BIOINFORMATICS REVIEW- OCTOBER 2017

OCTOBER 2017 VOL 3 ISSUE 10

“The human brain is an incredible pattern-matching machine.�

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Jeff Bezos

Intrinsically disordered proteins' predictors and databases: An overview

Predictors and databases

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Contents

October 2017

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Topics Editorial....

03 Databases Intrinsically disordered proteins' predictors and databases: An overview 07

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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 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.

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EDITORIAL

Bioinformatics Review (BiR): Bridging Between The Two Worlds Informatics and Biology are two sciences which are as different from each other as possible. One runs on the core concept of variation and another on strict reasoning. But still, these two have combined in a most natural way under the realm of “Bioinformatics”. For a biologist today it’s difficult to imagine a world without all biological databases and further no branch to decipher the huge enigma that it brings. Bioinformatics Review (BiR) journal is a platform to discover the latest happenings in this melting pot of two varied fields.

Dr. Roopam Sharma

Honorary Editor

The era of “omics” kick-started with the drafting of Human Genome Project (HGP) in 2003. Since then, a number of technological advancements especially, NGS has been generating mind-boggling data for the knowledge banks. Latest inventions like single-cell transcriptomics or metagenomics of most unusual habitats show how the evolution of technological advancements is directly resulting in breakthroughs in biological sciences.

Among various areas of biology which has benefited from these advancements is Pathology. In fact, deciphering the molecular and genetic basis of diseases in humans was the guiding force behind human genome sequencing Project. Bioinformatics has led to an impressive increase in recognition of possible pathogenic factors in varied systems, so much so that new techniques are being devised to increase the speed to actually test these factors in the wet lab. If we consider computationally, smaller but ever-changing genomes and transcriptomes of these pathogens, make them a much suitable candidate to test out many hypotheses for Bioinformatics studies. Effector Bioinformatics involves building custom pipelines for distinct species based on characteristics of effectors and size of the genome involved. These can be based on

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EDITORIAL

Homology or feature extraction or both, e.g. discovery of RXLR motifs in Oomycete effectors allowed many more effectors to be identified. This collaboration of two sciences for plant pathology has led to the development of many general use platforms like Broad-Fungal Genome Initiative, EuPathDB, PhytoPath and so on, but there is much need of developing specified resources like PHIbase for specific areas like effector biology. The use of machinelearning techniques like artificial neural network approach (which is actually based on biological neural networks) really shows how the two branches are so distinct yet so intertwined. All in all, it’s a brave new world where artificial communication is not only stimulating but also helping us understand the communication (between host and pathogen) going within the realm of life.

In this issue, BiR focusses on reviews related to some of the very basic techniques which have been used in computational biology and its applications in various biological studies. We look forward to continued support from our readers and contributors. For suggestions and feedback, do write to us at info@bioinformaticsreview.com

DATABASES

Intrinsically disordered proteins' predictors and databases: An overview Image Credit: Stock Photos

“In the last few years, the interactions between the IDPs and the other proteins have been an interesting research topic as their detailed analysis opens opportunities for therapeutic targeting.�

I

ntrinsically unstructured proteins (IUPs) are the natively unfolded proteins which must be unfolded or disordered in order to perform their functions. They are commonly referred to as intrinsically disordered proteins (IDPs) and play significant roles in regulating and signaling biological networks [1].

IDPs are also involved in the assembly of signaling complexes and in the dynamic self-assembly of membrane-less nuclear and cytoplasmic organelles [1]. The disordered regions in a protein can be highly conserved among the

species in respect of both the composition and the sequence [2].

IDPs have been found to be interacting frequently with the protein interaction networks [3,4].

The computational and bioinformatics analysis helps to identify and characterize disordered protein regions. Around fifteen years ago, there was only one predictor available for identifying the disordered protein regions, known as PONDR [5]. Today, around 50 predictors can be used to predict the disordered regions in a protein [6]

such as FoldIndex [7], GlobPlot [8], FoldUnfold [9-11], and so on. All these predictors are based on different algorithms. Structural disorders account to different states thereby rendering the prediction approach of single predictors ineffective. Therefore, some other combined algorithms were developed to predict IDPs more efficiently such as PONDR-FIT [12].

Some of the databases for IDPs also exist, for example, DisProt [13] and D2P2 [4].

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In the last few years, the interactions between the IDPs and the other proteins have been an interesting research topic as their detailed analysis opens opportunities for therapeutic targeting. Some of the studies based on IDP interactions has led to successful pharmaceutical targeting [15]. DIBS (DIsordered Binding Sites) is a recently developed database which stores interactions between the IDPs and the ordered proteins [16].

The annotations of order and disorder are grouped into three categories: 1. Proteins are marked as disordered from the direct proofs collected from the databases such as DisProt [13], and these proteins are referred to as 'Confirmed'.

2. Besides the direct experimental validation, the proteins are also marked as disordered in DIBS if its close homolog found to be lacking the intrinsic structure.

3. the third group comprises the proteins regions in the disordered state that bind via a known, short functional motif (either from ELM [17], UniProt [18], Pfam [19] or the literature).

The predictors' accuracy for predicting the disordered regions in proteins is assessed as a part of the critical assessment of structure prediction (CASP) experiment, and the best accuracy among the predictors has been found to be 85% [21].

This article is just a short introduction to IDPs predictors and the interaction databases. We will try to cover the algorithms of the predictors in detail in the upcoming articles. Meanwhile, keep telling us some topics related to bioinformatics which you are interested in, or tools/software to get their tutorials.

You can write us at info@bioinformaticsreview.com.

References 1.

Wright, P. E., & Dyson, H. J. (2015).

Intrinsically disordered proteins in cellular signaling and regulation. Nature reviews.

Molecular cell biology, 16(1), 18.

2.

Dyson, H. J., & Wright, P. E. (2005).

Intrinsically unstructured proteins and their functions. Nature reviews. Molecular cell biology, 6(3), 197.

3.

Dunker, A. K., Cortese, M. S., Romero, P., Iakoucheva, L. M., & Uversky, V. N. (2005).

Flexible nets. The FEBS journal, 272(20), 5129-5148.

4.

Kim, P. M., Sboner, A., Xia, Y., & Gerstein, M. (2008). The role of disorder in interaction networks: a structural analysis. Molecular systems biology, 4(1), 179.

5.

Garner, E., Cannon, P., Romero, P., Obradovic, Z., & Dunker, A. K. (1998).

Predicting disordered regions from amino acid sequence. Genome Informatics, 9, 201213.

6.

He, B., Wang, K., Liu, Y., Xue, B., Uversky, V. N., & Dunker, A. K. (2009). Predicting intrinsic disorder in proteins: an overview. Cell research, 19(8), 929.

7.

Prilusky, J., Felder, C. E., Zeev-BenMordehai, T., Rydberg, E. H., Man, O., Beckmann, J. S., ... & Sussman, J. L. (2005).

FoldIndexŠ: a simple tool to predict whether a given protein sequence is intrinsically unfolded. Bioinformatics, 21(16), 34353438.

8.

Linding, R., Russell, R. B., Neduva, V., & Gibson, T. J. (2003). GlobPlot: exploring protein sequences for globularity and disorder. Nucleic acids research, 31(13), 3701-3708.

9.

Garbuzynskiy SO, Lobanov MY, Galzitskaya OV (2004). To be fold-ed or to be unfolded.(13), 2871-2877

10. Galzitskaya, O. V., Garbuzynskiy, S. O., & Lobanov, M. Y. (2006). FoldUnfold: web server for the prediction of disordered regions in protein chain. Bioinformatics, 22(23), 2948-2949.

11. Galzitskaya, O. V., Garbuzynskiy, S. O., & Lobanov, M. Y. (2007). Expected packing density allows prediction of both amyloidogenic and disordered regions in protein chains. Journal of Physics: Condensed Matter, 19(28), 285225.

12. Xue, B., Dunbrack, R. L., Williams, R. W., Dunker, A. K., & Uversky, V. N. (2010).

PONDR-FIT: a meta-predictor of intrinsically disordered amino acids. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 1804(4), 996-1010.

13. Sickmeier, M., Hamilton, J. A., LeGall, T., Vacic, V., Cortese, M. S., Tantos, A., ... & Obradovic, Z. (2006). DisProt: the database

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of disordered proteins. Nucleic research, 35(suppl_1), D786-D793.

acids

14. Oates, M. E., Romero, P., Ishida, T., Ghalwash, M., Mizianty, M. J., Xue, B., ... & Dunker, A. K. (2012). D2P2: database of disordered protein predictions. Nucleic acids research, 41(D1), D508-D516.

15. Corbi-Verge, C., & Kim, P. M. (2016). Motif mediated protein-protein interactions as drug targets. Cell Communication and Signaling, 14(1), 8.

16. Schad, E., Fichó, E., Pancsa, R., Simon, I., Dosztányi, Z., & Mészáros, B. (2017). DIBS: a repository of disordered binding sites mediating interactions with ordered proteins. Bioinformatics, btx640.

17. Dinkel, H., Van Roey, K., Michael, S., Kumar, M., Uyar, B., Altenberg, B., ... & Dahl, S. L. (2015). ELM 2016—data update and new functionality of the eukaryotic linear motif resource. Nucleic acids research, 44(D1), D294-D300.

Evaluation of disorder predictions in CASP9. Proteins: Structure, Function, and Bioinformatics, 79(S10), 107-118.

18. UniProt Consortium. (2014). UniProt: a hub for protein information. Nucleic acids research, gku989.

19. Bateman, A., Coin, L., Durbin, R., Finn, R. D., Hollich, V., Griffiths‐Jones, S., ... & Studholme, D. J. (2004). The Pfam protein families database. Nucleic acids research, 32(suppl_1), D138-D141.c 20. Monastyrskyy, B., Fidelis, K., Moult, J., Tramontano, A., & Kryshtafovych, A. (2011).

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