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Nayar G, Altman RB. Heterogeneous network approaches to protein pathway prediction. Comput Struct Biotechnol J 2024; 23:2727-2739. [PMID: 39035835 PMCID: PMC11260399 DOI: 10.1016/j.csbj.2024.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/23/2024] Open
Abstract
Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.
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Affiliation(s)
- Gowri Nayar
- Department of Biomedical Data Science, Stanford University, United States
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, United States
- Department of Genetics, Stanford University, United States
- Department of Medicine, Stanford University, United States
- Department of Bioengineering, Stanford University, United States
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2
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González-Esparragoza D, Carrasco-Carballo A, Rosas-Murrieta NH, Millán-Pérez Peña L, Luna F, Herrera-Camacho I. In Silico Analysis of Protein-Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe. Curr Issues Mol Biol 2024; 46:4609-4629. [PMID: 38785548 PMCID: PMC11120530 DOI: 10.3390/cimb46050280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/26/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Ermp1 is a putative metalloprotease from Schizosaccharomyces pombe and a member of the Fxna peptidases. Although their function is unknown, orthologous proteins from rats and humans have been associated with the maturation of ovarian follicles and increased ER stress. This study focuses on proposing the first prediction of PPI by comparison of the interologues between humans and yeasts, as well as the molecular docking and dynamics of the M28 domain of Ermp1 with possible target proteins. As results, 45 proteins are proposed that could interact with the metalloprotease. Most of these proteins are related to the transport of Ca2+ and the metabolism of amino acids and proteins. Docking and molecular dynamics suggest that the M28 domain of Ermp1 could hydrolyze leucine and methionine residues of Amk2, Ypt5 and Pex12. These results could support future experimental investigations of other Fxna peptidases, such as human ERMP1.
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Affiliation(s)
- Dalia González-Esparragoza
- Laboratorio de Bioquímica y Biología Molecular, Centro de Química del Instituto de Ciencias (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico; (D.G.-E.); (N.H.R.-M.); (L.M.-P.P.)
- Laboratorio de Elucidación y Síntesis en Química Orgánica, Instituto de Ciencias de la Universidad Autónoma de Puebla (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico
| | - Alan Carrasco-Carballo
- Laboratorio de Elucidación y Síntesis en Química Orgánica, Instituto de Ciencias de la Universidad Autónoma de Puebla (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico
- Consejo Nacional de Humanidades Ciencia y Tecnología, Instituto de Ciencias de la Universidad Autónoma de Puebla (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico
| | - Nora H. Rosas-Murrieta
- Laboratorio de Bioquímica y Biología Molecular, Centro de Química del Instituto de Ciencias (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico; (D.G.-E.); (N.H.R.-M.); (L.M.-P.P.)
| | - Lourdes Millán-Pérez Peña
- Laboratorio de Bioquímica y Biología Molecular, Centro de Química del Instituto de Ciencias (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico; (D.G.-E.); (N.H.R.-M.); (L.M.-P.P.)
| | - Felix Luna
- Laboratorio de Neuroendocrinología, Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico;
| | - Irma Herrera-Camacho
- Laboratorio de Bioquímica y Biología Molecular, Centro de Química del Instituto de Ciencias (ICUAP), Benemérita Universidad Autónoma de Puebla, Puebla 72570, Mexico; (D.G.-E.); (N.H.R.-M.); (L.M.-P.P.)
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3
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Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods Mol Biol 2024; 2719:227-248. [PMID: 37803121 DOI: 10.1007/978-1-0716-3461-5_13] [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] [Indexed: 10/08/2023]
Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
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Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
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4
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Jarończyk M, Abagyan R, Totrov M. Software and Databases for Protein-Protein Docking. Methods Mol Biol 2024; 2780:129-138. [PMID: 38987467 DOI: 10.1007/978-1-0716-3985-6_8] [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] [Indexed: 07/12/2024]
Abstract
Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.
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Affiliation(s)
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
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5
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Esmaeilzadeh AA, Kashian M, Salman HM, Alsaffar MF, Jaber MM, Soltani S, Amiri Manjili D, Ilhan A, Bahrami A, Kastelic JW. Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing. BIOLOGY 2022; 11:1851. [PMID: 36552360 PMCID: PMC9776135 DOI: 10.3390/biology11121851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.
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Affiliation(s)
| | - Mahdis Kashian
- Department of Obstetrics and Gynecology, Medical College of Iran University, Tehran 14535, Iran;
| | - Hayder Mahmood Salman
- Department of Computer Science, Al-Turath University College Al Mansour, Baghdad 10011, Iraq;
| | - Marwa Fadhil Alsaffar
- Medical Laboratory Techniques Department, AL-Mustaqbal University College, Hillah 51001, Iraq;
| | - Mustafa Musa Jaber
- Computer Techniques Engineering Department, Dijlah University College, Baghdad 00964, Iraq;
- Computer Techniques Engineering Department, Al-Farahidi University, Baghdad 10011, Iraq
| | - Siamak Soltani
- Department of Forensic Medicine, School of Medicine, Iran University of Medical Sciences, Tehran 14535, Iran;
| | - Danial Amiri Manjili
- Department of Infectious Disease, School of Medicine, Babol University of Medical Sciences, Babol 47414, Iran
| | - Ahmet Ilhan
- Department of Medical Biochemistry, Faculty of Medicine, Cukurova University, Adana 01330, Turkey
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 1417643184, Iran;
- Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, 80333 Munich, Germany
| | - John W. Kastelic
- Department of Health, University of Calgary, Calgary, AB T2N 1N4, Canada;
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6
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Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications. Int J Mol Sci 2022; 23:ijms232213869. [PMID: 36430344 PMCID: PMC9692470 DOI: 10.3390/ijms232213869] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/05/2022] [Accepted: 11/07/2022] [Indexed: 11/12/2022] Open
Abstract
Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel therapeutic approaches are urgently needed. In this study, we utilized the systems biology method via genome-wide microarray data to explore the oncogenic mechanisms of MIBC and ABC to identify their respective drug targets for systems drug discovery. First, we constructed the candidate genome-wide genetic and epigenetic networks (GWGEN) through big data mining. Second, we applied the system identification and system order detection method to delete false positives in candidate GWGENs to obtain the real GWGENs of MIBC and ABC from their genome-wide microarray data. Third, we extracted the core GWGENs from the real GWGENs by selecting the significant proteins, genes and epigenetics via the principal network projection (PNP) method. Finally, we obtained the core signaling pathways from the corresponding core GWGEN through the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to investigate the carcinogenic mechanisms of MIBC and ABC. Based on the carcinogenic mechanisms, we selected the significant drug targets NFKB1, LEF1 and MYC for MIBC, and LEF1, MYC, NOTCH1 and FOXO1 for ABC. To design molecular drug combinations for MIBC and ABC, we employed a deep neural network (DNN)-based drug-target interaction (DTI) model with drug specifications. The DNN-based DTI model was trained by drug-target interaction databases to predict the candidate drugs for MIBC and ABC, respectively. Subsequently, the drug design specifications based on regulation ability, sensitivity and toxicity were employed as filter criteria for screening the potential drug combinations of Embelin and Obatoclax for MIBC, and Obatoclax, Entinostat and Imiquimod for ABC from their candidate drugs. In conclusion, we not only investigated the oncogenic mechanisms of MIBC and ABC, but also provided promising therapeutic options for MIBC and ABC, respectively.
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7
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Sadeghi M, Bahrami A, Hasankhani A, Kioumarsi H, Nouralizadeh R, Abdulkareem SA, Ghafouri F, Barkema HW. lncRNA-miRNA-mRNA ceRNA Network Involved in Sheep Prolificacy: An Integrated Approach. Genes (Basel) 2022; 13:genes13081295. [PMID: 35893032 PMCID: PMC9332185 DOI: 10.3390/genes13081295] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 02/06/2023] Open
Abstract
Understanding the molecular pattern of fertility is considered as an important step in breeding of different species, and despite the high importance of the fertility, little success has been achieved in dissecting the interactome basis of sheep fertility. However, the complex mechanisms associated with prolificacy in sheep have not been fully understood. Therefore, this study aimed to use competitive endogenous RNA (ceRNA) networks to evaluate this trait to better understand the molecular mechanisms responsible for fertility. A competitive endogenous RNA (ceRNA) network of the corpus luteum was constructed between Romanov and Baluchi sheep breeds with either good or poor genetic merit for prolificacy using whole-transcriptome analysis. First, the main list of lncRNAs, miRNAs, and mRNA related to the corpus luteum that alter with the breed were extracted, then miRNA−mRNA and lncRNA−mRNA interactions were predicted, and the ceRNA network was constructed by integrating these interactions with the other gene regulatory networks and the protein−protein interaction (PPI). A total of 264 mRNAs, 14 lncRNAs, and 34 miRNAs were identified by combining the GO and KEGG enrichment analyses. In total, 44, 7, 7, and 6 mRNAs, lncRNAs, miRNAs, and crucial modules, respectively, were disclosed through clustering for the corpus luteum ceRNA network. All these RNAs involved in biological processes, namely proteolysis, actin cytoskeleton organization, immune system process, cell adhesion, cell differentiation, and lipid metabolic process, have an overexpression pattern (Padj < 0.01). This study increases our understanding of the contribution of different breed transcriptomes to phenotypic fertility differences and constructed a ceRNA network in sheep (Ovis aries) to provide insights into further research on the molecular mechanism and identify new biomarkers for genetic improvement.
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Affiliation(s)
- Masoumeh Sadeghi
- Environmental Health, Zahedan University of Medical Sciences, Zahedan 98, Iran;
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 31, Iran; (A.H.); (F.G.)
- Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, 80333 Munich, Germany
- Correspondence: (A.B.); (R.N.); Tel.: +98-9199300065 (A.B.)
| | - Aliakbar Hasankhani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 31, Iran; (A.H.); (F.G.)
| | - Hamed Kioumarsi
- Department of Animal Science Research, Gilan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Rasht 43, Iran;
| | - Reza Nouralizadeh
- Department of Food and Drug Control, Faculty of Pharmacy, Jundishapour University of Medical Sciences, Ahvaz 63, Iran
- Correspondence: (A.B.); (R.N.); Tel.: +98-9199300065 (A.B.)
| | - Sarah Ali Abdulkareem
- Department of Computer Science, Al-Turath University College, Al Mansour, Baghdad 10011, Iraq;
| | - Farzad Ghafouri
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 31, Iran; (A.H.); (F.G.)
| | - Herman W. Barkema
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N4Z6, Canada;
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8
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Hephzibah Cathryn R, Udhaya Kumar S, Younes S, Zayed H, George Priya Doss C. A review of bioinformatics tools and web servers in different microarray platforms used in cancer research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:85-164. [PMID: 35871897 DOI: 10.1016/bs.apcsb.2022.05.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.
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Affiliation(s)
- R Hephzibah Cathryn
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - S Udhaya Kumar
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Salma Younes
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - Hatem Zayed
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
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9
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Tandon G, Yadav S, Kaur S. Pathway modeling and simulation analysis. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00007-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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10
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Paul M, Anand A. A New Family of Similarity Measures for Scoring Confidence of Protein Interactions Using Gene Ontology. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:19-30. [PMID: 34029194 DOI: 10.1109/tcbb.2021.3083150] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The large-scale protein-protein interaction (PPI) data has the potential to play a significant role in the endeavor of understanding cellular processes. However, the presence of a considerable fraction of false positives is a bottleneck in realizing this potential. There have been continuous efforts to utilize complementary resources for scoring confidence of PPIs in a manner that false positive interactions get a low confidence score. Gene Ontology (GO), a taxonomy of biological terms to represent the properties of gene products and their relations, has been widely used for this purpose. We utilize GO to introduce a new set of specificity measures: Relative Depth Specificity (RDS), Relative Node-based Specificity (RNS), and Relative Edge-based Specificity (RES), leading to a new family of similarity measures. We use these similarity measures to obtain a confidence score for each PPI. We evaluate the new measures using four different benchmarks. We show that all the three measures are quite effective. Notably, RNS and RES more effectively distinguish true PPIs from false positives than the existing alternatives. RES also shows a robust set-discriminating power and can be useful for protein functional clustering as well.
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11
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Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
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Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
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12
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Kan Y, Jiang L, Guo Y, Tang J, Guo F. Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes. Brief Bioinform 2021; 23:6426028. [PMID: 34791034 DOI: 10.1093/bib/bbab429] [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: 07/25/2021] [Revised: 08/30/2021] [Accepted: 09/18/2021] [Indexed: 11/14/2022] Open
Abstract
Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.
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Affiliation(s)
- Yingxin Kan
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Limin Jiang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yan Guo
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, U.S
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China
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13
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Mulas R, Sánchez-García RJ, MacArthur BD. Geometry and symmetry in biochemical reaction systems. Theory Biosci 2021; 140:265-277. [PMID: 34268705 PMCID: PMC8568762 DOI: 10.1007/s12064-021-00353-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/06/2021] [Indexed: 12/02/2022]
Abstract
Complex systems of intracellular biochemical reactions have a central role in regulating cell identities and functions. Biochemical reaction systems are typically studied using the language and tools of graph theory. However, graph representations only describe pairwise interactions between molecular species and so are not well suited to modelling complex sets of reactions that may involve numerous reactants and/or products. Here, we make use of a recently developed hypergraph theory of chemical reactions that naturally allows for higher-order interactions to explore the geometry and quantify functional redundancy in biochemical reactions systems. Our results constitute a general theory of automorphisms for oriented hypergraphs and describe the effect of automorphism group structure on hypergraph Laplacian spectra.
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Affiliation(s)
- Raffaella Mulas
- Mathematical Sciences, University of Southampton, Southampton, UK. .,Institute of Life Sciences, University of Southampton, Southampton, UK. .,The Alan Turing Institute, London, UK.
| | - Rubén J Sánchez-García
- Mathematical Sciences, University of Southampton, Southampton, UK.,Institute of Life Sciences, University of Southampton, Southampton, UK.,The Alan Turing Institute, London, UK
| | - Ben D MacArthur
- Mathematical Sciences, University of Southampton, Southampton, UK.,Institute of Life Sciences, University of Southampton, Southampton, UK.,The Alan Turing Institute, London, UK
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14
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Databases for Protein-Protein Interactions. Methods Mol Biol 2021; 2361:229-248. [PMID: 34236665 DOI: 10.1007/978-1-0716-1641-3_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Protein-protein interaction networks have a crucial role in biological processes. Proteins perform multiple functions in forming physical and functional interactions in cellular systems. Information concerning an enormous number of protein interactions in a wide range of species has accumulated and has been integrated into various resources for molecular biology and systems biology. This chapter provides a review of the representative databases and the major computational methods used for protein-protein interactions.
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15
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Fluorescence resonance energy transfer in revealing protein-protein interactions in living cells. Emerg Top Life Sci 2021; 5:49-59. [PMID: 33856021 DOI: 10.1042/etls20200337] [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: 11/29/2020] [Revised: 02/22/2021] [Accepted: 03/04/2021] [Indexed: 11/17/2022]
Abstract
Genes are expressed to proteins for a wide variety of fundamental biological processes at the cellular and organismal levels. However, a protein rarely functions alone, but rather acts through interactions with other proteins to maintain normal cellular and organismal functions. Therefore, it is important to analyze the protein-protein interactions to determine functional mechanisms of proteins, which can also guide to develop therapeutic targets for treatment of diseases caused by altered protein-protein interactions leading to cellular/organismal dysfunctions. There is a large number of methodologies to study protein interactions in vitro, in vivo and in silico, which led to the development of many protein interaction databases, and thus, have enriched our knowledge about protein-protein interactions and functions. However, many of these interactions were identified in vitro, but need to be verified/validated in living cells. Furthermore, it is unclear whether these interactions are direct or mediated via other proteins. Moreover, these interactions are representative of cell- and time-average, but not a single cell in real time. Therefore, it is crucial to detect direct protein-protein interactions in a single cell during biological processes in vivo, towards understanding the functional mechanisms of proteins in living cells. Importantly, a fluorescence resonance energy transfer (FRET)-based methodology has emerged as a powerful technique to decipher direct protein-protein interactions at a single cell resolution in living cells, which is briefly described in a limited available space in this mini-review.
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Datta S, Hett EC, Vora KA, Hazuda DJ, Oslund RC, Fadeyi OO, Emili A. The chemical biology of coronavirus host-cell interactions. RSC Chem Biol 2021; 2:30-46. [PMID: 34458775 PMCID: PMC8340996 DOI: 10.1039/d0cb00197j] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 12/06/2020] [Indexed: 12/25/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the current coronavirus disease 2019 (COVID-19) pandemic that has led to a global economic disruption and collapse. With several ongoing efforts to develop vaccines and treatments for COVID-19, understanding the molecular interaction between the coronavirus, host cells, and the immune system is critical for effective therapeutic interventions. Greater insight into these mechanisms will require the contribution and combination of multiple scientific disciplines including the techniques and strategies that have been successfully deployed by chemical biology to tease apart complex biological pathways. We highlight in this review well-established strategies and methods to study coronavirus-host biophysical interactions and discuss the impact chemical biology will have on understanding these interactions at the molecular level.
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Affiliation(s)
- Suprama Datta
- Center for Network Systems Biology, Department of Biochemistry, Boston University School of Medicine Boston MA USA
| | - Erik C Hett
- Exploratory Science Center, Merck & Co., Inc. Cambridge Massachusetts USA
| | - Kalpit A Vora
- Infectious Diseases and Vaccine Research, Merck & Co., Inc. West Point Pennsylvania USA
| | - Daria J Hazuda
- Exploratory Science Center, Merck & Co., Inc. Cambridge Massachusetts USA
- Infectious Diseases and Vaccine Research, Merck & Co., Inc. West Point Pennsylvania USA
| | - Rob C Oslund
- Exploratory Science Center, Merck & Co., Inc. Cambridge Massachusetts USA
| | | | - Andrew Emili
- Center for Network Systems Biology, Department of Biochemistry, Boston University School of Medicine Boston MA USA
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Abstract
iRefWeb is a resource that provides web interface to a large collection of protein-protein interactions aggregated from major primary databases. The underlying data-consolidation process, called iRefIndex, implements a rigorous methodology of identifying redundant protein sequences and integrating disparate data records that reference the same peptide sequences, despite many potential differences in data identifiers across various source databases. iRefWeb offers a unified user interface to all interaction records and associated information collected by iRefIndex, in addition to a number of data filters and visual features that present the supporting evidence. Users of iRefWeb can explore the consolidated landscape of protein-protein interactions, establish the provenance and reliability of each data record, and compare annotations performed by different data curator teams. The iRefWeb portal is freely available at http://wodaklab.org/iRefWeb .
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Omics Integration Analyses Reveal the Early Evolution of Malignancy in Breast Cancer. Cancers (Basel) 2020; 12:cancers12061460. [PMID: 32512721 PMCID: PMC7352609 DOI: 10.3390/cancers12061460] [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: 04/12/2020] [Revised: 05/29/2020] [Accepted: 05/31/2020] [Indexed: 12/12/2022] Open
Abstract
The majority of cancer evolution studies involve individual-based approaches that neglect the population dynamics necessary to build a global picture of cancer evolution for each cancer type. Here, we conducted a population-based study in breast cancer to understand the timing of malignancy evolution and its correlation to the genetic evolution of pathological stages. In an omics integrative approach, we integrated gene expression and genomic aberration data for pre-invasive (ductal carcinoma in situ; DCIS, early-stage) and post-invasive (invasive ductal carcinoma; IDC, late-stage) samples and investigated the evolutionary role of further genetic changes in later stages compared to the early ones. We found that single gene alterations (SGAs) and copy-number alterations (CNAs) work together in forward and backward evolution manners to fine-tune the signaling pathways operating in tumors. Analyses of the integrated point mutation and gene expression data showed that (i) our proposed fine-tuning concept is also applicable to metastasis, and (ii) metastases sometimes diverge from the primary tumor at the DCIS stage. Our results indicated that the malignant potency of breast tumors is constant over the pre- and post-invasive pathological stages. Indeed, further genetic alterations in later stages do not establish de novo malignancy routes; however, they serve to fine-tune antecedent signaling pathways.
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Yeast as a Model to Understand Actin-Mediated Cellular Functions in Mammals-Illustrated with Four Actin Cytoskeleton Proteins. Cells 2020; 9:cells9030672. [PMID: 32164332 PMCID: PMC7140605 DOI: 10.3390/cells9030672] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 03/05/2020] [Accepted: 03/05/2020] [Indexed: 12/31/2022] Open
Abstract
The budding yeast Saccharomyces cerevisiae has an actin cytoskeleton that comprises a set of protein components analogous to those found in the actin cytoskeletons of higher eukaryotes. Furthermore, the actin cytoskeletons of S. cerevisiae and of higher eukaryotes have some similar physiological roles. The genetic tractability of budding yeast and the availability of a stable haploid cell type facilitates the application of molecular genetic approaches to assign functions to the various actin cytoskeleton components. This has provided information that is in general complementary to that provided by studies of the equivalent proteins of higher eukaryotes and hence has enabled a more complete view of the role of these proteins. Several human functional homologues of yeast actin effectors are implicated in diseases. A better understanding of the molecular mechanisms underpinning the functions of these proteins is critical to develop improved therapeutic strategies. In this article we chose as examples four evolutionarily conserved proteins that associate with the actin cytoskeleton: (1) yeast Hof1p/mammalian PSTPIP1, (2) yeast Rvs167p/mammalian BIN1, (3) yeast eEF1A/eEF1A1 and eEF1A2 and (4) yeast Yih1p/mammalian IMPACT. We compare the knowledge on the functions of these actin cytoskeleton-associated proteins that has arisen from studies of their homologues in yeast with information that has been obtained from in vivo studies using live animals or in vitro studies using cultured animal cell lines.
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Gemovic B, Sumonja N, Davidovic R, Perovic V, Veljkovic N. Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. Curr Med Chem 2019; 26:3890-3910. [PMID: 29446725 DOI: 10.2174/0929867325666180214113704] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 09/14/2017] [Accepted: 01/29/2018] [Indexed: 01/04/2023]
Abstract
BACKGROUND The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.
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Affiliation(s)
- Branislava Gemovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Neven Sumonja
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Radoslav Davidovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Vladimir Perovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
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Chagoyen M, Ranea JAG, Pazos F. Applications of molecular networks in biomedicine. Biol Methods Protoc 2019; 4:bpz012. [PMID: 32395629 PMCID: PMC7200821 DOI: 10.1093/biomethods/bpz012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 08/20/2019] [Accepted: 08/28/2019] [Indexed: 12/12/2022] Open
Abstract
Due to the large interdependence between the molecular components of living systems, many phenomena, including those related to pathologies, cannot be explained in terms of a single gene or a small number of genes. Molecular networks, representing different types of relationships between molecular entities, embody these large sets of interdependences in a framework that allow their mining from a systemic point of view to obtain information. These networks, often generated from high-throughput omics datasets, are used to study the complex phenomena of human pathologies from a systemic point of view. Complementing the reductionist approach of molecular biology, based on the detailed study of a small number of genes, systemic approaches to human diseases consider that these are better reflected in large and intricate networks of relationships between genes. These networks, and not the single genes, provide both better markers for diagnosing diseases and targets for treating them. Network approaches are being used to gain insight into the molecular basis of complex diseases and interpret the large datasets associated with them, such as genomic variants. Network formalism is also suitable for integrating large, heterogeneous and multilevel datasets associated with diseases from the molecular level to organismal and epidemiological scales. Many of these approaches are available to nonexpert users through standard software packages.
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Affiliation(s)
- Monica Chagoyen
- Computational Systems Biology Group, Systems Biology Program, National Centre for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Juan A G Ranea
- Department of Molecular Biology and Biochemistry, University of Malaga, Malaga, Spain
- CIBER de Enfermedades Raras, Instituto de Salud Carlos III, Madrid, Spain
| | - Florencio Pazos
- Computational Systems Biology Group, Systems Biology Program, National Centre for Biotechnology (CNB-CSIC), Madrid, Spain
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22
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Ding D, Han S, Zhang H, He Y, Li Y. Predictive biomarkers of colorectal cancer. Comput Biol Chem 2019; 83:107106. [PMID: 31542707 DOI: 10.1016/j.compbiolchem.2019.107106] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 08/01/2019] [Accepted: 08/12/2019] [Indexed: 02/08/2023]
Abstract
Colorectal cancer is one of the top leading causes of cancer mortality worldwide, especially in China. However, most of the current treatments are invasive and can only be applied to very few cancers. The earlier a malignant tumor is diagnosed, the higher the patient's survival rate. In this study, we proposed a computational framework to identify highly-reliable and easierly-detectable biomarkers capable of secreting into blood, urine and saliva by integrating transcriptomics and proteomics data at the system biology level. First, a large number of transcriptome data were processed to identify candidate biomarkers for colorectal cancer. Second, three classified models are constructed to predict biomarkers for colorectal cancer capable of secreting into blood, urine and saliva, which are effective disease diagnosis media to facilitate clinical screening. Then biological functions and molecular mechanisms of the candidate biomarkers of colorectal cancer are inferred utilizing multi-source biological knowledge and literature mining. Furthermore, the classification power of different combinations of candidate biomarkers is verified by machine learning models. In addition, the targeted drugs of the predicted biomarkers are further analyzed to provide assistance for clinical treatment of colorectal cancer. In this paper, our proposed computational model not only provides the effective candidate biomarkers ESM1, CTHRC1, AZGP1 for colorectal cancer capable of secreting into blood, urine and saliva, but also helps to understand the molecular mechanism of colorectal cancer. This computational framework can span the huge gap between transcriptome and proteomics, which can easily be applied to the biomarker research for other types of tumor.
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Affiliation(s)
- Di Ding
- College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Siyu Han
- College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Hui Zhang
- College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Ye He
- College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Ying Li
- College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
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Sabetian S, Shamsir MS. Computer aided analysis of disease linked protein networks. Bioinformation 2019; 15:513-522. [PMID: 31485137 PMCID: PMC6704336 DOI: 10.6026/97320630015513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 12/26/2022] Open
Abstract
Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of complex biological systems including network pharmacology and disease network has also been discussed in this review.
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Affiliation(s)
- Soudabeh Sabetian
- Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia
- Infertility Research Center, Shiraz University, Shiraz 71454, Iran, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohd Shahir Shamsir
- Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia
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Yu L, Gao L. Human Pathway-Based Disease Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1240-1249. [PMID: 29990107 DOI: 10.1109/tcbb.2017.2774802] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Constructing disease-disease similarity network is important in elucidating the associations between the origin and molecular mechanism of diseases, and in researching disease function and medical research. In this paper, we use a high-quality protein interaction network and a collection of pathway databases to construct a Human Pathway-based Disease Network (HPDN) to explore the relationship between diseases and their intrinsic interactions. We find that the similarity of two diseases has a strong correlation with the number of their shared functional pathways and the interaction between their related gene sets. Comparing HPDN with disease networks based on genes and symptoms respectively, we find the three networks have high overlap rates. Additionally, HPDN can predict new disease-disease correlations, which are supported by Comparative Toxicogenomics Database (CTD) benchmark and large-scale biomedical literature database. The comprehensive, high-quality relations between diseases based on pathways can further be applied to study important matters in systems medicine, for instance, drug repurposing. Based on a dense subgraph in our network, we find two drugs, prednisone and folic acid, may have new indications, which will provide potential directions for the treatments of complex diseases.
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Bechtel W. From parts to mechanisms: research heuristics for addressing heterogeneity in cancer genetics. HISTORY AND PHILOSOPHY OF THE LIFE SCIENCES 2019; 41:27. [PMID: 31240400 DOI: 10.1007/s40656-019-0266-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Accepted: 06/17/2019] [Indexed: 06/09/2023]
Abstract
A major approach to cancer research in the late twentieth century was to search for genes that, when altered, initiated the development of a cell into a cancerous state (oncogenes) or failed to stop this development (tumor suppressor genes). But as researchers acquired the capacity to sequence tumors and incorporated the resulting data into databases, it became apparent that for many tumors no genes were frequently altered and that the genes altered in different tumors in the same tissue type were often distinct. To address this heterogeneity problem, many researchers looked to a higher level of organization-to mechanisms in which gene products (proteins) participated. They proposed to reduce heterogeneity by recognizing that multiple gene alterations affect the same mechanism and that it is the altered mechanism that is responsible for the cell developing one or more hallmarks of cancer. I examine how mechanisms figure in this research and focus on two heuristics researchers use to integrate proteins into mechanisms, one focusing on pathways and one focusing on clusters in networks.
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Affiliation(s)
- William Bechtel
- Department of Philosophy, University of California, San Diego, 92093-0119, USA.
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26
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Gopalakrishnan V, Jha K, Jin W, Zhang A. A survey on literature based discovery approaches in biomedical domain. J Biomed Inform 2019; 93:103141. [PMID: 30857950 DOI: 10.1016/j.jbi.2019.103141] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 02/17/2019] [Accepted: 02/19/2019] [Indexed: 02/06/2023]
Abstract
Literature Based Discovery (LBD) refers to the problem of inferring new and interesting knowledge by logically connecting independent fragments of information units through explicit or implicit means. This area of research, which incorporates techniques from Natural Language Processing (NLP), Information Retrieval and Artificial Intelligence, has significant potential to reduce discovery time in biomedical research fields. Formally introduced in 1986, LBD has grown to be a significant and a core task for text mining practitioners in the biomedical domain. Together with its inter-disciplinary nature, this has led researchers across domains to contribute in advancing this field of study. This survey attempts to consolidate and present the evolution of techniques in this area. We cover a variety of techniques and provide a detailed description of the problem setting, the intuition, the advantages and limitations of various influential papers. We also list the current bottlenecks in this field and provide a general direction of research activities for the future. In an effort to be comprehensive and for ease of reference for off-the-shelf users, we also list many publicly available tools for LBD. We hope this survey will act as a guide to both academic and industry (bio)-informaticians, introduce the various methodologies currently employed and also the challenges yet to be tackled.
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Affiliation(s)
| | | | - Wei Jin
- University of North Texas at Denton, TX, United States.
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Yang M, Chen J, Xu L, Shi X, Zhou X, An R, Wang X. A Network Pharmacology Approach to Uncover the Molecular Mechanisms of Herbal Formula Ban-Xia-Xie-Xin-Tang. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2018; 2018:4050714. [PMID: 30410554 PMCID: PMC6206573 DOI: 10.1155/2018/4050714] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 10/03/2018] [Indexed: 02/07/2023]
Abstract
Ban-Xia-Xie-Xin-Tang (BXXXT) is a classical formula from Shang-Han-Lun which is one of the earliest books of TCM clinical practice. In this work, we investigated the therapeutic mechanisms of BXXXT for the treatment of multiple diseases using a network pharmacology approach. Here three BXXXT representative diseases (colitis, diabetes mellitus, and gastric cancer) were discussed, and we focus on in silico methods that integrate drug-likeness screening, target prioritizing, and multilayer network extending. A total of 140 core targets and 72 representative compounds were finally identified to elucidate the pharmacology of BXXXT formula. After constructing multilayer networks, a good overlap between BXXXT nodes and disease nodes was observed at each level, and the network-based proximity analysis shows that the relevance between the formula targets and disease genes was significant according to the shortest path distance (SPD) and a random walk with restart (RWR) based scores for each disease. We found that there were 22 key pathways significantly associated with BXXXT, and the therapeutic effects of BXXXT were likely addressed by regulating a combination of targets in a modular pattern. Furthermore, the synergistic effects among BXXXT herbs were highlighted by elucidating the molecular mechanisms of individual herbs, and the traditional theory of "Jun-Chen-Zuo-Shi" of TCM formula was effectively interpreted from a network perspective. The proposed approach provides an effective strategy to uncover the mechanisms of action and combinatorial rules of BXXXT formula in a holistic manner.
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Affiliation(s)
- Ming Yang
- Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai, China
- Department of Chemistry, College of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jialei Chen
- Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai, China
| | - Liwen Xu
- Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai, China
| | - Xiufeng Shi
- Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai, China
| | - Xin Zhou
- Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai, China
| | - Rui An
- Department of Chemistry, College of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xinhong Wang
- Department of Chemistry, College of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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28
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Göktepe YE, Kodaz H. Prediction of Protein-Protein Interactions Using An Effective Sequence Based Combined Method. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.03.062] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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29
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Lagarde N, Carbone A, Sacquin-Mora S. Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions. Proteins 2018; 86:723-737. [DOI: 10.1002/prot.25506] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 03/23/2018] [Accepted: 04/07/2018] [Indexed: 02/06/2023]
Affiliation(s)
- Nathalie Lagarde
- Laboratoire de Biochimie Théorique, CNRS UPR9080, Institut de Biologie Physico-Chimique, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie; Paris 75005 France
| | - Alessandra Carbone
- Laboratoire de Biologie Computationnelle et Quantitative, CNRS UMR7238, UPMC Univ-Paris 6, Sorbonne Université, 4 place Jussieu; Paris 75005 France
- Institut Universitaire de France; Paris 75005 France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS UPR9080, Institut de Biologie Physico-Chimique, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie; Paris 75005 France
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30
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Wang YB, You ZH, Li X, Jiang TH, Chen X, Zhou X, Wang L. Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network. MOLECULAR BIOSYSTEMS 2018; 13:1336-1344. [PMID: 28604872 DOI: 10.1039/c7mb00188f] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. Although high-throughput technologies provide the possibility to detect large-scale PPIs, these cannot be used to detect whole PPIs, and unreliable data may be generated. To solve this problem, in this study, a novel computational method was proposed to effectively predict the PPIs using the information of a protein sequence. The present method adopts Zernike moments to extract the protein sequence feature from a position specific scoring matrix (PSSM). Then, these extracted features were reconstructed using the stacked autoencoder. Finally, a novel probabilistic classification vector machine (PCVM) classifier was employed to predict the protein-protein interactions. When performed on the PPIs datasets of Yeast and H. pylori, the proposed method could achieve average accuracies of 96.60% and 91.19%, respectively. The promising result shows that the proposed method has a better ability to detect PPIs than other detection methods. The proposed method was also applied to predict PPIs on other species, and promising results were obtained. To evaluate the ability of our method, we compared it with the-state-of-the-art support vector machine (SVM) classifier for the Yeast dataset. The results obtained via multiple experiments prove that our method is powerful, efficient, feasible, and make a great contribution to proteomics research.
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Affiliation(s)
- Yan-Bin Wang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
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31
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Li LP, Wang YB, You ZH, Li Y, An JY. PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation. Int J Mol Sci 2018; 19:ijms19041029. [PMID: 29596363 PMCID: PMC5979371 DOI: 10.3390/ijms19041029] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 03/20/2018] [Accepted: 03/21/2018] [Indexed: 11/30/2022] Open
Abstract
Protein–protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the Saccharomyces cerevisiae dataset. A high accuracy of 94.56%, with 94.79% sensitivity at 94.36% precision, was obtained. The experimental results illustrated that the proposed approach can extract the most significant features from each protein sequence and can be a bright and meaningful tool for the research of proteomics.
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Affiliation(s)
- Li-Ping Li
- Department of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Yan-Bin Wang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Zhu-Hong You
- Department of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Yang Li
- Department of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Ji-Yong An
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
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Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 2018; 6:484-495.e5. [PMID: 29605183 DOI: 10.1016/j.cels.2018.03.001] [Citation(s) in RCA: 173] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 12/19/2017] [Accepted: 02/28/2018] [Indexed: 12/27/2022]
Abstract
Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.
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Ray S, Maulik U, Mukhopadhyay A. A review of computational approaches for analysis of hepatitis C virus-mediated liver diseases. Brief Funct Genomics 2017; 17:428-440. [DOI: 10.1093/bfgp/elx040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Sumanta Ray
- Department of Computer Science and Engineering, Aliah University, Kolkata, India
| | - Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Anirban Mukhopadhyay
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, India
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Ansari S, Donato M, Saberian N, Draghici S. An approach to infer putative disease-specific mechanisms using neighboring gene networks. Bioinformatics 2017; 33:1987-1994. [PMID: 28200075 PMCID: PMC5870849 DOI: 10.1093/bioinformatics/btx097] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Revised: 01/18/2017] [Accepted: 02/10/2017] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The ultimate goal of any experiment is to understand the biological phenomena underlying the condition investigated. This process often results in genes network through which a certain biological mechanism is explained. Such networks have been proven to be extremely useful, for the prediction of mechanisms of action of drugs or the responses of an organism to a specific impact (e.g. a disease, a treatment, etc.). Here, we introduce an approach able to build a network that captures the putative mechanisms at play in the given condition, by using datasets from multiple experiments studying the same phenotype. This method takes advantage of known interactions extracted from multiple sources such as protein-protein interactions and curated biological pathways. Based on such prior knowledge, we overcome the drawbacks of snap-shot data by considering the possible effects of each gene on its neighbors. RESULTS We show the effectiveness of this approach in three different case studies and validate the results in two ways considering the identified genes and interactions between them. We compare our findings with the results of two widely-used methods in the same category as well as the classical approach of selecting differentially expressed (DE) genes in an investigated condition. The results show that 'neighbor-net' analysis is able to report biological mechanisms that are significantly relevant to the given diseases in all the three case studies, and performs better compared to all reference methods using both validation approaches. AVAILABILITY AND IMPLEMENTATION The proposed method is implemented as in R and will be available an a Bioconductor package. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sahar Ansari
- Department of Computer Science, Wayne State University, Detroit, MI, USA
| | - Michele Donato
- Department of Computer Science, Wayne State University, Detroit, MI, USA
| | - Nafiseh Saberian
- Department of Computer Science, Wayne State University, Detroit, MI, USA
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA
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Wang Y, You Z, Li X, Chen X, Jiang T, Zhang J. PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences. Int J Mol Sci 2017; 18:ijms18051029. [PMID: 28492483 PMCID: PMC5454941 DOI: 10.3390/ijms18051029] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 04/24/2017] [Accepted: 04/29/2017] [Indexed: 01/08/2023] Open
Abstract
Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.
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Affiliation(s)
- Yanbin Wang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Zhuhong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Xiao Li
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Tonghai Jiang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Jingting Zhang
- Department of Mathematics and Statistics, Henan University, Kaifeng 100190, China.
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Raja K, Patrick M, Gao Y, Madu D, Yang Y, Tsoi LC. A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries. Int J Genomics 2017; 2017:6213474. [PMID: 28331849 PMCID: PMC5346376 DOI: 10.1155/2017/6213474] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 02/09/2017] [Indexed: 12/13/2022] Open
Abstract
In the past decade, the volume of "omics" data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.
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Affiliation(s)
- Kalpana Raja
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Matthew Patrick
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Yilin Gao
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Desmond Madu
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Yuyang Yang
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Lam C. Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
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Lee SA, Huang KC. Epigenetic profiling of human brain differential DNA methylation networks in schizophrenia. BMC Med Genomics 2016; 9:68. [PMID: 28117656 PMCID: PMC5260790 DOI: 10.1186/s12920-016-0229-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Background Epigenetics of schizophrenia provides important information on how the environmental factors affect the genetic architecture of the disease. DNA methylation plays a pivotal role in etiology for schizophrenia. Previous studies have focused mostly on the discovery of schizophrenia-associated SNPs or genetic variants. As postmortem brain samples became available, more and more recent studies surveyed transcriptomics of the diseases. In this study, we constructed protein-protein interaction (PPI) network using the disease associated SNP (or genetic variants), differentially expressed disease genes and differentially methylated disease genes (or promoters). By combining the different datasets and topological analyses of the PPI network, we established a more comprehensive understanding of the development and genetics of this devastating mental illness. Results We analyzed the previously published DNA methylation profiles of prefrontal cortex from 335 healthy controls and 191 schizophrenic patients. These datasets revealed 2014 CpGs identified as GWAS risk loci with the differential methylation profile in schizophrenia, and 1689 schizophrenic differential methylated genes (SDMGs) identified with predominant hypomethylation. These SDMGs, combined with the PPIs of these genes, were constructed into the schizophrenic differential methylation network (SDMN). On the SDMN, there are 10 hypermethylated SDMGs, including GNA13, CAPNS1, GABPB2, GIT2, LEFTY1, NDUFA10, MIOS, MPHOSPH6, PRDM14 and RFWD2. The hypermethylation to differential expression network (HyDEN) were constructed to determine how the hypermethylated promoters regulate gene expression. The enrichment analyses of biochemical pathways in HyDEN, including TNF alpha, PDGFR-beta signaling, TGF beta Receptor, VEGFR1 and VEGFR2 signaling, regulation of telomerase, hepatocyte growth factor receptor signaling, ErbB1 downstream signaling and mTOR signaling pathway, suggested that the malfunctioning of these pathways contribute to the symptoms of schizophrenia. Conclusions The epigenetic profiles of DNA differential methylation from schizophrenic brain samples were investigated to understand the regulatory roles of SDMGs. The SDMGs interplays with SCZCGs in a coordinated fashion in the disease mechanism of schizophrenia. The protein complexes and pathways involved in SDMN may be responsible for the etiology and potential treatment targets. The SDMG promoters are predominantly hypomethylated. Increasing methylation on these promoters is proposed as a novel therapeutic approach for schizophrenia. Electronic supplementary material The online version of this article (doi:10.1186/s12920-016-0229-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sheng-An Lee
- Department of Information Management, Kainan University, Taoyuan, Taiwan
| | - Kuo-Chuan Huang
- Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan. .,Department of Nursing, Ching Kuo Institute of Management and Health, Keelung, Taiwan.
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Segura-Cabrera A, Singh N, Komurov K. An integrated network platform for contextual prioritization of drugs and pathways. MOLECULAR BIOSYSTEMS 2016; 11:2850-9. [PMID: 26315485 DOI: 10.1039/c5mb00444f] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Repurposing of drugs to novel disease indications has promise for faster clinical translation. However, identifying the best drugs for a given pathological context is not trivial. We developed an integrated random walk-based network framework that combines functional biomolecular relationships and known drug-target interactions as a platform for contextual prioritization of drugs, genes and pathways. We show that the use of gene-centric or drug-centric data, such as gene expression data or a phenotypic drug screen, respectively, within this network platform can effectively prioritize drugs and pathways, respectively, to the studied biological context. We demonstrate that various genomic data can be used as contextual cues to effectively prioritize drugs to the studied context, while similarly, phenotypic drug screen data can be used to effectively prioritize genes and pathways to the studied phenotypic context. As a proof-of-principle, we showcase the use of our platform to identify known and novel drug indications against different subsets of breast cancers through contextual prioritization based on genome-wide gene expression, shRNA and drug screen and clinical survival data. The integrated network and associated methods are incorporated into the NetWalker suite for functional genomics analysis ().
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Affiliation(s)
- Aldo Segura-Cabrera
- Cincinnati Children's Hospital Medical Center, Division of Experimental Hematology and Cancer Biology, 3333 Burnet Ave, Cincinnati, Ohio, USA.
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Vamparys L, Laurent B, Carbone A, Sacquin-Mora S. Great interactions: How binding incorrect partners can teach us about protein recognition and function. Proteins 2016; 84:1408-21. [PMID: 27287388 PMCID: PMC5516155 DOI: 10.1002/prot.25086] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 06/01/2016] [Accepted: 06/02/2016] [Indexed: 12/29/2022]
Abstract
Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Lydie Vamparys
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France
| | - Benoist Laurent
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC Univ-Paris 6, CNRS UMR7238, Laboratoire De Biologie Computationnelle Et Quantitative, 15 Rue De L'Ecole De Médecine, Paris, 75006, France.,Institut Universitaire De France, Paris, 75005, France
| | - Sophie Sacquin-Mora
- Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, 13 Rue Pierre Et Marie Curie, Paris, 75005, France.
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Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4783801. [PMID: 27314023 PMCID: PMC4893571 DOI: 10.1155/2016/4783801] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Accepted: 04/12/2016] [Indexed: 01/08/2023]
Abstract
We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.
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41
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Integration of multiple biological features yields high confidence human protein interactome. J Theor Biol 2016; 403:85-96. [PMID: 27196966 DOI: 10.1016/j.jtbi.2016.05.020] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Revised: 05/01/2016] [Accepted: 05/11/2016] [Indexed: 01/05/2023]
Abstract
The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level.
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Ruggiero C, Fragassi G, Grossi M, Picciani B, Di Martino R, Capitani M, Buccione R, Luini A, Sallese M. A Golgi-based KDELR-dependent signalling pathway controls extracellular matrix degradation. Oncotarget 2016; 6:3375-93. [PMID: 25682866 PMCID: PMC4413660 DOI: 10.18632/oncotarget.3270] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 12/12/2014] [Indexed: 12/24/2022] Open
Abstract
We recently identified an endomembrane-based signalling cascade that is activated by the KDEL receptor (KDELR) on the Golgi complex. At the Golgi, the KDELR acts as a traffic sensor (presumably via binding to chaperones that leave the ER) and triggers signalling pathways that balance membrane fluxes between ER and Golgi. One such pathway relies on Gq and Src. Here, we examine if KDELR might control other cellular modules through this pathway. Given the central role of Src in extracellular matrix (ECM) degradation, we investigated the impact of the KDELR-Src pathway on the ability of cancer cells to degrade the ECM. We find that activation of the KDELR controls ECM degradation by increasing the number of the degradative structures known as invadopodia. The KDELR induces Src activation at the invadopodia and leads to phosphorylation of the Src substrates cortactin and ASAP1, which are required for basal and KDELR-stimulated ECM degradation. This study furthers our understanding of the regulatory circuitry underlying invadopodia-dependent ECM degradation, a key phase in metastases formation and invasive growth.
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Affiliation(s)
- Carmen Ruggiero
- Unit of Genomic Approaches to Membrane Traffic, Fondazione Mario Negri Sud, Santa Maria Imbaro, Chieti, Italy.,Current address: Institut de Pharmacologie Moléculaire et Cellulaire CNRS and Associated International Laboratory (LIA) NEOGENEX CNRS and University of Nice-Sophia-Antipolis, Valbonne, France
| | - Giorgia Fragassi
- Unit of Genomic Approaches to Membrane Traffic, Fondazione Mario Negri Sud, Santa Maria Imbaro, Chieti, Italy
| | - Mauro Grossi
- Unit of Genomic Approaches to Membrane Traffic, Fondazione Mario Negri Sud, Santa Maria Imbaro, Chieti, Italy
| | - Benedetta Picciani
- Unit of Genomic Approaches to Membrane Traffic, Fondazione Mario Negri Sud, Santa Maria Imbaro, Chieti, Italy
| | - Rosaria Di Martino
- Institute of Protein Biochemistry National Research Council, Naples, Italy
| | - Mirco Capitani
- Unit of Genomic Approaches to Membrane Traffic, Fondazione Mario Negri Sud, Santa Maria Imbaro, Chieti, Italy
| | - Roberto Buccione
- Laboratory of Tumour Cell Invasion, Fondazione Mario Negri Sud, Santa Maria Imbaro, Chieti, Italy
| | - Alberto Luini
- Institute of Protein Biochemistry National Research Council, Naples, Italy
| | - Michele Sallese
- Unit of Genomic Approaches to Membrane Traffic, Fondazione Mario Negri Sud, Santa Maria Imbaro, Chieti, Italy
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Dugger JW, Webb LJ. Preparation and Characterization of Biofunctionalized Inorganic Substrates. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2015; 31:10331-40. [PMID: 26135514 DOI: 10.1021/acs.langmuir.5b01876] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Integrating the function of biological molecules into traditional inorganic materials and substrates couples biologically relevant function to synthetic devices and generates new materials and capabilities by combining biological and inorganic functions. At this so-called "bio/abio interface," basic biological functions such as ligand binding and catalysis can be co-opted to detect analytes with exceptional sensitivity or to generate useful molecules with chiral specificity under entirely benign reaction conditions. Proteins function in dynamic, complex, and crowded environments (the living cell) and are therefore appropriate for integrating into multistep, multiscale, multimaterial devices such as integrated circuits and heterogeneous catalysts. However, the goal of reproducing the highly specific activities of biomolecules in the perturbed chemical and electrostatic environment at an inorganic interface while maintaining their native conformations is challenging to achieve. Moreover, characterizing protein structure and function at a surface is often difficult, particularly if one wishes to compare the activity of the protein to that of the dilute, aqueous solution phase. Our laboratory has developed a general strategy to address this challenge by taking advantage of the structural and chemical properties of alkanethiol self-assembled monolayers (SAMs) on gold surfaces that are functionalized with covalently tethered peptides. These surface-bound peptides then act as the chemical recognition element for a target protein, generating a biomimetic surface in which protein orientation, structure, density, and function are controlled and variable. Herein we discuss current research and future directions related to generating a chemically tunable biofunctionalization strategy that has potential to successfully incorporate the highly specialized functions of proteins onto inorganic substrates.
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Affiliation(s)
- Jason W Dugger
- Department of Chemistry, Center for Nano- and Molecular Science and Technology, and Institute for Cell and Molecular Biology, The University of Texas at Austin , 105 E. 24th Street, STOP A5300, Austin, Texas 78712-1224, United States
| | - Lauren J Webb
- Department of Chemistry, Center for Nano- and Molecular Science and Technology, and Institute for Cell and Molecular Biology, The University of Texas at Austin , 105 E. 24th Street, STOP A5300, Austin, Texas 78712-1224, United States
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Kulej K, Sidoli S, Palmisano G, Edwards AV, Robinson PJ, Larsen MR. Optimization of calmodulin-affinity chromatography for brain and organelles. EUPA OPEN PROTEOMICS 2015. [DOI: 10.1016/j.euprot.2015.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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45
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Li R, Chen T, Li S. Network-based method to infer the contributions of proteins to the etiology of drug side effects. QUANTITATIVE BIOLOGY 2015. [DOI: 10.1007/s40484-015-0051-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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46
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Liu W, Chen L, Li B. A New Protein-Protein Interaction Prediction Algorithm Based on Conditional Random Field. ACTA ACUST UNITED AC 2015. [DOI: 10.1007/978-3-319-22186-1_36] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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47
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Remmele CW, Luther CH, Balkenhol J, Dandekar T, Müller T, Dittrich MT. Integrated inference and evaluation of host-fungi interaction networks. Front Microbiol 2015; 6:764. [PMID: 26300851 PMCID: PMC4523839 DOI: 10.3389/fmicb.2015.00764] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Accepted: 07/13/2015] [Indexed: 12/18/2022] Open
Abstract
Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data.
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Affiliation(s)
| | | | | | - Thomas Dandekar
- Department of Bioinformatics, University of Würzburg Würzburg, Germany
| | - Tobias Müller
- Department of Bioinformatics, University of Würzburg Würzburg, Germany
| | - Marcus T Dittrich
- Department of Bioinformatics, University of Würzburg Würzburg, Germany ; Department of Human Genetics, University of Würzburg Würzburg, Germany
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48
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Kim WY, Kurmar S. Sensible method for updating motif instances in an increased biological network. Methods 2015; 83:71-9. [PMID: 25869675 DOI: 10.1016/j.ymeth.2015.04.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2015] [Revised: 04/04/2015] [Accepted: 04/06/2015] [Indexed: 11/20/2022] Open
Abstract
A network motif is defined as an over-represented subgraph pattern in a network. Network motif based techniques have been widely applied in analyses of biological networks such as transcription regulation networks (TRNs), protein-protein interaction networks (PPIs), and metabolic networks. The detection of network motifs involves the computationally expensive enumeration of subgraphs, NP-complete graph isomorphism testing, and significance testing through the generation of many random graphs to determine the statistical uniqueness of a given subgraph. These computational obstacles make network motif analysis unfeasible for many real-world applications. We observe that the fast growth of biotechnology has led to the rapid accretion of molecules (vertices) and interactions (edges) to existing biological network databases. Even with a small percentage of additions, revised networks can have a large number of differing motif instances. Currently, no existing algorithms recalculate motif instances in 'updated' networks in a practical manner. In this paper, we introduce a sensible method for efficiently recalculating motif instances by performing motif enumeration from only updated vertices and edges. Preliminary experimental results indicate that our method greatly reduces computational time by eliminating the repeated enumeration of overlapped subgraph instances detected in earlier versions of the network. The software program implementing this algorithm, defined as SUNMI (Sensible Update of Network Motif Instances), is currently a stand-alone java program and we plan to upgrade it as a web-interactive program that will be available through http://faculty.washington.edu/kimw6/research.htm in near future. Meanwhile it is recommended to contact authors to obtain the stand-alone SUNMI program.
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Affiliation(s)
- W Y Kim
- Computing and Software Systems, School of Science, Technology, Engineering, and Mathematics, University of Washington Bothell, Bothell, WA 98011-8246, United States.
| | - S Kurmar
- Computing and Software Systems, School of Science, Technology, Engineering, and Mathematics, University of Washington Bothell, Bothell, WA 98011-8246, United States.
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49
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MENG FANXIN, YOU YU, LIU ZHILIANG, LIU JIANMING, DING HU, XU RUXIANG. Neuronal calcium signaling pathways are associated with the development of epilepsy. Mol Med Rep 2015; 11:196-202. [PMID: 25339366 PMCID: PMC4237086 DOI: 10.3892/mmr.2014.2756] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2013] [Accepted: 08/29/2014] [Indexed: 12/12/2022] Open
Abstract
Epilepsy is the most common serious neurological disorder worldwide, however, the specific causative factors and mechanisms underlying epilepsy remain unclear. The current study aimed to study the potential genes or pathways associated with epilepsy, based on rat miRNA expression profiles. The microarray dataset GSE49850 was downloaded and analyzed with the TimeCourse R software package, which was used to generate comparisons between the control and electrically-stimulated groups. The target genes of differentially expressed miRNAs were queried in the miRWalk database and functional enrichment was conducted using the Database for Annotation, Visualization and Integrated Discovery software tools. The interaction network of the target genes was constructed based on the Biomolecular Interaction Network Database and clustered using ClusterONE. In total, 152 differentially expressed miRNAs were identified, with rno-miR-21-5p being the most significantly differentially expressed. A total of 526 target genes of the differentially expressed miRNAs were obtained. Functional analysis indicated that these genes were predominantly involved in responses to stimuli. The interaction network showed that the GRIN and STX gene family, which are involved in synaptic signal transmission, were significant. In conclusion, the present study identified that the development of epilepsy was closely associated with neuronal calcium signaling pathways.
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Affiliation(s)
| | | | - ZHILIANG LIU
- Department of Neuronal Surgery, The Military General Hospital of Beijing PLA, Beijing 100007, P.R. China
| | - JIANMING LIU
- Department of Neuronal Surgery, The Military General Hospital of Beijing PLA, Beijing 100007, P.R. China
| | - HU DING
- Department of Neuronal Surgery, The Military General Hospital of Beijing PLA, Beijing 100007, P.R. China
| | - RUXIANG XU
- Department of Neuronal Surgery, The Military General Hospital of Beijing PLA, Beijing 100007, P.R. China
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50
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Abstract
Years of meticulous curation of scientific literature and increasingly reliable computational predictions have resulted in creation of vast databases of protein interaction data. Over the years, these repositories have become a basic framework in which experiments are analyzed and new directions of research are explored. Here we present an overview of the most widely used protein-protein interaction databases and the methods they employ to gather, combine, and predict interactions. We also point out the trade-off between comprehensiveness and accuracy and the main pitfall scientists have to be aware before adopting protein interaction databases in any single-gene or genome-wide analysis.
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