Amino Acid Structures from Klug & Cummings. 10/7/2003 CAP/CGS 5991: Lecture 7 1
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1 Amino Acid Structures from Klug & Cummings 10/7/2003 CAP/CGS 5991: Lecture 7 1
2 Amino Acid Structures from Klug & Cummings 10/7/2003 CAP/CGS 5991: Lecture 7 2
3 Amino Acid Structures from Klug & Cummings 10/7/2003 CAP/CGS 5991: Lecture 7 3
4 Amino Acid Structures from Klug & Cummings 10/7/2003 CAP/CGS 5991: Lecture 7 4
5 Secondary Structure Prediction Software 10/7/2003 CAP/CGS 5991: Lecture 7 5
6 Secondary Structure Prediction [NN based] PSI-pred, nnpredict (2-layer, feedforward NN), Pred2ary [Consensus Approach] JPRED, SOPMA [K-nearest neighbor] NNSSP, PREDATOR [HMM] PSA ZPRED SSP PHD (See Sample) 10/7/2003 CAP/CGS 5991: Lecture 7 6
7 Motif Detection Tools PROSITE (Database of protein families & domains) Try PDOC Also Try PS00041 PRINTS Sample Output BLOCKS (multiply aligned ungapped segments for highly conserved regions of proteins; automatically created) Sample Output Pfam (Protein families database of alignments & HMMs) Multiple Alignment, domain architectures, species distribution, links: Try MoST PROBE ProDom DIP 10/7/2003 CAP/CGS 5991: Lecture 7 7
8 Protein Information Sites SwissPROT & GenBank InterPRO is a database of protein families, domains and functional sites in which identifiable features found in known proteins can be applied to unknown protein sequences. See sample. PIR Sample Protein page 10/7/2003 CAP/CGS 5991: Lecture 7 8
9 Modular Nature of Proteins Proteins are collections of modular domains. For example, Coagulation Factor XII F2 E F2 E K Catalytic Domain PLAT F2 E K K Catalytic Domain 10/7/2003 CAP/CGS 5991: Lecture 7 9
10 Domain Architecture Tools CDART Protein AAH24495; Domain Architecture; It s domain relatives; Multiple alignment for 2 nd domain SMART 10/7/2003 CAP/CGS 5991: Lecture 7 10
11 Predicting Specialized Structures COILS Predicts coiled coil motifs TMPred predicts transmembrane regions SignalP predicts signal peptides SEG predicts nonglobular regions 10/7/2003 CAP/CGS 5991: Lecture 7 11
12 Tertiary & Quaternary Protein Structures Experimental methods X-ray crystallography [More accurate!] Nuclear Magnetic Resonance Spectroscopy (NMR) If protein unfolded (denatured) and released, then it goes back to its native 3-d structure. The tertiary structure is a structure of minimum energy. Angles φ and ψ are constrained. Proteins structures often have hydrophobic core. 10/7/2003 CAP/CGS 5991: Lecture 7 12
13 Protein Folding Unfolded Molten Globule State Folded Native State Rapid (< 1s) Slow ( s) How to find minimum energy configuration? 10/7/2003 CAP/CGS 5991: Lecture 7 13
14 Modular Nature of Protein Structures Example: Diphtheria Toxin 10/7/2003 CAP/CGS 5991: Lecture 7 14
15 Structural Classification of Proteins SCOP (Structural Classification of Proteins) Based on structurla & evolutionary relationships. Contains ~ 40,000 domains Classes (groups of folds), Folds (proteins sharing folds), Families (proteins related by function/evolution), Superfamilies (distantly related proteins) 10/7/2003 CAP/CGS 5991: Lecture 7 15
16 SCOP Family View 10/7/2003 CAP/CGS 5991: Lecture 7 16
17 CATH: Protein Structure Classification Semi-automatic classification; ~36K domains 4 levels of classification: Class (C), depends on sec. Str. Content Architecture (A), orientation of sec. Str. Topolgy (T), topological connections & Homologous Superfamily (H), similar str and functions. 10/7/2003 CAP/CGS 5991: Lecture 7 17
18 DALI Domain Dictionary Completely automated; 3724 domains Criteria of compactness & recurrence Each domain is assigned a Domain Classification number DC_l_m_n_p representing fold space attractor region (l), globular folding topology (m), functional family (n) and sequence family (p). 10/7/2003 CAP/CGS 5991: Lecture 7 18
19 5 Fold Space classes Attractor 1 can be characterized as alpha/beta, attractor 2 as all-beta, attractor 3 as all-alpha, attractor 5 as alpha-beta meander (1mli), and attractor 4 contains antiparallel beta-barrels e.g. OB-fold (1prtF). 10/7/2003 CAP/CGS 5991: Lecture 7 19
20 Fold Types & Neighbors Structural neighbours of 1urnA (top left). 1mli (bottom right) has the same topology even though there are shifts in the relative orientation of secondary structure elements. 10/7/2003 CAP/CGS 5991: Lecture 7 20
21 Sequence Alignment of Fold Neighbors 10/7/2003 CAP/CGS 5991: Lecture 7 21
22 Frequent Fold Types 10/7/2003 CAP/CGS 5991: Lecture 7 22
23 Motifs in Protein Sequences Motifs are combinations of secondary structures in proteins with a specific structure and a specific function. They are also called super-secondary structures. Examples: Helix-Turn-Helix, Zinc-finger, Homeobox domain, Hairpin-beta motif, Calcium-binding motif, Beta-alpha-beta motif, Coiled-coil motifs. Several motifs may combine to form domains. Serine proteinase domain, Kringle domain, calciumbinding domain, homeobox domain. 10/7/2003 CAP/CGS 5991: Lecture 7 23
24 Motif Detection Problem Input: Set, S, of known (aligned) examples of a motif M, A new protein sequence, P. Output: Does P have a copy of the motif M? Example: Zinc Finger Motif YKCGLCERSFVEKSALSRHORVHKN Input: Output: Database, D, of known protein sequences, A new protein sequence, P. What interesting patterns from D are present in P? 10/7/2003 CAP/CGS 5991: Lecture 7 24
25 Helix-Turn-Helix Motifs Structure 3-helix complex Length: 22 amino acids Turn angle Function Gene regulation by binding to DNA 10/7/2003 CAP/CGS 5991: Lecture 7 25
26 DNA Binding at HTH Motif 10/7/2003 CAP/CGS 5991: Lecture 7 26
27 HTH Motifs: Examples Loc Helix 2 Turn 10/7/2003 CAP/CGS 5991: Lecture 7 27
28 Basis for New Algorithm Combinations of residues in specific locations (may not be contiguous) contribute towards stabilizing a structure. Some reinforcing combinations are relatively rare. 10/7/2003 CAP/CGS 5991: Lecture 7 28
29 New Motif Detection Algorithm Pattern Generation: Aligned Motif Examples Pattern Generator Motif Detection: New Protein Sequence Motif Detector Pattern Dictionary Detection Results 10/7/2003 CAP/CGS 5991: Lecture 7 29
30 Patterns Loc Protein Helix 2 Turn Helix 3 Name Q1 G9 N20 A5 G9 V10 I15 10/7/2003 CAP/CGS 5991: Lecture 7 30
31 Pattern Mining Algorithm Algorithm Pattern-Mining Input: Motif length m, support threshold T, list of aligned motifs M. Output: Dictionary L of frequent patterns. 1. L 1 := All frequent patterns of length 1 2. for i = 2 to m do 3. C i := Candidates(L i-1 ) 4. L i := Frequent candidates from C i 5. if ( L i <= 1) then 6. return L as the union of all L j, j <= i. 10/7/2003 CAP/CGS 5991: Lecture 7 31
32 Candidates Function L 3 G1, V2, S3 G1, V2, T6 G1, V2, I7 G1, V2, E8 G1, S3, T6 G1, T6, I7 V2, T6, I7 V2, T6, E8 C 4 G1, V2, S3, T6 G1, V2, S3, I7 G1, V2, S3, E8 G1, V2, T6, I7 G1, V2, T6, E8 G1, V2, I7, E8 V2, T6, I7, E8 L 4 G1, V2, S3, T6 G1, V2, S3, I7 G1, V2, S3, E8 G1, V2, T6, E8 V2, T6, I7, E8 10/7/2003 CAP/CGS 5991: Lecture 7 32
33 Motif Detection Algorithm Algorithm Motif-Detection Input : Motif length m, threshold score T, pattern dictionary L, and input protein sequence P[1..n]. Output : Information about motif(s) detected. 1. for each location i do 2. S := MatchScore(P[i..i+m-1], L). 3. if (S > T) then 4. Report it as a possible motif 10/7/2003 CAP/CGS 5991: Lecture 7 33
34 Experimental Results: GYM 2.0 Motif HTH Motif (22) Protein Number GYM = DE Number GYM = Annot. Family Tested Agree Annotated Master (100 %) Sigma (98 %) Negates (92 %) 0 0 LysR (98 %) AraC (84 %) Rreg (85 %) Total (94 %) (88 %) 10/7/2003 CAP/CGS 5991: Lecture 7 34
35 Experiments Basic Implementation (Y. Gao) Improved implementation & comprehensive testing (K. Mathee, GN). Implementation for homeobox domain detection (X. Wang). Statistical methods to determine thresholds (C. Bu). Use of substitution matrix (C. Bu). Study of patterns causing errors (N. Xu). Negative training set (N. Xu). NN implementation & testing (J. Liu & X. He). HMM implementation & testing (J. Liu & X. He). 10/7/2003 CAP/CGS 5991: Lecture 7 35
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