1. Protein structure prediction Einat Granot Liron Atedgi

2. Protein folding • Protein folding determined by A”A sequence Why knowing the folding is importance ? • Determine it’s functionality • Find distant evolutionary relationship • Design drugs

3. Protein structures • Primary structure • Secondary structure • Tertiary structure

4. Two prediction methods • PSI-PRED– secondary structure prediction based on PSIBLAST • GenTHREADER– tertiary structure prediction Were developed by the group of David T.Jones,University of Warwick

5. Methods general format Sequence Alignment + Additional data Neuron networks Structure prediction

6. Neuron networks

7. Neuron networks Output Numerical inputs Units Why do we call it neuron network ? Every unit performs weighted calculation

8. Neuron network hidden layer with the increasing number of added layers the mean square error is lower Hidden layer

9. Neuron networks training • Network connections and weights determined by training process • Training performs by samples of input and expected output. • The learning algorithm is called back propagation

10. Network training & testing After training we perform testing • Training and testing groups must be chosen very carefully • What problems can arise ? • Insufficient training or testing • Testing group may be biased

11. Neuron networks is a “black-box” • The specific algorithm ofa working neuron networkis not known • It’s hard to deduce new biological principles about the solved problem

12. PSI-PRED Secondary structureprediction

13. Secondary structure prediction • In DSSP – 8 secondary structures categories • In PSI-PRED – were joined into 3:Strand(E), Helix(H) and Coil(C) AA: RLMPHIKRSAIPVNHGQCRWEDNVDERTNCMIQYVLIMRD Pred: CCCCCHHHCCCCCCEEEEEECCCCCCHHHHEEEEEECCCC

14. PSI-PRED sequence alignment (Find homologous) Create protein profile Insert to first neuron network Insert to second neuron network Final prediction

15. sequence alignment • Finding homologous for target protein using PSI-BLAST Reminder … ? What is PSI-BLAST…? Position Specific Iterated Blast,giving output to PSSM.

16. PSI-BLAST Pros & Cons Pros : • Sensitive to distant homologous • Reliable • Accessible from every workstation Cons : • Sensitive to distant homologous - Result might be biased • Sensitive to repetitive sequences

17. Solving PSI-BLAST problems • A special DB of 340,000 sequences was constructed for PSI-PRED • This DB contains only unique and unrepetitive sequences

18. PSI-PRED sequence alignment (Find homologous) Create protein profile Insert to first neuron network Insert to second neuron network Final prediction

19. Create protein profile • PSI-PRED uses the PSSM from PSI-BLAST produced after 3 iteration • This matrix is processed by transformation f(x) = , so the final values are between 0 to 1

20. PSSM – Output of PSI-BLAST Transformation

21. Create protein profile • The matrix size is M x 20, when M is the sequence length • Addition column is added which defined the N/C terminus -> M x 21 matrix

22. PSI-PRED sequence alignment (Find homologous) Create protein profile Insert to first neuron network Insert to second neuron network Final prediction

23. Networks training & testing • 187 proteins were selected according to CATH and PSI-BLAST • CATH filters proteins according to their folding domains configuration (T-level) • This considered to be a strict selection

24. First neuron network Every time, a sequence of 15 A”A long is inserted into the first network The output is a matrix 15 x 3

25. PSI-PRED sequence alignment (Find homologous) Create protein profile Insert to first neuron network Insert to second neuron network Final prediction

26. Second neuron network The input for the 2nd network is the output from the 1st one Again, another column is added, indicates the N/C terminus

27. Why do we need a second network? Let’s examine a possible prediction from the 1st network… What is the problem with this prediction ? Seq VLFLNDNLDDVVIGRPKRTYTAITL Pred EEEECCCCHHHCCCHCCCEEEECC A single A”A helix does not exist The 2nd network maintains the coherency between adjacent A”A and improves the accuracy

28. PSI-PRED sequence alignment (Find homologous) Create protein profile Insert to first neuron network Insert to second neuron network Final prediction

29. Final prediction Image of prediction Degree Of confidence Target sequence Secondary structure

30. PSI-PRED evaluation • CASP– Critical Assessment of technique for protein Structure Prediction experiments • At CASP3 PSI-PRED achieved the best results from all other methods participated

31. PSI-PRED evaluation Q3 average : PSI-PRED - 76.3% JPRED – 72.4% DSC - 67.3% Q3 score – percentage of A”A predicted correctly

32. Reasons for success • The use of PSI-BLAST • More sensitive (iterative algorithm) • More accurate (pairwise local alignments) • Usage of neuron networks • Strict selection for training & testing

33. Possible improvements • Larger data bases (training & alignment) • Combinations with other methods (JPRED) • Predict more than 3 secondary structure

34. Bring out the food…

35. GenTHREADER Tertiary structure Prediction

36. Threading methods • Trying to thread a target A”A sequence on a template 3D structure M Q S N I L D V R E R A Q T V L C N K

37. Templates collection • Target sequence is compared against a collection of sequences with known folding • The collection was taken from Brookhaven Protein Data Bank and includes unique sequences

38. GenTHREADER Sequence alignment Calculate threading potential Insert to neuron network Final prediction

39. Sequence alignment • The target sequence is aligned against each of the templates twice: • Target profile against template sequence • Target sequence against template profile • The best result is taken

40. Creating a profile Steps for creating a profile : • Alignment against OWL DB(A DB for coding sequences) • Selection of sequences with E-Value lower than 0.01 • Constructing a profile using BLOSUM50

41. Creating a profile A L M P H I K R S A I P V N H G Y V I M Q C R W E D N S T K V

42. GenTHREADER Sequence alignment Calculate threading potential Insert to neuron network Final prediction

43. Calculate threading potential Threading potential includes : • pairwise potential • solvation potential

44. Pairwise potential • Potential for interaction between two A”A • Considerate analysis of known structure and favorable energy configuration • Lower pairwise potential indicates a favorable state

45. Solvation potential • Calculated per A”A and proportional to its degree of burial • Degree of burial (DOB)– The num of other A”A located in a radius of 10Å • Hydrophobic acids - a high DOB is preferred • Hydrophilic acids - a low DOB is preferred

46. GenTHREADER Sequence alignment Calculate threading potential Insert to neuron network Final prediction

47. Insert to neuron network • Prediction is very complex therefore a neuron network is used

48. Neuron network • Again, the 6 input parameters were converted to values between 0 – 1 using the function f(x) = • The output is a value between 0 -1 showing the confidence of the match

49. Network training & testing • The network was trained using pairs of proteins with known folding patterns • Again the training and testing sets were separated to avoid bias

50. GenTHREADER Sequence alignment Calculate threading potential Insert to neuron network Final prediction