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

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

Jeong et al. Nature 411, 41 - 42 (2001)

The Protein-Protein Interaction Network in yeast

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

Metabolites

Enzymatic conversion

Protein-Metabolite

A

B

Metabolic

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 a network?

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)

Networks can help to predict function

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

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

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

A1191

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 network

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 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

Course Summary

What did we learn

• Pairwise alignment –

Local and Global Alignments

When? How ?

Tools : for local blast2seq , for global best use MSA tools such as Clustal X, Muscle

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

Tools : Clustal X (for DNA and RNA), MUSCLE (for proteins)Tools for phylogenetic trees: PHYLIP …

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

- PHIBLAST

Searching for a short pattern within a protein

What did we learn• Motif search

When? How ?

- Searching for known motifs in a given promoter (JASPAR)

-Searching for overabundance of unknown regulatory motifs in a set of sequences ; e.g promoters of genes which have similar expression pattern (MEME)

Tools : MEME, logo, Databases of motifs : JASPAR (Transcription Factors binding sites)PRATT in PROSITE (searching for motifs in protein sequences)

What did we learn• Protein Function Prediction

When? How ?

- Pfam (database to search for protein motifs/domain (PfamA/PfamB)

- PROSITE

- Protein annotations in UNIPROT

(SwissProt/ Tremble)

What did we learn• Protein Secondary Structure Prediction-

When? How ?– Helix/Beta/Coil(PHDsec,PSIPRED).– Predicts transmembrane helices (PHDhtm,TMHMM).– Solvent accessibility: important for the prediction of

ligand binding sites (PHDacc).

What did we learn• Protein Tertiary Structure Prediction-

When? How ?– First we must look at sequence identity to a sequence with a known

structure!!– Homology modeling/Threading– MODEBase- database of models

Remember : Low quality models can be miss leading !!

Tools : SWISS-MODEL ,genTHREADER, MODEBase

What did we learn• RNA Structure and Function Prediction-

When? How ?– RNAfold – good for local interactions, several predictions of low

energy structures– Alifold – adding information from MSA– RFAM

– Specific database and search tools: tRNA, microRNA …..

What did we learn• Gene expression

When? How ?– Many database of gene expression

GEO …– Clustering analysis

EPClust (different clustering methods K-means, Hierarchical Clustering, trasformations row/columns/both…)

– GO annotation (analysis of gene clusters..)

So How do we start …

• Given a hypothetical sequence predict its function….

What should we do???

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 ?