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00186","url":null,"abstract":"It has been proven that the microbiome in human bodies can promote or inhibit the treatment effects of the drugs by affecting their toxicities and activities. Therefore, identifying drug-related microbes helps in understanding how drugs exert their functions under the influence of these microbes. Most recent methods for drug-related microbe prediction are developed based on graph learning. However, those methods fail to fully utilize the diverse characteristics of drug and microbe entities from the perspective of a knowledge graph, as well as the contextual relationships among multiple meta-paths from the meta-path perspective. Moreover, previous methods overlook the consistency between the entity features derived from the knowledge graph and the node semantic features extracted from the meta-paths. To address these limitations, we propose a knowledge-graph transformer and node category-sensitive contrastive learning-based drug and microbe association prediction model (KNDM). This model learns the diverse features of drug and microbe entities, encodes the contextual relationships across multiple meta-paths, and integrates the feature consistency. First, we construct a knowledge graph consisting of drug and microbe entities, which aids in revealing similarities and associations between any two entities. Second, considering the heterogeneity of entities in the knowledge graph, we propose an entity category-sensitive transformer to integrate the diversity of multiple entity types and the various relationships among them. Third, multiple meta-paths are constructed to capture and embed the semantic relationships based on similarities and associations among drug and microbe nodes. A meta-path semantic feature learning strategy with recursive gating is proposed to capture specific semantic features of individual meta-paths while fusing contextual relationships among multiple meta-paths. Finally, we develop a node-category-sensitive contrastive learning strategy to enhance the consistency between entity features and node semantic features. Extensive experiments demonstrate that KNDM outperforms eight state-of-the-art drug-microbe association prediction models, while ablation studies validate the effectiveness of its key innovations. Additionally, case studies on candidate microbes associated with three drugs-curcumin, epigallocatechin gallate, and ciprofloxacin-further showcase KNDM's capability to identify potential drug-microbe associations.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"7 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rational Computational Workflow for Structure-Guided Discovery of a Novel USP7 Inhibitor.","authors":"Mitul Srivastava,Deepika Kumari,Sushanta Majumder,Nitu Singh,Rajani Mathur,Tushar Kanti Maiti,Ajay Kumar,Shailendra Asthana","doi":"10.1021/acs.jcim.4c01400","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01400","url":null,"abstract":"Rationally applied, structurally guided computational methods hold the promise of identifying potent and distinct chemotypes while enabling the precise targeting of structural determinants. Here, we implemented a computational workflow combining insights from cocrystal poses and monitoring the dynamical structural determinants from our previous studies for the identification of potential candidates against USP7. We identified and tested several diverse chemical scaffolds, which underwent in vitro validation across six cancer cell lines. Among these hits, compound M15, belonging to the benzothiazole chemical class, exhibited remarkable anticancer activities, demonstrating dose-dependent reduction in cancer cell viability across all cell lines and indicating that it is a promising candidate to explore as a potent anticancer drug. Biophysical binding confirms binding of M15 on USP7. M15 also exhibited certain USP7 inhibitory activity, as observed in the enzymatic assay. A comparative cocrystal mining of reported USP7 inhibitors unveiled a distinct binding mode of M15, which nicely cross-corroborated with MD and binding-pose metadynamics simulations. Notably, M15 occupies both the determinants, i.e., BL1 and the allosteric checkpoint, which has not yet been underscored as a druggable site. In essence, our study presents a robust and multifaceted computational method for the discovery and characterization of a novel inhibitor scaffold, exemplified by the identification and mechanistic elucidation of M15 against USP7. This integrated approach not only advances our understanding of USP7 inhibition and underscores mechanistic determinants but also offers promising avenues for the discovery of target-specific therapeutic intervention.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"35 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Strategies for Broad Spectrum Venom Phospholipase A2 Inhibitors.","authors":"David A Poole,Laura-Oana Albulescu,Jeroen Kool,Nicholas R Casewell,Daan P Geerke","doi":"10.1021/acs.jcim.5c00045","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00045","url":null,"abstract":"Snakebite envenoming is a persistent cause of mortality and morbidity worldwide due to the logistical challenges and costs of current antibody-based treatments. Their persistence motivates a broad interest in the discovery of inhibitors against multispecies venom phospholipase A2 (PLA2), which are underway as an alternative or supplemental treatment to improve health outcomes. Here, we present new computational strategies for improved inhibitor classification for challenging metalloenzyme targets across many species, including both a new method to utilize existing molecular docking, and subsequent data normalization. These methods were improved to support experimental screening efforts estimating the broader efficacy of candidate PLA2 inhibitors against diverse viper and elapid venoms.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"108 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling Disease-Associated PIWI-Interacting RNAs with a Contrastive Learning Methods.","authors":"Xiaowen Hu,Hao Sun,Linchao Shan,Chenxi Ma,Hanming Quan,Yuanpeng Zhang,Jiaxuan Zhang,Ziyu Fan,Yongjun Tang,Lei Deng","doi":"10.1021/acs.jcim.5c00173","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00173","url":null,"abstract":"PIWI-interacting RNAs (piRNAs) are a class of small, non-coding RNAs predominantly expressed in the germ cells of animals and play a crucial role in maintaining genomic integrity, mediating transposon suppression, and ensuring gene stability. Beyond their functions in reproductive cells, piRNAs also play roles in various human diseases, including cancer, suggesting their potential as significant biomarkers critical for disease diagnosis and treatment. Wet-lab methods to identify piRNA-disease associations require substantial resources and are often hit-or-miss. With advancements in computational technologies, an increasing number of researchers are employing computational methods to efficiently predict potential piRNA-disease associations. The sparsity of data in piRNA-disease association studies significantly limits model performance improvement. In this study, we propose a novel computational model, iPiDA_CL, to predict potential piRNA-disease associations through contrastive learning methods, which do not require negative samples. The model represents piRNA-disease association pairs as a bipartite graph and computes the initial embeddings of piRNAs and diseases using Gaussian kernel similarity, with features updated via LightGCN. Based on the siamese network framework, iPiDA_CL constructs online and target networks and employs data augmentation in the target network to build a contrastive learning objective that optimizes model parameters without introducing negative samples. Finally, cross-prediction methods are used to calculate specific piRNA-disease association scores. A series of experimental results demonstrate that iPiDA_CL surpasses state-of-the-art methods in both performance and computational efficiency. The application of iPiDA_CL to the miRNA-disease association dataset underscores its versatility across various ncRNA-disease association task. Furthermore, a case study highlights iPiDA_CL as an efficient and promising tool for predicting piRNA-disease associations.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"6 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SeqMG-RPI: A Sequence-Based Framework Integrating Multi-Scale RNA Features and Protein Graphs for RNA-Protein Interaction Prediction.","authors":"Teng Ma,Mingjian Jiang,Shunpeng Pang,Zhi Zhang,Huaibin Hang,Wei Zhou,Yuanyuan Zhang","doi":"10.1021/acs.jcim.5c00176","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00176","url":null,"abstract":"RNA-protein interaction (RPI) plays a crucial role in cell biology, and accurate prediction of RPI is essential to understand molecular mechanisms and advance disease research. Some existing RPI prediction methods typically rely on a single feature and there is significant room for improvement. In this paper, we propose a novel sequence-based RPI prediction method, called SeqMG-RPI. For RNA, SeqMG-RPI introduces an innovative multi-scale RNA feature that integrates three sequence-based representations: a multi-channel RNA feature, a k-mer frequency feature, and a k-mer sparse matrix feature. For protein, SeqMG-RPI utilizes a graph-based protein feature to capture protein information. Moreover, a novel neural network architecture is constructed for feature extraction and RPI prediction. Through experiments from multiple perspectives across various datasets, it is demonstrated that the proposed method outperforms existing methods, which has better performance and generalization.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"13 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}