In Silico Protein Behavior. Predicting the Activity of p53 Tumor Suppressor Protein Mutants Using Features Derived From Homology Modeling. Sam Danziger Department of Biomedical Engineering University of California, Irvine. Dr. Rainer Brachmann School of Medicine. Dr. Richard Lathrop - PowerPoint PPT Presentation
In Silico In Silico Protein Protein Behavior Behavior Predicting the Activity of p53 Predicting the Activity of p53 Tumor Suppressor Protein Mutants Tumor Suppressor Protein Mutants Using Features Derived From Using Features Derived From Homology Modeling Homology Modeling Sam Danziger Department of Biomedical Engineering University of California, Irvine Dr. Rainer Brachmann School of Medicine Dr. Richard Lathrop School of Information and Computer Science Jue Zeng School of Medicine
Transcript
In SilicoIn Silico Protein Protein BehaviorBehavior
Predicting the Activity of p53 Tumor Predicting the Activity of p53 Tumor Suppressor Protein Mutants Using Suppressor Protein Mutants Using Features Derived From Homology Features Derived From Homology
ModelingModeling
Sam DanzigerDepartment of Biomedical Engineering
University of California, Irvine
Dr. Rainer Brachmann
School of Medicine
Dr. Richard LathropSchool of Information and
Computer Science
Jue ZengSchool of Medicine
What Am I DoingWhat Am I Doing: : Big PictureBig Picture
Biology Homology ModelingMake a protein
and test it in-vitro
PRO: Real
CON: Slow
Predict a protein
structure using a
template
PRO: Fast
CON: Inaccurate, so what does it tell us?
Machine
Learning
Use Homology
Modeling to guide
biological research
What is p53?What is p53? Tumor Suppressor ProteinTumor Suppressor Protein p53 Mutations are present p53 Mutations are present
in ~50% of human cancers.in ~50% of human cancers. Receives upstream signals Receives upstream signals
in response to cellular in response to cellular stress.stress.
Arrests cell growth if there Arrests cell growth if there is repairable DNA damage.is repairable DNA damage.
Triggers apoptosis if DNA Triggers apoptosis if DNA damage is irreparable.damage is irreparable.
Second site mutations Second site mutations can rescue cancer can rescue cancer mutants.mutants. p53 core domain bound to DNA
Rescue
Mutation
Cancer
Mutation
P53 Cancer Rescue P53 Cancer Rescue MutantsMutants
What is Homology What is Homology Modeling?Modeling?
Modeling done using Amber™ with zinc ion characteristics tuned by Dr. Qiang Lu working in Dr. Ray Lui’s lab.Modeling done using Amber™ with zinc ion characteristics tuned by Dr. Qiang Lu working in Dr. Ray Lui’s lab.
1. Use a wild type crystal structure of the protein in question.
2. Substitute one or more amino acids to mutate the protein.
3. Apply simulated physical laws to determine an energy function.
4. Minimize the energy of the new mutant protein.
How Do We Use Homology How Do We Use Homology Models?Models?
FF1111 FF1212 …… FF1n1n
3D p53 Molecule
2D Surface Map
Features from a grid
What is Machine What is Machine Learning?Learning?
Training: Set the parameters with n features.
Testing: Use the parameters to predict unknown classes
WW11
WW22
……
WWnn
FF1111 FF1212 …… FF1n1n
FF2121 …… …… ……
…… …… …… ……
FFmm
11
…… …… FFmm
nn
Example 1Example 1
Example 2Example 2
……
Example mExample m
Class 1Class 1
Class 2Class 2
……
Class mClass m
Unknown Unknown FF1111 FF1212 …… FF1n1n
WW11
WW22
……
WWnn
PredictioPredictionn
What do we know about What do we know about Rescue Mutants?Rescue Mutants?
261 mutants created 261 mutants created in-vitroin-vitro 5 sites for mutation = 5 sites for mutation =
We know 1 in every 2.47 * 10We know 1 in every 2.47 * 101515
It takes light about 1 month to go It takes light about 1 month to go 10101515 meters. meters. http://www.alcyone.com/max/physics/orders/metre.htmlhttp://www.alcyone.com/max/physics/orders/metre.html
Where to look next?Where to look next?
Spiral Galaxy M101
http://hubblesite.org/
Known Mutants
Let The Computer Pick The Let The Computer Pick The Next ExperimentsNext Experiments
1.1. Find interesting cancer rescue regions Find interesting cancer rescue regions by random sampling.by random sampling.
2.2. Focus the classifier on these regions Focus the classifier on these regions for detailed analysis.for detailed analysis.
3.3. Predict the behavior of putative rescue Predict the behavior of putative rescue mutants and create them mutants and create them in-vitroin-vitro..
4.4. Improve the classifier with knowledge Improve the classifier with knowledge about these new mutants.about these new mutants.
Ultimate GoalUltimate Goal
Broken p53
Engineered
Small Molecul
e
+ =
Functional Complex
Specific: Build models to understand how p53 breaks and help guide biological research by
mapping the space of p53 mutants.General: Build a generalized toolset to explore any protein
with functional mutations.
Intermediate Intermediate GoalsGoals
AcknowledgmentsAcknowledgments
People:People: Pierre Baldi, Josh Swamidass, Pierre Baldi, Josh Swamidass, Richard Chamberlin, Jonathan Chen, Richard Chamberlin, Jonathan Chen, Jianlin Cheng, Melanie Cocco, Richard Jianlin Cheng, Melanie Cocco, Richard Colman, John Coroneus, Lawrence Colman, John Coroneus, Lawrence Dearth, Vinh Hoang, Qiang Lu, Hartmut Dearth, Vinh Hoang, Qiang Lu, Hartmut Luecke, Ray Luo, Hiroto Saigo, Don Luecke, Ray Luo, Hiroto Saigo, Don Senear, Ying WangSenear, Ying Wang
Funding:Funding: NIH, NSF, Harvey Fellowship NIH, NSF, Harvey Fellowship (JS), UCI Medical Scientist Training (JS), UCI Medical Scientist Training Program, UCI Office of Research and Program, UCI Office of Research and Graduate Studies, UCI Institute for Graduate Studies, UCI Institute for Genomics and BioinformaticsGenomics and Bioinformatics
Questions?Questions?
1) Predictions
in silico
5) NewMutants
To Explore
2) Testsin vitro
4) NewTheories
3) Results ImprovePredictor
Thanks to Rainer Brachmann, Jue Zeng, Richard Thanks to Rainer Brachmann, Jue Zeng, Richard Lathrop, and everyone else who contributedLathrop, and everyone else who contributed
