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Benchmarking and Extension of Protein3DFit Nasir Mahmood
Benchmarking and Extension of Protein3DFit ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Introduction   Method  Results  Outlook
Why Align Structures?   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Introduction   Method  Results  Outlook
Structure Alignment: Issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Introduction   Method  Results  Outlook
Simple case – two closely related proteins with the same number of amino acids. Structure alignment Problem A B Introduction   Method  Results  Outlook T Find a transformation to achieve the best  superposition
Structure Alignment Problem Two sets of Points in 3D space:   A = (a1, a2, …, an)   B = (b1, b2, …, bm) A(P) ,  B(Q):   Optimal subsets   with | A(P) |=| B(Q) |,   Introduction   Method  Results  Outlook Optimal rigid body transformation (calculated by Diamond (1998) method) Distance { d(  A(P)  -  T( B(Q) )  ) } min T
Two Subproblems ,[object Object],[object Object],[object Object],Introduction   Method  Results  Outlook
Correspondence set ,[object Object],[object Object],[object Object],Introduction   Method  Results  Outlook
Superposition ,[object Object],[object Object],[object Object],[object Object],T Introduction   Method  Results  Outlook
Aim of the project ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Introduction   Method  Results  Outlook
Benchmarking and Extension of Protein3DFit ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Introduction  Method  Results  Outlook
Algorithm  Intra-Molecular distance Seed Alignments (Fragments & Quartets) Next Best Frag Quartet 1 Quartet 2 .............. .............. Alignment Dynamic Programming i<N No Stop C α  Match? increased Yes No Sup. Archive Yes Optimization Best  Supp. Display  Best Alignment Introduction  Method  Results  Outlook Structures Superimpose
Intra-molecular Distance Matrices 1  2  3  4  5 . . . . . N  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Structure A Structure B 1.4 3.0 0.7 1.9 0.0 4.7 0.0 2.9 0.0 0.0 2.1 0.3 0.0 3.1 0.6 2.9 0.0 0.0 0.0 0.4 0.0 5.9 0.0 2.3 1.4 3.0 0.7 1.9 0.0 4.7 0.0 2.9 2.1 0.3 0.0 3.1 0.6 2.9 0.0 0.0 0.4 0.0 5.9 0.0 2.3 0.4 0.0 5.9 0.0 2.3 3.1 0.6 2.9 0.0 0.6 2.9 0.0 1 2 3 4 5 ........................... N 0.0 0.0 0.0 1.4 3.0 0.0 1.9 0.0 4.7 0.0 2.9 0.0 0.0 2.1 0.3 0.0 3.1 0.6 2.9 0.0 0.0 0.0 0.4 0.0 5.9 0.0 2.3 1.4 1.4 1.4 1 2 3 4 5 ............................ M 1  2  3  4  5 . . . . . M  i i+1 i+2 i+3 i+4 i+5 i+6 i+7 Introduction  Method  Results  Outlook i i+1 i+2 i+3 i+4 i+5 i+6 i+7
Seed Alignments Extend Fragment Make Fragment Quartet Introduction  Method  Results  Outlook
Algorithm Intra-Molecular distance Seed Alignments (Fragments & Quartets) Next Best Frag Quartet 1 Quartet 2 .............. .............. Alignment Dynamic Programming i<N No Stop C α  Match? increased Yes No Sup. Archive Yes Optimization Best  Supp. Display  Best Alignment Introduction  Method  Results  Outlook Structures Superimpose
Algorithm- Pitfalls ,[object Object],[object Object],[object Object],Introduction  Method  Results  Outlook
Algorithm- Pitfalls ,[object Object],[object Object],[object Object],[object Object],Introduction  Method  Results  Outlook
Algorithm- Pitfalls Reference: 10 ‘difficult‘ structures from (Fischer et al.) obtained by Dali (Holm & Sander, 1993)   ,  VAST (Madej et al., 1995) & CE (Shindyalov & Bourne, 1998) 187/3.2 211/3.4 100/1.2 1EDE:_ 1CRL:_ 7 116/2.9 108/2.0 82/1.7 70/1.1 4FGF:_ 1TIE:_ 10 94/4.1 98/3.5 74/2.5 52/1.1 2GMF:A 1BGE:B 9 264/3.0 286/3.8 284/3.8 129/1.2 1NSB:A 2SIM:_ 8 94/2.7 95/3.3 85/2.2 59/1.1 2RHE:_ 1CID:_ 6 69/1.9 81/2.3 71/1.9 54/1.2 1MOL:A 1CEW:I 5 85/2.9 _ 74/2.2 52/1.2 1PAZ:_ 2AZA:A 4 85/3.5 63/2.5 _ 42/1.1 2RHE:_ 3HLA:B 3 87/1.9 86/1.9 78/1.6 71/1.0 3HHR:B 1TEN:_ 2 _ _ 48/2.1 39/1.0 1UBQ:_ 1FXI:A 1 CE DALI VAST Protein3DFit Chain 2 Chain  1 No
Algorithm - Extension Introduction  Method  Results  Outlook
C α  Match increased Yes Intra-Mol distance based Seed Alignments (Fragments & Quartets) Next No Best Frag Quartet 1 Quartet 2 .............. .............. Alignment Dynamic Programming Optimization C α Matches ? Increase Yes No Improved? i<N Sup. Archive Yes No i<M Yes Yes No Optimization Stop Get  Supp. Display  Best Alignment Next No Superimpose Structures Superimpose
Similarity Score : similarity score used in DP   :distance :distance resulting in half-maximal  similarity M   :maximum score for a match   Source: Gerstein Levitt, 1998 Introduction  Method  Results  Outlook
Quality Function Q score, ratio of no.  of aligned residues & RMSD :number of matches (equivalent residues) :length of structure 1 & 2 respectively :empirical parameter measuring Relative significance of  & RMSD Source: Krissinel & Henrich (2004) Introduction  Method  Results  Outlook
Benchmarking and Extension of Protein3DFit ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Introduction  Method  Results  Outlook
Results source: 10 ‘difficult‘ structures from (Fischer et al.) obtained by Dali (Holm & Sander, 1993)   , VAST (Madej et al., 1995) & CE (Shindyalov & Bourne, 1998). 39/1.0 71/1.0 42/1.1 52/1.2 54/1.2 59/1.1 100/1.2 129/1.2 52/1.1 70/1.1 Introduction  Method  Results   Outlook 187/3.2 211/3.4 181/3.2 1EDE:_ 1CRL:_ 7 116/2.9 108/2.0 82/1.7 105/2.6 4FGF:_ 1TIE:_ 10 94/4.1 98/3.5 74/2.5 81/3.0 2GMF:A 1BGE:B 9 264/3.0 286/3.8 284/3.8 265/3.2 1NSB:A 2SIM:_ 8 94/2.7 95/3.3 85/2.2 91/2.6 2RHE:_ 1CID:_ 6 69/1.9 81/2.3 71/1.9 76/2.1 1MOL:A 1CEW:I 5 85/2.9 _ 74/2.2 80/2.3 1PAZ:_ 2AZA:A 4 85/3.5 63/2.5 _ 69/3.4 2RHE:_ 3HLA:B 3 87/1.9 86/1.9 78/1.6 82/1.7 3HHR:B 1TEN:_ 2 _ _ 48/2.1 59/2.9 1UBQ:_ 1FXI:A 1 CE DALI VAST Protein3DFit Chain 2 Chain  1 No
Results Introduction  Method  Results   Outlook
CE   Dali 3Dfit-old 3Dfit-ext Introduction  Method  Results   Outlook
CE   Dali 3Dfit-old 3Dfit-ext
3Dfit-ext 3Dfit-old
Outlook / Possible Extensions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Acknowlegdments ,[object Object],[object Object],[object Object]
Questions? Thanks a lot for your patience!

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Protein Structure Alignment

  • 1. Benchmarking and Extension of Protein3DFit Nasir Mahmood
  • 2.
  • 3.
  • 4.
  • 5. Simple case – two closely related proteins with the same number of amino acids. Structure alignment Problem A B Introduction Method Results Outlook T Find a transformation to achieve the best superposition
  • 6. Structure Alignment Problem Two sets of Points in 3D space: A = (a1, a2, …, an) B = (b1, b2, …, bm) A(P) , B(Q): Optimal subsets with | A(P) |=| B(Q) |, Introduction Method Results Outlook Optimal rigid body transformation (calculated by Diamond (1998) method) Distance { d( A(P) - T( B(Q) ) ) } min T
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12. Algorithm Intra-Molecular distance Seed Alignments (Fragments & Quartets) Next Best Frag Quartet 1 Quartet 2 .............. .............. Alignment Dynamic Programming i<N No Stop C α Match? increased Yes No Sup. Archive Yes Optimization Best Supp. Display Best Alignment Introduction Method Results Outlook Structures Superimpose
  • 13. Intra-molecular Distance Matrices 1 2 3 4 5 . . . . . N 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Structure A Structure B 1.4 3.0 0.7 1.9 0.0 4.7 0.0 2.9 0.0 0.0 2.1 0.3 0.0 3.1 0.6 2.9 0.0 0.0 0.0 0.4 0.0 5.9 0.0 2.3 1.4 3.0 0.7 1.9 0.0 4.7 0.0 2.9 2.1 0.3 0.0 3.1 0.6 2.9 0.0 0.0 0.4 0.0 5.9 0.0 2.3 0.4 0.0 5.9 0.0 2.3 3.1 0.6 2.9 0.0 0.6 2.9 0.0 1 2 3 4 5 ........................... N 0.0 0.0 0.0 1.4 3.0 0.0 1.9 0.0 4.7 0.0 2.9 0.0 0.0 2.1 0.3 0.0 3.1 0.6 2.9 0.0 0.0 0.0 0.4 0.0 5.9 0.0 2.3 1.4 1.4 1.4 1 2 3 4 5 ............................ M 1 2 3 4 5 . . . . . M i i+1 i+2 i+3 i+4 i+5 i+6 i+7 Introduction Method Results Outlook i i+1 i+2 i+3 i+4 i+5 i+6 i+7
  • 14. Seed Alignments Extend Fragment Make Fragment Quartet Introduction Method Results Outlook
  • 15. Algorithm Intra-Molecular distance Seed Alignments (Fragments & Quartets) Next Best Frag Quartet 1 Quartet 2 .............. .............. Alignment Dynamic Programming i<N No Stop C α Match? increased Yes No Sup. Archive Yes Optimization Best Supp. Display Best Alignment Introduction Method Results Outlook Structures Superimpose
  • 16.
  • 17.
  • 18. Algorithm- Pitfalls Reference: 10 ‘difficult‘ structures from (Fischer et al.) obtained by Dali (Holm & Sander, 1993) , VAST (Madej et al., 1995) & CE (Shindyalov & Bourne, 1998) 187/3.2 211/3.4 100/1.2 1EDE:_ 1CRL:_ 7 116/2.9 108/2.0 82/1.7 70/1.1 4FGF:_ 1TIE:_ 10 94/4.1 98/3.5 74/2.5 52/1.1 2GMF:A 1BGE:B 9 264/3.0 286/3.8 284/3.8 129/1.2 1NSB:A 2SIM:_ 8 94/2.7 95/3.3 85/2.2 59/1.1 2RHE:_ 1CID:_ 6 69/1.9 81/2.3 71/1.9 54/1.2 1MOL:A 1CEW:I 5 85/2.9 _ 74/2.2 52/1.2 1PAZ:_ 2AZA:A 4 85/3.5 63/2.5 _ 42/1.1 2RHE:_ 3HLA:B 3 87/1.9 86/1.9 78/1.6 71/1.0 3HHR:B 1TEN:_ 2 _ _ 48/2.1 39/1.0 1UBQ:_ 1FXI:A 1 CE DALI VAST Protein3DFit Chain 2 Chain 1 No
  • 19. Algorithm - Extension Introduction Method Results Outlook
  • 20. C α Match increased Yes Intra-Mol distance based Seed Alignments (Fragments & Quartets) Next No Best Frag Quartet 1 Quartet 2 .............. .............. Alignment Dynamic Programming Optimization C α Matches ? Increase Yes No Improved? i<N Sup. Archive Yes No i<M Yes Yes No Optimization Stop Get Supp. Display Best Alignment Next No Superimpose Structures Superimpose
  • 21. Similarity Score : similarity score used in DP :distance :distance resulting in half-maximal similarity M :maximum score for a match Source: Gerstein Levitt, 1998 Introduction Method Results Outlook
  • 22. Quality Function Q score, ratio of no. of aligned residues & RMSD :number of matches (equivalent residues) :length of structure 1 & 2 respectively :empirical parameter measuring Relative significance of & RMSD Source: Krissinel & Henrich (2004) Introduction Method Results Outlook
  • 23.
  • 24. Results source: 10 ‘difficult‘ structures from (Fischer et al.) obtained by Dali (Holm & Sander, 1993) , VAST (Madej et al., 1995) & CE (Shindyalov & Bourne, 1998). 39/1.0 71/1.0 42/1.1 52/1.2 54/1.2 59/1.1 100/1.2 129/1.2 52/1.1 70/1.1 Introduction Method Results Outlook 187/3.2 211/3.4 181/3.2 1EDE:_ 1CRL:_ 7 116/2.9 108/2.0 82/1.7 105/2.6 4FGF:_ 1TIE:_ 10 94/4.1 98/3.5 74/2.5 81/3.0 2GMF:A 1BGE:B 9 264/3.0 286/3.8 284/3.8 265/3.2 1NSB:A 2SIM:_ 8 94/2.7 95/3.3 85/2.2 91/2.6 2RHE:_ 1CID:_ 6 69/1.9 81/2.3 71/1.9 76/2.1 1MOL:A 1CEW:I 5 85/2.9 _ 74/2.2 80/2.3 1PAZ:_ 2AZA:A 4 85/3.5 63/2.5 _ 69/3.4 2RHE:_ 3HLA:B 3 87/1.9 86/1.9 78/1.6 82/1.7 3HHR:B 1TEN:_ 2 _ _ 48/2.1 59/2.9 1UBQ:_ 1FXI:A 1 CE DALI VAST Protein3DFit Chain 2 Chain 1 No
  • 25. Results Introduction Method Results Outlook
  • 26. CE Dali 3Dfit-old 3Dfit-ext Introduction Method Results Outlook
  • 27. CE Dali 3Dfit-old 3Dfit-ext
  • 29.
  • 30.
  • 31. Questions? Thanks a lot for your patience!

Notas del editor

  1. Good Afternoon everybody! I am Nasir Mahmood Currently I am a Postgraduate Diploma Student Cologne University Bioinformatics Center (CUBIC), Cologne. Today, Here I am going to talk about Protein Structure Alignment.