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Virus Host co-evolution in sight of their proteomes and codon preferences

Virus Host co-evolution in sight of their proteomes and codon preferences. Bioinformatics project 2007 Yaar Reuveni Instructor - Michal Linial. Outline:. My project is composed of two phases:

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Virus Host co-evolution in sight of their proteomes and codon preferences

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  1. Virus Host co-evolution in sight of their proteomes and codon preferences Bioinformatics project 2007 Yaar Reuveni Instructor - Michal Linial

  2. Outline: My project is composed of two phases: • Phase I: The virus host web tool – VirOsNet. You are welcome to visit at: www.virosnet.cs.huji.ac.il • Phase II: Virus Host co-evolution research using codon usage analysis.

  3. Viruses: • Basically a cpasid envelope that contains genetic information. • Viruses can not replicate by themselves, and depend on the host for reproduction. • It’s main purpose in life enter a host, and use it’s facilities to reproduce

  4. Viruses fight back:

  5. Phase I: VirOsNet VirOsNet provides database and tools for exploring virus evolution and virus-host co-evolution

  6. Background and Motivation: • Ample of examples suggest that often viruses steal information from their hosts. • Viruses must optimize their amount of genetic material and physical size. • Viruses have very fast evolution: • Hard to trace. • Might change by switching hosts. • Shuffle their genetic material.

  7. Phase (I) main objective: Compare all viral proteins to all known proteins and detect resemblance. Meaning: in what way do viral proteins "resemble" any of all other known proteins in our world?

  8. Objectives and possible outcomes (i) • Clever search: Provide crossbreeding factors when searching • Offer comparisons of viruses relative to the proteome of their known hosts • Stolen elements: where were they stolen from? Was it from the host? • Mimicking phenomenon: detect host - protein mimicry • When did it happen: Evolutionary tracking

  9. Objectives and possible outcomes (ii) • Recent event – indicative by similarity search results that are exceptional. • Insights on viruses and their proteomes. Long term: • Pharmaceutics applications. Proposal of drug targets

  10. Methods: • Data is from the ProtoNet DB (currently ~ 1.8 million proteins) All proteins are from UniProt. • New tables to the DB -specialized for host-virus relations. • Pre computed BLAST (BLOSUM62) and dynamic BLAST options. • Entry is a Viral Protein, BLAST search results are sorted by the descending E-values. • Several display schemes. • Each result associated with domain information (InterPro) • Download options for next phase analysis

  11. Tool overview: The tool works in a 4 steps scheme: • Step 1: search for a virus to query on using one of the search methods • Step 2: choose a specific virus • Step 3: choose one of it’s proteins, and the BLAST properties • Step 4: choosing one of the BLAST results to get it’s pairwise alignment

  12. 7,763 viruses and 199,563 proteins

  13. Some Statistics Entry point to viruses according to their genetic material complexity

  14. Example: check all dsRNA viruses Affecting Eukaryotes

  15. Case study: • Abelson murine leukemia virus: a VERY close homolog of human and a mouse protein tyrosine kinase that: • Regulates cytoskeleton during cell differentiation, cell division and cell adhesion • Regulates DNA repair potentially in severe demage. The viral protein causes cancer(active site mutation) Lets look at it……

  16. Active site

  17. Summery Phase I: • Pros: • Platform for studying viruses relative to hosts • A discovery tool • Rich BLAST options for evolutionary wider view • Crossbreeding with host data (i.e. IntrPro Domains). • Dynamic view on BLAST result as a group (ProtoMesh) • Cons: • Still to improve the usability to the average biologist • VirOsNet can get very slow on overload or in some of the filtering options.

  18. Phase II: Codon usage Figure adapted from L. Merkel, N. Budisa, BIOspektrum 2006 , 12 , 41. Veränderung des genetischen Codes. Virus-host classification using codon usage analysis with SVM

  19. RNA codons:

  20. Main question: Given a viral protein, determine who might be a potential host of the virus. The basis for the hypothesis: An optimization of the viruses toward their hosts

  21. Objectives: • Create a classification tool, that receives a viral protein and will give a prediction on its potential hosts. • Classify all the proteins to different classes, using a maximum-margin hyperplane. • Provide different levels of classification. • Create a “host rank” for a given viral protein for each of its potential hosts. • Results: May suggest a “virus cross-species potential index”

  22. Methods: • Collect and arrange all the codon usage data (or other relevant data for this classification). • Analyze the data, normalization and processing. • Unsupervised learning and clustering for better understanding of the data. • Given all codon usage for all species, use the SVM algorithm to create a predictor for a new specimens. • Provide various levels of classifying classes for the codon data.

  23. Codon usage species 1 . . . 64 About the data: • Codon usage is calculated for each species. • Each species is represented by a 64 positions vector. • The question of normalization: • standard normalize to 1. • functional per amino-acid, or by entropy. • percentage – per column

  24. Bacteria

  25. Primates

  26. Data from Nakamura: • Codon usage tabulated from the international DNA sequence databases Nakamura, Y., Gojobori, T. and Ikemura, T. (2000) Nucl. Acids Res. 28, 292. • Downloading the codon usage table • The data covers all species (including viruses).

  27. Usage distribution: Primates Bacteria Invertebrates Plants Rodents Viral

  28. Usage distribution: Positions 1-13

  29. Our data: • It was expected to find diverse codon usage between different taxonomy groups. • There are 703 distinct known hosts in our DB and 2152 distinct known hosted viruses. • I created an interface for extracting the CDS data from the coding data we have in ProtoNet. • I used the same convention for the vector

  30. In ProtoNet (version 5.1): 16,567 viruses and 409,726 proteins

  31. Dividing our data in to groups:

  32. 7 Rodents Fungi 2 302 112 32 647 Plants Primates 6 0 137 151 226 Aves 308 70 Bacteria Tetrapoda Fish Arthropoda 16 (+99) distributed Who infect what? 6 Others

  33. These are all diferent viruses groups:

  34. Comparison: Positions 1-12 Looks Promising!

  35. Clustering: • preliminary results • Using a set of COMPACT tool (COMPACT: A Comparative Package for Clustering Assessment) • Varshavsky et al, 2005 ISPA: 159-167. Visualization of results Scoring

  36. Hierarchal - Percentage Normalization

  37. Hierarchal - Standard Normalization

  38. Summery phase II: • All data is organized, accessible and will update along with the ProtoNet DB. • Comprehensive analysis, created a good understanding of the data. • Future plans: • Decide on a good division into classes. • Use SVM algorithm to create a classifier, given a virus codon preferences guess potential hosts. • Create an interface that offers this service.

  39. Acknowledgements: Thank you to all the people that helped: • Michal Linial • Iris Bahir • Menachem Fromer • Alexander Savenok • Michael Dvorkin • Roy Varshavsky

  40. Thank You!

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