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Li YX, Zhang M, Shimadate Y, Kato A, Wang JZ, Jia YM, Fleet GWJ, Yu CY. C-2 fluorinated castanospermines as potent and specific α-glucosidase inhibitors: synthesis and structure-activity relationship study. Org Biomol Chem 2025; 23:2854-2877. [PMID: 39976182 DOI: 10.1039/d4ob01542h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
C-2 Fluorinated castanospermines have been synthesized from a well-protected aldehyde precursor and evaluated as glycosidase inhibitors in comparison with castanospermine, 1-epi-castanospermine and C-1 fluorinated castanospermines. While C-1 fluorinated castanospermines lose nearly all the glycosidase inhibition shown by castanospermine and 1-epi-castanospermine, C-2 fluorinated derivatives of castanospermine were found to be potent and highly specific α-glucosidase inhibitors; however, the C-2 fluorinated 1-epi-castanospermines showed a sharp decrease in inhibition towards all tested enzymes. Docking calculations attributed the sharp decrease of glycosidase inhibition of C-1 fluorinated castanospermines to the disappearance of hydrogen bonds between the original C-1 hydroxyls and residues Arg-526 and Asp-327. The retained potent and specific α-glucosidase inhibition of C-2 fluorinated castanospermines was achieved by the fluorine-induced reestablishment of the docking mode in the active site; and the sharply decreased inhibition of C-2 fluorinated 1-epi-castanospermines can be attributed to obvious binding distorsion and disappearance of the hydrogen bonding with residues His-600 and Arg-526. Reliability of the docking results was evaluated by Molecular Dynamics (MD) simulation, which provided necessary calibrations to the calculation results. The interaction modes of fluorine reported herein are different from the "mimic effect" of fluorine for hydrogen, offering insights and extending our previous work on fluorinated casuarines. These results would be important for the development of castanospermine-related drug candidates for the treatment of diabetes, viral infections and Pompe disease.
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Affiliation(s)
- Yi-Xian Li
- Beijing National Laboratory for Molecular Science (BNLMS), CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ming Zhang
- Beijing National Laboratory for Molecular Science (BNLMS), CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuna Shimadate
- Department of Hospital Pharmacy, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan.
| | - Atsushi Kato
- Department of Hospital Pharmacy, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan.
| | - Jun-Zhe Wang
- Beijing National Laboratory for Molecular Science (BNLMS), CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue-Mei Jia
- Beijing National Laboratory for Molecular Science (BNLMS), CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - George W J Fleet
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Mansfield Road, Oxford, OX1 3TA, UK
| | - Chu-Yi Yu
- Beijing National Laboratory for Molecular Science (BNLMS), CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
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Kumari P, Pradhan B, Koromina M, Patrinos GP, Steen KV. Discovery of new drug indications for COVID-19: A drug repurposing approach. PLoS One 2022; 17:e0267095. [PMID: 35609015 PMCID: PMC9129022 DOI: 10.1371/journal.pone.0267095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/03/2022] [Indexed: 11/19/2022] Open
Abstract
Motivation
The outbreak of coronavirus health issues caused by COVID-19(SARS-CoV-2) creates a global threat to public health. Therefore, there is a need for effective remedial measures using existing and approved therapies with proven safety measures has several advantages. Dexamethasone (Pubchem ID: CID0000005743), baricitinib(Pubchem ID: CID44205240), remdesivir (PubchemID: CID121304016) are three generic drugs that have demonstrated in-vitro high antiviral activity against SARS-CoV-2. The present study aims to widen the search and explore the anti-SARS-CoV-2 properties of these potential drugs while looking for new drug indications with optimised benefits via in-silico research.
Method
Here, we designed a unique drug-similarity model to repurpose existing drugs against SARS-CoV-2, using the anti-Covid properties of dexamethasone, baricitinib, and remdesivir as references. Known chemical-chemical interactions of reference drugs help extract interactive compounds withimprovedanti-SARS-CoV-2 properties. Here, we calculated the likelihood of these drug compounds treating SARS-CoV-2 related symptoms using chemical-protein interactions between the interactive compounds of the reference drugs and SARS-CoV-2 target genes. In particular, we adopted a two-tier clustering approach to generate a drug similarity model for the final selection of potential anti-SARS-CoV-2 drug molecules. Tier-1 clustering was based on t-Distributed Stochastic Neighbor Embedding (t-SNE) and aimed to filter and discard outlier drugs. The tier-2 analysis incorporated two cluster analyses performed in parallel using Ordering Points To Identify the Clustering Structure (OPTICS) and Hierarchical Agglomerative Clustering (HAC). As a result, itidentified clusters of drugs with similar actions. In addition, we carried out a docking study for in-silico validation of top candidate drugs.
Result
Our drug similarity model highlighted ten drugs, including reference drugs that can act as potential therapeutics against SARS-CoV-2. The docking results suggested that doxorubicin showed the least binding energy compared to reference drugs. Their practical utility as anti-SARS-CoV-2 drugs, either individually or in combination, warrants further investigation.
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Affiliation(s)
- Priyanka Kumari
- GIGA-R Medical Genomics - BIO3 Systems Genomics, University of Liège, Liège, Belgium
- Laboratory of Pharmaceutical Analytical Chemistry, CIRM, University of Liège, Liège, Belgium
- * E-mail: (PK); (KVS)
| | - Bikram Pradhan
- Indian Space Research Organisation (ISRO) Headquarters, Bengaluru, India
| | - Maria Koromina
- University of Patras, School of Health Sciences, Department of Pharmacy, Patras, Greece
| | - George P. Patrinos
- University of Patras, School of Health Sciences, Department of Pharmacy, Patras, Greece
- Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
- Zayed Bin Sultan Center for Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Kristel Van Steen
- GIGA-R Medical Genomics - BIO3 Systems Genomics, University of Liège, Liège, Belgium
- Department of Human Genetics - BIO3 Systems Medicine, University of Leuven, Leuven, Belgium
- * E-mail: (PK); (KVS)
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Edeh MO, Dalal S, Dhaou IB, Agubosim CC, Umoke CC, Richard-Nnabu NE, Dahiya N. Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease. Front Public Health 2022; 10:892371. [PMID: 35570979 PMCID: PMC9092454 DOI: 10.3389/fpubh.2022.892371] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/17/2022] [Indexed: 12/15/2022] Open
Abstract
Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.
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Affiliation(s)
- Michael Onyema Edeh
- Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria
| | - Surjeet Dalal
- College of Computing Science & Information Technology, Teerthanker Mahaveer University, Moradabad, India
| | - Imed Ben Dhaou
- Department of Computer Science, Hekma School of Engineering, Computing and Informatics, Dar Al-Hekma University, Jeddah, Saudi Arabia
| | | | - Chukwudum Collins Umoke
- Department of Vocational and Technical Education, Alex Ekwueme Federal University Ndufu Alike Ikwo (AE- FUNAI), Abakaliki, Nigeria
| | - Nneka Ernestina Richard-Nnabu
- Department of Computer Science/Informatics, Alex Ekwueme Federal University Ndufu Alike Ikwo (AE-FUNAI), Abakaliki, Nigeria
| | - Neeraj Dahiya
- Department of Computer Science and Engineering, SRM University Delhi-NCR, Sonipat, India
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Tran TD, Pham DT. Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks. Sci Rep 2021; 11:14095. [PMID: 34238960 PMCID: PMC8266823 DOI: 10.1038/s41598-021-93336-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 06/23/2021] [Indexed: 12/16/2022] Open
Abstract
Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.
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Affiliation(s)
- Tien-Dzung Tran
- Complex Systems and Bioinformatics Lab, Faculty of Information and Communication Technology, Hanoi University of Industry, Bac Tu Liem District, 298 Cau Dien street, Hanoi, Vietnam. .,Department of Software Engineering, Faculty of Information and Communication Technology, Hanoi University of Industry, Bac Tu Liem District, 298 Cau Dien street, Hanoi, Vietnam.
| | - Duc-Tinh Pham
- Complex Systems and Bioinformatics Lab, Faculty of Information and Communication Technology, Hanoi University of Industry, Bac Tu Liem District, 298 Cau Dien street, Hanoi, Vietnam.,Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
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5
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Biziukova N, Tarasova O, Ivanov S, Poroikov V. Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies. Front Genet 2021; 11:618862. [PMID: 33414815 PMCID: PMC7783389 DOI: 10.3389/fgene.2020.618862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 11/26/2020] [Indexed: 12/16/2022] Open
Abstract
Text analysis can help to identify named entities (NEs) of small molecules, proteins, and genes. Such data are very important for the analysis of molecular mechanisms of disease progression and development of new strategies for the treatment of various diseases and pathological conditions. The texts of publications represent a primary source of information, which is especially important to collect the data of the highest quality due to the immediate obtaining information, in comparison with databases. In our study, we aimed at the development and testing of an approach to the named entity recognition in the abstracts of publications. More specifically, we have developed and tested an algorithm based on the conditional random fields, which provides recognition of NEs of (i) genes and proteins and (ii) chemicals. Careful selection of abstracts strictly related to the subject of interest leads to the possibility of extracting the NEs strongly associated with the subject. To test the applicability of our approach, we have applied it for the extraction of (i) potential HIV inhibitors and (ii) a set of proteins and genes potentially responsible for viremic control in HIV-positive patients. The computational experiments performed provide the estimations of evaluating the accuracy of recognition of chemical NEs and proteins (genes). The precision of the chemical NEs recognition is over 0.91; recall is 0.86, and the F1-score (harmonic mean of precision and recall) is 0.89; the precision of recognition of proteins and genes names is over 0.86; recall is 0.83; while F1-score is above 0.85. Evaluation of the algorithm on two case studies related to HIV treatment confirms our suggestion about the possibility of extracting the NEs strongly relevant to (i) HIV inhibitors and (ii) a group of patients i.e., the group of HIV-positive individuals with an ability to maintain an undetectable HIV-1 viral load overtime in the absence of antiretroviral therapy. Analysis of the results obtained provides insights into the function of proteins that can be responsible for viremic control. Our study demonstrated the applicability of the developed approach for the extraction of useful data on HIV treatment.
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Affiliation(s)
- Nadezhda Biziukova
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Olga Tarasova
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Sergey Ivanov
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia.,Department of Bioinformatics, Faculty of Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Vladimir Poroikov
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
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Wang W, Yang X, Wu C, Yang C. CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph. BMC Bioinformatics 2020; 21:544. [PMID: 33243142 PMCID: PMC7689985 DOI: 10.1186/s12859-020-03899-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 11/19/2020] [Indexed: 11/19/2022] Open
Abstract
Background Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological network-based approaches have been proven to be effective in predicting chemical-gene interactions.
Results We present CGINet, a graph convolutional network-based method for identifying chemical-gene interactions in an integrated multi-relational graph containing three types of nodes: chemicals, genes, and pathways. We investigate two different perspectives on learning node embeddings. One is to view the graph as a whole, and the other is to adopt a subgraph view that initial node embeddings are learned from the binary association subgraphs and then transferred to the multi-interaction subgraph for more focused learning of higher-level target node representations. Besides, we reconstruct the topological structures of target nodes with the latent links captured by the designed substructures. CGINet adopts an end-to-end way that the encoder and the decoder are trained jointly with known chemical-gene interactions. We aim to predict unknown but potential associations between chemicals and genes as well as their interaction types. Conclusions We study three model implementations CGINet-1/2/3 with various components and compare them with baseline approaches. As the experimental results suggest, our models exhibit competitive performances on identifying chemical-gene interactions. Besides, the subgraph perspective and the latent link both play positive roles in learning much more informative node embeddings and can lead to improved prediction.
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Affiliation(s)
- Wei Wang
- College of Computer, National University of Defense Technology, Changsha, 410073, China
| | - Xi Yang
- College of Computer, National University of Defense Technology, Changsha, 410073, China
| | - Chengkun Wu
- College of Computer, National University of Defense Technology, Changsha, 410073, China. .,State Key Laboratory of High-Performance Computing, National University of Defense Technology, Changsha, 410073, China.
| | - Canqun Yang
- College of Computer, National University of Defense Technology, Changsha, 410073, China
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7
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Zhou B, Zhao X, Lu J, Sun Z, Liu M, Zhou Y, Liu R, Wang Y. Relating Substructures and Side Effects of Drugs with Chemical-chemical Interactions. Comb Chem High Throughput Screen 2020; 23:285-294. [DOI: 10.2174/1386207322666190702102752] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 03/11/2019] [Accepted: 04/16/2019] [Indexed: 12/17/2022]
Abstract
Background:Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs and most of them used the fact that similar drugs always have similar side effects. However, previous studies did not analyze which substructures are highly related to which kind of side effect.Method:In this study, we conducted a computational investigation. In this regard, we extracted a drug set for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was constructed by picking up drugs owing such substructure. The relationship between one side effect and one substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in an Es value. Then, the statistical significance of Es value was measured by a permutation test.Results and Conclusion:A number of highly related pairs of side effects and substructures were obtained and some were extensively analyzed to confirm the reliability of the results reported in this study.
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Affiliation(s)
- Bo Zhou
- Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Xian Zhao
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Jing Lu
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai 264005, China
| | - Zuntao Sun
- Informatization Office, Shanghai Maritime University, Shanghai 201306, China
| | - Min Liu
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Yilu Zhou
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Rongzhi Liu
- Center for Medical Device Evaluation, China Drug Administration, State Administration for Market Regulation, Beijing 100081, China
| | - Yihua Wang
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom
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Patel RD, Prasanth Kumar S, Pandya HA, Solanki HA. MDCKpred: a web-tool to calculate MDCK permeability coefficient of small molecule using membrane-interaction chemical features. Toxicol Mech Methods 2018; 28:685-698. [PMID: 29998769 DOI: 10.1080/15376516.2018.1499840] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Structure-based models to understand the transport of small molecules through biological membrane can be developed by enumerating intermolecular interactions of the small molecule with a biological membrane, usually a dimyristoylphosphatidylcholine (DMPC) monolayer. This ADME (absorption, distribution, metabolism, and excretion) property based on Madin-Darby Canine Kidney (MDCK) cell line demonstrates intestinal drug absorption of small molecules and correlated to human intestinal absorption which acts as a determining factor to forecast small-molecule prioritization in drug-discovery projects. We present here the development of MDCKpred web-tool which calculates MDCK permeability coefficient of small molecule based on the regression model, developed using membrane-interaction chemical features. The web-tool allows users to calculate the MDCK permeability coefficient (nm/s) instantly by providing simple descriptor inputs. The chemical-interaction features are derived from different parts of the DMPC molecule viz. head, middle, and tail regions and accounts overall intermolecular contacts of the small molecule when passively diffused through the phospholipid-rich biological membrane. The MDCKpred model is both internally (R2 = .76; [Formula: see text]= .68; Rtrain = .87; Rtest = .69) and externally (Rext = .55) validated. Furthermore, we used natural molecules as application examples to demonstrate its utility in lead exploration and optimization projects. The MDCKpred web-tool can be accessed freely at http://www.mdckpred.in . This web-tool is designed to offer an intuitive way of prioritizing small molecules based on calculated MDCK permeabilities.
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Affiliation(s)
- Rikin D Patel
- a Department of Bioinformatics, Applied Botany Centre (ABC), University School of Sciences , Gujarat University , Ahmedabad , India
| | | | - Himanshu A Pandya
- a Department of Bioinformatics, Applied Botany Centre (ABC), University School of Sciences , Gujarat University , Ahmedabad , India
| | - Hitesh A Solanki
- a Department of Bioinformatics, Applied Botany Centre (ABC), University School of Sciences , Gujarat University , Ahmedabad , India
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Chen L, Lu J, Huang T, Cai YD. A computational method for the identification of candidate drugs for non-small cell lung cancer. PLoS One 2017; 12:e0183411. [PMID: 28820893 PMCID: PMC5562320 DOI: 10.1371/journal.pone.0183411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 08/03/2017] [Indexed: 11/25/2022] Open
Abstract
Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.
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Affiliation(s)
- Lei Chen
- College of Life Science, Shanghai University, Shanghai, People’s Republic of China
- College of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China
| | - Jing Lu
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, People’s Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Yu-Dong Cai
- College of Life Science, Shanghai University, Shanghai, People’s Republic of China
- * E-mail:
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10
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Liao JM, Wang YT, Lin CLS. A fragment-based docking simulation for investigating peptide–protein bindings. Phys Chem Chem Phys 2017; 19:10436-10442. [DOI: 10.1039/c6cp07136h] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
We developed a fragment-based docking strategy for long peptide docking simulations, which separates a long peptide into halves for docking, and then recombined to rebuild whole-peptide docking conformations. With further screening, optimizations and MM/GBSA scoring, our method was capable of efficiently predicting the near-native peptide binding conformations.
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Affiliation(s)
- Jun-min Liao
- Graduate School of Medicine
- Kaohsiung Medical University
- Taiwan
| | - Yeng-Tseng Wang
- Department of Biochemistry
- Kaohsiung Medical University
- Taiwan
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Ma N, Yang GZ, Liu XW, Yang YJ, Mohamed I, Liu GR, Li JY. Impact of Aspirin Eugenol Ester on Cyclooxygenase-1, Cyclooxygenase-2, C-Reactive Protein, Prothrombin and Arachidonate 5-Lipoxygenase in Healthy Rats. IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH : IJPR 2017; 16:1443-1451. [PMID: 29552053 PMCID: PMC5843306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Aspirin eugenol ester (AEE) is a promising drug candidate which is used for the treatment of inflammation, pain, fever, and the prevention of cardiovascular diseases. This study focuses on the effect of AEE on five proteins which are related to inflammation and thrombosis, including cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2), C-reactive protein (CRP), prothrombin (FII) and arachidonate 5-lipoxygenase (ALOX5). Meanwhile, the study was administrated to compare the drug effect between AEE and its precursor from the view of chemical-protein interactions. Healthy rats were given AEE, aspirin, eugenol and integration of aspirin and eugenol. Carboxyl methyl cellulose sodium (CMC-Na) was used as control. After drugs were administered intragastrically for seven days, the blood samples were collected to measure the proteins concentration by enzyme linked immuno-sorbent assay (ELISA). The results showed that the concentrations of key endogenic bioactive enzymes were significantly reduced in AEE groups when compared with CMC-Na and aspirin groups (P < 0.01). Drug effects of AEE on five proteins were stronger than aspirin and eugenol. From the view of chemical-protein interactions, AEE had positive effects on anti-inflammation and anti-thrombosis and showed stronger effects than aspirin and eugenol.
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Affiliation(s)
- Ning Ma
- Key Lab of New Animal Drug Project of Gansu Province, Key Lab of Veterinary Pharmaceutical Development, Ministry of Agriculture, Lanzhou Institute of Husbandry and Pharmaceutical Science of CAAS, Lanzhou730050, P.R. China.
| | - Guan-Zhou Yang
- School of Pharmacy Lanzhou University, Lanzhou 730020, P.R. China.
| | - Xi-Wang Liu
- Key Lab of New Animal Drug Project of Gansu Province, Key Lab of Veterinary Pharmaceutical Development, Ministry of Agriculture, Lanzhou Institute of Husbandry and Pharmaceutical Science of CAAS, Lanzhou730050, P.R. China.
| | - Ya-Jun Yang
- Key Lab of New Animal Drug Project of Gansu Province, Key Lab of Veterinary Pharmaceutical Development, Ministry of Agriculture, Lanzhou Institute of Husbandry and Pharmaceutical Science of CAAS, Lanzhou730050, P.R. China.
| | - Isam Mohamed
- Key Lab of New Animal Drug Project of Gansu Province, Key Lab of Veterinary Pharmaceutical Development, Ministry of Agriculture, Lanzhou Institute of Husbandry and Pharmaceutical Science of CAAS, Lanzhou730050, P.R. China.
| | - Guang-Rong Liu
- Key Lab of New Animal Drug Project of Gansu Province, Key Lab of Veterinary Pharmaceutical Development, Ministry of Agriculture, Lanzhou Institute of Husbandry and Pharmaceutical Science of CAAS, Lanzhou730050, P.R. China.
| | - Jian-Yong Li
- Key Lab of New Animal Drug Project of Gansu Province, Key Lab of Veterinary Pharmaceutical Development, Ministry of Agriculture, Lanzhou Institute of Husbandry and Pharmaceutical Science of CAAS, Lanzhou730050, P.R. China.
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12
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Zhang J, Yang J, Huang T, Shu Y, Chen L. Identification of novel proliferative diabetic retinopathy related genes on protein–protein interaction network. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.09.136] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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13
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The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life. PLoS One 2016; 11:e0165496. [PMID: 27780226 PMCID: PMC5079577 DOI: 10.1371/journal.pone.0165496] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2016] [Accepted: 10/12/2016] [Indexed: 02/07/2023] Open
Abstract
A drug's biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives.
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14
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Zhu L, Chen X, Kong X, Cai YD. Investigation of the roles of trace elements during hepatitis C virus infection using protein-protein interactions and a shortest path algorithm. Biochim Biophys Acta Gen Subj 2016; 1860:2756-68. [PMID: 27208424 DOI: 10.1016/j.bbagen.2016.05.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 05/05/2016] [Accepted: 05/13/2016] [Indexed: 12/12/2022]
Abstract
BACKGROUND Hepatitis is a type of infectious disease that induces inflammation of the liver without pinpointing a particular pathogen or pathogenesis. Type C hepatitis, as a type of hepatitis, has been reported to induce cirrhosis and hepatocellular carcinoma within a very short amount of time. It is a great threat to human health. Some studies have revealed that trace elements are associated with infection with and immune rejection against hepatitis C virus (HCV). However, the mechanism underlying this phenomenon is still unclear. METHODS In this study, we aimed to expand our knowledge of this phenomenon by designing a computational method to identify genes that may be related to both HCV and trace element metabolic processes. The searching procedure included three stages. First, a shortest path algorithm was applied to a large network, constructed by protein-protein interactions, to identify potential genes of interest. Second, a permutation test was executed to exclude false discoveries. Finally, some rules based on the betweenness and associations between candidate genes and HCV and trace elements were built to select core genes among the remaining genes. RESULTS 12 lists of genes, corresponding to 12 types of trace elements, were obtained. These genes are deemed to be associated with HCV infection and trace elements metabolism. CONCLUSIONS The analyses indicate that some genes may be related to both HCV and trace element metabolic processes, further confirming the associations between HCV and trace elements. The method was further tested on another set of HCV genes, the results indicate that this method is quite robustness. GENERAL SIGNIFICANCE The newly found genes may partially reveal unknown mechanisms between HCV infection and trace element metabolism. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
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Affiliation(s)
- LiuCun Zhu
- School of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China
| | - XiJia Chen
- School of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China
| | - Xiangyin Kong
- The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200025, People's Republic of China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China.
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15
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Chen L, Zhang YH, Zou Q, Chu C, Ji Z. Analysis of the chemical toxicity effects using the enrichment of Gene Ontology terms and KEGG pathways. Biochim Biophys Acta Gen Subj 2016; 1860:2619-26. [PMID: 27208425 DOI: 10.1016/j.bbagen.2016.05.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 04/25/2016] [Accepted: 05/13/2016] [Indexed: 02/06/2023]
Abstract
BACKGROUND Chemical toxicity is one of the major barriers for designing and detecting new chemical entities during drug discovery. Unexpected toxicity of an approved drug may lead to withdrawal from the market and significant loss of the associated costs. Better understanding of the mechanisms underlying various toxicity effects can help eliminate unqualified candidate drugs in early stages, allowing researchers to focus their attention on other more viable candidates. METHODS In this study, we aimed to understand the mechanisms underlying several toxicity effects using Gene Ontology (GO) terms and KEGG pathways. GO term and KEGG pathway enrichment theories were adopted to encode each chemical, and the minimum redundancy maximum relevance (mRMR) was used to analyze the GO terms and the KEGG pathways. Based on the feature list obtained by the mRMR method, the most related GO terms and KEGG pathways were extracted. RESULTS Some important GO terms and KEGG pathways were uncovered, which were concluded to be significant for determining chemical toxicity effects. CONCLUSIONS Several GO terms and KEGG pathways are highly related to all investigated toxicity effects, while some are specific to a certain toxicity effect. GENERAL SIGNIFICANCE The findings in this study have the potential to further our understanding of different chemical toxicity mechanisms and to assist scientists in developing new chemical toxicity prediction algorithms. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China.
| | - Yu-Hang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China.
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, People's Republic of China.
| | - Chen Chu
- Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China.
| | - Zhiliang Ji
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, People's Republic of China.
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16
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Liu L, Chen L, Zhang YH, Wei L, Cheng S, Kong X, Zheng M, Huang T, Cai YD. Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection. J Biomol Struct Dyn 2016; 35:312-329. [PMID: 26750516 DOI: 10.1080/07391102.2016.1138142] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Drug-drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.
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Affiliation(s)
- Lili Liu
- a Intelligence Research Department, Information Center , Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203 , P. R. China
| | - Lei Chen
- b College of Information Engineering, Shanghai Maritime University , Shanghai 201306 , P. R. China
| | - Yu-Hang Zhang
- c Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031 , P. R. China
| | - Lai Wei
- b College of Information Engineering, Shanghai Maritime University , Shanghai 201306 , P. R. China
| | - Shiwen Cheng
- b College of Information Engineering, Shanghai Maritime University , Shanghai 201306 , P. R. China
| | - Xiangyin Kong
- c Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031 , P. R. China
| | - Mingyue Zheng
- d State Key Laboratory of Drug Research, Drug Discovery and Design Center , Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203 , P. R. China
| | - Tao Huang
- c Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031 , P. R. China
| | - Yu-Dong Cai
- e School of Life Sciences, Shanghai University , Shanghai 200444 , P. R. China
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17
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Lu J, Chen L, Yin J, Huang T, Bi Y, Kong X, Zheng M, Cai YD. Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm. J Biomol Struct Dyn 2016; 34:906-17. [PMID: 26849843 DOI: 10.1080/07391102.2015.1060161] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.
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Affiliation(s)
- Jing Lu
- a School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong , Yantai University , Yantai , 264005 , P.R. China
| | - Lei Chen
- b College of Information Engineering , Shanghai Maritime University , Shanghai 201306 , P.R. China
| | - Jun Yin
- b College of Information Engineering , Shanghai Maritime University , Shanghai 201306 , P.R. China
| | - Tao Huang
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Yi Bi
- a School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong , Yantai University , Yantai , 264005 , P.R. China
| | - Xiangyin Kong
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Mingyue Zheng
- d Drug Discovery and Design Center , Shanghai Institute of Materia Medica , Shanghai 201203 , P.R. China
| | - Yu-Dong Cai
- e College of Life Science , Shanghai University , Shanghai 200444 , P.R. China
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18
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Chen L, Yang J, Huang T, Kong X, Lu L, Cai YD. Mining for novel tumor suppressor genes using a shortest path approach. J Biomol Struct Dyn 2015. [PMID: 26209080 DOI: 10.1080/07391102.2015.1042915] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Cancer, being among the most serious diseases, causes many deaths every year. Many investigators have devoted themselves to designing effective treatments for this disease. Cancer always involves abnormal cell growth with the potential to invade or spread to other parts of the body. In contrast, tumor suppressor genes (TSGs) act as guardians to prevent a disordered cell cycle and genomic instability in normal cells. Studies on TSGs can assist in the design of effective treatments against cancer. In this study, we propose a computational method to discover potential TSGs. Based on the known TSGs, a number of candidate genes were selected by applying the shortest path approach in a weighted graph that was constructed using protein-protein interaction network. The analysis of selected genes shows that some of them are new TSGs recently reported in the literature, while others may be novel TSGs.
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Affiliation(s)
- Lei Chen
- a College of Life Science , Shanghai University , Shanghai 200444 , P.R. China.,b College of Information Engineering , Shanghai Maritime University , Shanghai 201306 , P.R. China
| | - Jing Yang
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Tao Huang
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Xiangyin Kong
- c The Key Laboratory of Stem Cell Biology , Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS) , Shanghai 200025 , P.R. China
| | - Lin Lu
- d Department of Radiology , Columbia University Medical Center , New York , NY 10032 , USA
| | - Yu-Dong Cai
- a College of Life Science , Shanghai University , Shanghai 200444 , P.R. China
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19
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Identification of Chemical Toxicity Using Ontology Information of Chemicals. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:246374. [PMID: 26508991 PMCID: PMC4609800 DOI: 10.1155/2015/246374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Revised: 03/20/2015] [Accepted: 03/22/2015] [Indexed: 12/26/2022]
Abstract
With the advance of the combinatorial chemistry, a large number of synthetic compounds have surged. However, we have limited knowledge about them. On the other hand, the speed of designing new drugs is very slow. One of the key causes is the unacceptable toxicities of chemicals. If one can correctly identify the toxicity of chemicals, the unsuitable chemicals can be discarded in early stage, thereby accelerating the study of new drugs and reducing the R&D costs. In this study, a new prediction method was built for identification of chemical toxicities, which was based on ontology information of chemicals. By comparing to a previous method, our method is quite effective. We hope that the proposed method may give new insights to study chemical toxicity and other attributes of chemicals.
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20
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Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:462363. [PMID: 26504486 PMCID: PMC4609422 DOI: 10.1155/2015/462363] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2015] [Accepted: 02/24/2015] [Indexed: 12/26/2022]
Abstract
Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer.
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21
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Chen L, Chu C, Lu J, Kong X, Huang T, Cai YD. A computational method for the identification of new candidate carcinogenic and non-carcinogenic chemicals. MOLECULAR BIOSYSTEMS 2015; 11:2541-50. [PMID: 26194467 DOI: 10.1039/c5mb00276a] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cancer is one of the leading causes of human death. Based on current knowledge, one of the causes of cancer is exposure to toxic chemical compounds, including radioactive compounds, dioxin, and arsenic. The identification of new carcinogenic chemicals may warn us of potential danger and help to identify new ways to prevent cancer. In this study, a computational method was proposed to identify potential carcinogenic chemicals, as well as non-carcinogenic chemicals. According to the current validated carcinogenic and non-carcinogenic chemicals from the CPDB (Carcinogenic Potency Database), the candidate chemicals were searched in a weighted chemical network constructed according to chemical-chemical interactions. Then, the obtained candidate chemicals were further selected by a randomization test and information on chemical interactions and structures. The analyses identified several candidate carcinogenic chemicals, while those candidates identified as non-carcinogenic were supported by a literature search. In addition, several candidate carcinogenic/non-carcinogenic chemicals exhibit structural dissimilarity with validated carcinogenic/non-carcinogenic chemicals.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China.
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22
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Gao YF, Yuan F, Liu J, Li LP, He YC, Gao RJ, Cai YD, Jiang Y. Identification of New Candidate Genes and Chemicals Related to Esophageal Cancer Using a Hybrid Interaction Network of Chemicals and Proteins. PLoS One 2015; 10:e0129474. [PMID: 26058041 PMCID: PMC4461353 DOI: 10.1371/journal.pone.0129474] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 05/10/2015] [Indexed: 01/04/2023] Open
Abstract
Cancer is a serious disease responsible for many deaths every year in both developed and developing countries. One reason is that the mechanisms underlying most types of cancer are still mysterious, creating a great block for the design of effective treatments. In this study, we attempted to clarify the mechanism underlying esophageal cancer by searching for novel genes and chemicals. To this end, we constructed a hybrid network containing both proteins and chemicals, and generalized an existing computational method previously used to identify disease genes to identify new candidate genes and chemicals simultaneously. Based on jackknife test, our generalized method outperforms or at least performs at the same level as those obtained by a widely used method - the Random Walk with Restart (RWR). The analysis results of the final obtained genes and chemicals demonstrated that they highly shared gene ontology (GO) terms and KEGG pathways with direct and indirect associations with esophageal cancer. In addition, we also discussed the likelihood of selected candidate genes and chemicals being novel genes and chemicals related to esophageal cancer.
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Affiliation(s)
- Yu-Fei Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Fei Yuan
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of China
| | - Junbao Liu
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Li-Peng Li
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Yi-Chun He
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Ru-Jian Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
| | - Yu-Dong Cai
- College of Life Science, Shanghai University, Shanghai 200444, People’s Republic of China
| | - Yang Jiang
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, People’s Republic of China
- * E-mail:
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23
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Chen L, Yang J, Zheng M, Kong X, Huang T, Cai YD. The Use of Chemical-Chemical Interaction and Chemical Structure to Identify New Candidate Chemicals Related to Lung Cancer. PLoS One 2015; 10:e0128696. [PMID: 26047514 PMCID: PMC4457841 DOI: 10.1371/journal.pone.0128696] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Accepted: 04/29/2015] [Indexed: 11/19/2022] Open
Abstract
Lung cancer causes over one million deaths every year worldwide. However, prevention and treatment methods for this serious disease are limited. The identification of new chemicals related to lung cancer may aid in disease prevention and the design of more effective treatments. This study employed a weighted network, constructed using chemical-chemical interaction information, to identify new chemicals related to two types of lung cancer: non-small lung cancer and small-cell lung cancer. Then, a randomization test as well as chemical-chemical interaction and chemical structure information were utilized to make further selections. A final analysis of these new chemicals in the context of the current literature indicates that several chemicals are strongly linked to lung cancer.
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Affiliation(s)
- Lei Chen
- College of Life Science, Shanghai University, Shanghai, 200444, People’s Republic of China
- College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China
| | - Jing Yang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China
| | - Mingyue Zheng
- Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Shanghai, 201203, People’s Republic of China
| | - Xiangyin Kong
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China
- * E-mail: (TH); (YDC)
| | - Yu-Dong Cai
- College of Life Science, Shanghai University, Shanghai, 200444, People’s Republic of China
- * E-mail: (TH); (YDC)
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24
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Chen L, Chu C, Lu J, Kong X, Huang T, Cai YD. Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. PLoS One 2015; 10:e0126492. [PMID: 25951454 PMCID: PMC4423955 DOI: 10.1371/journal.pone.0126492] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 04/02/2015] [Indexed: 12/22/2022] Open
Abstract
Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural similarities rather than biological relevance. In this study, we proposed for the first time a "GO and KEGG enrichment score" method to represent a certain category of drug molecules by further classification and interpretation of the DTI database. A benchmark dataset consisting of 2,015 drugs that are assigned to nine categories ((1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens) was constructed by collecting data from KEGG. We analyzed each category and each drug for its contribution in GO terms and KEGG pathways using the popular feature selection "minimum redundancy maximum relevance (mRMR)" method, and key GO terms and KEGG pathways were extracted. Our analysis revealed the top enriched GO terms and KEGG pathways of each drug category, which were highly enriched in the literature and clinical trials. Our results provide for the first time the biological relevance among drugs, targets and biological functions, which serves as a new basis for future DTI predictions.
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Affiliation(s)
- Lei Chen
- College of Life Science, Shanghai University, Shanghai, People’s Republic of China
- College of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China
| | - Chen Chu
- Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Jing Lu
- Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Shandong, Yantai, People’s Republic of China
| | - Xiangyin Kong
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Yu-Dong Cai
- College of Life Science, Shanghai University, Shanghai, People’s Republic of China
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25
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Chen L, Chu C, Huang T, Kong X, Cai YD. Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models. Amino Acids 2015; 47:1485-93. [PMID: 25894890 DOI: 10.1007/s00726-015-1974-5] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 03/27/2015] [Indexed: 12/26/2022]
Abstract
Cell-penetrating peptides, a group of short peptides, can traverse cell membranes to enter cells and thus facilitate the uptake of various molecular cargoes. Thus, they have the potential to become powerful drug delivery systems. The correct identification of peptides as cell-penetrating or non-cell-penetrating would accelerate this application. In this study, we determined which features were important for a peptide to be cell-penetrating or non-cell-penetrating and built a predictive model based on the key features extracted from this analysis. The investigated peptides were retrieved from a previous study, and each was encoded as a numeric vector according to six properties of amino acids-amino acid frequency, codon diversity, electrostatic charge, molecular volume, polarity, and secondary structure-by the pseudo-amino acid composition method. Methods of minimum redundancy maximum relevance and incremental feature selection were then employed to analyze these features, and some were found to be key determinants of cell penetration. In parallel, an optimal random forest prediction model was built. We hope that our findings will provide new resources for the study of cell-penetrating peptides.
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Affiliation(s)
- Lei Chen
- College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China,
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26
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Jiang Y, Zhang P, Li LP, He YC, Gao RJ, Gao YF. Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm. BIOMED RESEARCH INTERNATIONAL 2015; 2015:964795. [PMID: 25874234 PMCID: PMC4385622 DOI: 10.1155/2015/964795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2014] [Accepted: 12/05/2014] [Indexed: 02/07/2023]
Abstract
Thyroid cancer is a typical endocrine malignancy. In the past three decades, the continued growth of its incidence has made it urgent to design effective treatments to treat this disease. To this end, it is necessary to uncover the mechanism underlying this disease. Identification of thyroid cancer-related genes and chemicals is helpful to understand the mechanism of thyroid cancer. In this study, we generalized some previous methods to discover both disease genes and chemicals. The method was based on shortest path algorithm and applied to discover novel thyroid cancer-related genes and chemicals. The analysis of the final obtained genes and chemicals suggests that some of them are crucial to the formation and development of thyroid cancer. It is indicated that the proposed method is effective for the discovery of novel disease genes and chemicals.
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Affiliation(s)
- Yang Jiang
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Peiwei Zhang
- The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li-Peng Li
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Yi-Chun He
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Ru-jian Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - Yu-Fei Gao
- Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
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Vu TH, Le Lamer AC, Lalli C, Boustie J, Samson M, Lohézic-Le Dévéhat F, Le Seyec J. Depsides: lichen metabolites active against hepatitis C virus. PLoS One 2015; 10:e0120405. [PMID: 25793970 PMCID: PMC4368788 DOI: 10.1371/journal.pone.0120405] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Accepted: 01/25/2015] [Indexed: 11/18/2022] Open
Abstract
A thorough phytochemical study of Stereocaulon evolutum was conducted, for the isolation of structurally related atranorin derivatives. Indeed, pilot experiments suggested that atranorin (1), the main metabolite of this lichen, would interfere with the lifecycle of hepatitis C virus (HCV). Eight compounds, including one reported for the first time (2), were isolated and characterized. Two analogs (5, 6) were also synthesized, to enlarge the panel of atranorin-related structures. Most of these compounds were active against HCV, with a half-maximal inhibitory concentration of about 10 to 70 µM, with depsides more potent than monoaromatic phenols. The most effective inhibitors (1, 5 and 6) were then added at different steps of the HCV lifecycle. Interestingly, atranorin (1), bearing an aldehyde function at C-3, inhibited only viral entry, whereas the synthetic compounds 5 and 6, bearing a hydroxymethyl and a methyl function, respectively, at C-3 interfered with viral replication.
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Affiliation(s)
- Thi Huyen Vu
- CNRS, UMR-6226, Institut des Sciences Chimiques de Rennes (ISCR), Rennes, France
- Université de Rennes 1, Rennes, France
| | - Anne-Cécile Le Lamer
- CNRS, UMR-6226, Institut des Sciences Chimiques de Rennes (ISCR), Rennes, France
- Université de Rennes 1, Rennes, France
- Université de Toulouse III, Toulouse, France
| | - Claudia Lalli
- CNRS, UMR-6226, Institut des Sciences Chimiques de Rennes (ISCR), Rennes, France
- Université de Rennes 1, Rennes, France
| | - Joël Boustie
- CNRS, UMR-6226, Institut des Sciences Chimiques de Rennes (ISCR), Rennes, France
- Université de Rennes 1, Rennes, France
| | - Michel Samson
- Université de Rennes 1, Rennes, France
- INSERM, UMR-1085, Institut de Recherche Santé Environnement & Travail (IRSET), Rennes, France
- Fédération de Recherche BioSit de Rennes, Rennes, France
| | - Françoise Lohézic-Le Dévéhat
- CNRS, UMR-6226, Institut des Sciences Chimiques de Rennes (ISCR), Rennes, France
- Université de Rennes 1, Rennes, France
- * E-mail: (FLLD); (JLS)
| | - Jacques Le Seyec
- Université de Rennes 1, Rennes, France
- INSERM, UMR-1085, Institut de Recherche Santé Environnement & Travail (IRSET), Rennes, France
- Fédération de Recherche BioSit de Rennes, Rennes, France
- * E-mail: (FLLD); (JLS)
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Prediction of drug indications based on chemical interactions and chemical similarities. BIOMED RESEARCH INTERNATIONAL 2015; 2015:584546. [PMID: 25821813 PMCID: PMC4363546 DOI: 10.1155/2015/584546] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2014] [Accepted: 09/11/2014] [Indexed: 12/13/2022]
Abstract
Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.
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Chen L, Chu C, Kong X, Huang T, Cai YD. Discovery of new candidate genes related to brain development using protein interaction information. PLoS One 2015; 10:e0118003. [PMID: 25635857 PMCID: PMC4311913 DOI: 10.1371/journal.pone.0118003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Accepted: 01/03/2015] [Indexed: 12/18/2022] Open
Abstract
Human brain development is a dramatic process composed of a series of complex and fine-tuned spatiotemporal gene expressions. A good comprehension of this process can assist us in developing the potential of our brain. However, we have only limited knowledge about the genes and gene functions that are involved in this biological process. Therefore, a substantial demand remains to discover new brain development-related genes and identify their biological functions. In this study, we aimed to discover new brain-development related genes by building a computational method. We referred to a series of computational methods used to discover new disease-related genes and developed a similar method. In this method, the shortest path algorithm was executed on a weighted graph that was constructed using protein-protein interactions. New candidate genes fell on at least one of the shortest paths connecting two known genes that are related to brain development. A randomization test was then adopted to filter positive discoveries. Of the final identified genes, several have been reported to be associated with brain development, indicating the effectiveness of the method, whereas several of the others may have potential roles in brain development.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People’s Republic of China
| | - Chen Chu
- Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of China
| | - Xiangyin Kong
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, People’s Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, People’s Republic of China
- * E-mail: (TH); (YDC)
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai 200444, People’s Republic of China
- * E-mail: (TH); (YDC)
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