Sequence analysis: Macromolecular motif recognition Sylvia Nagl.
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Transcript of Sequence analysis: Macromolecular motif recognition Sylvia Nagl.
Sequence analysis:Macromolecular motif recognition
Sylvia Nagl
Amino acid primary sequence
2. Homologue(s) with known 3D structure?
Homology modellingavailable
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
•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
TerminologyTerminology
EXAMPLE: CATHEPSIN A
PEPTIDASE FAMILY S10
EC # 3.4.16.5
3-D representation
3D profile
(PROCAT)
Active site recognition
419IAFLTIKGAGHMVPTDKP436
1ac5
1ivy
438LTFVSVYNASHMVPFDKS455
Active site motifs
Conserved seq patterns
Domain recognition
Kringle domain from plasminogen protein
EGF-like domain from coagulation factor X
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
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
What do we need?
•Method for defining motifs
•Algorithm for finding them
•Statistics to evaluate matches
Macromolecular motif recognition
Methods for defining motifs:
•Regular expression (patterns)
•Profiles
•Hidden Markov Model (HMM)
Macromolecular motif recognition
1-D representation: Primary amino acid sequenceMIRAAPPPLFLLLLLLLLLVSWASRGEAAPDQDEIQRLPGLAKQPSFRQYSGYLKSSGSKHLHYWFVESQKDPENSPVVLWLNGGPGCSSLDGLLTEHGPFLVQPDGVTLEYNPYSWNLIANVLYLESPAGVGFSYSDDKFYATNDTEVAQSNFEALQDFFRLFPEYKNNKL...
Computational sequence analysis
Query secondary databases over the
Internet
Macromolecular motif recognition
http://www.ebi.ac.uk/interpro/
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)
419IAFLTIKGAGHMVPTDKP436
1ac5
1ivy
438LTFVSVYNASHMVPFDKS455
Active site motifs
Conserved seq patterns
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
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.
Regular expressions contd.
Regular expressions, such as PROSITE patterns, are matched to primary amino acid sequences using finite state automata.
“all-or-none”
Prints: Residue frequency matrices
Motif 1 NPESWTNFANMLWNPYSWVNLTNVLW 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 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
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 QY P I V G Q E L L GF P V V K E A I L KF K V L A A V I A DL E F I S E C I I Q
Motif modelling methods
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.
Profiles contd.
•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
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”
B EMi
Di
Ii
Macromolecular motif recognition
Pfam and Prosite: Hidden Markov Models(HMMs)
B EMi
Di
Ii
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
•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
•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
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