1. Biological Networks

2. Building models from parts lists
DNA, RNA,
proteins
-Sequences
-2D Structures
-3D structures ?
-Gene
Expression
(coding,
non-coding)
-Proteomics

3. 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

4. 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

5. Interaction data = Biological Networks
Jeong et al. Nature 411, (2001)

6. Different types of Biological Networks
Proteins
Physical Interaction
Protein-Protein A B
Protein Interaction
Transcription factor
Target genes
Transcriptional
Interaction
Protein-DNA A B
Nodes
Edges

7. 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 ?

8. Hubs are usually critical proteins for the species
Lethal
Slow-growth
Non-lethal
Unknown
Jeong et al. Nature 411, (2001)

9. Networks can help to predict function

10. 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

11. Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002

12. Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002

13. Networks can help to predict function
Begley TJ, Mol Cancer Res

14. A network approach to predict
new drug targets
Hilda David-Eden

15. Keats ( )
Kafka ( )
Orwell ( )
Chopin ( )
Mozart ( )
Schubert ( )

16. 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)

17. Aim :to identify critical positions on the ribosome which could be potential targets of new antibiotics

18. The ribosome is a target for approximately half of antibiotics characterized to date
Antibiotics targets of the large ribosomal subunit

19. Looking at the ribosome as a network

20. Many biological network have characteristics of a Small World Network
Every node can be reached from every other by a small number of steps

21. What can we learn from the ribosome network?
Critical sites in the ribosome network may represent functional sites
(not discovered before)
2. New functional sites may be good sites for drug design

22. Looking for critical positions in a network
נשאלת השאלה מי הם הצמתים הכי חשובים ברשת?
נדגים זאת על רשת פשוטה "רשת העפיפון".

23. Looking for critical positions in a network
Degree: the number of edges that a node has.
לכל צומת ניתן לחשב את מספר הקשתות היוצאות ממנה. פרמטר זה נקרא דיגרי.
הצומת הצבועה בירוק היא בעלת הדיגרי הכי גבוה.
The node with the highest degree in the graph (HUB)

24. Looking for critical positions in a network
Degree: the number of edges that a node has.
לכל צומת ניתן לחשב את מספר הקשתות היוצאות ממנה. פרמטר זה נקרא דיגרי.
הצומת הצבועה בירוק היא בעלת הדיגרי הכי גבוה.
The node with the highest degree in the graph (HUB)

25. Closeness (centrality)
Closeness: measure how close a node to all other nodes in the network.
ניתן לחשב עד כמה צומת קרובה לשאר הצמתים ברשת, לפרמטר זה נקרא קלוסנס.
קירבה בין צמתים מחושבת על פי הדרך הקצרה בניהם כלומר כמספר מנימלי של קשתות שצריך לעבור כדי להגיע מצומת לצומת.
בדוגמה זו הצמתים הצבועים האדום הם עם הקלוסנס הכי גבוה.
The nodes with the highest closeness

26. Betweenness (connectivity)
Betweenness: quantify the number of all shortest paths that pass through a node.
הפרמטר נוסף שמבוסס על חישוב הדרך הקצרה מכל צומת לכל צומת נקרא ביטוייננס.
ביטויינס כמת כמה דרכים קצרות עוברות על צומת.
תוכלו לזכור את משמעותו במהלך ההרצאה מהמילה ביטויין.
ברשת זו אם נסיר את הצומת הסגולה בעלת הביטיינס הכי גבוה נקבל שתי רשתות נפרדות.
The node with the highest betweenness

27. Looking for critical positions in a network
נסתכל על שלושת התכונות ביחד. ברשת זו רואים כי אין חפיפה בין התכונות.
The node with the highest degree
The node with the highest betweenness
The nodes with the highest closeness

28. Looking at macromolecular structures as a network
A1191 have the highest closeness, betwenness, and degree.
אם נחשב את ערכי הרשת בדוגמה זו נקבל שהנוקלאוטיד המסומן מקיים את כל התכונות: כלומר בעל ערכי קלוסנס ביטויינס ודגרי הכי גבוהים.
A1191

29. (is there a?) property best characterizes the known function sites?
How can the network approach help identify functional sites in the ribosome ?
Which
(is there a?) property best characterizes the known function sites? ?
Characterize the whole ribosome as a network
Calculate the network properties of each nucleotide

30. When mutating the critical site on the ribosome the bacteria will not grow2 1
Lethal mutations
Neutral mutations

31. Lethal Mutations Neutral Mutations
Critical site on the ribosome have very high centrality values (closeness)
Lethal Mutations
Neutral Mutations
nucleotides with the highest
closeness
nucleotideswith the highest
closeness
P-value~0
P-value=1
David-Eden et al, 2008

32. Lethal Mutations Neutral Mutations
Critical site on the ribosome have very high connectivity (betweenness)
Lethal Mutations
Neutral Mutations
nucleotides with the highest
betweennes
nucleotideswith the highest
betweennes
P-value~0
P-value=1
David-Eden et al, 2008

33. 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)

34. Based on the network property
‘Druggability Index’
Based on the network property
Bad site
Good site
David-Eden et al. NAR (2010)

35. overlap known drug binding sites
Pockets with the highest ‘Druggability Index’
overlap known drug binding sites
DI=1
DI=0.98
Erythromycin
Telithromycin
Girodazole
DI=0.94
DI=0.93
David-Eden et al. NAR (2010)

36. Course Summary (What did we learn and additional useful tools) and How to start working on your project

37. 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_DEF=blastn&BLAST_PROG_DEF=megaBlast&BLAST_SPEC=blast2seq
For global best use MSA tools such as Clustal W2, Muscle (see next slide)

38. 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 (best for DNA and RNA), MUSCLE (best for proteins)
Phylogeny.fr phylogenetic trees

39. 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

40. 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 tools
GEO
EPclust
David

41. 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
DRIMUST

42. 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
Suggested tools: RNAfold
RFAM

43. What did we learn Protein Secondary Structure Prediction- When? 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:

44. 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
Remember : Low quality models can be miss leading !!
Database and tools
Protein Data Bank
Suggested tool for molecular visualization
Good tool for homology modeling

45. 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
Biogrid
Cytoscape
Fanmod

46. Most useful databases Genomic database Protein database
The human genome browser
Protein database
Uniprot
Structure database
PDB (RCSB)
Gene expression database
GEO

47. So How do we start …
Now that you have selected a project you should carefully plan your next steps:
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. .

48. 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

49. GOOD LUCK!!! Key date reminder 11.1 Meetings with supervisors
9.3 Poster submission
16.3 Poster presentation
(POSTER DAY 12:30-14:30)
GOOD LUCK!!!