BIOINFORMATICS REVIEW- AUGUST 2018

AUGUST 2018 VOL 4 ISSUE 8

“Two things are infinite: the universe and human stupidity; and I'm not sure about the universe.�

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Albert Einstein

Most widely used tools for phylogenetic customization

Video Tutorial: How to perform docking using Autodock-Vina

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Contents

August 2018

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Topics Editorial....

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03 Tools Most widely used tools for phylogenetic tree customization 06

05 Software 04 Tutorial Video Tutorial: How to perform docking using Autodock Vina 08

Web-based tools for protein-peptide docking

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FOUNDER TARIQ ABDULLAH EDITORIAL EXECUTIVE EDITOR TARIQ ABDULLAH FOUNDING EDITOR MUNIBA FAIZA SECTION EDITORS FOZAIL AHMAD ALTAF ABDUL KALAM MANISH KUMAR MISHRA SANJAY KUMAR PRAKASH JHA NABAJIT DAS

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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

Bioinformatics- A broad future ahead: Editorial

EDITORIAL

It has been a wonderful time since BiR came into existence. As we enter a new year, BiR tries to look forward towards the development and wonderful achievements and providing the best knowledge regarding bioinformatics. In the past two years, BiR has hit a long road from a few readers to several thousand.

Muniba Faiza

Founding Editor

Every complimentary and appreciation mail we get feels like an achievement for us. Bioinformatics has got a great future ahead of it with a better understanding and precise methodologies for both dry and the wet lab experimentations.

In the last two years, BiR has advanced in many aspects. We have come up with an android app which helps our readers to stay connected with the latest updates, our articles have started to appear in Google Scholar, we get a lot of cherishing emails, and collaboration proposals. BiR is trying to broaden the horizons by covering different domains of bioinformatics. Since bioinformatics is multidisciplinary, to date, the team of BiR has tried to go through almost every aspect of it including big data, sequence analysis, structural bioinformatics, data mining, tools, software, biostatistics, and so on. This year BiR is more focused to provide a rich content to our readers and help to understand the concepts of bioinformatics more easily. The team of BiR is trying to reach to the students to encourage them for their career in bioinformatics and to the researchers currently working in the same area. The last internship at BiR was a great success and we got an amazing response from our interns. We are looking forward to presenting our work at school and college level to introduce this to the young minds who are more fascinated by the technology.

We have such a long road to drive on which is not possible without the support of our readers, subscribers, and contributors. We are thankful to our readers wholeheartedly for their support and suggestions and wish them a very happy and prosperous new year

Letters and responses: info@bioinformaticsreview.com

with new hopes and great achievements. We would like to hear your thoughts and feedback about BiR, and what other kinds of articles you would like to read.

EDITORIAL

Please write us at info@bioinformaticsrevew.com

TOOLS

Most widely used tools for phylogenetic tree customization Image Credit: Google Images

“The basic requirement for using any bioinformatics software/tool is the file format and it is very difficult to deal with the phylogenetic tree conversions for the beginners sometimes. There are a bunch of tools available to visualize and annotate phylogenetic trees.� ost of the times, it is a very tedious job to convert file formats in bioinformatics, especially when we are dealing with phylogeny. Most of the available online servers mess your file and the output format is also not supported by the other programs.

Additionally, it is quite difficult to perform other customizations on the phylogeny tree.

M

The basic requirement for using any bioinformatics software/tool is the file format and it is very difficult to deal with the phylogenetic tree conversions for the beginners sometimes. There are a bunch of tools available to visualize and annotate phylogenetic trees. Some of the most widely used software/tools are discussed below:

1. MEGA [1]

2. Dendroscope [2]

MEGA is a useful software in constructing phylogenies and visualizing them, and also for data conversion. It can easily convert alignment files to other formats such as nexus, paup, phylip, and fasta, and so on. The MEGA tree explorer is helpful in editing trees very easily, subtrees can also be selected and edited separately. Some tree image export options are also available. The input formats are newick, phylip, mega, and nexus. The phylogenetic tree can also be converted in newick format but it falls short on converting it into other formats such as phylip which is required in other analyses such as selection analysis.

It is helpful in visualizing large trees and provides several options to export their graphics with a command line.

Several different views are also available, trees can be easily rerooted and node labels and branches can be easily formatted. It can export trees in newick and nexus format.

Although users will have to register themselves first to use this feature.

3. FigTree [3] It is actually designed to visualize trees that are produced by BEAST [4] program. Tip labels and node labels can be easily edited. It can easily export trees in nexus, newick, and JSON format with some graphics export options such as emf, pdf, sg, png, etc.

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4. Phylotree.js [5]

References

It is a javascript based library to visualize and annotate trees and offer some other customizations. It has a wide application in Datamonkey [6] comparative analyses. A user can upload trees using Phylotree.js where a user can easily select test and reference branches, and any changes can be mapped to their position on the corresponding structure. It is also good for comparison of trees with links between leaves known as a tanglegram, where crossings can represent evolutionary events. It also offers several export options and other built-in features [5].

1.

Kumar, S., Stecher, G., & Tamura, K. (2016).

MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Molecular biology and evolution, 33(7), 1870-1874.

2.

Huson, D. H., & Scornavacca, C. (2012).

Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks. Systematic biology, 61(6), 1061-1067.

3.

http://tree.bio.ed.ac.uk/software/figtree/

4.

http://tree.bio.ed.ac.uk/software/beast/

5.

Shank, S. D., Weaver, S., & Pond, S. L. K. (2018). phylotree. js-a JavaScript library for application development and interactive data visualization in phylogenetics. BMC bioinformatics, 19(1), 276.

6.

Delport, W., Poon, A. F., Frost, S. D., & Kosakovsky Pond, S. L. (2010). Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology. Bioinformatics, 26(19), 2455-2457.

7.

Yu, G., Smith, D. K., Zhu, H., Guan, Y., & Lam, T. T. Y. (2017). ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution, 8(1), 28-36.

8.

Perrière, G. and Gouy, M. (1996) WWW-Query: An on-line retrieval system for biological sequence banks. Biochimie, 78, 364-369.

9.

Stöver B C, Müller K F: TreeGraph 2: Combining and visualizing evidence from different phylogenetic analyses. BMC Bioinformatics 2010, 11:7

5. ggtree [7] ggtree is an R package for phylogenetic tree visualization and annotation. It also displays annotation data on the tree apart from visualizing it. Users can annotate trees with their own data and can easily convert trees into a data frame, and a lot of other features are available (https://guangchuangyu.github.io/sof tware/ggtree/).

There is various other software available to visualize and customize phylogenetic trees such as NJplot [8], TreeGraph2 [9], and so on. We will be discussing their functional detail with examples in the upcoming articles.

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TUTORIAL

Video Tutorial: How to perform docking using Autodock Vina

Image Credit: Stock Photos

“Autodock Vina is a program for molecular docking and virtual screening. It is a most widely used tool for site-specific docking.�

T

his is an video addition to our existing tutorial (How to perform site-specific docking using Autodock

Vina).

It is also available on YouTube.

Reference 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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DOCKING

Web-based tools for proteinpeptide docking Image Credit: Stock photos

“Protein–peptide docking methods can be divided into three categories: template-based docking; local docking; and global docking.� rotein-protein interactions are considered necessary in the interactome analysis as they play an important in various biological processes such as posttranslational modifications and signal transduction, and short peptides mediate around 40% of protein-protein interactions[1]. They are also found involved in some kind of infections and critical human diseases such as cancer [2,3].

P

Given the importance of proteinpeptide interactions, it is necessary to determine their structure complexes to understand the involved mechanisms and then study further for its applications in the development of personalized drugs [4].

Protein-peptide docking methods are categorized into three: local docking, global docking, and

template-based docking (for reading in detail, click here).

This article explains some of the widely used web-based tools for protein-peptide docking.

1. CABS-dock [5] It is a global protein-peptide docking web server without a priori information about the binding sites.

It searches for the binding sites with random conformations of the protein and positions of the peptide [5]. It follows four major steps: first, it generates random structures of the peptide, second, it simulates the binding and docking models using Replica Exchange Monte Carlo dynamics producing 10 trajectories for 10 replicas with each consisting of 1000 time-stamped simulation snapshots; in the third step, final

models are selected on the basis of initial-filtering during which all unbound models are discarded and 100 models showing lowest energy are selected, followed by k-medoids clustering performed 100 times giving 10 models; finally, these 10 models are reconstructed and optimized using Modeller [6].

The web server (http://biocomp.chem.uw.ed u.pl/CABSdock/) offers various options for users. They can either upload the PDB structure of the protein or provide the PDB code along with the peptide sequence or structure and set the simulation cycles.

2. Rosetta FlexPepDock [7] It is a local protein-peptide docking based tool which refines proteinBioinformatics Review | 9

peptide complex structures using Monte-Carlo minimization approach. The input structures of both the protein and the peptide undergo optimization during which side-chain conformers are optimized to reduce internal clashes of the structures. Later, a refinement step is followed to provide one single model, during which reduced repulsive Van der Waals term and increased attractive Van der Waals terms are optimized in 10 cycles.

The web server (http://flexpepdock.furmanlab.cs.h uji.ac.il/cite.php) offers various advanced options such as constraint file upload, reference structure, and so forth [7].

3. GalaxyPepDock [8] This is a template-based docking method which uses known structures as the templates to generate a protein-peptide complex.

It involves two major steps: template selection and model building. In the first step, the templates are searched in PepBind database [9] on the basis of interaction and structure similarity calculating a Scomplex score for each complex. In the second step, 50 complex models are built using GalaxyTBM [10], which undergo optimization and ultimately, 10 complex structures are selected depending on the best energy values

and are further processed using GALAXY refinement method [11] which adjusts the side-chains and backbone using molecular dynamics.

The web server (http://galaxy.seoklab.org/cgibin/submit.cgi?type=PEPDOCK) is also user-friendly and also provides a stand-alone version.

4. pepATTRACT [12] This is a completely global proteinpeptide docking method for blind and large-scale docking. It follows two major steps: protein-peptide docking; and interface analysis and prediction. During the first step, three model structures are generated using PeptideBuilder [13] followed by filling missing atoms using PDB2PQR [14]. These structures are later converted into ATTRACT coarse-grained atom type representation [15] followed by docking using ATTRACT [15]. The 50 docked models are selected for each of which interface propensity is calculated and finally presented to the user.

The web server (http://bioserv.rpbs.univ-parisdiderot.fr/services/pepATTRACT/) requires the simple protein and peptide structures/sequences as the input.

Reference 1.

Petsalaki, E., & Russell, R. B. (2008).

Peptide-mediated interactions in biological systems: new discoveries and applications. Current opinion in biotechnology, 19(4), 344-350.

2.

Soni, V., Cahir-McFarland, E., & Kieff, E. (2007). LMP1 TRAFficking activates growth and survival pathways. In TNF Receptor Associated Factors (TRAFs) (pp. 173-187).

Springer, New York, NY.

3.

MacLaine, N. J., & Hupp, T. R. (2011). How phosphorylation controls p53. Cell Cycle, 10(6), 916-921.

4.

Craik, D. J., Fairlie, D. P., Liras, S., & Price, D. (2013). The future of peptide‐based drugs. Chemical biology & drug design, 81(1), 136-147.

5.

Eswar, N., Webb, B., Marti‐Renom, M. A., Madhusudhan, M. S., Eramian, D., Shen, M. Y., ... & Sali, A. (2006). Comparative protein structure modeling using Modeller. Current protocols in bioinformatics, 15(1), 5-6.

6.

Eswar, N., Webb, B., Marti‐Renom, M. A., Madhusudhan, M. S., Eramian, D., Shen, M. Y., ... & Sali, A. (2006). Comparative protein structure modeling using Modeller. Current protocols in bioinformatics, 15(1), 5-6.

7.

London, N., Raveh, B., Cohen, E., Fathi, G., & Schueler-Furman, O. (2011). Rosetta FlexPepDock web server—high resolution modeling of peptide–protein interactions. Nucleic acids research, 39(suppl_2), W249-W253.

8.

Lee, H., Heo, L., Lee, M. S., & Seok, C. (2015).

GalaxyPepDock: a protein–peptide docking tool based on interaction similarity and energy optimization. Nucleic acids research, 43(W1), W431-W435.

9.

Das, A. A., Sharma, O. P., Kumar, M. S., Krishna, R., & Mathur, P. P. (2013). PepBind: a comprehensive database and computational tool for analysis of protein–

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peptide interactions. Genomics, proteomics & bioinformatics, 11(4), 241246.

10. Ko, J., Park, H., & Seok, C. (2012).

GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions. BMC bioinformatics, 13(1), 198.

11. Heo, L., Park, H., & Seok, C. (2013).

GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic acids research, 41(W1), W384-W388.

12. de Vries, S. J., Rey, J., Schindler, C. E., Zacharias, M., & Tuffery, P. (2017). The pepATTRACT web server for blind, largescale peptide–protein docking. Nucleic acids research, 45(W1), W361-W364.

13. Tien, M. Z., Sydykova, D. K., Meyer, A. G., & Wilke, C. O. (2013). PeptideBuilder: A simple Python library to generate model peptides. PeerJ, 1, e80.

Poisson–Boltzmann electrostatics calculations. Nucleic acids research, 32(suppl_2), W665-W667.

15. Zacharias, M. (2003). Protein-protein docking with a reduced protein model accounting for side‐chain flexibility. Protein Science, 12(6), 1271-1282.

14. Dolinsky, T. J., Nielsen, J. E., McCammon, J. A., & Baker, N. A. (2004). PDB2PQR: an automated pipeline for the setup of

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