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Biological Networks. Can a biologist fix a radio?. Lazebnik, Cancer Cell, 2002. Building models from parts lists. Lazebnik, Cancer Cell, 2002. Building models from parts lists. Computational tools are needed to distill pathways of interest from large molecular interaction databases. - PowerPoint PPT Presentation
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Computational tools are needed to distill pathways of interest from large molecular interaction databases
Thinking computationally about biological process may lead to more accurate models,which in turn can be used to improve the design of algorithms
Navlakha an Bar-Joseph 2011
gene A gene Bregulates
protein A Protein Bbinds
Network Representation
edge (link)
Directional
Non-directional
node
Proteins
Physical Interaction
Protein-Protein
A
B
Protein Interaction
Transcription factorTarget genes
TranscriptionalInteraction
Protein-DNA
A
B
Transcriptional
Different types of Biological Networks
Nodes
Edges
Small-world Network
Biological networks exhibit small-world network (SWN) characteristics
(similar to social networks, internet etc)
Every node can be reached from every other by a small number of steps
SWN vs Random NetworksSmall World Network (SWN)Random Network
SWN have a small number of highly connected nodes
What can we learn from Biological Networks
• Hubs tend to be “older” proteins
• Hubs are evolutionary conserved
Hubs are highlyconnected nodes
Are hubs functionally important ?
Hubs are usually critical proteins for the species
LethalSlow-growthNon-lethalUnknown
Jeong et al. Nature 411, 41 - 42 (2001)
Can the network help to predict function
Begley TJ, Mol Cancer Res. 2002
•Systematic phenotyping of 1615 gene knockout strains in yeast•Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents)•Screening against a network of 12,232 protein interactions
A network approach to predict new drug targets
Aim :to identify critical positions on the ribosome which could be potential
targets of new antibiotics
Case Study
Keats (1795-1821) Kafka (1883-1924) Orwell (1903-1950)
Mozart (1756-1791) Schubert (1797-1828) Chopin (1810-1849)
In our days…
Infectious diseases are still number 1 cause of premature death
(0-44 years of age) worldwide..
Annually kill >13 million people (~33% of all deaths)
Antibiotics targets of the large ribosomal subunit
The ribosome is a target for approximately half of antibiotics characterized to date
Looking at the ribosome as a network
1. Critical sites in the ribosome network may represent functional sites
(not discovered before)
2. New functional sites may be good site for drug design
Looking for critical positions in a networkLooking for critical positions in a networkDegree: the number of edges that a node has.
The node with the highest degree in the graph (HUB)
Degree: the number of edges that a node has.
The node with the highest degree in the graph (HUB)
Looking for critical positions in a networkLooking for critical positions in a network
ClosenessClosenessCloseness: measure how close a node to all other nodes in the network.
The nodes with the highest closeness
BetweennessBetweenness
The node with the highest betweenness
Betweenness: quantify the number of all shortest paths that pass through a node.
The node with the highest degree
The node with the highest betweenness
The nodes with the highest closeness
Looking for critical positions in a networkLooking for critical positions in a network
Looking at macromolecular structures as a Looking at macromolecular structures as a networknetwork
A1191
A1191 have the highest closeness, betwenness, and degree.
Which (is there a?) property best characterizes
the known function sites?
How can the network approach help How can the network approach help identify functional sites in the identify functional sites in the
ribosome ? ribosome ?
Characterize the whole ribosome as a network
Calculate the network properties of each nucleotide
?
Strong mutations
Mild mutations
12
When mutating the critical site on the When mutating the critical site on the ribosomeribosome
the bacteria will not grow the bacteria will not grow
p~0
p~0
p=0.01
Critical site on the ribosomeCritical site on the ribosomehave unique network properties have unique network properties
Strong mutations Mild mutations
David-Eden et al, NAR (2008)
‘ ‘Druggability Index’Druggability Index’Based on the network propertyBased on the network property
David-Eden et al. NAR (2010)
Bad site Good site
Pockets with the highest ‘Druggability Pockets with the highest ‘Druggability Index’Index’
overlap known drug binding siteoverlap known drug binding sitess
David-Eden et al. NAR (2010)
DI=1 DI=0.98
Erythromycin Telithromycin
Girodazole
DI=0.94 DI=0.93
What did we learn
• Pairwise alignment – Dynamic Programing
Local and Global Alignments
When? How ?
Recommended Tools : for local alignment blast2seqlast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch&PROG_DEF=blastn&BLAST_PROG_DEF=megaBlast&BLAST_SPEC=blast2seq
For global best use MSA tools such as Clustal W2, Muscle (see next slide)
What did we learn• Multiple alignments (MSA)
When? How ?
MSA are needed as an input for many different purposes: searching motifs, phylogenetic analysis, protein and RNA structure predictions, conservation of specific nts/residues
Recommended Tools : Clustal W2 http://www.ebi.ac.uk/Tools/msa/clustalw2/ (best for DNA and RNA), MUSCLE http://www.drive5.com/muscle/ (best for proteins)Phylogeny.fr phylogenetic trees http://www.phylogeny.fr/
What did we learn• Search a sequence against a database
When? How ? - BLAST :Remember different option for BLAST!!! (blastP blastN…. ), make
sure to search the right database!!!
DO NOT FORGET –You can change the scoring matrices, gap penalty etc
- PSIBLAST
Searching for remote homologies
BLAST http://blast.ncbi.nlm.nih.gov/Blast.cgi
What did we learn >Motif search
When? How ?
-Searching for overabundance of unknown regulatory motifs in a set of sequences ; e.g promoters of genes which have similar expression pattern (MEME)
>Domain search
Pfam (database to search for protein domains)
Suggested Tools : MEME http://meme.nbcr.net/meme/DRIMUST http://drimust.technion.ac.il/
PFAM http://pfam.sanger.ac.uk/
What did we learn• Protein Secondary Structure Prediction-
When? How ?– Helix/Beta/Coil– Most successful approaches rely on
information from the environment and MSA
- Predictions level around 80%
Suggested toolsGOR: http://gor.bb.iastate.edu/
Jpred: http://www.compbio.dundee.ac.uk/www-jpred/
What did we learn• Protein Tertiary Structure Prediction-
When? How ?– First we must look at sequence identity to a sequence with
a known structure!!– Sequence homology based methods-Homology modeling– Structure homology based methods- Threading
Remember : Low quality models can be miss leading !! Database and tools
Protein Data Bank http://www.rcsb.org/pdb/home/home.doSuggested tool for molecular visualization http://www.pymol.org/Good tool for homology modeling http://swissmodel.expasy.org/
What did we learn• RNA Structure and Function Prediction-
When? How ?– MFE based methods– good for local interactions, several
predictions of low energy structures
– Adding information from MSA can help but usually not available
– RNA families are characterized by their structure (Rfam).
Suggested tools: RNAfold http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi
RFAM http://rfam.sanger.ac.uk/
What did we learn• Gene expression
When? How ?> Unsupervised methods-
Different clustering methods : K-means, Hierarchical Clustering
> Supervised methods-such as SVM–GO annotation (analysis of gene clusters..)
Selected databases and toolsGEO http://www.ncbi.nlm.nih.gov/geo/EPclust http://www.bioinf.ebc.ee/EP/EP/EPCLUST/David http://david.abcc.ncifcrf.gov/
Most useful databases
Genomic databaseThe human genome browser
http://genome.ucsc.edu/
Protein databaseUniprothttp://www.uniprot.org/
Structure databasePDB (RCSB)http://www.rcsb.org
Gene expression databaseGEOhttp://www.ncbi.nlm.nih.gov/geo/
So How do we start …Now that you have selected a project you should carefully plan your next steps:
A.Make sure you understand the problem and read the necessary background to proceed
B. formulate your working plan, step by step
C. After you have a plan, start from extracting the necessary data and decide on the relevant tools to use at the first step. When running a tool make sure to summarize the results and extract the relevant information you need to answer your question, it is recommended to save the raw data for your records , don't present raw data in your final project. Your initial results should guide you towards your next steps.
D. When you feel you explored all tools you can apply to answer your question you should summarize and get to conclusions. Remember NO is also an answer as long as you are sure it is NO. Also remember this is a course project not only a HW exercise. .
Example
• Amyloids are proteins which tend to aggregate in solution. Abnormal accumulation of amyloid in organs is assumed to play a role in various neurodegenerative diseases.
Question : can we predict whether a protein X is an amyolid ?
Preparing a poster
Prepare in PPT poster size 90-120 cmTitle of the project Names and affiliation of the students presenting
The poster should include 5 sections :Background should include description of your question (can add figure)Goal and Research Plan: Describe the main objective and the research planResults (main section) : Present your results in 3-4 figures, describe each figure (figure legends) and give a title to each result Conclusions : summarized in points the conclusions of your projectReferences : List the references of paper/databases/tools used for your project