1. Sequence similarity search Glance to the protein world

2. WHATS TODAY? BLASTing Proteins -Similarity scores for protein sequences -Advanced BLAST (PSI BLAST)

3. Protein Sequence Alignment  Rule of thumb: Proteins are homologous if 25% identical (length >100) DNA sequences are homologous if 70% identical

4. Protein Pairwise Sequence Alignment The alignment tools are similar to the DNA alignment tools BLASTN for nucleotides BLASTP for proteins Main difference: instead of scoring match (+2) and mismatch (-1) we have similarity scores: Score s(i,j) > 0 if amino acids i and j have similar properties Score s(i,j) is  0 otherwise How should we score s(i,j)?

5. The 20 Amino Acids

6. Chemical Similarities Between Amino Acids Acids & AmidesDENQ (Asp, Glu, Asn, Gln) Basic HKR (His, Lys, Arg) AromaticFYW (Phe, Tyr, Trp) Hydrophilic ACGPST (Ala, Cys, Gly, Pro, Ser, Thr) HydrophobicILMV (Ile, Leu, Met, Val)

7. Sequence Alignment based on AA similarity TQSPSSLSASVGDTVTITCRASQSISTYLNWYQQKP----GKAPKLLIYAASSSQSGVPS || + |||| +|| ||| | +| | | | | TQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIKILGNQGSFLTKGPSKLNDRADS RFSGSGSGTDFTLTINSLQPEDFATYYCQ---------------QSYSTPHFSQGTKLEI | | | +| | | +|+ || || |+ + | | || | + RRSLWDQG-NFPLIIKNLKIEDSDTYICEVEDQKEEVQLLVFGLTANSDTHLLQGQSLTL ---KRTVAAPSVFIFPPSDEQLKSGTASVVCLLN---------NFYPREAKVQWKVD ++||| | + ++ | | | + ||++|+| TLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKID | = identity+ = similarity

8. Amino Acid Substitutions Matrices When scoring protein sequence alignments it is common to use a matrix of 20  20, representing all pairwise comparisons : -Score Matrix -Substitution Matrix

9. Scoring Matrices Scoring Matrix -match/mismatch score –Not bad for similar sequences –Does not show distantly related sequences Substitution matrix –Scores residues dependent upon likelihood substitution is found in nature –More applicable for amino acid sequences

10. Given an alignment of closely related sequences we can score the relation between amino acids based on how frequently they substitute each other In this column E & D are found 7/8 M G Y D E M G Y E E M G Y D E M G Y Q E M G Y D E M G Y E E Substitution Matrix

11. CH +H3N+H3N COO - HCH C O-O- O CH +H3N+H3N C COO - HCH O-O- O Aspartate (Asp, D) Glutamate (Glu, E) D / E

12. PAM - Point Accepted Mutations Developed by Margaret Dayhoff, 1978. Analyzed very similar protein sequences “Accepted” mutations – do not negatively affect a protein’s fitness Used global alignment. Counted the number of substitutions (i,j) per amino acid pair: Many i j substitutions => high score s(i,j)

13. Basic matrix normalized probabilities multiplied by 10000 Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val A R N D C Q E G H I L K M F P S T W Y V A 9867 2 9 10 3 8 17 21 2 6 4 2 6 2 22 35 32 0 2 18 R 1 9913 1 0 1 10 0 0 10 3 1 19 4 1 4 6 1 8 0 1 N 4 1 9822 36 0 4 6 6 21 3 1 13 0 1 2 20 9 1 4 1 D 6 0 42 9859 0 6 53 6 4 1 0 3 0 0 1 5 3 0 0 1 C 1 1 0 0 9973 0 0 0 1 1 0 0 0 0 1 5 1 0 3 2 Q 3 9 4 5 0 9876 27 1 23 1 3 6 4 0 6 2 2 0 0 1 E 10 0 7 56 0 35 9865 4 2 3 1 4 1 0 3 4 2 0 1 2 G 21 1 12 11 1 3 7 9935 1 0 1 2 1 1 3 21 3 0 0 5 H 1 8 18 3 1 20 1 0 9912 0 1 1 0 2 3 1 1 1 4 1 I 2 2 3 1 2 1 2 0 0 9872 9 2 12 7 0 1 7 0 1 33 L 3 1 3 0 0 6 1 1 4 22 9947 2 45 13 3 1 3 4 2 15 K 2 37 25 6 0 12 7 2 2 4 1 9926 20 0 3 8 11 0 1 1 M 1 1 0 0 0 2 0 0 0 5 8 4 9874 1 0 1 2 0 0 4 F 1 1 1 0 0 0 0 1 2 8 6 0 4 9946 0 2 1 3 28 0 P 13 5 2 1 1 8 3 2 5 1 2 2 1 1 9926 12 4 0 0 2 S 28 11 34 7 11 4 6 16 2 2 1 7 4 3 17 9840 38 5 2 2 T 22 2 13 4 1 3 2 2 1 11 2 8 6 1 5 32 9871 0 2 9 W 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 9976 1 0 Y 1 0 3 0 3 0 1 0 4 1 1 0 0 21 0 1 1 2 9945 1 V 13 2 1 1 3 2 2 3 3 57 11 1 17 1 3 2 10 0 2 9901

14. Log Odds Matrices PAM matrices converted to log-odds matrix –Calculate odds ratio for each substitution Taking scores in previous matrix Divide by frequency of amino acid –Convert ratio to log10 and multiply by 10 –Take average of log odds ratio for converting A to B and converting B to A –Result: Symmetric matrix

15. PAM250 Log odds matrix Entry (i,i) is greater than any entry (i,j), j  i. Entry (i,j): the score of aligning amino acid i against amino acid j. Simliar aa have high score

16. Selecting a PAM Matrix There are different PAM matrices (PAM 1- PAM250). The matrices are derived from each other by multiplying the PAM1 matrices N times Low PAM numbers: short sequences, strong local similarities. High PAM numbers: long sequences, weak similarities. –PAM120 recommended for general use (40% identity) –PAM60 for close relations (60% identity) –PAM250 for distant relations (20% identity) If uncertain, try several different matrices –PAM40, PAM120, PAM250 recommended

17. BLOSUM Blocks Substitution Matrix –Steven and Jorga G. Henikoff (1992) Based on BLOCKS database ( www.blocks.fhcrc.org) –Families of proteins with identical function –Highly conserved protein domains Ungapped local alignment to identify motifs –Each motif is a block of local alignment –Counts amino acids observed in same column –Symmetrical model of substitution AABCDA… BBCDA DABCDA. A.BBCBB BBBCDABA.BCCAA AAACDAC.DCBCDB CCBADAB.DBBDCC AAACAA… BBCCC

18. BLOSUM Matrices Different BLOSUMn matrices are calculated independently from BLOCKS BLOSUMn is based on blocks that are at most n percent identical.

19. Selecting a BLOSUM Matrix For BLOSUMn, higher n suitable for sequences which are more similar –BLOSUM62 recommended for general use –BLOSUM80 for close relations –BLOSUM45 for distant relations

20. Summary: BLOSUM matrices are based on the replacement patterns found in more highly conserved regions of the sequences without gaps =Loacl alignment PAM matrices based on mutations observed throughout a global alignment, includes both highly conserved and highly mutable regions BLAST uses BLOSUM62 as a default REMEMBER !!!! you can always change it

21. Gap penalty in protein alignments Scoring for gap opening & for extension Depends on the substitution matrix used Default gap parameters are given for each matrix: –PAM30: open=9, extension=1 –PAM250: open=14, extension=2

22. Remote homologues Sometimes BLAST isn’t enough. Large protein family, and BLAST only gives close members. We want more distant members PSI-BLAST

23. [1] Select a query and search it against a protein database [2] PSI-BLAST constructs a multiple sequence alignment then creates a “profile” or specialized position-specific scoring matrix (PSSM) Page 138

24. R,I,KCD,E,TK,R,TN,L,Y,G

25. A R N D C Q E G H I L K M F P S T W Y V 1 M -1 -2 -2 -3 -2 -1 -2 -3 -2 1 2 -2 6 0 -3 -2 -1 -2 -1 1 2 K -1 1 0 1 -4 2 4 -2 0 -3 -3 3 -2 -4 -1 0 -1 -3 -2 -3 3 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 4 V 0 -3 -3 -4 -1 -3 -3 -4 -4 3 1 -3 1 -1 -3 -2 0 -3 -1 4 5 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 6 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 7 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 8 L -1 -3 -3 -4 -1 -3 -3 -4 -3 2 2 -3 1 3 -3 -2 -1 -2 0 3 9 L -1 -3 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 2 10 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 11 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 12 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 13 W -2 -3 -4 -4 -2 -2 -3 -4 -3 1 4 -3 2 1 -3 -3 -2 7 0 0 14 A 3 -2 -1 -2 -1 -1 -2 4 -2 -2 -2 -1 -2 -3 -1 1 -1 -3 -3 -1 15 A 2 -1 0 -1 -2 2 0 2 -1 -3 -3 0 -2 -3 -1 3 0 -3 -2 -2 16 A 4 -2 -1 -2 -1 -1 -1 3 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 -1... 37 S 2 -1 0 -1 -1 0 0 0 -1 -2 -3 0 -2 -3 -1 4 1 -3 -2 -2 38 G 0 -3 -1 -2 -3 -2 -2 6 -2 -4 -4 -2 -3 -4 -2 0 -2 -3 -3 -4 39 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -3 -2 0 40 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 41 Y -2 -2 -2 -3 -3 -2 -2 -3 2 -2 -1 -2 -1 3 -3 -2 -2 2 7 -1 42 A 4 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0

26. A R N D C Q E G H I L K M F P S T W Y V 1 M -1 -2 -2 -3 -2 -1 -2 -3 -2 1 2 -2 6 0 -3 -2 -1 -2 -1 1 2 K -1 1 0 1 -4 2 4 -2 0 -3 -3 3 -2 -4 -1 0 -1 -3 -2 -3 3 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 4 V 0 -3 -3 -4 -1 -3 -3 -4 -4 3 1 -3 1 -1 -3 -2 0 -3 -1 4 5 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 6 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 7 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 8 L -1 -3 -3 -4 -1 -3 -3 -4 -3 2 2 -3 1 3 -3 -2 -1 -2 0 3 9 L -1 -3 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 2 10 L -2 -2 -4 -4 -1 -2 -3 -4 -3 2 4 -3 2 0 -3 -3 -1 -2 -1 1 11 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 12 A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 13 W -2 -3 -4 -4 -2 -2 -3 -4 -3 1 4 -3 2 1 -3 -3 -2 7 0 0 14 A 3 -2 -1 -2 -1 -1 -2 4 -2 -2 -2 -1 -2 -3 -1 1 -1 -3 -3 -1 15 A 2 -1 0 -1 -2 2 0 2 -1 -3 -3 0 -2 -3 -1 3 0 -3 -2 -2 16 A 4 -2 -1 -2 -1 -1 -1 3 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 -1... 37 S 2 -1 0 -1 -1 0 0 0 -1 -2 -3 0 -2 -3 -1 4 1 -3 -2 -2 38 G 0 -3 -1 -2 -3 -2 -2 6 -2 -4 -4 -2 -3 -4 -2 0 -2 -3 -3 -4 39 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -3 -2 0 40 W -3 -3 -4 -5 -3 -2 -3 -3 -3 -3 -2 -3 -2 1 -4 -3 -3 12 2 -3 41 Y -2 -2 -2 -3 -3 -2 -2 -3 2 -2 -1 -2 -1 3 -3 -2 -2 2 7 -1 42 A 4 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0

27. PSI-BLAST [1] Select a query and search it against a protein database [2] PSI-BLAST constructs a multiple sequence alignment then creates a “profile” or specialized position-specific scoring matrix (PSSM) [3] The PSSM is used as a query against the database [4] PSI-BLAST estimates statistical significance (E values) [5] Repeat steps [3] and [4] iteratively, typically 5 times. At each new search, a new profile is used as the query. Page 138

28. Searching for remote homology using PSI-BLAST

29. The universe of lipocalins (each dot is a protein) retinol-binding protein odorant-binding protein apolipoprotein D Retinol binding Protein B-lactoglubolin

30. Score = 46.2 bits (108), Expect = 2e-04 Identities = 40/150 (26%), Positives = 70/150 (46%), Gaps = 37/150 (24%) Query: 27 VKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVC 86 V+ENFD ++ G WY + +K P + I A +S+ E G + K ++ Sbjct: 33 VQENFDVKKYLGRWYEI-EKIPASFEKGNCIQANYSLMENGNIEVLNK---------ELS 82 Query: 87 ADMVGTF---------TDTEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCR 137 D GT ++ +PAK +++++ + +WI+ TDY+ YA+ YSC Sbjct: 83 PD--GTMNQVKGEAKQSNVSEPAKLEVQFFPLMP-----PAPYWILATDYENYALVYSCT 135 Query: 138 ----LLNLDGTCADSYSFVFSRDPNGLPPE 163 L ++D + ++ R+P LPPE Sbjct: 136 TFFWLFHVD------FFWILGRNPY-LPPE 158 PSI-BLAST alignment of RBP (retinol binding protein) and  -lactoglobulin: iteration 1 Example is taken from Bioinformatics and Functional Genomics by Jonathan Pevsner (ISBN 0-471-21004-8). Copyright © 2003 by John Wiley & Sons, Inc.

31. PSI-BLAST alignment of RBP and  -lactoglobulin: iteration 2 Score = 140 bits (353), Expect = 1e-32 Identities = 45/176 (25%), Positives = 78/176 (43%), Gaps = 33/176 (18%) Query: 4 VWALLLLAAWAAAERDCRVSSF--------RVKENFDKARFSGTWYAMAKKDPEGLFLQD 55 V L+ LA A + +F V+ENFD ++ G WY + +K P + Sbjct: 2 VTMLMFLATLAGLFTTAKGQNFHLGKCPSPPVQENFDVKKYLGRWYEI-EKIPASFEKGN 60 Query: 56 NIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMV---GTFTDTEDPAKFKMKYWGVASF 112 I A +S+ E G + K + D + V ++ +PAK +++++ + Sbjct: 61 CIQANYSLMENGNIEVLNKEL-----SPDGTMNQVKGEAKQSNVSEPAKLEVQFFPL--- 112 Query: 113 LQKGNDDHWIVDTDYDTYAVQYSCR----LLNLDGTCADSYSFVFSRDPNGLPPEA 164 +WI+ TDY+ YA+ YSC L ++D + ++ R+P LPPE Sbjct: 113 --MPPAPYWILATDYENYALVYSCTTFFWLFHVD------FFWILGRNPY-LPPET 159

32. PSI-BLAST alignment of RBP and  -lactoglobulin: iteration 3 Score = 159 bits (404), Expect = 1e-38 Identities = 41/170 (24%), Positives = 69/170 (40%), Gaps = 19/170 (11%) Query: 3 WVWALLLLAAWAAAERD--------CRVSSFRVKENFDKARFSGTWYAMAKKDPEGLFLQ 54 V L+ LA A + S V+ENFD ++ G WY + K Sbjct: 1 MVTMLMFLATLAGLFTTAKGQNFHLGKCPSPPVQENFDVKKYLGRWYEIEKIPASFE-KG 59 Query: 55 DNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQ 114 + I A +S+ E G + K V + ++ +PAK +++++ + Sbjct: 60 NCIQANYSLMENGNIEVLNKELSPDGTMNQVKGE--AKQSNVSEPAKLEVQFFPL----- 112 Query: 115 KGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADSYSFVFSRDPNGLPPEA 164 +WI+ TDY+ YA+ YSC + ++ R+P LPPE Sbjct: 113 MPPAPYWILATDYENYALVYSCTTFFWL--FHVDFFWILGRNPY-LPPET 159

33. Score = 159 bits (404), Expect = 1e-38 Identities = 41/170 (24%), Positives = 69/170 (40%), Gaps = 19/170 (11%) Query: 3 WVWALLLLAAWAAAERD--------CRVSSFRVKENFDKARFSGTWYAMAKKDPEGLFLQ 54 V L+ LA A + S V+ENFD ++ G WY + K Sbjct: 1 MVTMLMFLATLAGLFTTAKGQNFHLGKCPSPPVQENFDVKKYLGRWYEIEKIPASFE-KG 59 Query: 55 DNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQ 114 + I A +S+ E G + K V + ++ +PAK +++++ + Sbjct: 60 NCIQANYSLMENGNIEVLNKELSPDGTMNQVKGE--AKQSNVSEPAKLEVQFFPL----- 112 Query: 115 KGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADSYSFVFSRDPNGLPPEA 164 +WI+ TDY+ YA+ YSC + ++ R+P LPPE Sbjct: 113 MPPAPYWILATDYENYALVYSCTTFFWL--FHVDFFWILGRNPY-LPPET 159 Score = 46.2 bits (108), Expect = 2e-04 Identities = 40/150 (26%), Positives = 70/150 (46%), Gaps = 37/150 (24%) Query: 27 VKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVC 86 V+ENFD ++ G WY + +K P + I A +S+ E G + K ++ Sbjct: 33 VQENFDVKKYLGRWYEI-EKIPASFEKGNCIQANYSLMENGNIEVLNK---------ELS 82 Query: 87 ADMVGTF---------TDTEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCR 137 D GT ++ +PAK +++++ + +WI+ TDY+ YA+ YSC Sbjct: 83 PD--GTMNQVKGEAKQSNVSEPAKLEVQFFPLMP-----PAPYWILATDYENYALVYSCT 135 Query: 138 ----LLNLDGTCADSYSFVFSRDPNGLPPE 163 L ++D + ++ R+P LPPE Sbjct: 136 TFFWLFHVD------FFWILGRNPY-LPPE 158 1 3

34. The universe of lipocalins (each dot is a protein) retinol-binding protein odorant-binding protein apolipoprotein D

35. Scoring matrices let you focus on the big (or small) picture retinol-binding protein

36. Scoring matrices let you focus on the big (or small) picture retinol-binding protein retinol-binding protein PAM250 PAM30 Blosum45 Blosum80

37. PSI-BLAST generates scoring matrices more powerful than PAM or BLOSUM retinol-binding protein retinol-binding protein

38. PSI-BLAST -PSI-BLAST is useful to detect weak but biologically meaningful relationships between proteins. -The main source of false positives is the spurious amplification of sequences not related to the query. -Once even a single spurious protein is included in a PSI-BLAST search above threshold, it will not go away. Page 144

39. PSI-BLAST Three approaches to prevent false positive results: [1] Apply filtering [2] Adjust E value to a lower value [3] Visually inspect the output from each iteration. Remove suspicious hits. Page 144

40. PHI-BLAST Searching a specific sequence pattern with local alignments surrounding the match. Page 145 PHI-BLAST may be preferable to just searching for pattern occurrences because it filters out those cases where the pattern occurrence is probably random and not indicative of homology. EXAMPLE: Search for a short sequence motif in the lipocalin family

41. PHI-BLAST Given 1) protein sequence S 2) pattern P occurring in S, PHI-BLAST helps answer the question: What other protein sequences both contain an occurrence of P and are homologous to S in the vicinity of the pattern occurrences? Page 145

42. 1 50 ecblc MRLLPLVAAA TAAFLVVACS SPTPPRGVTV VNNFDAKRYL GTWYEIARFD vc MRAIFLILCS V...LLNGCL G..MPESVKP VSDFELNNYL GKWYEVARLD hsrbp ~~~MKWVWAL LLLAAWAAAE RDCRVSSFRV KENFDKARFS GTWYAMAKKD Align three lipocalins (RBP and two bacterial lipocalins)

43. 1 50 ecblc MRLLPLVAAA TAAFLVVACS SPTPPRGVTV VNNFDAKRYL GTWYEIARFD vc MRAIFLILCS V...LLNGCL G..MPESVKP VSDFELNNYL GKWYEVARLD hsrbp ~~~MKWVWAL LLLAAWAAAE RDCRVSSFRV KENFDKARFS GTWYAMAKKD GTWYEI K AV M Concentrate on the conserved region of interest and see which amino acid residues are used

44. 1 50 ecblc MRLLPLVAAA TAAFLVVACS SPTPPRGVTV VNNFDAKRYL GTWYEIARFD vc MRAIFLILCS V...LLNGCL G..MPESVKP VSDFELNNYL GKWYEVARLD hsrbp ~~~MKWVWAL LLLAAWAAAE RDCRVSSFRV KENFDKARFS GTWYAMAKKD GTWYEI K AV M GXW[YF][EA][IVLM] Create a pattern using the appropriate syntax

45. Results