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Dar KB, Bhat AH, Amin S, Anjum S, Reshi BA, Zargar MA, Masood A, Ganie SA. Exploring Proteomic Drug Targets, Therapeutic Strategies and Protein - Protein Interactions in Cancer: Mechanistic View. Curr Cancer Drug Targets 2020; 19:430-448. [PMID: 30073927 DOI: 10.2174/1568009618666180803104631] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 07/17/2018] [Accepted: 07/19/2018] [Indexed: 12/31/2022]
Abstract
Protein-Protein Interactions (PPIs) drive major signalling cascades and play critical role in cell proliferation, apoptosis, angiogenesis and trafficking. Deregulated PPIs are implicated in multiple malignancies and represent the critical targets for treating cancer. Herein, we discuss the key protein-protein interacting domains implicated in cancer notably PDZ, SH2, SH3, LIM, PTB, SAM and PH. These domains are present in numerous enzymes/kinases, growth factors, transcription factors, adaptor proteins, receptors and scaffolding proteins and thus represent essential sites for targeting cancer. This review explores the candidature of various proteins involved in cellular trafficking (small GTPases, molecular motors, matrix-degrading enzymes, integrin), transcription (p53, cMyc), signalling (membrane receptor proteins), angiogenesis (VEGFs) and apoptosis (BCL-2family), which could possibly serve as targets for developing effective anti-cancer regimen. Interactions between Ras/Raf; X-linked inhibitor of apoptosis protein (XIAP)/second mitochondria-derived activator of caspases (Smac/DIABLO); Frizzled (FRZ)/Dishevelled (DVL) protein; beta-catenin/T Cell Factor (TCF) have also been studied as prospective anticancer targets. Efficacy of diverse molecules/ drugs targeting such PPIs although evaluated in various animal models/cell lines, there is an essential need for human-based clinical trials. Therapeutic strategies like the use of biologicals, high throughput screening (HTS) and fragment-based technology could play an imperative role in designing cancer therapeutics. Moreover, bioinformatic/computational strategies based on genome sequence, protein sequence/structure and domain data could serve as competent tools for predicting PPIs. Exploring hot spots in proteomic networks represents another approach for developing targetspecific therapeutics. Overall, this review lays emphasis on a productive amalgamation of proteomics, genomics, biochemistry, and molecular dynamics for successful treatment of cancer.
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Affiliation(s)
- Khalid Bashir Dar
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India.,Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Aashiq Hussain Bhat
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India.,Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Shajrul Amin
- Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Syed Anjum
- Amity Institute of Biotechnology, Amity University, Rajasthan, India
| | - Bilal Ahmad Reshi
- Department of Biotechnology, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Mohammad Afzal Zargar
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Akbar Masood
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Showkat Ahmad Ganie
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
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Simplified Swarm Optimization-Based Function Module Detection in Protein–Protein Interaction Networks. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7040412] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Engler MS, Scheubert K, Schubert US, Böcker S. New Statistical Models for Copolymerization. Polymers (Basel) 2016; 8:E240. [PMID: 30979335 PMCID: PMC6432000 DOI: 10.3390/polym8060240] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 06/07/2016] [Accepted: 06/15/2016] [Indexed: 11/16/2022] Open
Abstract
For many years, copolymerization has been studied using mathematical and statistical models. Here, we present new Markov chain models for copolymerization kinetics: the Bernoulli and Geometric models. They model copolymer synthesis as a random process and are based on a basic reaction scheme. In contrast to previous Markov chain approaches to copolymerization, both models take variable chain lengths and time-dependent monomer probabilities into account and allow for computing sequence likelihoods and copolymer fingerprints. Fingerprints can be computed from copolymer mass spectra, potentially allowing us to estimate the model parameters from measured fingerprints. We compare both models against Monte Carlo simulations. We find that computing the models is fast and memory efficient.
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Affiliation(s)
- Martin S Engler
- Chair of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Kerstin Scheubert
- Chair of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Ulrich S Schubert
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany.
- Jena Center for Soft Matter (JCMS), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany.
| | - Sebastian Böcker
- Chair of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
- Jena Center for Soft Matter (JCMS), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany.
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Markov mean properties for cell death-related protein classification. J Theor Biol 2014; 349:12-21. [DOI: 10.1016/j.jtbi.2014.01.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 01/21/2014] [Accepted: 01/24/2014] [Indexed: 11/18/2022]
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Munteanu CR, Pedreira N, Dorado J, Pazos A, Pérez-Montoto LG, Ubeira FM, González-Díaz H. LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design. Mol Inform 2014; 33:276-85. [DOI: 10.1002/minf.201300027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 12/11/2014] [Indexed: 01/05/2023]
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Fernandez-Lozano C, Fernández-Blanco E, Dave K, Pedreira N, Gestal M, Dorado J, Munteanu CR. Improving enzyme regulatory protein classification by means of SVM-RFE feature selection. MOLECULAR BIOSYSTEMS 2014; 10:1063-71. [DOI: 10.1039/c3mb70489k] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Aguiar-Pulido V, Munteanu CR, Seoane JA, Fernández-Blanco E, Pérez-Montoto LG, González-Díaz H, Dorado J. Naïve Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer. MOLECULAR BIOSYSTEMS 2012; 8:1716-22. [PMID: 22466084 DOI: 10.1039/c2mb25039j] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Fast cancer diagnosis represents a real necessity in applied medicine due to the importance of this disease. Thus, theoretical models can help as prediction tools. Graph theory representation is one option because it permits us to numerically describe any real system such as the protein macromolecules by transforming real properties into molecular graph topological indices. This study proposes a new classification model for proteins linked with human colon cancer by using spiral graph topological indices of protein amino acid sequences. The best quantitative structure-disease relationship model is based on eleven Shannon entropy indices. It was obtained with the Naïve Bayes method and shows excellent predictive ability (90.92%) for new proteins linked with this type of cancer. The statistical analysis confirms that this model allows diagnosing the absence of human colon cancer obtaining an area under receiver operating characteristic of 0.91. The methodology presented can be used for any type of sequential information such as any protein and nucleic acid sequence.
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Affiliation(s)
- Vanessa Aguiar-Pulido
- Department of Information and Communications Technologies, University of A Coruña, A Coruña, Spain
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González-Díaz H, Munteanu CR, Postelnicu L, Prado-Prado F, Gestal M, Pazos A. LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria. MOLECULAR BIOSYSTEMS 2012; 8:851-62. [DOI: 10.1039/c2mb05432a] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Salmaso S, Caliceti P. Self assembling nanocomposites for protein delivery: supramolecular interactions of soluble polymers with protein drugs. Int J Pharm 2011; 440:111-23. [PMID: 22209998 DOI: 10.1016/j.ijpharm.2011.12.029] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2011] [Revised: 12/04/2011] [Accepted: 12/16/2011] [Indexed: 12/11/2022]
Abstract
Translation of therapeutic proteins to pharmaceutical products is often encumbered by their inadequate physicochemical and biopharmaceutical properties, namely low stability and poor bioavailability. Over the last decades, several academic and industrial research programs have been focused on development of biocompatible polymers to produce appropriate formulations that provide for enhanced therapeutic performance. According to their physicochemical properties, polymers have been exploited to obtain a variety of formulations including biodegradable microparticles, 3-dimensional hydrogels, bioconjugates and soluble nanocomposites. Several soluble polymers bearing charges or hydrophobic moieties along the macromolecular backbone have been found to physically associate with proteins to form soluble nanocomplexes. Physical complexation is deemed a valuable alternative tool to the chemical bioconjugation. Soluble protein/polymer nanocomplexes formed by physical specific or unspecific interactions have been found in fact to possess peculiar physicochemical, and biopharmaceutical properties. Accordingly, soluble polymeric systems have been developed to increase the protein stability, enhance the bioavailability, promote the absorption across the biological barriers, and prolong the protein residence in the bloodstream. Furthermore, a few polymers have been found to favour the protein internalisation into cells or boost their immunogenic potential by acting as immunoadjuvant in vaccination protocols.
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Affiliation(s)
- Stefano Salmaso
- Department of Pharmaceutical Sciences, University of Padua, Via F. Marzolo, 5, 35131 Padua, Italy
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Fuzzy clustering of physicochemical and biochemical properties of amino acids. Amino Acids 2011; 43:583-94. [PMID: 21993537 PMCID: PMC3397137 DOI: 10.1007/s00726-011-1106-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2011] [Accepted: 09/23/2011] [Indexed: 12/03/2022]
Abstract
In this article, we categorize presently available experimental and theoretical knowledge of various physicochemical and biochemical features of amino acids, as collected in the AAindex database of known 544 amino acid (AA) indices. Previously reported 402 indices were categorized into six groups using hierarchical clustering technique and 142 were left unclustered. However, due to the increasing diversity of the database these indices are overlapping, therefore crisp clustering method may not provide optimal results. Moreover, in various large-scale bioinformatics analyses of whole proteomes, the proper selection of amino acid indices representing their biological significance is crucial for efficient and error-prone encoding of the short functional sequence motifs. In most cases, researchers perform exhaustive manual selection of the most informative indices. These two facts motivated us to analyse the widely used AA indices. The main goal of this article is twofold. First, we present a novel method of partitioning the bioinformatics data using consensus fuzzy clustering, where the recently proposed fuzzy clustering techniques are exploited. Second, we prepare three high quality subsets of all available indices. Superiority of the consensus fuzzy clustering method is demonstrated quantitatively, visually and statistically by comparing it with the previously proposed hierarchical clustered results. The processed AAindex1 database, supplementary material and the software are available at http://sysbio.icm.edu.pl/aaindex/.
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González-Díaz H, Prado-Prado F, Sobarzo-Sánchez E, Haddad M, Maurel Chevalley S, Valentin A, Quetin-Leclercq J, Dea-Ayuela MA, Teresa Gomez-Muños M, Munteanu CR, José Torres-Labandeira J, García-Mera X, Tapia RA, Ubeira FM. NL MIND-BEST: A web server for ligands and proteins discovery—Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. J Theor Biol 2011; 276:229-49. [DOI: 10.1016/j.jtbi.2011.01.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2010] [Revised: 12/02/2010] [Accepted: 01/10/2011] [Indexed: 10/18/2022]
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Investigation of the complexation of proteins with neutral water soluble polymers through model analysis method. POLYMER 2011. [DOI: 10.1016/j.polymer.2011.01.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Roy K, Ojha PK. Advances in quantitative structure–activity relationship models of antimalarials. Expert Opin Drug Discov 2010; 5:751-78. [DOI: 10.1517/17460441.2010.497812] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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