Proteins-Structure Function and Bioinformatics最新文献_第2页

Exploring Compactness and Dynamics of Apomyoglobin. 无肌红蛋白的致密性和动力学研究。

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-05-01 Epub Date: 2024-12-23 DOI: 10.1002/prot.26786

Anna V Glyakina, Mariya Y Suvorina, Nikita V Dovidchenko, Natalya S Katina, Alexey K Surin, Oxana V Galzitskaya

{"title":"Exploring Compactness and Dynamics of Apomyoglobin.","authors":"Anna V Glyakina, Mariya Y Suvorina, Nikita V Dovidchenko, Natalya S Katina, Alexey K Surin, Oxana V Galzitskaya","doi":"10.1002/prot.26786","DOIUrl":"10.1002/prot.26786","url":null,"abstract":"Hydrogen-deuterium exchange mass spectrometry (HDX-MS) approach has become a valuable analytical complement to traditional methods. HDX-MS allows the identification of dynamic surfaces in proteins. We have shown that the introduction of various mutations into the amino acid sequence of whale apomyoglobin (apoMb) leads to a change in the number of exchangeable hydrogen atoms, which is associated with a change in its compactness in the native-like condition. Thus, amino acid substitutions V10A, A15S, P120G, and M131A result in an increase in the number of exchangeable hydrogen atoms at the native-like condition, while the mutant form A144S leads to a decrease in the number of exchangeable hydrogen atoms. This may be due to a decrease and increase in the compactness of apoMb structure compared to the wild-type apoMb, respectively. The L9F and L9E mutations did not affect the compactness of the molecule compared to the wild type. We have demonstrated that V10A and M131A substitutions lead to the maximum and large increase correspondently in the average number of exchangeable hydrogen atoms for deuterium, since these substitutions lead to the loss of contacts between important parts of myoglobin structure: helices A, G, and H, which are structured at the early stage of folding.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":"997-1008"},"PeriodicalIF":3.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Protein-RNA Docking Benchmark v3.0 Integrated With Binding Affinity.

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-04-09 DOI: 10.1002/prot.26825

Shri Kant, Chandran Nithin, Sunandan Mukherjee, Atanu Maity, Ranjit Prasad Bahadur

{"title":"Protein-RNA Docking Benchmark v3.0 Integrated With Binding Affinity.","authors":"Shri Kant, Chandran Nithin, Sunandan Mukherjee, Atanu Maity, Ranjit Prasad Bahadur","doi":"10.1002/prot.26825","DOIUrl":"https://doi.org/10.1002/prot.26825","url":null,"abstract":"We introduce an updated non-redundant protein-RNA docking benchmark version 3.0 (PRDBv3.0) containing 197 test cases curated from 288 unique protein-RNA complexes available in the Protein Data Bank until July 2024. Among these, 27 are unbound-unbound (UU) type where both the binding partners are available in their unbound states, 160 are unbound-bound (UB) type where only the protein is available in unbound state and remaining 10 are bound-unbound (BU) type where only the RNA is available in unbound state. The benchmark is categorized into three classes based on the conformational flexibility of the protein interface: 117 rigid-body (R) complexes with minimal structural changes, 41 semi-flexible (S) complexes showing moderate conformational changes and 29 full-flexible (F) complexes with significant conformational changes. The current benchmark represents a 62% increase in the number of test cases compared to its previous version. Binding affinity (Kd) values for a subset of 105 protein-RNA complexes from PRDBv3.0 are catalogued along with additional experimental details to develop a comprehensive protein-RNA affinity benchmark. Moreover, a total of 255 unique RNA-binding domains, present in RNA-binding proteins, are also catalogued in this updated benchmark. PRDBv3.0 will facilitate the evaluation of both rigid-body and flexible docking methods as well as the methods that aim to predict binding affinity. The updated benchmark is freely available at http://www.csb.iitkgp.ac.in/applications/PRDBv3/PRDBv3.php.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Protein-Ligand Structure and Affinity Prediction in CASP16 Using a Geometric Deep Learning Ensemble and Flow Matching.

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-04-08 DOI: 10.1002/prot.26827

Alex Morehead, Jian Liu, Pawan Neupane, Nabin Giri, Jianlin Cheng

{"title":"Protein-Ligand Structure and Affinity Prediction in CASP16 Using a Geometric Deep Learning Ensemble and Flow Matching.","authors":"Alex Morehead, Jian Liu, Pawan Neupane, Nabin Giri, Jianlin Cheng","doi":"10.1002/prot.26827","DOIUrl":"https://doi.org/10.1002/prot.26827","url":null,"abstract":"Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein-ligand structure (poses) produced by the latest deep learning methods to identify the best poses and how to accurately estimate the binding affinity between a protein target and a list of ligand candidates. Further, a blind benchmarking and assessment of protein-ligand structure and binding affinity prediction is necessary to ensure it generalizes well to new settings. Towards this end, we introduce MULTICOM_ligand, a deep learning-based protein-ligand structure and binding affinity prediction ensemble featuring structural consensus ranking for unsupervised pose ranking and a new deep generative flow matching model for joint structure and binding affinity prediction. Notably, MULTICOM_ligand ranked among the top-5 ligand prediction methods in both protein-ligand structure prediction and binding affinity prediction in the 16th Critical Assessment of Techniques for Structure Prediction (CASP16), demonstrating its efficacy and utility for real-world drug discovery efforts. The source code for MULTICOM_ligand is freely available on GitHub.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Homocysteine Thiolactone Modification of Ribonuclease A: Thermodynamics and Kinetics. 同半胱氨酸硫内酯修饰核糖核酸酶 A：热力学和动力学。

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-04-04 DOI: 10.1002/prot.26824

Kabira Sabnam, Swagata Dasgupta

{"title":"Homocysteine Thiolactone Modification of Ribonuclease A: Thermodynamics and Kinetics.","authors":"Kabira Sabnam, Swagata Dasgupta","doi":"10.1002/prot.26824","DOIUrl":"https://doi.org/10.1002/prot.26824","url":null,"abstract":"Homocysteine thiolactone is a metabolite associated with various diseases at elevated levels in humans. Lysine residues in proteins are modified through N-homocysteinylation and homocysteinylated proteins are prone to form dimers and oligomers through disulfide cross-linkages. This study investigates the effects of N-homocysteinylation on Ribonuclease A (RNase A). The formation of dimers and higher oligomers in RNase A have been confirmed by SDS-PAGE and MALDI-ToF. Agarose-gel assays revealed an altered ribonucleolytic activity due to Lys modification. Fluorescence spectroscopy indicates local changes in the Tyr microenvironment. CD melting studies reveal that β-sheet formation is slightly enhanced with a reduction in the α-helical content in case of modified RNase A. However, the similar melting temperature of both native and modified RNase A indicates overall structural integrity with local changes in secondary structural components. ITC and UV-visible kinetics show reduced ribonucleolytic activity in homocysteinylated RNase A compared to the unmodified enzyme. These findings provide insights into the structural and functional consequences of RNase A homocysteinylation, contributing to our understanding of hyperhomocysteinemia-related pathologies.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Enzyme Commission Number Prediction With Contrastive Learning and Agent Attention.

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-04-02 DOI: 10.1002/prot.26822

Wendi Zhao, Qiaoling Han, Fan Yang, Yue Zhao

{"title":"Enhancing Enzyme Commission Number Prediction With Contrastive Learning and Agent Attention.","authors":"Wendi Zhao, Qiaoling Han, Fan Yang, Yue Zhao","doi":"10.1002/prot.26822","DOIUrl":"https://doi.org/10.1002/prot.26822","url":null,"abstract":"The accurate prediction of enzyme function is crucial for elucidating disease mechanisms and identifying drug targets. Nevertheless, existing enzyme commission (EC) number prediction methods are limited by database coverage and the depth of sequence information mining, hindering the efficiency and precision of enzyme function annotation. Therefore, this study introduces ProteEC-CLA (Protein EC number prediction model with Contrastive Learning and Agent Attention). ProteEC-CLA utilizes contrastive learning to construct positive and negative sample pairs, which not only enhances sequence feature extraction but also improves the utilization of unlabeled data. This process helps the model learn the differences in sequence features, thereby enhancing its ability to predict enzyme function. Integrating the pre-trained protein language model ESM2, the model generates informative sequence embeddings for deep functional correlation analysis, significantly enhancing prediction accuracy. With the incorporation of the Agent Attention mechanism, ProteEC-CLA's ability to comprehensively capture local details and global features is enhanced, ensuring high-accuracy predictions on complex sequences. The results demonstrate that ProteEC-CLA performs exceptionally well on two independent and representative datasets. In the standard dataset, it achieves 98.92% accuracy at the EC4 level. In the more challenging clustered split dataset, ProteEC-CLA achieves 93.34% accuracy and an F1-score of 94.72%. With only enzyme sequences as input, ProteEC-CLA can accurately predict EC numbers up to the fourth level, significantly enhancing annotation efficiency and accuracy, which makes it a highly efficient and precise functional annotation tool for enzymology research and applications.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

α/β Hydrolases: Toward Unraveling Entangled Classification. α/β水解酶：解开缠结分类。

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-04-01 Epub Date: 2024-12-02 DOI: 10.1002/prot.26776

Fatih Ozhelvaci, Kamil Steczkiewicz

{"title":"α/β Hydrolases: Toward Unraveling Entangled Classification.","authors":"Fatih Ozhelvaci, Kamil Steczkiewicz","doi":"10.1002/prot.26776","DOIUrl":"10.1002/prot.26776","url":null,"abstract":"α/β Hydrolase-like enzymes form a large and functionally diverse superfamily of proteins. Despite retaining a conserved structural core consisting of an eight-stranded, central β-sheet flanked with six α-helices, they display a modular architecture allowing them to perform a variety of functions, like esterases, lipases, peptidases, epoxidases, lyases, and others. At the same time, many α/β hydrolase-like families, even enzymatically distinct, share a high degree of sequence similarity. This imposes several problems for their annotation and classification, because available definitions of particular α/β hydrolase-like families overlap significantly, so the unambiguous functional assignment of these superfamily members remains a challenging task. For instance, two large and important peptidase families, namely S9 and S33, blend with lipases, epoxidases, esterases, and other enzymes unrelated to proteolysis, which hinders automatic annotations in high-throughput projects. With the use of thorough sequence and structure analyses, we newly annotate three protein families as α/β hydrolase-like and revise current classifications of the realm of α/β hydrolase-like superfamily. Based on manually curated structural superimpositions and multiple sequence and structure alignments, we comprehensively demonstrate structural conservation and diversity across the whole superfamily. Eventually, after detailed pairwise sequence similarity assessments, we develop a new clustering of the α/β hydrolases and provide a set of family profiles allowing for detailed, reliable, and automatic functional annotations of the superfamily members.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":"855-870"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Myotubularin Related Proteins and the Untapped Interaction Potential of Their Disordered C-Terminal Regions. 肌小管蛋白相关蛋白及其无序c端区域未开发的相互作用潜力。

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-04-01 Epub Date: 2024-11-30 DOI: 10.1002/prot.26774

Daniel Saar, Caroline L E Lennartsson, Philip Weidner, Elke Burgermeister, Birthe B Kragelund

{"title":"The Myotubularin Related Proteins and the Untapped Interaction Potential of Their Disordered C-Terminal Regions.","authors":"Daniel Saar, Caroline L E Lennartsson, Philip Weidner, Elke Burgermeister, Birthe B Kragelund","doi":"10.1002/prot.26774","DOIUrl":"10.1002/prot.26774","url":null,"abstract":"Intrinsically disordered regions (IDRs) of proteins remain understudied with enigmatic sequence features relevant to their functions. Members of the myotubularin-related protein (MTMR) family contain uncharacterized IDRs. After decades of research on their phosphatase activity, recent work on the C-terminal IDRs of MTMR7 revealed new interactions and important new functions beyond the phosphatase function. Here we take a broader look at the C-terminal domains (CTDs) of 14 human MTMRs and use bioinformatic tools and biophysical methods to ask which other functions may be probable in this protein family. The predictions show that the CTDs are disordered and carry short linear motifs (SLiMs) important for targeting of MTMRs to defined subcellular compartments and implicating them in signaling, phase separation, interaction with diverse proteins, including transcription factors and are of relevance for cancer research and neuroscience. We also present experimental methods to study the CTDs and use them to characterize the coiled coil (CC) domains of MTMR7 and MTMR9. We show homo- and hetero-oligomerization with preference for MTMR7-CC to form dimers, while MTMR9-CC forms trimers. We relate the results to sequence features and make predictions for the structural landscape of other MTMRs. Our work gives a broad insight into the so far unrecognized features and SLiMs in MTMR-CTDs, and provides the basis for more in-depth experimental research on this diverse protein family and understudied IDRs in proteins in general.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":"831-854"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Short Molecular Dynamics Simulations to Determine the Important Features of Interactions in Antibody-Protein Complexes. 利用短分子动力学模拟确定抗体-蛋白质复合物相互作用的重要特征。

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-04-01 Epub Date: 2024-11-27 DOI: 10.1002/prot.26773

A Clay Richard, Robert J Pantazes

{"title":"Using Short Molecular Dynamics Simulations to Determine the Important Features of Interactions in Antibody-Protein Complexes.","authors":"A Clay Richard, Robert J Pantazes","doi":"10.1002/prot.26773","DOIUrl":"10.1002/prot.26773","url":null,"abstract":"The last few years have seen the rapid proliferation of machine learning methods to design binding proteins. Although these methods have shown large increases in experimental success rates compared to prior approaches, the majority of their predictions fail when they are experimentally tested. It is evident that computational methods still struggle to distinguish the features of real protein binding interfaces from false predictions. Short molecular dynamics simulations of 20 antibody-protein complexes were conducted to identify features of interactions that should occur in binding interfaces. Intermolecular salt bridges, hydrogen bonds, and hydrophobic interactions were evaluated for their persistences, energies, and stabilities during the simulations. It was found that only the hydrogen bonds where both residues are stabilized in the bound complex are expected to persist and meaningfully contribute to binding between the proteins. In contrast, stabilization was not a requirement for salt bridges and hydrophobic interactions to persist. Still, interactions where both residues are stabilized in the bound complex persist significantly longer and have significantly stronger energies than other interactions. Two hundred and twenty real antibody-protein complexes and 8194 decoy complexes were used to train and test a random forest classifier using the features of expected persistent interactions identified in this study and the macromolecular features of interaction energy (IE), buried surface area (BSA), IE/BSA, and shape complementarity. It was compared to a classifier trained only on the expected persistent interaction features and another trained only on the macromolecular features. Inclusion of the expected persistent interaction features reduced the false positive rate of the classifier by two- to five-fold across a range of true positive classification rates.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":"812-830"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142735041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Protein-Variant-Phenotype Study of NBAS Using AlphaFold in the Aspect of SOPH Syndrome. 在SOPH综合征中应用AlphaFold研究NBAS蛋白变异表型。

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-04-01 Epub Date: 2024-12-06 DOI: 10.1002/prot.26764

Leonid Zhozhikov, Filipp Vasilev, Nadezhda Maksimova

引用次数: 0

The High-Resolution Structure of a Variable Lymphocyte Receptor From Petromyzon marinus Capable of Binding to the Brain Extracellular Matrix. 能与脑细胞外基质结合的海百合可变淋巴细胞受体的高分辨率结构。

IF 3.2 4区生物学

Proteins-Structure Function and Bioinformatics Pub Date : 2025-04-01 Epub Date: 2024-11-27 DOI: 10.1002/prot.26768

Elizabeth A Appelt, James B Thoden, Seth A Gehrke, Hannah D Bachmeier, Ivan Rayment, Eric V Shusta, Hazel M Holden

{"title":"The High-Resolution Structure of a Variable Lymphocyte Receptor From Petromyzon marinus Capable of Binding to the Brain Extracellular Matrix.","authors":"Elizabeth A Appelt, James B Thoden, Seth A Gehrke, Hannah D Bachmeier, Ivan Rayment, Eric V Shusta, Hazel M Holden","doi":"10.1002/prot.26768","DOIUrl":"10.1002/prot.26768","url":null,"abstract":"Variable lymphocyte receptors (VLRs) are antigen receptors derived from the adaptive immune system of jawless vertebrates such as lamprey ( Petromyzon marinus ). First discovered in 2004, VLRs have been the subject of numerous biochemical and structural investigations. Due to their unique antigen binding properties, VLRs have been leveraged as possible drug delivery agents. One such VLR, previously identified and referred to as P1C10, was shown to bind to the brain extracellular matrix. Here, we present the high-resolution X-ray crystal structure of this VLR determined to 1.3 Å resolution. The fold is dominated by a six-stranded mixed β-sheet which provides a concave surface for possible antigen binding. Electron density corresponding to a 4-(2-hydroxyethyl)piperazine-1-propanesulfonic acid buffer molecule (HEPPS) was found in this region. By comparing the P1C10 molecular architecture and its buffer binding residues with those of other VLRs previously reported, it was possible to illustrate how this unique class of proteins can accommodate diverse binding partners. Additionally, we provide an analysis of the experimentally determined structure compared to the models generated by the commonly used AlphaFold and iTASSER structure prediction software packages.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":" ","pages":"801-811"},"PeriodicalIF":3.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142735033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0