DECEMBER 2018 VOL 4 ISSUE 12
“Science is a beautiful gift to humanity; we should not distort it.” -
Dr. A. P. J. Abdul Kalam
Bioinformatics and stem cell researchA mini review
Video Tutorial: Autodock Vina Result Analysis with PyMol
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Contents
December 2018
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Topics Editorial....
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03 Docking Video Tutorial: Autodock Vina Result Analysis with PyMol
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05 Tools Bioinformatics and stem cell research- A mini review 10
04 Tutorial Tutorial: Vina Output Analysis Using PyMol 08
FOUNDER TARIQ ABDULLAH EDITORIAL EXECUTIVE EDITOR TARIQ ABDULLAH FOUNDING EDITOR MUNIBA FAIZA SECTION EDITORS FOZAIL AHMAD ALTAF ABDUL KALAM MANISH KUMAR MISHRA SANJAY KUMAR NABAJIT DAS
REPRINTS AND PERMISSIONS You must have permission before reproducing any material from Bioinformatics Review. Send E-mail requests to info@bioinformaticsreview.com. Please include contact detail in your message. BACK ISSUE Bioinformatics Review back issues can be downloaded in digital format from bioinformaticsreview.com at $5 per issue. Back issue in print format cost $2 for India delivery and $11 for international delivery, subject to availability. Pre-payment is required CONTACT PHONE +91. 991 1942-428 / 852 7572-667 MAIL Editorial: 101 FF Main Road Zakir Nagar, Okhla New Delhi IN 110025 STAFF ADDRESS To contact any of the Bioinformatics Review staff member, simply format the address as firstname@bioinformaticsreview.com PUBLICATION INFORMATION Volume 1, Number 1, Bioinformatics Reviewâ„¢ is published monthly for one year (12 issues) by Social and Educational Welfare Association (SEWA) trust (Registered under Trust Act 1882). Copyright 2015 Sewa Trust. All rights reserved. Bioinformatics Review is a trademark of Idea Quotient Labs and used under license by SEWA trust. Published in India
EDITORIAL
Bioinformatics is prediction- and simulationbased: Let's rephrase the conversation!
Dr. Prashant Pant Editor-in-Chief Muniba Faiza Founding Editor
Since the field of Bioinformatics has come into existence, general opinion has been that 'Bioinformatics is all about predictions, experimental-based, and talking everything in the imagination', or 'the probabilistic outcomes must be approved in real world' or ' the Bioinformatics results will have to be testified using the wet lab'. This indeed is true, all the predictions made by Bioinformatics drafted experiments, must be testified using the wet lab. We cannot ride on a long way just on the basis of predictions and assumptions. But what if this is just one side of the coin? We still need to flip over to see the other side of Bioinformatics. The prevailing opinion about the Bioinformatics is that it is more of a prediction-based and simulation-based field, which may be a half-truth. Indeed the predictions need to be verified in the real world but this is not the case with Bioinformatics only, it can also be related to the drug-discovery process where each and every drug is subjected to clinical trials before being manufactured as the complete drug, or even the other fields of science such as physics. This could be an application of Bioinformatics to be utilized in the other biological fields (including biophysics and biochemistry) where we model and simulate the macromolecules, predict potential functions and roles. Science is about studying nature, discovering new opportunities, providing better insights, and developing new things leading to advancements. Bioinformatics bridges two worlds: biological science and computer science, which provides insights into the biological world and the latter incorporates the algorithm
Letters and responses: info@bioinformaticsreview.com
development and programming skills. With that in favor, bioinformaticists and bioinformaticians have the capability to advance in science and develop novel important techniques, tools, and software to study nature. Research is endless, additionally, most of the times, the outcomes are different than what we assumed for but it always reveals something important though and for that sometimes 'out-of-thebox thinking' is required. This may also be the case here, Bioinformatics needs more exploration and inspiration which may take it to the next level.
EDITORIAL
Please share your thoughts at info@bioinformaticsreview.com
DOCKING
Video Tutorial: Autodock Vina Result Analysis with PyMol Image Credit: Stock photos
“This is a continuation video tutorial of docking.� his is a video tutorial to demonstrate the analysis of Autodock Vina results using PyMol, in continuation of our existing docking tutorial.
T
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TUTORIAL
Tutorial: Vina Output Analysis Using PyMol Image Credit: Stock Photos
“This is the basic introductory tutorial; you can always explore more to represent the complex more beautifully. There are several ways to represent the protein and the ligand such as ribbon, surface, cartoon, and more. You can also show only binding pockets of the protein as per your requirements.�
T
he analysis of Autodock Vina [1] results is a bit tricky in the sense of viewing all interactions and selecting the best pose. In our last video tutorial, we explained how to analyze docking results obtained from Vina using PyMol. This article is the written guide for the same. We need a PDB file of protein and vina output file in .pdbqt format. Here, our protein file is 2bxa.pdb and the vina output file is cmpf.pdbqt. Let's get started! 1. Open PyMol then go to 'File' --> 'Open' --> then select the PDB file of your protein (here, 2bxa.pdb) --> click 'Open'. It will display the
protein.
2. Now open the vina output file.
Again go
to 'File' --> 'Open' --> then select the pdbqt file (here, cmpf.pdbqt) --> click 'Open'.
You can now see the protein and the ligand but there are no bondings/interactions between the two. Now, in order to see the interactions, follow these steps: Look at the middle right side in PyMol window, there you can see some tabs named as 'all', '2bxa.pdb', and 'cmpf.pdbqt', and some letters such as 'A' (Action), 'S' (Show), 'H' (Hide), and 'L' (Label) right in front of these tabs. 3. Go to the tab 'all' --> click 'A' (it will show you a drop-down menu) --
> 'preset' --> 'ligand sites' --> 'cartoon'.
It will re-center your complex and show the interactions the protein is making with the ligand. You can also measure the bond lengths following these steps: 1. Go
to 'Wizard' 'Measurement'.
-->
It will ask to select the atoms. 2. Select
two atoms from both the protein and the ligand showing interaction. It will display the length.
Similarly, you can label the residues as well by going to 'Wizard' --> 'Label'
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Now, there comes the point where you need to choose the best docking pose. Well, the best pose is supposed to be the first docking pose generated by Vina which shows 'zero' RMSD value and best binding affinity. You may also need to make sure whether your ligand is interacting to all the binding/catalytic residues you defined for docking. Sometimes, your second pose may also show the required maximum interactions within the binding pocket. This is the basic introductory tutorial; you can always explore more to represent the complex more beautifully. There are several ways to represent the protein and the ligand such as ribbon, surface, cartoon, and more. You can also show only binding pockets of the protein as per your requirements. I hope this tutorial helps! For any query and suggestions, please write to us at info@bioinformaticsreview.com and tariq@bioinformaticsreview.com Reference 1.
Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.
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TOOLS
Bioinformatics and stem cell research- A mini review Image Credit: Stock photos
“The use of bioinformatics in stem cell biology initially revolved around self-renewal dynamics of adult stem cells [9] that later saw the application of molecular biology data along with the use of genome sequencing. ”
tem cells are cells that can be differentiated into other types and are thus pluripotent with the ability to become cells of all lineages. Cells found in the blastocyst of embryos are called as embryonic stem cells or ESCs [1, 2] that are considered “gold” standard of pluripotency [3]. There are also adult stem cells found in several tissues for the purpose of repair such as mesenchymal stem cells that have been differentiated into various other tissues [4].
S
These pluripotent stem cells hold promise to aid in studying embryo development, differentiation of cells and regenerative medicine that aims at “personalized” medicine [3].
Another class of stem cell known as induced pluripotent stem cells (iPSCs) were created by expressing key transcription factors in adult cells [5]. The field of regenerative medicine has seen plenty of research articles on stem cells. While some stem cell types have been differentiated into specific cell types to cure several diseases such as neurodegenerative disorders, cancer, diabetes, heart disease etc. [2], iPSCs have been differentiated into retinal cells, endothelial cells, and neurons [6]. Where does bioinformatics fit in? Bioinformatics hardware,
is
a merger of mathematics,
networking, and databases to develop tools that can be used by a person interested in life sciences to process and analyze data [7]. Bioinformatic tools can potentially help in identifying its possible function, for example, KEGG can identify pathways, orthologs, and functions of sequences submitted [8]. The use of bioinformatics in stem cell biology initially revolved around self-renewal dynamics of adult stem cells [9] that later saw the application of molecular biology data along with the use of genome sequencing. With molecular profiling of single cells and systems biology that aid in modeling stem cell patterns, the field of bioinformatics
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can play a key role in stem cell biology [10]. A few tools: Let’s discuss a few examples to gain a better understanding. The transcriptome of pluripotent stem cells has been first studied using DNA microarrays with classification algorithms that aid in distinguishing among differentiated, multipotent and pluripotent stem cells [11]. In the case of larger datasets, the classification of pluripotent stem cells can be facilitated by the use of machine learning. One of such tools is an algorithm PluriTest that uses measurements of DNA microarrays to analyze pluripotent cells using bioinformatics models [12]. PluriNetWork can uncover mechanisms and molecules involved in pluripotency of stem cells using a combination of links to literature, gene ontology and automated analysis [2]. Mechanisms in stem cells such as regulation associated at a post-transcriptional level have been studied using next-generation sequencing techniques [3]. For example, the involvement of ZFP217, a zinc finger protein associated with chromatin in the regulation of pluripotency in human embryonic stem cells is shown with a MeRIP-Seq method [13].
Taken together, these and many other genome-wide molecular profiling studies have collectively contributed to our understanding of the multilayered regulation of pluripotency, and have further served as models to understand the regulation of cell type identity for other, less-investigated lineage [10]. A common curated system used a combination of social networking software as well as Wiki to combine research data, key genes and protein circuits to be used with ease and analysis with Cytoscape software [14]. Such a network is a common system composed of literature and details of transcription factors and signals that is tailor-made for a particular requirement [2].
Another application of bioinformatics in stem cell biology is to assess the differentiation ability of a stem cell using a “scorecard” approach. Bock et al, 2011 developed a deviation scorecard with methylation patterns and gene expression of human ESCs as they hypothesized that any deviation here could prevent differentiation to particular lineages. Differences in iPSC lines in comparison to ESCs were tabulated [15]. Several genes were listed as markers of germ layers, that when expressed at early stages indicate the differentiation potential, for example, hypermethylation of GRM (glutamate receptor) in motor neurons [3, 10].
The field of epigenetics makes an entry to analyze differences between ESCs and iPSCs as well as to study patterns seen with iPSCs such as their bias towards lineages of a donor [3, 10]. For instance, a study published in 2011 used a support vector machine learning algorithm based on methylation data of ESCs and iPSCs [15] that could identify the ESCs with precision but iPSCs at 61% sensitivity [3]. Regions of differential methylation were analyzed using ‘comprehensive high-throughput arrays for relative methylation’ (CHARM) to uncover promoters of factors for distinct lineages [16, 17].
An algorithm TeratoScore uses gene expression of teratomas to evaluate the differentiation ability of human pluripotent stem cells as they can differentiate into all three germ layers. The origin of a tumor, either pluripotent or tissue-specific cells can be classified by the tool [18]. Another tool CellNet uses gene expression profiles to give a prediction of a specific cell type in the query along with transcription factors [19]. The efficiency of differentiation of pluripotent stem cells can be predicted using a platform called KeyGenes that uses
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RNA-Seq or microarray data of human fetal tissues [20]. A data repository for stem cells called the Cellfinder looks at augmenting human embryonic stem cell registry (hESCreg) into a tool that facilitates the design of projects and analysis of the registry [21]. Additionally, a web-interface called StemBase contains SAGE (Serial Analysis of Gene Expression) data of mouse and human stem cells and allows for studying specific genes or markers [22].
Conclusion This short review has highlighted a few of the tools that find use in stem cell research. The above-mentioned tools show that the field of bioinformatics holds much promise in analyzing stem cells using web interfaces and tools. With further inputs from the various “OMICS” that unravel the roles of molecules in a single cell, the use of bioinformatics can aid in analyzing fates of cells as well as potentially delve deeper into this exciting field of stem cells that are being pitched in as a panacea for several diseases that would help us realize an important goal of stem cell biology:
a detailed glimpse into understanding the nuances of the cells vital for development and maintenance of life. References 1.
Evans M. Discovering Pluripotency: 30 years of mouse embryonic stem cells. Nat Rev Mol Cell Biol. 2011;12(10):680- 6. 2. Babu PBR and Krishnamoorthy P. Applications of Bioinformatics Tools in Stem Cell Research: An Update. J Pharm Res. 2012;5(9),4863-6. 3. Nestor MW and Noggle SA. Standardization of human stem cell pluripotency using bioinformatics. Stem Cell Res Ther. 2013;4:37. 4. Pacini S. Deterministic and stochastic approaches in the clinical application of mesenchymal stromal cells (MSCs). Front Cell Dev Biol. 2014;12:50. 5. Takahashi K and Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006;126:663–6. 6. Bilic J and Izpisua Belmonte JC. Concise review: Induced pluripotent stem cells versus embryonic stem cells: close enough or yet too far apart? Stem Cells. 2012; 30:33–41. 7. Orozco A, Morera J, Jiménez S, Boza R. A review of Bioinformatics training applied to research in Molecular Medicine, Agriculture and Biodiversity in Costa Rica and Central America. Brief Bioinform. 2013;14(5):661– 70. 8. Kyoto Encyclopedia of Genes and Available on: https://www.genome.jp/kegg/ 9. Till JE, et al. A stochastic model of stem cell proliferation, based on the growth of spleen colony-forming cells. Proc Natl Acad Sci U.S.A. 1964;51:29–36. 10. Bian Q and Cahan P. Computational Tools for Stem Cell Biology. Trends Biotechnol. 2016;34(12).
11. Müller FJ, Laurent LC, Kostka D, Ulitsky I, Williams R, Lu C, et al. Regulatory networks define phenotypic classes of human stem cell lines. Nature. 2008;455:401–5. 12. Müller F-J, Schuldt BM, Williams R, Mason D, Altun G, Papapetrou EP, et al. A bioinformatic assay for pluripotency in human cells. Nat Methods. 2011;8:315. 13. Aguilo F, et al. Coordination of m6A mRNA methylation and gene transcription by ZFP217 regulates pluripotency and reprogramming. Cell Stem Cell. 2015;17:689–704. 14. Narad P, Upadhyaya K and Som A. Reconstruction, visualization and explorative analysis of human pluripotency network. Network Biology. 2017;7:57-75. 15. Bock C, Kiskinis E, Verstappen G, Gu H, Boulting G, Smith ZD, et al. Reference maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines. Cell. 2011;144:439–52. 16. Kim K, et al. Epigenetic memory in induced pluripotent stem cells. Nature. 2010;467:285–90. 17. Kim K, et al. Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells. Nat. Biotechnol. 2011;29:1117–9. 18. Avior Y, et al. TeratoScore: assessing the differentiation potential of human pluripotent stem cells by quantitative expression analysis of teratomas. Stem Cell Rep. 2015;4:967–74. 19. Cahan P, et al. CellNet: network biology applied to stem cell engineering. Cell. 2014;158:903–15. 20. Roost MS, et al. KeyGenes, a tool to probe tissue differentiation using a human fetal transcriptional atlas. Stem Cell Rep. 2015;4:1112–24. 21. Borstlap J, Luong MX, Rooke HM, et al. International stem cell registries. In Vitro Cell Dev Biol - Animal. 2010;46(3-4):242-6. 22. Sandie R, Palidwor GA, Huska MR, et al. Recent developments in Stembase: a tool to study gene expression in human and murine stem cells. BMC Res Notes. 2009;2:39.
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