1. Sequence analysis: Macromolecular motif recognition Sylvia Nagl

2. Amino acid primary sequence 2. Homologue(s) with known 3D structure? Homology modelling available 1. Search for sequence homologue(s) and construct an alignment 3. Motif recognition: Search secondary databases Secondary structure prediction Fold assignment Physico-chemical properties (e. g., using EMBOSS suite) DNA sequence Automatic translation Primary db searches FASTA, BLAST

3. Motif: the biological object one attempts to model - a functional or structural domain, active site, phosphorylation site etc. Pattern: a qualitative motif description based on a regular expression-like syntax Profile: a quantitative motif description - assigns a degree of similarity to a potential match Terminology

4. EXAMPLE: CATHEPSIN A PEPTIDASE FAMILY S10 EC # 3.4.16.5 3-D representation 3D profile (PROCAT) Active site recognition

5. 419IAFLTIKGAGHMVPTDKP436 1ac5 1ivy 438LTFVSVYNASHMVPFDKS455 Active site motifs Conserved seq patterns

6. Domain recognition Kringle domain from plasminogen protein EGF-like domain from coagulation factor X

7. Why search for motifs? to find “homologous” sequences apply existing information to new sequence find functionally important sites to find templates for homology modelling -lecture on homology modelling Macromolecular motif recognition

8. Percent identity Method 100 90 80 70 60 50 40 30 20 10 0 Twilight zone Midnight zone Automatic pairwise Alignment BLAST, Fasta) Macromolecular motif recognition Structure prediction Different analysis methods

9. What do we need? Method for defining motifs Algorithm for finding them Statistics to evaluate matches Macromolecular motif recognition

10. Methods for defining motifs: Regular expression (patterns) Profiles Hidden Markov Model (HMM) Macromolecular motif recognition

11. 1-D representation: Primary amino acid sequence MIRAAPPPLFLLLLLLLLLVSWASRGEAAPDQDEIQRLPGLAKQPSFRQYSGYLKSSGSKHLHYWFVESQKDPE NSPVVLWLNGGPGCSSLDGLLTEHGPFLVQPDGVTLEYNPYSWNLIANVLYLESPAGVGFSYSDDKFYATNDTE VAQSNFEALQDFFRLFPEYKNNKL... Computational sequence analysis Query secondary databases over the Internet Macromolecular motif recognition http://www.ebi.ac.uk/interpro/

12. Macromolecular motif recognition full domain alignment single motif multiple motifs exact regular expression (PROSITE) residue frequency matrices (PRINTS) profile (PROSITE) Hidden Markov Model (Pfam, PROSITE)

13. 419IAFLTIKGAGHMVPTDKP436 1ac5 1ivy 438LTFVSVYNASHMVPFDKS455 Active site motifs Conserved seq patterns

14. Prosite: Regular expressions CARBOXYPEPT_SER_HIS [LIVF]-x(2)-[LIVSTA]-x-[IVPST]-x-[GSDNQL]-[SAGV]-[SG]-H-x- [IVAQ]-P-x(3)-[PSA] Regular expressions represent features by logical combinations of characters. A regular expression defines a sequence pattern to be matched. Motif modelling methods

15. Basic rules for regular expressions Each position is separated by a hyphen “-” A symbol X is a regular expression matching itself x means ‘any residue’ [ ] surround ambiguities - a string [XYZ] matches any of the enclosed symbols A string [R]* matches any number of strings that match { } surround forbidden residues ( ) surround repeat counts Model formation Restricted to key conserved features in order to reduce the “noise” level Built by hand in a stepwise fashion from multiple alignments Regular expressions contd.

16. Regular expressions, such as PROSITE patterns, are matched to primary amino acid sequences using finite state automata. “all-or-none”

17. Prints: Residue frequency matrices Motif 1 NPESWTNFANMLW NPYSWVNLTNVLW REYSWHQNHHMIY NEGSWISKGDLLF NPYSWTNLTNVVY NEYSWNKMASVVY NDFGWDQESNLIY NENSWNNYANMIY NEYGWDQVSNLLY NPYAWSKVSTMIY NPYSWNGNASIIY NEYAWNKFANVLF NPYSWNRVSNILY NPYSWNLIANVLY NEYRWNKVANVLF Motif 2 LDQPFGTGYSQ VDNPVGAGFSY VDQPVGTGFSL VDQPGGTGFSS IDNPVGTGFSF IDQPTGTGFSV VDQPLGTGYSY IDQPAGTGFSP LESPIGVGFSY LDQPVGSGFSY LDQPINTGFSN LDQPIGAGFSY LDAPAGVGFSY LDQPVGAGFSY Motif 3 FFQHFPEYQTNDFHIAGESYAGHYIP FFNKFPEYQNRPFYITGESYGGIYVP WVERFPEYKGRDFYIVGESYAGNGLM FLSKFPEYKGRDFWITGESYAGVYIP WFQLYPEFLSNPFYIAGESYAGVYVP FFEAFPHLRSNDFHIAGESYAGHYIP FFRLFPEYKDNKLFLTGESYAGIYIP FLTRFPQFIGRETYLAGESYGGVYVP FFNEFPQYKGNDFYVTGESYGGIYVP WMSRFPQYQYRDFYIVGESYAGHYVP FFRLFPEYKNNKLFLTGESYAGIYIP FFRLFPEYKNNKLFLTGESYAGIYIP WLERFPEYKGREFYITGESYAGHYVP WMSRFPQYRYRDFYIVGESYAGHYVP WFEKFPEHKGNEFYIAGESYAGIYVP Motif 4 LAFTLSNSVGHMAP LQFWWILRAGHMVA LMWAETFQSGHMQP LTYVRVYNSSHMVP LQEVLIRNAGHMVP LTFVSVYNASHMVP LTFARIVEASHMVP LTFSSVYLSGHEIP IDVVTVKGSGHFVP MTFATIKGSGHTAE MTFATIKGGGHTAE FGYLRLYEAGHMVP MTFATVKGSGHTAE ITLISIKGGGHFPA MTFATVKGSGHTAE Motif modelling methods a collection of protein “fingerprints” that exploit groups of motifs to build characteristic family signatures motifs are encoded in ungapped ”raw” sequence format different scoring methods may be superimposed onto the data, e..g. BLAST improved diagnostic reliability mutual context provided by motif neighbours

18. Prosite: Profiles Feature is represented as a matrix with a score for every possible character. Matrix is derived from a sequence alignment, e.g.: F K L L S H C L L V F K A F G Q T M F Q Y P I V G Q E L L G F P V V K E A I L K F K V L A A V I A D L E F I S E C I I Q Motif modelling methods

19. Derived matrix: A -18 -10 -1 -8 8 -3 3 -10 -2 -8 C -22 -33 -18 -18 -22 -26 22 -24 -19 -7 D -35 0 -32 -33 -7 6 -17 -34 -31 0 E -27 15 -25 -26 -9 23 -9 -24 -23 -1 F 60 -30 12 14 -26 -29 -15 4 12 -29 G -30 -20 -28 -32 28 -14 -23 -33 -27 -5 H -13 -12 -25 -25 -16 14 -22 -22 -23 -10 I 3 -27 21 25 -29 -23 -8 33 19 -23 K -26 25 -25 -27 -6 4 -15 -27 -26 0 L 14 -28 19 27 -27 -20 -9 33 26 -21 M 3 -15 10 14 -17 -10 -9 25 12 -11 N -22 -6 -24 -27 1 8 -15 -24 -24 -4 P -30 24 -26 -28 -14 -10 -22 -24 -26 -18 Q -32 5 -25 -26 -9 24 -16 -17 -23 7 R -18 9 -22 -22 -10 0 -18 -23 -22 -4 S -22 -8 -16 -21 11 2 -1 -24 -19 -4 T -10 -10 -6 -7 -5 -8 2 -10 -7 -11 V 0 -25 22 25 -19 -26 6 19 16 -16 W 9 -25 -18 -19 -25 -27 -34 -20 -17 -28 Y 34 -18 -1 1 -23 -12 -19 0 0 -18 Alignment positions Profiles contd.

20. inclusion of all possible information to maximise overall signal of protein/domain i. e., a full representation of features in the aligned sequences can detect distant relationships with only few well conserved residues position-dependent weights/penalties for all 20 amino acids -- BASED ON AMINO ACID SUBSTITUTION MATRICES -- and for gaps and insertions dynamic programming algorithms for scoring hits

21. Pfam and Prosite: Hidden Markov Models (HMMs) Feature is represented by a probabilistic model of interconnecting match, delete or insert states contains statistical information on observed and expected positional variation - “platonic ideal of protein family” BEMiMi DiDi IiIi Macromolecular motif recognition

22. Pfam and Prosite: Hidden Markov Models (HMMs) BEMiMi DiDi IiIi Macromolecular motif recognition P of a given amino acid to occurs in a particular state (M, I, D) - at particular position in sequence (for all 20, profile-like) P of transition state

23. Statistical tests aim to assess the likelihood that a match of a query sequence to a profile, regular expression, HMM, etc, is the result of chance. They control for such factors as sequence (match) length, amino acid composition and size of the database searched. Statistical significance

24. log-odds score: this number is the log of the ratio between two probabilities - P that the sequence belongs to the positive set, and P that the result was obtained by chance due to the amino acid distribution in the positive set (random model). Z-score: one needs to estimate an average score and a standard deviation as a function of sequence length. Then, one uses the number of standard deviations each sequence is away from the average as the score. e-value (Expect value): given a database search result with alignment score S, the e-value is the expected number of sequences of score >= S that would be found by random chance. p-value: the probability that one or more sequences of score >= S would have been found randomly. Statistical significance

25. INTERPRO The InterPro database allows efficient searching An integrated annotation resource for protein families, domains and functional sites that amalgamates the efforts of the PROSITE, PRINTS, Pfam, ProDom, SMART and TIGRFAMs secondary database projects. http://www.ebi.ac.uk/interpro