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P53_Final_Presentation
- 1. By Jonah Kohen Advised by Prof. Brian Chen
- 2. 1) Who are we and what do we do? 2) DNA and p53: Basics 3) Research problem 4) Approach 1: electrostatic complementarity across whole region. 5) Approach 2: complementarity at the 14 binding amino acids. 6) Approach 3: Cubes 7) Conclusions
- 3. Jonah Kohen – B.S. Computer Engineering (expected May 2015), Lehigh University Sara Grogan – B.S. IBE Chemical Engineer, minor Biotechnology (expected May 2017), Lehigh University Prof. Brian Chen – P.C. Rossin Assistant Professor of Computer Science and Engineering at Lehigh University Sara Grogan Brian Chen
- 4. Prof. Chen works in structural bioinformatics and has created programs modeling biomolecule interactions to aid in bioinformatics research. I chose to study p53 because it plays a pivotal role in cancer.
- 5. Electrostatic analysis of the interaction between DNA and p53 mutants can be used to predict whether or not the mutation will lead to cancer.
- 6. 1) Who are we and what do we do? 2) DNA and p53: Basics 3) Research problem 4) Approach 1: electrostatic complementarity across whole region. 5) Approach 2: complementarity at the 14 binding amino acids. 6) Approach 3: Cubes 7) Conclusions
- 7. The DNA in the nucleus of cells contains the instructions for the production of proteins. p53 is one such protein. Proteins are large molecules composed of amino acids that perform certain functions like cellular metabolic processes.
- 8. p53 performs its function by binding to a particular region on the DNA.
- 9. p53 becomes activated in response to cellular stress, for example DNA damage. p53 in turn activates DNA repair proteins, suspends cell division, and initiates apoptosis (cell death). These damage control mechanisms help prevent cancer. Repair Suspend Division Cell Death
- 10. When functioning normally, p53 suppresses the proliferation of cancer cells. Mutations to p53 may hinder this function. Mutations in the p53 tumor suppressor are the most frequently observed genetic alterations in human cancer. Each of these variants may carry one or several substitutions. 3 p53 proteins interacting with DNA.
- 11. A p53 mutant is “active” if this variant of p53 is functioning normally. A p53 mutant is “inactive” if this variant of p53 is unable to function normally. Active: Good Inactive: Bad
- 12. To design an algorithm that can reliably classify a p53 mutant as active or inactive. Several predictors have been proposed, but most are unreliable. A reliable predictor would help us diagnose a mutation as cancerous or not.
- 13. p53 is composed of 393 amino acids. The region responsible for DNA binding is between amino acids number 102-292. Within this region, I am looking only at 14 amino acids that directly bind to DNA.
- 14. Previous research has found that these 14 amino acids are: a119, a276, n239, n247, r248, r273, r280, r283, c275, c277, l120, m243, s121, and s241. l120 s121 n239 s241 r248 r273 c277 r283
- 15. 1) Who are we and what do we do? 2) DNA and p53: Basics 3) Research problem 4) Approach 1: electrostatic complementarity across whole region. 5) Approach 2: complementarity at the 14 binding amino acids. 6) Approach 3: Cubes 7) Conclusions
- 16. If the 273rd amino acid of p53 (arginine) is changed to histidine (abbreviated r273h), p53 is inactivated. r273h
- 17. If the 273rd amino acid of p53 is changed to histidine AND the 263rd amino acid of p53 (asparagine) is changed to valine (abbrev. r273h/n263v), p53 remains functional. r273h/n263v
- 18. Why are mutants like r273h inactive while other mutants like r273h/n263v active? r273h r273h/n263v ???
- 19. Data set of 541 pdb files, each one describing a different p53 mutant. 143 active mutants, 77 involving the 14 key amino acids. 398 inactive mutants, 155 involving the 14 key amino acids. Activity determined by in vivo analysis. Source: Richard H. Lathrop, UC Irvine.
- 20. pdb file of unmutated p53, viewed in Pymol (3D structure). = Segment of same pdb file viewed as text.
- 21. Leads to… r273h/s240q Observable changes in structural and electrostatic properties. Mutations involving any of the 14 binding amino acids (one or more). Which we want to use to… Reliably classify p53 mutants as active or inactive.
- 22. p53 and DNA are both primarily negatively charged molecules. However, p53 has positive pockets that interact with the negatively charged DNA.
- 23. The electrostatic complementarity region is defined as a region where negatively charged DNA overlaps with positively charged p53. +1 +2 +3 -1 -2 -3 protein +1 isopotential DNA -1 isopotential
- 24. Compute +1 isopotential around p53 mutant, -1 isopotential around DNA, and find the electrostatic complementarity region between the two. Isopotentials generated by “surfaceExtractor”, Boolean intersections generated by VASP (Volumetric Analysis of Surface Properties).
- 25. The -1 isopotential region of the p53- binding DNA motif. DNA -1 isopotential, yellow indicates negative charge
- 27. Intersection of DNA and unmutated (wild type) p53
- 28. 1) Who are we and what do we do? 2) DNA and p53: Basics 3) Research problem 4) Approach 1: electrostatic complementarity across whole region. 5) Approach 2: complementarity at the 14 binding amino acids. 6) Approach 3: Cubes 7) Conclusions
- 29. The volume of the electrostatic complementarity region between amino acids 102-292 is computed for every mutant. This picture is the visual representation of a volumetric computation.
- 30. 0 1/20 1/10 3/20 1/5 1/4 3/10 360 380 400 420 440 460 480 500 520 540 560 580 600 Rel. Freq. active Rel. Freq. Inactive Complementarity Region Volume Relative Frequency
- 31. Given a random complementarity region volume, it is mostly impossible to determine whether the mutant is active or inactive. 0 1/20 1/10 3/20 1/5 1/4 3/10 360 380 400 420 440 460 480 500 520 540 560 580 600 Rel. Freq. active Rel. Freq. Inactive Complementarity Region Volume RelativeFrequency Random mutant: Volume = 380, effect = inactive Random mutant: Volume = 580, effect = inactive Random mutant: Volume = 480, effect = ????
- 32. 1) Who are we and what do we do? 2) DNA and p53: Basics 3) Research problem 4) Approach 1: electrostatic complementarity across whole region. 5) Approach 2: complementarity at the 14 binding amino acids. 6) Approach 3: Cubes 7) Conclusions
- 33. What happens if we only analyze the volume near the 14 amino acids directly involved in DNA binding? Again, these amino acids are: a119, a276, n239, n247, r248, r273, r280, r283, c275, c277, l120, m243, s121, and s241.
- 34. From the entire electrostatic complementarity region, we take all subregions that are within five cubic Angstroms of any of the 14 binding amino acids. The rest of the region is discarded. Electrostatic Complementarity region for unmutated p53 within 5 cubic Angstroms of the 13 binding amino acids
- 35. Results of this test indicate that if the region volume is below a certain threshold, it is guaranteed inactive. These lower volumes correspond to real life mutations of r248 and r283. 0 0.05 0.1 0.15 0.2 0.25 Rel. Freq. Active Rel. Freq. Inactive Complementarity Region Volume Relative Frequency
- 36. 0 1/20 1/10 3/20 1/5 1/4 3/10 360 380 400 420 440 460 480 500 520 540 560 580 600 Rel. Freq. active Rel. Freq. Inactive Complementarity Region Volume RelativeFrequency 0 0.05 0.1 0.15 0.2 0.25 Rel. Freq. Active Rel. Freq. Inactive Complementarity Region Volume RelativeFrequencyApproach 1 Approach 2
- 37. 1) Who are we and what do we do? 2) DNA and p53: Basics 3) Research problem 4) Approach 1: electrostatic complementarity across whole region. 5) Approach 2: complementarity at the 14 binding amino acids. 6) Approach 3: Cubes 7) Conclusions
- 38. We divide the EC region into 39 sub regions defined by cubes. We calculate the volume of electrostatic complementarity in each cube. Intersection region for wild type p53 separated into cubes
- 39. Each mutation is an observation with 39 features. Each feature is the volume of the electrostatic complementarity region contained in a particular cube. r273r 41.082862 78.972403 16.34071 Mutation Name Cube 11 Cube 60 Cube 101
- 40. The collection of all observations is a matrix with 39 columns (cube volumes) and 232 rows (mutations). Mutation Cube 3 Cube 4 Cube 11 r158l_s227f_n239y 7.445915 10.34335 41.34778 r249m_n235k_n239y 5.358244 10.58604 37.30913 r273c_d281g_e285g 7.348197 10.26282 42.42635
- 41. A “true positive” is a correctly recognized active mutant. A “true negative” is a correctly recognized inactive mutant.
- 42. 1) Who are we and what do we do? 2) DNA and p53: Basics 3) Research problem 4) Approach 1: electrostatic complementarity across whole region. 5) Approach 2: complementarity at the 14 binding amino acids. 6) Approach 3: Cubes 7) Conclusions
- 43. Volumetric analysis of electrostatic complementarity regions holds promising applications to p53 study. A prediction algorithm with high sensitivity is possible from analysis of cube data. Our hope is that, using more refined analysis techniques, the prediction algorithm can be made even more specific.
- 44. Support Vector Machines (SVM) can be used to calculate a basis function that separates active mutants from inactive mutants based on cube volumes.
- 48. 0 1/10 1/5 3/10 360 380 400 420 440 460 480 500 520 540 560 580 600 Intersection Volumes+1 Relative Frequency Rel. Freq. active Rel. Freq. Inactive Complementarity Region Volume RelativeFrequency 0 0.1 0.2 0.3 210 225 240 255 270 285 300 315 More Binding Acid regions +1 Relative Frequency Rel. Freq. Active Rel. Freq. Inactive Complementarity Region Volume RelativeFrequency
- 49. In both histograms, it is clear that there is a certain lower and upper bound that all active mutants fall between. If these thresholds can be detected, it is possible to accurately predict inactive p53 mutants. Using the entire intersection volume for this purpose is not as accurate.
- 50. For each cube, we calculate the lower threshold (minimum volume across all mutations) and the higher threshold (maximum volume across all mutations). Mutation Cube 3 Cube 4 Cube 11 r158l_s227f_n239y 7.445915 10.34335 41.34778 r249m_n235k_n239y 5.358244 10.58604 37.30913 r273c_d281g_e285g 7.348197 10.26282 42.42635 Lower threshold 5.358244 10.26282 37.30913 Upper threshold 7.445915 10.58604 42.42635
- 51. The remaining data set is divided into two parts: a second training set to determine which cubes indicate inactivity, and a test set to analyze the predictive power of the chosen cubes. For each mutation, 39 true/false values (one for each cube) are computed. Each cube with a volume above or below the thresholds set for that particular cube in stage 1 gets a “true” value.
- 52. Total Dataset Dataset minus training set Training Set 1 (only actives) Stage 1 Test Set Training Set 2 Thresholds generated in Training Set 1 used to extract inactivity indicators from Training Set 2 Stage 2 Inactivity indicators are applied to the test set.
- 53. Active mutants have, on average, far fewer “true” values (threshold violations) than inactive mutants. Depending on the training and test sets, the worst active mutants had anywhere from 3-6 true values, while inactives can have more than 13. 1 violation: 46 2 violations: 29 3 violations: 25 4 violations: 9 5 violations: 5 6 violations: 1 7 violations: 0 8 violations: 1 9 violations: 0 10 violations: 0 11 violations: 0 12 violations: 0 13 violations: 0 Num violators: 116 1 violation: 6 2 violations: 2 3 violations: 1 4 violations: 0 5 violations: 0 6 violations: 0 Num violators: 9 # of Active Mutants with n threshold violations # of Inactive Mutants with n threshold violations
- 54. A simple example: Mutations a-e have violations in cubes 1-5 if they have a “TRUE”. For each cube, the number of mutations with true values in that cube and 0, 1, 2, etc other violations is counted. Mutation 1 2 3 4 5 A TRUE TRUE TRUE TRUE B TRUE TRUE TRUE TRUE C TRUE TRUE TRUE TRUE D TRUE E TRUE TRUE
- 55. Cube Number of violators w/ 0 other violations Number of violators w/ 1 other violation Number of violators w/ 2 other violations Number of violators w/ 3 other violations 1 3 3 2 2 2 3 2 2 2 3 3 3 3 3 4 3 3 3 3 5 3 3 2 2 The cubes marked in red are the cubes of interest. The amount of cubes with violations in them does not change between 0 and 3 other threshold violations.
- 56. For each cube, the amount of mutations in the second training set with a threshold violation in that cube were counted. Separate counts were generated for mutations that had a violation in that cube along with 1, 2, 3, etc other cubes.
- 57. If all active mutations have 3 or less threshold violations, then cubes that sustain the same count between 0 and 3 neighboring violations are only present in inactive mutations. These inactivity indicators are used to classify the mutants in the test set as either active or inactive. Test Set Training Set 2 Inactivity indicators are applied to the test set.
- 58. The algorithm searches for inactive mutants. Any mutant with more than three threshold violations is automatically counted as inactive. From the remaining pool, all mutants with threshold violations in the cubes that do not have their counts change between 0 and 3 concomitant violations are also counted as inactive.
- 59. Cubes that guarantee inactivity have still not been completely identified. The best inactivity indicators generated thus far are not the most sensitive. Next steps involve either a refinement of the threshold violations algorithm or the use of other methods.
Hinweis der Redaktion
- I chose this title because that’s the title Brian submitted to the NSF when getting funding. SAY THAT! Say that the various components of the title will be explained as we go along.
- ADD SARA GROGAN!!
- Say in your presentation: “Don’t worry, I’ll explain what each one of these terms means.”
- Cell division suspended in the G1/S phase. Apoptosis initiated when cell sustains too much damage.
- Active/inactive classification is binary. Did not consider partial loss of function mutations.
- Changes to these amino acids can drastically alter the structure of the binding site.
- R273h/n263v is a rescue mutation. The phenomenon of mutations in p53 counteracting each other has been described as rescue mutations, and is heavily researched. We just concern ourselves with the activity classification of the protein.
- R273h/n263v is a rescue mutation. The phenomenon of mutations in p53 counteracting each other has been described as rescue mutations, and is heavily researched. We just concern ourselves with the activity classification of the protein.
- R273h/n263v is a rescue mutation. The phenomenon of mutations in p53 counteracting each other has been described as rescue mutations, and is heavily researched. We just concern ourselves with the activity classification of the protein.
- Because we only analyze substitutions of DNA binding AAs, we use 77 of the actives and 155 of the inactives A protein data bank (pdb) file contains twofold information about proteins: the sequence of the protein’s amino acids and the 3D structure of the protein.
- 1TSR.pdb contains three chains, each a copy of the p53 DNA binding domain. Chain B interacts directly with the binding region of DNA.
- What conformational changes to p53’s contact site determine activity vs. inactivity? Can we generalize our findings to any mutation within the DNA binding region?
- An electrostatic isopotential maps all regions of a protein with a particular charge. A +1/-1 isopotential contains all regions with a charge of 1 kT/e or greater/less, where k = Boltzmann constant, T = Temperature, and e = electron charge. The regions where the oppositely charged regions of the isopotential overlap are the regions of electrostatic complementarity, which govern binding.
- An electrostatic isopotential maps all regions of a protein with a particular charge. A +1/-1 isopotential contains all regions with a charge of 1 kT/e or greater/less, where k = Boltzmann constant, T = Temperature, and e = electron charge. The regions where the oppositely charged regions of the isopotential overlap are the regions of electrostatic complementarity, which govern binding.
- Both of these programs were created by Prof. Brian Chen
- We removed the -1 isopotential of p53 from the image and made the p53 +1 isopotential transparent
- This picture is the visual representation of such a computation
- Unfortunately, calculating the volume of the intersection region does not yield viable classification. Explain every feature about this graph: x axis, y axis, blue vs red. For example: about ¼ of all active mutants have EC region volumes around 470 cubic angstroms. “Am I happy with this diagram? Of course not (transition to next slide).”
- The choice of 5 cubic angstroms produced the clearest return values.
- By looking at a small subset of the electrostatic complementarity region, we are able to get a much clearer division between active and inactive volumes, at least for some part of the histogram. Unfortunately the biggest part of the histogram is still tangled. What this shows is that by subdividing the EC region into subsection, we can improve our results. Hence the next approach.
- 39 cubic sub regions were generated. The volume of the electrostatic complementarity region contained in each cube was computed.
- Say the following: “After that, the learning algorithm I have developed becomes complicated, so allow me to go to the results. If anyone is interested in the algorithm I am at your disposal to take it offline.”
- There are far too many false positives. Obviously I am still not happy with these results. While the sensitivity is good, the specificity is not. There are far too many false positives.
- In the graphs of both approaches, we notice the existence of very low volumes that only inactive mutants tend to have. The Approach 1 graph indicates the existence of certain extraneous regions of complementarity that are detrimental to binding. In Approach 2 these regions are removed because we are only looking at the complementarity region needed for binding.
- These maximum and minimum volumes determine the upper and lower volume thresholds, respectively, for each cube. These thresholds are used to in the remaining stages to classify mutations in the test set.