Displaying publications 1 - 20 of 31 in total

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  1. Construction of Protein Expression Network
    Afiqah-Aleng N, Mohamed-Hussein ZA
    Methods Mol Biol, 2021;2189:119-132.
    PMID: 33180298 DOI: 10.1007/978-1-0716-0822-7_10
    In this post-genomic era, protein network can be used as a complementary way to shed light on the growing amount of data generated from current high-throughput technologies. Protein network is a powerful approach to describe the molecular mechanisms of the biological events through protein-protein interactions. Here, we describe the computational methods used to construct the protein network using expression data. We provide a list of available tools and databases that can be used in constructing the network.
    Matched MeSH terms: Protein Interaction Mapping*; Protein Interaction Maps*
  2. Evaluation of the proteomic landscape of HPV E7‑induced alterations in human keratinocytes reveal therapeutically relevant pathways for cervical cancer
    Gandhi S, Mohamad Razif MF, Othman S, Chakraborty S, Nor Rashid N
    Mol Med Rep, 2023 Feb;27(2).
    PMID: 36633133 DOI: 10.3892/mmr.2023.12933
    The lack of specific and accurate therapeutic targets poses a challenge in the treatment of cervical cancer (CC). Global proteomics has the potential to characterize the underlying and intricate molecular mechanisms that drive the identification of therapeutic candidates for CC in an unbiased manner. The present study assessed human papillomavirus (HPV)‑induced proteomic alterations to identify key cancer hallmark pathways and protein‑protein interaction (PPI) networks, which offered the opportunity to evaluate the possibility of using these for targeted therapy in CC. Comparative proteomic profiling of HPV‑transfected (HPV16/18 E7), HPV‑transformed (CaSki and HeLa) and normal human keratinocyte (HaCaT) cells was performed using the liquid chromatography‑tandem mass spectrometry (LC‑MS/MS) technique. Both label‑free quantification and differential expression analysis were performed to assess differentially regulated proteins in HPV‑transformed and ‑transfected cells. The present study demonstrated that protein expression was upregulated in HPV‑transfected cells compared with in HPV‑transformed cells. This was probably due to the ectopic expression of E7 protein in the former cell type, in contrast to its constitutive expression in the latter cell type. Subsequent pathway visualization and network construction demonstrated that the upregulated proteins in HPV16/18 E7‑transfected cells were predominantly associated with a diverse array of cancer hallmarks, including the mTORC1 signaling pathway, MYC targets V1, hypoxia and glycolysis. Among the various proteins present in the cancer hallmark enrichment pathways, phosphoglycerate kinase 1 (PGK1) was present across all pathways. Therefore, PGK1 may be considered as a potential biomarker. PPI analysis demonstrated a direct interaction between p130 and polyubiquitin B, which may lead to the degradation of p130 via the ubiquitin‑proteasome proteolytic pathway. In summary, elucidation of the key signaling pathways in HPV16/18‑transfected and ‑transformed cells may aid in the design of novel therapeutic strategies for clinical application such as targeted therapy and immunotherapy against cervical cancer.
    Matched MeSH terms: Protein Interaction Maps*
  3. Fulltext Computer aided analysis of disease linked protein networks
    Sabetian S, Shamsir MS
    Bioinformation, 2019;15(7):513-522.
    PMID: 31485137 DOI: 10.6026/97320630015513
    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.
    Matched MeSH terms: Protein Interaction Maps
  4. Comparative proteomic analysis of oil palm leaves infected with Ganoderma boninense revealed changes in proteins involved in photosynthesis, carbohydrate metabolism, and immunity and defense
    Jeffery Daim LD, Ooi TE, Ithnin N, Mohd Yusof H, Kulaveerasingam H, Abdul Majid N, et al.
    Electrophoresis, 2015 Aug;36(15):1699-710.
    PMID: 25930948 DOI: 10.1002/elps.201400608
    The basidiomycete fungal pathogen Ganoderma boninense is the causative agent for the incurable basal stem rot (BSR) disease in oil palm. This disease causes significant annual crop losses in the oil palm industry. Currently, there is no effective method for disease control and elimination, nor is any molecular marker for early detection of the disease available. An understanding of how BSR affects protein expression in plants may help identify and/or assist in the development of an early detection protocol. Although the mode of infection of BSR disease is primarily via the root system, defense-related genes have been shown to be expressed in both the root and leafs. Thus, to provide an insight into the changes in the global protein expression profile in infected plants, comparative 2DE was performed on leaf tissues sampled from palms with and without artificial inoculation of the Ganoderma fungus. Comparative 2DE revealed that 54 protein spots changed in abundance. A total of 51 protein spots were successfully identified by LC-QTOF MS/MS. The majority of these proteins were those involved in photosynthesis, carbohydrate metabolism as well as immunity and defense.
    Matched MeSH terms: Protein Interaction Maps/physiology
  5. Potential shared pathogenic mechanisms between endometriosis and inflammatory bowel disease indicate a strong initial effect of immune factors
    Zhang H, Mo Y, Wang L, Zhang H, Wu S, Sandai D, et al.
    Front Immunol, 2024;15:1339647.
    PMID: 38660311 DOI: 10.3389/fimmu.2024.1339647
    INTRODUCTION: Over the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD.

    METHODS: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.

    RESULTS: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.

    DISCUSSION: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

    Matched MeSH terms: Protein Interaction Maps*
  6. Construction and analysis of protein-protein interaction network to identify the molecular mechanism in laryngeal cancer
    Sarahani Harun, Nurulisa Zulkifle
    Sains Malaysiana, 2018;47:2933-2940.
    Laryngeal cancer is the most common head and neck cancer in the world and its incidence is on the rise. However, the
    molecular mechanism underlying laryngeal cancer pathogenesis is poorly understood. The goal of this study was to
    develop a protein-protein interaction (PPI) network for laryngeal cancer to predict the biological pathways that underlie
    the molecular complexes in the network. Genes involved in laryngeal cancer were extracted from the OMIM database
    and their interaction partners were identified via text and data mining using Agilent Literature Search, STRING and
    GeneMANIA. PPI network was then integrated and visualised using Cytoscape ver3.6.0. Molecular complexes in the
    network were predicted by MCODE plugin and functional enrichment analyses of the molecular complexes were performed
    using BiNGO. 28 laryngeal cancer-related genes were present in the OMIM database. The PPI network associated with
    laryngeal cancer contained 161 nodes, 661 edges and five molecular complexes. Some of the complexes were related to
    the biological behaviour of cancer, providing the foundation for further understanding of the mechanism of laryngeal
    cancer development and progression.
    Matched MeSH terms: Protein Interaction Maps
  7. Multiobjective optimization to reconstruct biological networks
    Naef A, Abdullah R, Abdul Rashid N
    Biosystems, 2018 Sep 17;174:22-36.
    PMID: 30236951 DOI: 10.1016/j.biosystems.2018.09.003
    Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup.
    Matched MeSH terms: Protein Interaction Maps
  8. Functional features and protein network of human sperm-egg interaction
    Sabetian S, Shamsir MS, Abu Naser M
    Syst Biol Reprod Med, 2014 Dec;60(6):329-37.
    PMID: 25222562 DOI: 10.3109/19396368.2014.955896
    Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human sperm-egg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that sperm-egg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new computationally resolved interactions and the genetic links between sperm-egg interaction abnormalities and the associated disease.
    Matched MeSH terms: Protein Interaction Maps*
  9. Iron-associated protein interaction networks reveal the key functional modules related to survival and virulence of Pasteurella multocida
    Jatuponwiphat T, Chumnanpuen P, Othman S, E-Kobon T, Vongsangnak W
    Microb Pathog, 2019 Feb;127:257-266.
    PMID: 30550841 DOI: 10.1016/j.micpath.2018.12.013
    Pasteurella multocida causes respiratory infectious diseases in a multitude of birds and mammals. A number of virulence-associated genes were reported across different strains of P. multocida, including those involved in the iron transport and metabolism. Comparative iron-associated genes of P. multocida among different animal hosts towards their interaction networks have not been fully revealed. Therefore, this study aimed to identify the iron-associated genes from core- and pan-genomes of fourteen P. multocida strains and to construct iron-associated protein interaction networks using genome-scale network analysis which might be associated with the virulence. Results showed that these fourteen strains had 1587 genes in the core-genome and 3400 genes constituting their pan-genome. Out of these, 2651 genes associated with iron transport and metabolism were selected to construct the protein interaction networks and 361 genes were incorporated into the iron-associated protein interaction network (iPIN) consisting of nine different iron-associated functional modules. After comparing with the virulence factor database (VFDB), 21 virulence-associated proteins were determined and 11 of these belonged to the heme biosynthesis module. From this study, the core heme biosynthesis module and the core outer membrane hemoglobin receptor HgbA were proposed as candidate targets to design novel antibiotics and vaccines for preventing pasteurellosis across the serotypes or animal hosts for enhanced precision agriculture to ensure sustainability in food security.
    Matched MeSH terms: Protein Interaction Maps*
  10. Topologically significant directed random walk with applied walker network in cancer environment
    Seah CS, Kasim S, Saedudin RR, Md Fudzee MF, Mohamad MS, Hassan R, et al.
    Pak J Pharm Sci, 2019 May;32(3 Special):1395-1408.
    PMID: 31551221
    Numerous cancer studies have combined different datasets for the prognosis of patients. This study incorporated four networks for significant directed random walk (sDRW) to predict cancerous genes and risk pathways. The study investigated the feasibility of cancer prediction via different networks. In this study, multiple micro array data were analysed and used in the experiment. Six gene expression datasets were applied in four networks to study the effectiveness of the networks in sDRW in terms of cancer prediction. The experimental results showed that one of the proposed networks is outstanding compared to other networks. The network is then proposed to be implemented in sDRW as a walker network. This study provides a foundation for further studies and research on other networks. We hope these finding will improve the prognostic methods of cancer patients.
    Matched MeSH terms: Protein Interaction Maps/genetics
  11. Biomarkers for ischemic stroke subtypes: A protein-protein interaction analysis
    Wei LK, Quan LS
    Comput Biol Chem, 2019 Dec;83:107116.
    PMID: 31561071 DOI: 10.1016/j.compbiolchem.2019.107116
    According to the Trial of Org 10172 in Acute Stroke Treatment, ischemic stroke is classified into five subtypes. However, the predictive biomarkers of ischemic stroke subtypes are still largely unknown. The utmost objective of this study is to map, construct and analyze protein-protein interaction (PPI) networks for all subtypes of ischemic stroke, and to suggest the predominant biological pathways for each subtypes. Through 6285 protein data retrieved from PolySearch2 and STRING database, the first PPI networks for all subtypes of ischemic stroke were constructed. Notably, F2 and PLG were identified as the critical proteins for large artery atherosclerosis (LAA), lacunar, cardioembolic, stroke of other determined etiology (SOE) and stroke of undetermined etiology (SUE). Gene ontology and DAVID analysis revealed that GO:0030193 regulation of blood coagulation and GO:0051917 regulation of fibrinolysis were the important functional clusters for all the subtypes. In addition, inflammatory pathway was the key etiology for LAA and lacunar, while FOS and JAK2/STAT3 signaling pathways might contribute to cardioembolic stroke. Due to many risk factors associated with SOE and SUE, the precise etiology for these two subtypes remained to be concluded.
    Matched MeSH terms: Protein Interaction Maps*
  12. PNMA family: Protein interaction network and cell signalling pathways implicated in cancer and apoptosis
    Pang SW, Lahiri C, Poh CL, Tan KO
    Cell Signal, 2018 05;45:54-62.
    PMID: 29378289 DOI: 10.1016/j.cellsig.2018.01.022
    Paraneoplastic Ma Family (PNMA) comprises a growing number of family members which share relatively conserved protein sequences encoded by the human genome and is localized to several human chromosomes, including the X-chromosome. Based on sequence analysis, PNMA family members share sequence homology to the Gag protein of LTR retrotransposon, and several family members with aberrant protein expressions have been reported to be closely associated with the human Paraneoplastic Disorder (PND). In addition, gene mutations of specific members of PNMA family are known to be associated with human mental retardation or 3-M syndrome consisting of restrictive post-natal growth or dwarfism, and development of skeletal abnormalities. Other than sequence homology, the physiological function of many members in this family remains unclear. However, several members of this family have been characterized, including cell signalling events mediated by these proteins that are associated with apoptosis, and cancer in different cell types. Furthermore, while certain PNMA family members show restricted gene expression in the human brain and testis, other PNMA family members exhibit broader gene expression or preferential and selective protein interaction profiles, suggesting functional divergence within the family. Functional analysis of some members of this family have identified protein domains that are required for subcellular localization, protein-protein interactions, and cell signalling events which are the focus of this review paper.
    Matched MeSH terms: Protein Interaction Maps*
  13. Fulltext Protein-Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome
    Ramly B, Afiqah-Aleng N, Mohamed-Hussein ZA
    Int J Mol Sci, 2019 Jun 18;20(12).
    PMID: 31216618 DOI: 10.3390/ijms20122959
    Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein-protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher's exact test and comorbidity data were used to identify PCOS-disease subnetworks. Pathway enrichment analysis was performed on the PCOS-disease subnetworks to identify significant pathways that are highly involved in the PCOS-disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.
    Matched MeSH terms: Protein Interaction Mapping*; Protein Interaction Maps*
  14. Minireview: applied structural bioinformatics in proteomics
    Choong YS, Tye GJ, Lim TS
    Protein J, 2013 Oct;32(7):505-11.
    PMID: 24096348 DOI: 10.1007/s10930-013-9514-1
    The limited sequence similarity of protein sequences with known structures has led to an indispensable need for computational technology to predict their structures. Structural bioinformatics (SB) has become integral in elucidating the sequence-structure-function relationship of a protein. This report focuses on the applications of SB within the context of protein engineering including its limitation and future challenges.
    Matched MeSH terms: Protein Interaction Maps
  15. Proteomic and metabolomic study of wax apple (Syzygium samarangense) fruit during ripening process
    Jamil NAM, Rahmad N, Rosli NHM, Al-Obaidi JR
    Electrophoresis, 2018 12;39(23):2954-2964.
    PMID: 30074628 DOI: 10.1002/elps.201800185
    Wax apple is one of the underutilized fruits that is considered a good source of fibers, vitamins, minerals as well as antioxidants. In this study, a comparative analysis of the developments of wax fruit ripening at the proteomic and metabolomic level was reported. 2D electrophoresis coupled with MALDI-TOF/TOF was used to compare the proteome profile from three developmental stages named immature, young, and mature fruits. In general, the protein expression profile and the identified proteins function were discussed for their potential roles in fruit physiological development and ripening processes. The metabolomic investigation was also performed on the same samples using quadrupole LC-MS (LC-QTOF/MS). Roles of some of the differentially expressed proteins and metabolites are discussed in relation to wax apple ripening during the development. This is the first study investigating the changes in the proteins and metabolites in wax apple at different developmental stages. The information obtained from this research will be helpful in developing biomarkers for breeders and help the plant researchers to avoid wax apple cultivation problems such as fruit cracking.
    Matched MeSH terms: Protein Interaction Maps
  16. Fulltext A side-effect free method for identifying cancer drug targets
    Ashraf MI, Ong SK, Mujawar S, Pawar S, More P, Paul S, et al.
    Sci Rep, 2018 04 27;8(1):6669.
    PMID: 29703908 DOI: 10.1038/s41598-018-25042-2
    Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.
    Matched MeSH terms: Protein Interaction Maps/drug effects
  17. Fulltext The FBXW7-NOTCH interactome: A ubiquitin proteasomal system-induced crosstalk modulating oncogenic transformation in human tissues
    Kar R, Jha SK, Ojha S, Sharma A, Dholpuria S, Raju VSR, et al.
    Cancer Rep (Hoboken), 2021 08;4(4):e1369.
    PMID: 33822486 DOI: 10.1002/cnr2.1369
    BACKGROUND: Ubiquitin ligases or E3 ligases are well programmed to regulate molecular interactions that operate at a post-translational level. Skp, Cullin, F-box containing complex (or SCF complex) is a multidomain E3 ligase known to mediate the degradation of a wide range of proteins through the proteasomal pathway. The three-dimensional domain architecture of SCF family proteins suggests that it operates through a novel and adaptable "super-enzymatic" process that might respond to targeted therapeutic modalities in cancer.

    RECENT FINDINGS: Several F-box containing proteins have been characterized either as tumor suppressors (FBXW8, FBXL3, FBXW8, FBXL3, FBXO1, FBXO4, and FBXO18) or as oncogenes (FBXO5, FBXO9, and SKP2). Besides, F-box members like βTrcP1 and βTrcP2, the ones with context-dependent functionality, have also been studied and reported. FBXW7 is a well-studied F-box protein and is a tumor suppressor. FBXW7 regulates the activity of a range of substrates, such as c-Myc, cyclin E, mTOR, c-Jun, NOTCH, myeloid cell leukemia sequence-1 (MCL1), AURKA, NOTCH through the well-known ubiquitin-proteasome system (UPS)-mediated degradation pathway. NOTCH signaling is a primitive pathway that plays a crucial role in maintaining normal tissue homeostasis. FBXW7 regulates NOTCH protein activity by controlling its half-life, thereby maintaining optimum protein levels in tissue. However, aberrations in the FBXW7 or NOTCH expression levels can lead to poor prognosis and detrimental outcomes in patients. Therefore, the FBXW7-NOTCH axis has been a subject of intense study and research over the years, especially around the interactome's role in driving cancer development and progression. Several studies have reported the effect of FBXW7 and NOTCH mutations on normal tissue behavior. The current review attempts to critically analyze these mutations prognostic value in a wide range of tumors. Furthermore, the review summarizes the recent findings pertaining to the FBXW7 and NOTCH interactome and its involvement in phosphorylation-related events, cell cycle, proliferation, apoptosis, and metastasis.

    CONCLUSION: The review concludes by positioning FBXW7 as an effective diagnostic marker in tumors and by listing out recent advancements made in cancer therapeutics in identifying protocols targeting the FBXW7-NOTCH aberrations in tumors.

    Matched MeSH terms: Protein Interaction Maps/genetics*
  18. Fulltext Delineating the Plausible Molecular Vaccine Candidates and Drug Targets of Multidrug-Resistant Acinetobacter baumannii
    Mujawar S, Mishra R, Pawar S, Gatherer D, Lahiri C
    Front Cell Infect Microbiol, 2019;9:203.
    PMID: 31281799 DOI: 10.3389/fcimb.2019.00203
    Nosocomial infections have become alarming with the increase of multidrug-resistant bacterial strains of Acinetobacter baumannii. Being the causative agent in ~80% of the cases, these pathogenic gram-negative species could be deadly for hospitalized patients, especially in intensive care units utilizing ventilators, urinary catheters, and nasogastric tubes. Primarily infecting an immuno-compromised system, they are resistant to most antibiotics and are the root cause of various types of opportunistic infections including but not limited to septicemia, endocarditis, meningitis, pneumonia, skin, and wound sepsis and even urinary tract infections. Conventional experimental methods including typing, computational methods encompassing comparative genomics, and combined methods of reverse vaccinology and proteomics had been proposed to differentiate and develop vaccines and/or drugs for several outbreak strains. However, identifying proteins suitable enough to be posed as drug targets and/or molecular vaccines against the multidrug-resistant pathogenic bacterial strains has probably remained an open issue to address. In these cases of novel protein identification, the targets either are uncharacterized or have been unable to confer the most coveted protection either in the form of molecular vaccine candidates or as drug targets. Here, we report a strategic approach with the 3,766 proteins from the whole genome of A. baumannii ATCC19606 (AB) to rationally identify plausible candidates and propose them as future molecular vaccine candidates and/or drug targets. Essentially, we started with mapping the vaccine candidates (VaC) and virulence factors (ViF) of A. baumannii strain AYE onto strain ATCC19606 to identify them in the latter. We move on to build small networks of VaC and ViF to conceptualize their position in the network space of the whole genomic protein interactome (GPIN) and rationalize their candidature for drugs and/or molecular vaccines. To this end, we propose new sets of known proteins unearthed from interactome built using key factors, KeF, potent enough to compete with VaC and ViF. Our method is the first of its kind to propose, albeit theoretically, a rational approach to identify crucial proteins and pose them for candidates of vaccines and/or drugs effective enough to combat the deadly pathogenic threats of A. baumannii.
    Matched MeSH terms: Protein Interaction Maps/drug effects; Protein Interaction Maps/genetics
  19. Fulltext Comprehensive computational target fishing approach to identify Xanthorrhizol putative targets
    Shahid M, Azfaralariff A, Law D, Najm AA, Sanusi SA, Lim SJ, et al.
    Sci Rep, 2021 01 15;11(1):1594.
    PMID: 33452398 DOI: 10.1038/s41598-021-81026-9
    Xanthorrhizol (XNT), is a bioactive compound found in Curcuma xanthorrhiza Roxb. This study aimed to determine the potential targets of the XNT via computational target fishing method. This compound obeyed Lipinski's and Veber's rules where it has a molecular weight (MW) of 218.37 gmol-1, TPSA of 20.23, rotatable bonds (RBN) of 4, hydrogen acceptor and donor ability is 1 respectively. Besides, it also has half-life (HL) values 3.5 h, drug-likeness (DL) value of 0.07, oral bioavailability (OB) of 32.10, and blood-brain barrier permeability (BBB) value of 1.64 indicating its potential as therapeutic drug. Further, 20 potential targets were screened out through PharmMapper and DRAR-CPI servers. Co-expression results derived from GeneMANIA revealed that these targets made connection with a total of 40 genes and have 744 different links. Four genes which were RXRA, RBP4, HSD11B1 and AKR1C1 showed remarkable co-expression and predominantly involved in steroid metabolic process. Furthermore, among these 20 genes, 13 highly expressed genes associated with xenobiotics by cytochrome P450, chemical carcinogenesis and steroid metabolic pathways were identified through gene ontology (GO) and KEGG pathway analysis. In conclusion, XNT is targeting multiple proteins and pathways which may be exploited to shape a network that exerts systematic pharmacological effects.
    Matched MeSH terms: Protein Interaction Maps/drug effects
  20. Systematic Analysis of Protein Interaction Network Associated with Azoospermia
    Sabetian S, Shamsir MS
    Int J Mol Sci, 2016 Nov 10;17(11).
    PMID: 27834916
    Non-obstructive azoospermia is a severe infertility factor. Currently, the etiology of this condition remains elusive with several possible molecular pathway disruptions identified in the post-meiotic spermatozoa. In the presented study, in order to identify all possible candidate genes associated with azoospermia and to map their relationship, we present the first protein-protein interaction network related to azoospermia and analyze the complex effects of the related genes systematically. Using Online Mendelian Inheritance in Man, the Human Protein Reference Database and Cytoscape, we created a novel network consisting of 209 protein nodes and 737 interactions. Mathematical analysis identified three proteins, ar, dazap2, and esr1, as hub nodes and a bottleneck protein within the network. We also identified new candidate genes, CREBBP and BCAR1, which may play a role in azoospermia. The gene ontology analysis suggests a genetic link between azoospermia and liver disease. The KEGG analysis also showed 45 statistically important pathways with 31 proteins associated with colorectal, pancreatic, chronic myeloid leukemia and prostate cancer. Two new genes and associated diseases are promising for further experimental validation.
    Matched MeSH terms: Protein Interaction Maps/genetics*
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