Transcript lecture11_2015

Biological Networks
Building models from parts lists
DNA, RNA,
proteins
-Sequences
-2D Structures
-3D structures
?
-Gene
Expression
(coding,
non-coding)
-Proteomics
Building models from parts lists
DNA, RNA,
proteins
-Sequences
-2D Structures
-3D structures
-Protein-protein
-Protein-RNA
-Protein-DNA
-Gene
Expression
(coding,
non-coding)
-Proteomics
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 and Bar-Joseph 2011
Interaction data = Biological Networks
Jeong et al. Nature 411, 41 - 42 (2001)
Different types of Biological Networks
Nodes
Edges
Protein Interaction
Transcriptional
Proteins
Transcription factor
Target genes
Physical Interaction
Transcriptional
Interaction
Protein-Protein
Protein-DNA
A
A
B
B
What can we learn from the
topology of biological networks
Hubs are
highly
connected
nodes
• Hubs tend to be
“older” proteins
• Hubs are
evolutionary
conserved
Are hubs functionally important ?
Hubs are usually critical proteins for the species
Lethal
Slow-growth
Non-lethal
Unknown
Jeong et al. Nature 411, 41 - 42 (2001)
Networks can help to predict
function
Can the network help to predict function
•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
Begley TJ, Mol Cancer Res. 2002
Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002
Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002
Networks can help to predict
function
Begley TJ, Mol Cancer Res. 2002.
A network approach to predict
new drug targets
Hilda David-Eden
Keats (1795-1821)
Mozart (1756-1791)
Kafka (1883-1924)
Orwell (1903-1950)
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)
Aim :to identify critical positions on the
ribosome which could be potential targets of
new antibiotics
The ribosome is a target for approximately half of
antibiotics characterized to date
Antibiotics targets of the large ribosomal subunit
Looking at the ribosome
as a network
A1191
Many biological network have
characteristics of a
Small World Network
Every node can be reached from every
other by a small number of steps
What can we learn from the
ribosome network?
1. Critical sites in the ribosome network may
represent functional sites
(not discovered before)
2. New functional sites may be good sites for
drug design
Looking for critical positions in a network
Looking for critical positions in a network
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 network
Degree: the number of edges that a node has.
The node with the highest degree in the graph (HUB)
Closeness (centrality)
Closeness: measure how close a node to all other nodes in the network.
The nodes with the highest closeness
Betweenness (connectivity)
Betweenness: quantify the number of all shortest paths that pass
through a node.
The node with the highest betweenness
Looking for critical positions in a network
The node with the highest degree
The node with the highest betweenness
The nodes with the highest closeness
Looking at macromolecular structures as a network
A1191 have the highest closeness, betwenness, and degree.
A1191
How can the network approach help
identify functional sites in the ribosome ?
Characterize
the whole
ribosome as
a network
Calculate
the network
properties
of each
nucleotide
?
Which
(is there a?)
property best
characterizes
the known
function sites?
When mutating the critical site on the ribosome
the bacteria will not grow
2
Lethal mutations
Neutral mutations
1
Critical site on the ribosome
have very high centrality values (closeness)
Lethal Mutations
Neutral Mutations
nucleotides
with the
highest
closeness
nucleotides
with the
highest
closeness
P-value~0
P-value=1
David-Eden et al, 2008
Critical site on the ribosome
have very high connectivity (betweenness)
Lethal Mutations
Neutral Mutations
nucleotides
with the
highest
betweennes
nucleotides
with the
highest
betweennes
P-value~0
P-value=1
David-Eden et al, 2008
Critical site on the ribosome
have unique network properties
Lethal mutations
Neutral mutations
p~0
p~0
p=0.01
David-Eden et al, NAR (2008)
‘Druggability Index’
Based on the network property
Bad site
Good site
David-Eden et al. NAR (2010)
Pockets with the highest ‘Druggability Index’
overlap known drug binding sites
DI=1
Erythromycin
DI=0.98
Telithromycin
Girodazole
DI=0.94
DI=0.93
David-Eden et al. NAR (2010)
Course Summary
(What did we learn and additional useful tools)
and
How to start working on your
project
What did we learn
• Pairwise alignment – Dynamic Programing
Local and Global Alignments
When? How ?
Recommended Tools : for local alignment blast2seq
last.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch&PROG_DE
F=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
• Phylogenetic trees and 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
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
• Gene expression
When? How ?
> Unsupervised methodsDifferent clustering methods : K-means,
Hierarchical Clustering
> Supervised methods-such as SVM
– GO annotation (analysis of gene clusters..)
Selected databases and tools
GEO
http://www.ncbi.nlm.nih.gov/geo/
EPclust http://www.bioinf.ebc.ee/EP/EP/EPCLUST/
David
http://david.abcc.ncifcrf.gov/
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, DRIMUST)
Suggested Tools : MEME http://meme.nbcr.net/meme/
DRIMUST http://drimust.technion.ac.il/
What did we learn
• RNA Structure and Function PredictionWhen? How ?
– MFE based methods– good for local
interactions, several predictions of low energy
structures
– Adding information from MSA can help but
usually not available
Suggested tools: RNAfold http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi
RFAM http://rfam.sanger.ac.uk/
What did we learn
• Protein Secondary Structure PredictionWhen? How ?
– Helix/Beta/Coil
– Most successful approaches rely on
dependency between the positions (HMM)
- Evolutionary information can contribute to
predictions
- Predictions levels are very high (>80%)
Suggested tools
Jpred:
http://www.compbio.dundee.ac.uk/www-jpred/
What did we learn
• Protein Tertiary Structure PredictionWhen? How ?
– First we must look at sequence identity to a
sequence with a known structure!!
– Sequence homology based methodsHomology modeling
Remember : Low quality models can be miss
leading !!
Database and tools
Protein Data Bank http://www.rcsb.org/pdb/home/home.do
Suggested tool for molecular visualization http://www.pymol.org/
Good tool for homology modeling http://modbase.compbio.ucsf.edu/
What did we learn?
Biological Networks
• Different types of Biological Networks
Protein-Protein (non-directed)
Regulatory networks (directed)
structural networks
• Network Topology
• Network motifs
Selected tools
String http://string-db.org/
Biogrid http://thebiogrid.org/
Cytoscape http://www.cytoscape.org/
Fanmod http://theinf1.informatik.uni-jena.de/motifs/
Most useful databases
Genomic database
The human genome browser
http://genome.ucsc.edu/
Protein database
Uniprot
http://www.uniprot.org/
Structure database
PDB (RCSB)
http://www.rcsb.org
Gene expression database
GEO
http://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.
.
Preparing a poster
Prepare in PPT poster size 90-120 cm
Title 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 plan
Results (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 project
References : List the references of paper/databases/tools used for your
project
Key date reminder
11.1
18.1
9.3
16.3
Meetings with supervisors
Meetings with supervisors
Poster submission
Poster presentation
(POSTER DAY 12:30-14:30)