Download Characterization and prediction of drug binding sites in proteins

•
•
•
•
•
•
Background
Goals
Methods
Results
Conclusions
Implications
• Proteins – organic compounds that constitute the basic
functional and computational unit in the cell. They are able to
bind other molecules specifically and tightly.
• Pocket – The region of the protein responsible for binding.
• Ligand – Substance that is able to bind to a biomolecule.
• Drug – Substance that alters normal body function.
• Most drugs achieve their effects by binding to a protein at a
specific binding site and modifying its activity.
• One may want a drug that binds to a specific location in a
protein to prevent side effects.
• Identifying those binding sites in proteins experimentally is
time & resource consuming.
This will shorten the drug development time significantly…
• Collecting data – Pocket creation
• Choosing attributes & analysis of pockets
accordingly
• Machine Learning
Choosing drugs (ligands):
Choosing Proteins:
Docking algorithm: method to predict binding orientation.
•Count the number of each amino acid.
•Charge in physiological PH.
•Shape matching =
•Connectivity =
With
:
•Accessibility calculation is done by simulation of rolling water
molecules over the protein surface.
With
:
•Accessibility difference of protein atoms before binding the
ligand and after.
•Accessibility difference of ligand atoms before binding to the
pocket and after.
With HBPLUS:
•Number of hydrogen bonds between ligand and pocket.
Positive set size: 285
Negative set size: ~10,600
Number of Attributes: 26
With WEKA – using LibSVM:
Training:
True: 200
Data
False1,000-10,000
Testing:
True: 85
False: 544-9,544
Precision/Recall change in the Positive Set
1.2
Precision/Recall Value
1
0.8
0.6
recall
Precision
0.4
0.2
0
0
2000
4000
6000
Learning Set Size
8000
10000
12000
Recall/Precision Change in the Negative Set
1.02
1
0.98
Recall/Precision Value
0.96
recall
0.94
precision
0.92
0.9
0.88
0.86
0
2000
4000
6000
Learning Set Size
8000
10000
12000
ROC Graph
0.5
0.45
0.4
True Positive Rate
0.35
0.3
0.25
TP
0.2
0.15
0.1
0.05
0
-0.002
0
0.002
0.004
0.006
0.008
False Positive Rate
0.01
0.012
0.014
0.016
•We were able to distinguish between real & non-biological
binding sites without using computationally expensive energy
functions or evolutionary conservation.
•It is not possible to distinguish between binding sites with
PatchDock alone.
•Using the combination of simple and computationally “cheap”
tools such as SVM, PatchDock and the algorithms for pocket
analysis mentioned earlier, it is possible to give a good prediction
regarding the nature of the binding site.
•The advantage of the method is its simplicity: Taking the best
docking conformations and comparing with characteristics of real
and non-biological binding sites. (No need to compare entire
proteins).
•The few negative binding sites classified as positives may be
potentially real binding sites. (Need to be checked
experimentally).
The method can be improved and refined:
•More attributes
•More drugs and proteins
•Analysis of attribute significance
•Bigger learning set
•Bigger positive set in relation to the negative set in the learning
set (help the learning algorithm)
•The tool can be used to check possible side effects during drug
development.
•Drug Repurposing - Find new targets for existing drugs.
•Can significantly shorten the drug toxicity check during
development.
•Dr. Yanay Ofran
•Dr. Olga Leiderman
•Dr. Guy Nimrod
•Vered Kunik
•Rotem Snir
•Sivan Ophir
For your dedicated help!