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Yu L, Liu J, Jia J, Yang J, Tong R, Zhang X, Zhang Y, Yin S, Li J, Sun D. Fusion Genes Landscape of Lung Cancer Patients From Inner Mongolia, China. Genes Chromosomes Cancer 2024; 63:e23258. [PMID: 39011998 DOI: 10.1002/gcc.23258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/04/2024] [Accepted: 06/19/2024] [Indexed: 07/17/2024] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths globally. Gene fusion, a key driver of tumorigenesis, has led to the identification of numerous driver gene fusions for lung cancer diagnosis and treatment. However, previous studies focused on Western populations, leaving the possibility of unrecognized lung cancer-associated gene fusions specific to Inner Mongolia due to its unique genetic background and dietary habits. To address this, we conducted DNA sequencing analysis on tumor and adjacent nontumor tissues from 1200 individuals with lung cancer in Inner Mongolia. Our analysis established a comprehensive fusion gene landscape specific to lung cancer in Inner Mongolia, shedding light on potential region-specific molecular mechanisms underlying the disease. Compared to Western cohorts, we observed a higher occurrence of ALK and RET fusions in Inner Mongolian patients. Additionally, we discovered eight novel fusion genes in three patients: SLC34A2-EPHB1, CCT6P3-GSTP1, BARHL2-APC, HRAS-MELK, FAM134B-ERBB2, ABCB1-GIPC1, GPR98-ALK, and FAM134B-SALL1. These previously unreported fusion genes suggest potential regional specificity. Furthermore, we characterized the fusion genes' structures based on breakpoints and described their impact on major functional gene domains. Importantly, the identified novel fusion genes exhibited significant clinical and pathological relevance. Notably, patients with SLC34A2-EPHB1, CCT6P3-GSTP1, and BARHL2-APC fusions showed sensitivity to the combination of chemotherapy and immunotherapy. Patients with HRAS-MELK, FAM134B-ERBB2, and ABCB1-GIPC1 fusions showed sensitivity to chemotherapy. In summary, our study provides novel insights into the frequency, distribution, and characteristics of specific fusion genes, offering valuable guidance for the development of effective clinical treatments, particularly in Inner Mongolia.
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Affiliation(s)
- Lan Yu
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Jinyang Liu
- Department of Sciences, Geneis Beijing Co. Ltd., Beijing, China
- Department of Data Mining, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jianchao Jia
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Jie Yang
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Ruiying Tong
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Xiao Zhang
- Clinical Medical Research Center, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Yun Zhang
- Department of Sciences, Geneis Beijing Co. Ltd., Beijing, China
- Department of Data Mining, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Songtao Yin
- Department of Medical Imaging, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Junlin Li
- Department of Medical Imaging, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
| | - Dejun Sun
- Inner Mongolia Academy of Medical Sciences, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
- Pulmonary and Critical Care Medicine, Inner Mongolian People's Hospital, Hohhot, Inner Mongolia, China
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Poorinmohammad N, Salavati R. Prioritization of Trypanosoma brucei editosome protein interactions interfaces at residue resolution through proteome-scale network analysis. BMC Mol Cell Biol 2024; 25:3. [PMID: 38279116 PMCID: PMC10811811 DOI: 10.1186/s12860-024-00499-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/19/2024] [Indexed: 01/28/2024] Open
Abstract
BACKGROUND Trypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed due to a lack of functional information on many of the pathogen's proteins. Accordingly, this paper presents a computational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene or protein characterization, potentially accelerating drug discovery efforts. RESULTS By integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporating them into gene set analysis and differential expression analysis of existing quantitative proteomics data across various life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot residues, which could guide drug candidate selection and functional studies. CONCLUSION RNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces that play central roles in the pathogen's life cycle. This study introduces an integrative drug target identification workflow combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully realize its potential in advancing urgently needed antiparasitic drug development.
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Affiliation(s)
- Naghmeh Poorinmohammad
- Institute of Parasitology, McGill University, Ste. Anne de Bellevue, Montreal, Quebec, H9X 3V9, Canada
| | - Reza Salavati
- Institute of Parasitology, McGill University, Ste. Anne de Bellevue, Montreal, Quebec, H9X 3V9, Canada.
- Department of Biochemistry, McGill University, Montreal, Quebec, H3G 1Y6, Canada.
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Ma CH, Zhao JF, Zhang XG, Ding CH, Hao HH, Ji YH, Li LP, Guo ZT, Liu WS. Discovery of ellagic acid as a competitive inhibitor of Src homology phosphotyrosyl phosphatase 2 (SHP2) for cancer treatment: In vitro and in silico study. Int J Biol Macromol 2024; 254:127845. [PMID: 37935292 DOI: 10.1016/j.ijbiomac.2023.127845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/29/2023] [Accepted: 10/31/2023] [Indexed: 11/09/2023]
Abstract
Targeting SHP2 has become a potential cancer treatment strategy. In this study, ellagic acid was first reported as a competitive inhibitor of SHP2, with an IC50 value of 0.69 ± 0.07 μM, and its inhibitory potency was 34.86 times higher that of the positive control NSC87877. Ellagic acid also had high inhibitory activity on the SHP2-E76K and SHP2-E76A mutants, with the IC50 values of 1.55 ± 0.17 μM and 0.39 ± 0.05 μM, respectively. Besides, the IC50 values of ellagic acid on homologous proteins SHP1, PTP1B, and TCPTP were 0.93 ± 0.08 μM, 2.04 ± 0.28 μM, and 11.79 ± 0.83 μM, with selectivity of 1.35, 2.96, and 17.09 times, respectively. The CCK8 proliferation experiment exhibited that ellagic acid would inhibit the proliferation of various cancer cells. It was worth noting that the combination of ellagic acid and KRASG12C inhibitor AMG510 would produce a strong synergistic effect in inhibiting NCI-H358 cells. Western blot experiment exhibited that ellagic acid would downregulate the phosphorylation levels of Erk and Akt in NCI-H358 and MDA-MB-468 cells. Molecular docking and molecular dynamics studies revealed the binding information between SHP2 and ellagic acid. In summary, this study provides new ideas for the development of SHP2 inhibitors.
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Affiliation(s)
- Chun-Hui Ma
- Department of Clinical Laboratory, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong, China; Clinical Research Center, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong Province, China
| | - Ji-Feng Zhao
- Shandong Key Laboratory of Medicine and Health (Clinical Applied Pharmacology), Department of Pharmacy, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong Province, China; Clinical Research Center, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong Province, China
| | - Xu-Guang Zhang
- Department of Clinical Laboratory, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong, China
| | - Chuan-Hua Ding
- Shandong Key Laboratory of Medicine and Health (Clinical Applied Pharmacology), Department of Pharmacy, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong Province, China
| | - Hui-Hui Hao
- Shandong Key Laboratory of Medicine and Health (Clinical Applied Pharmacology), Department of Pharmacy, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong Province, China
| | - Ying-Hui Ji
- Shandong Key Laboratory of Medicine and Health (Clinical Applied Pharmacology), Department of Pharmacy, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong Province, China
| | - Li-Peng Li
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin 300070, China
| | - Zhen-Tao Guo
- Department of Nephrology, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong, China.
| | - Wen-Shan Liu
- Shandong Key Laboratory of Medicine and Health (Clinical Applied Pharmacology), Department of Pharmacy, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong Province, China; Clinical Research Center, Affiliated Hospital of Weifang Medical University, Weifang 261041, Shandong Province, China.
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Franke L, Peter C. Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding. J Chem Theory Comput 2023; 19:2985-2995. [PMID: 37122117 DOI: 10.1021/acs.jctc.2c01228] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Characterizing the structural dynamics of proteins with heterogeneous conformational landscapes is crucial to understanding complex biomolecular processes. To this end, dimensionality reduction algorithms are used to produce low-dimensional embeddings of the high-dimensional conformational phase space. However, identifying a compact and informative set of input features for the embedding remains an ongoing challenge. Here, we propose to harness the power of Residue Interaction Networks (RINs) and their centrality measures, established tools to provide a graph theoretical view on molecular structure. Specifically, we combine the closeness centrality, which captures global features of the protein conformation at residue-wise resolution, with EncoderMap, a hybrid neural-network autoencoder/multidimensional-scaling like dimensionality reduction algorithm. We find that the resulting low-dimensional embedding is a meaningful visualization of the residue interaction landscape that resolves structural details of the protein behavior while retaining global interpretability. This feature-based graph embedding of temporal protein graphs makes it possible to apply the general descriptive power of RIN formalisms to the analysis of protein simulations of complex processes such as protein folding and multidomain interactions requiring no protein-specific input. We demonstrate this on simulations of the fast folding protein Trp-Cage and the multidomain signaling protein FAT10. Due to its generality and modularity, the presented approach can easily be transferred to other protein systems.
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Affiliation(s)
- Leon Franke
- Department of Chemistry, University of Konstanz, Universitätsstraße 10, Konstanz 78457, Germany
- Konstanz Research School Chemical Biology, University of Konstanz, Universitätsstraße 10, Konstanz 78457, Germany
| | - Christine Peter
- Department of Chemistry, University of Konstanz, Universitätsstraße 10, Konstanz 78457, Germany
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5
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Pozzati G, Kundrotas P, Elofsson A. Scoring of protein–protein docking models utilizing predicted interface residues. Proteins 2022; 90:1493-1505. [PMID: 35246997 PMCID: PMC9314140 DOI: 10.1002/prot.26330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/23/2022] [Accepted: 02/28/2022] [Indexed: 11/08/2022]
Abstract
Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today's best scoring functions can significantly increase the number of top‐ranked models but still fail for most targets. Here, we examine the possibility of utilizing predicted interface residues to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the regions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. This study systematically tests different interface prediction methods for scoring >300.000 low‐resolution rigid‐body template free docking decoys. Overall we find that contact‐based interface prediction by BIPSPI is the best method to score docking solutions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high‐importance metric when estimating interface prediction quality, focusing on docking constraints production. Finally, we discussed several limitations for adopting interface predictions as constraints in a docking protocol.
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Affiliation(s)
- Gabriele Pozzati
- Department of Biochemistry and Biophysics and Science for Life Laboratory Stockholm University Solna Sweden
| | - Petras Kundrotas
- Department of Biochemistry and Biophysics and Science for Life Laboratory Stockholm University Solna Sweden
- Center for Bioinformatics and Department of Molecular Biosciences University of Kansas Lawrence Kansas USA
| | - Arne Elofsson
- Department of Biochemistry and Biophysics and Science for Life Laboratory Stockholm University Solna Sweden
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Exploring the cause of the dual allosteric targeted inhibition attaching to allosteric sites enhancing SHP2 inhibition. Mol Divers 2021; 26:1567-1580. [PMID: 34338914 DOI: 10.1007/s11030-021-10286-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/22/2021] [Indexed: 10/20/2022]
Abstract
SHP2 is a protein tyrosine phosphatase (PTP) that can regulate the tyrosine phosphorylation level. Overexpression of SHP2 will promote the development of cancer diseases, so SHP2 has become one of the popular targets for the treatment of cancer. Studies have reported that both SHP099 and SHP844 are inhibitors of SHP2 and bind to different allosteric sites 1 and 2, respectively. Studies have shown that combining SHP099 with SHP844 will enhance pharmacological pathway inhibition in cells. This study uses molecular dynamic simulations to explore the dual allosteric targeted inhibition mechanism. The result shows that the residues THR108-TRP112 (allosteric site 1) move to LEU236-GLN245 (αB-αC link loop in PTP domain) , the residues of GLN79-GLN87 (allosteric site 2) get close to LEU262-GLN269 (αA-αB link loop in PTP domain) and HIS458-ARG465 (P-loop) come near to ARG501-THR507 (Q-loop) in SHP2-SHP099-SHP844 system, which makes the "inactive conformation" more stable and prevents the substrate from entering the catalytic site. Meanwhile, residue GLU110 (allosteric site 1), ARG265 (allosteric site 2), and ARG501 (Q-loop) are speculated to be the key residues that causing the SHP2 protein in auto-inhibition conformation. It is hoped that this study will provide clues for the development of the dual allosteric targeted inhibition of SHP2.
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Brysbaert G, Lensink MF. Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding. FRONTIERS IN BIOINFORMATICS 2021; 1:684970. [PMID: 36303777 PMCID: PMC9581030 DOI: 10.3389/fbinf.2021.684970] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/17/2021] [Indexed: 12/21/2022] Open
Abstract
Residue interaction networks (RINs) describe a protein structure as a network of interacting residues. Central nodes in these networks, identified by centrality analyses, highlight those residues that play a role in the structure and function of the protein. However, little is known about the capability of such analyses to identify residues involved in the formation of macromolecular complexes. Here, we performed six different centrality measures on the RINs generated from the complexes of the SKEMPI 2 database of changes in protein–protein binding upon mutation in order to evaluate the capability of each of these measures to identify major binding residues. The analyses were performed with and without the crystallographic water molecules, in addition to the protein residues. We also investigated the use of a weight factor based on the inter-residue distances to improve the detection of these residues. We show that for the identification of major binding residues, closeness, degree, and PageRank result in good precision, whereas betweenness, eigenvector, and residue centrality analyses give a higher sensitivity. Including water in the analysis improves the sensitivity of all measures without losing precision. Applying weights only slightly raises the sensitivity of eigenvector centrality analysis. We finally show that a combination of multiple centrality analyses is the optimal approach to identify residues that play a role in protein–protein interaction.
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Role of protein-protein interactions in allosteric drug design for DNA methyltransferases. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 121:49-84. [PMID: 32312426 DOI: 10.1016/bs.apcsb.2019.12.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
DNA methyltransferases (DNMTs) not only play key roles in epigenetic gene regulation, but also serve as emerging targets for several diseases, especially for cancers. Due to the multi-domains of DNMT structures, targeting allosteric sites of protein-protein interactions (PPIs) is becoming an attractive strategy in epigenetic drug discovery. This chapter aims to review the major contemporary approaches utilized for the drug discovery based on PPIs in different dimensions, from the enumeration of allosteric mechanism to the identification of allosteric pockets. These include the construction of protein structure networks (PSNs) based on molecular dynamics (MD) simulations, performing elastic network models (ENMs) and perturbation response scanning (PRS) calculation, the sequence-based conservation and coupling analysis, and the allosteric pockets identification. Furthermore, we complement this methodology by highlighting the role of computational approaches in promising practical applications for the computer-aided drug design, with special focus on two DNMTs, namely, DNMT1 and DNMT3A.
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Ma YC, Yang B, Wang X, Zhou L, Li WY, Liu WS, Lu XH, Zheng ZH, Ma Y, Wang RL. Identification of novel inhibitor of protein tyrosine phosphatases delta: structure-based pharmacophore modeling, virtual screening, flexible docking, molecular dynamics simulation, and post-molecular dynamics analysis. J Biomol Struct Dyn 2019; 38:4432-4448. [PMID: 31625456 DOI: 10.1080/07391102.2019.1682050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Owing to their unique functions in regulating the synapse activity of protein tyrosine phosphatases delta (PTPδ) that has drawn special attention for developing drugs to autism spectrum disorders (ASDs). In this study, the PTPδ pharmacophore was first established by the structure-based pharmacophore method. Subsequently, 10 compounds contented Lipinski's rule of five was acquired by the virtual screening of the PTPδ pharmacophore against ZINC and PubChem databases. Then, the 10 identified molecules were discovered that had better binding affinity than a known PTPδ inhibitors compound SCHEMBL16375396. Two compounds SCHEMBL16375408 and ZINC19796658 with high binding score, low toxicity were gained. They were observed by docking analysis and molecular dynamics simulations that the novel potential inhibitors not only possessed the same function as SCHEMBL16375396 did in inhibiting PTPδ, but also had more favorable conformation to bind with the catalytic active regions. This study provides a new method for identify PTPδ inhibitor for the treatment of ASDs disease.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yang-Chun Ma
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Bing Yang
- Department of Cell Biology, School of Basic Medical Science, Tianjin Medical University, Tianjin, China
| | - Xin Wang
- Tasly Pharmaceutical Group Co., Ltd., Tianjin, China
| | - Liang Zhou
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Wei-Ya Li
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Wen-Shan Liu
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Xin-Hua Lu
- New Drug Research and Development Center of North China Pharmaceutical Group Corporation, National Microbial Medicine Engineering and Research Center, Hebei Industry Microbial Metabolic Engineering & Technology Research Center, Key Laboratory for New Drug Screening Technology of Shijiazhuang City, Shijiazhuang, Hebei, China
| | - Zhi-Hui Zheng
- New Drug Research and Development Center of North China Pharmaceutical Group Corporation, National Microbial Medicine Engineering and Research Center, Hebei Industry Microbial Metabolic Engineering & Technology Research Center, Key Laboratory for New Drug Screening Technology of Shijiazhuang City, Shijiazhuang, Hebei, China
| | - Ying Ma
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Run-Ling Wang
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
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Liu WS, Wang RR, Sun YZ, Li WY, Li HL, Liu CL, Ma Y, Wang RL. Exploring the effect of inhibitor AKB-9778 on VE-PTP by molecular docking and molecular dynamics simulation. J Cell Biochem 2019; 120:17015-17029. [PMID: 31125141 DOI: 10.1002/jcb.28963] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/03/2019] [Accepted: 03/15/2019] [Indexed: 01/02/2023]
Abstract
Diabetic macular edema, also known as diabetic eye disease, is mainly caused by the overexpression of vascular endothelial protein tyrosine phosphatase (VE-PTP) at hypoxia/ischemic. AKB-9778 is a known VE-PTP inhibitor that can effectively interact with the active site of VE-PTP to inhibit the activity of VE-PTP. However, the binding pattern of VE-PTP with AKB-9778 and the dynamic implications of AKB-9778 on VE-PTP system at the molecular level are poorly understood. Through molecular docking, it was found that the AKB-9778 was docked well in the binding pocket of VE-PTP by the interactions of hydrogen bond and Van der Waals. Furthermore, after molecular dynamic simulations on VE-PTP system and VE-PTP AKB-9778 system, a series of postdynamic analyses found that the flexibility and conformation of the active site undergone an obvious transition after VE-PTP binding with AKB-9778. Moreover, by constructing the RIN, it was found that the different interactions in the active site were the detailed reasons for the conformational differences between these two systems. Thus, the finding here might provide a deeper understanding of AKB-9778 as VE-PTP Inhibitor.
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Affiliation(s)
- Wen-Shan Liu
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Rui-Rui Wang
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Ying-Zhan Sun
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Wei-Ya Li
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Hong-Lian Li
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Chi-Lu Liu
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Ying Ma
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Run-Ling Wang
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
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Kim P, Zhou X. FusionGDB: fusion gene annotation DataBase. Nucleic Acids Res 2019; 47:D994-D1004. [PMID: 30407583 PMCID: PMC6323909 DOI: 10.1093/nar/gky1067] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 10/05/2018] [Accepted: 11/01/2018] [Indexed: 12/26/2022] Open
Abstract
Gene fusion is one of the hallmarks of cancer genome via chromosomal rearrangement initiated by DNA double-strand breakage. To date, many fusion genes (FGs) have been established as important biomarkers and therapeutic targets in multiple cancer types. To better understand the function of FGs in cancer types and to promote the discovery of clinically relevant FGs, we built FusionGDB (Fusion Gene annotation DataBase) available at https://ccsm.uth.edu/FusionGDB. We collected 48 117 FGs across pan-cancer from three representative fusion gene resources: the improved database of chimeric transcripts and RNA-seq data (ChiTaRS 3.1), an integrative resource for cancer-associated transcript fusions (TumorFusions), and The Cancer Genome Atlas (TCGA) fusions by Gao et al. For these ∼48K FGs, we performed functional annotations including gene assessment across pan-cancer fusion genes, open reading frame (ORF) assignment, and retention search of 39 protein features based on gene structures of multiple isoforms with different breakpoints. We also provided the fusion transcript and amino acid sequences according to multiple breakpoints and transcript isoforms. Our analyses identified 331, 303 and 667 in-frame FGs with retaining kinase, DNA-binding, and epigenetic factor domains, respectively, as well as 976 FGs lost protein-protein interaction. FusionGDB provides six categories of annotations: FusionGeneSummary, FusionProtFeature, FusionGeneSequence, FusionGenePPI, RelatedDrug and RelatedDisease.
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Affiliation(s)
- Pora Kim
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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12
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Carugo O. Atomic displacement parameters in structural biology. Amino Acids 2018; 50:775-786. [DOI: 10.1007/s00726-018-2574-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 04/19/2018] [Indexed: 01/14/2023]
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Song J, Li F, Takemoto K, Haffari G, Akutsu T, Chou KC, Webb GI. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. J Theor Biol 2018; 443:125-137. [DOI: 10.1016/j.jtbi.2018.01.023] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 01/17/2018] [Accepted: 01/18/2018] [Indexed: 10/18/2022]
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