![T1 T2 T3 Q1 Q2 HMAP2 – STL in C++
(generic programming)
Algorithm
Evaluator Enumerator
dynamic alignment
Format
pgram’ing set
matrix [pair list]
sparks? optimal
HMAP gnoali gn2 S4 Waterman RC ?
T HMAP Q = DPM aabbccdef
aa---cdef
Fasta, PIR
aabbccdef (formatted
ENU
M
DPM = AS ---aacdef output)](https://image.slidesharecdn.com/hmap2-12526106496748-phpapp02/85/Structural-information-in-protein-sequence-alignment-accuracy-7-320.jpg)
![HMAP secondary
structure gap
sequence profile open, extn
nf.
H E C
co
.01 .02 … 0.45 ... 0.02 0 0 1 1 3.7 0.3
T
…… 1 0 0 1 SQ,T = dot [ aaQ , aaT ] * exp [ W * ssQT ]
E
M …. … 1 0 0 1
…… 1 0 0 1 1 * confQ : if ssQ = ssT
P ssQT
… …. 0 0 1 1
L -0.5 * confQ : if ssQ ≠ ssT
A …… 0 1 0 1
T …. … 0 1 0 1 W = 0.5 (new opt value = 0.55)
E … .04 .025 0.02 0 1 0 1 12.8 0.9
ZQ,T = (SQ,T - µ) / σ
PSIPRED
gap
sequence profile open, extn
nf.
H E C
co
.02 .08 … 0.25 ... 0.02 0 0 1
Q
U …… 1 0 0 3.7,0.3 : if ssT = coil
…. …
GI,GE
E 1 0 0 12.8,0.9 : if ssT ≠ coil
R …… 1 0 0
… .03 .015 0.05 0 0 1
Y
= continuously valued from [0..1]](https://image.slidesharecdn.com/hmap2-12526106496748-phpapp02/85/Structural-information-in-protein-sequence-alignment-accuracy-10-320.jpg)
![Sparks scoring functions
• Sparks 2
– Sequence-based profile-to-profile
– Secondary structure prediction using PSIPRED (Jones) [+1/-1]
• SP3
– Sparks 2 plus…
– Residue-depth dependent profile
• SP4
– SP3 plus…
– Solvent accessibility prediction using SABLE (Adamczak, Porollo, Meller)
• Trained (parameterized)
– using ProSup (Sippl; 2000) alignments
• Tests performed
– Fold recognition (FR) + Model building: Lindahl FR set
– FR + Model building: LiveBench 8 (MaxSub)
– FR + Model building: CASP7 (GDT Z-score)
– Alignment: Sali’s test set (200 pairs, 65% overlap, 3.5 Å) (TM overlap)](https://image.slidesharecdn.com/hmap2-12526106496748-phpapp02/85/Structural-information-in-protein-sequence-alignment-accuracy-11-320.jpg)
![Should we use dot [ aaQ , aaT ] ?](https://image.slidesharecdn.com/hmap2-12526106496748-phpapp02/85/Structural-information-in-protein-sequence-alignment-accuracy-15-320.jpg)
Structural information in protein sequence alignment accuracy
- 1. Comparisons of Sequence Alignment Scoring Functions: On the Use of Structural Information to Improve Performance Feb 6, 2008
- 2. What’s a scoring function? a b b c d d d e f g a b c d a b b c d d d e f g e f a b - c d - - e f g g a b b c d d d e f g a b - c c d - e f g - d - Optimal :: MAX ( ∑ S (•) - ∑C ( – ) ) similarity S cost C > 0 Aims Optimal alignment problem: Native alignment scores best SO alignment sampling problem: Native alignment scores best & Poor alignments kept at minimum & Avoid “unproductive” alignments
- 3. Productive versus Unproductive Alignment Sampling AK AML YYY XXX A A XXXAAAYYY XXXAAADEFAAAYYY XXX--aYYY a XXX-AKLMA---YYY A A X XXXAAAYYY XX XXXAAADEFAAAYYY MLK YYY XXX--AKLMA--YYY XXX XXX-a-YYY Y YY A XXXAAAYYY XXXAAADEFAAAYYY XXXa--YYY XXX---AKLMA-YYY LKA YYY XXX MA Non-redundant (good) Redundant (not good)
- 4. Classes of Methods for Sampling Suboptimal Alignments • Top-down Enumeration – Classical Waterman (Near-optimal alignments) Path > Opt-δ • Iterative Elimination (IE) – Waterman & Eggert – Saqi, Bates & Sternberg • Parametric Sampling (PS) – Chivian & Baker; 2006 • Combined IE + PS – Jaroszewski, Li & Godzik; 2002 sample over lots of … • Stochastic Sampling P (sim1,gap1,ss1) – John & Sali; 2003 P (sim2,gap1,ss1) … P (simn,gapn,ssn) • Fragment Set Approach (S4)
- 5. Critical Questions Am I ranking the most native alignment first? Within the scope of the scoring function Am I eliminating poor/impossible alignments? Within the scope of Am I sampling efficiently/with little redundancy? alignment sampling New GN2 v. HMAP – sp2 – sp3 – sp4
- 6. Organization Talk about software library for doing sequence alignment Talk about the HMAP and Sparks-family of scoring functions New method: GN2 Benchmark design & results
- 7. T1 T2 T3 Q1 Q2 HMAP2 – STL in C++ (generic programming) Algorithm Evaluator Enumerator dynamic alignment Format pgram’ing set matrix [pair list] sparks? optimal HMAP gnoali gn2 S4 Waterman RC ? T HMAP Q = DPM aabbccdef aa---cdef Fasta, PIR aabbccdef (formatted ENU M DPM = AS ---aacdef output)
- 8. primary secondary Structure residue depth sequence structure contact sequence- numbers, solvent depth-dep. hydro- based prof. distances, HBs accessibility a.a. freq philicity PSI BLAST Template Profile Algorithm Alignments sequence database NR Query Profile Models primary Sequence- PSIPRED sequence based prof. prediction SABLE prediction
- 9. a b c d e a b x y e Affine gaps Arbitrary gaps Double-sided gaps abcd--e (zigzag alignment) ab--xye ss coil G 0 1 2… l 0 1 2… l Fast, good for Nonlinear gaps, Most flexible, DB search structure-derived gaps potentially most costly (HMAP) (AS Yang - 2002) (A Sali - 2006)
- 10. HMAP secondary structure gap sequence profile open, extn nf. H E C co .01 .02 … 0.45 ... 0.02 0 0 1 1 3.7 0.3 T …… 1 0 0 1 SQ,T = dot [ aaQ , aaT ] * exp [ W * ssQT ] E M …. … 1 0 0 1 …… 1 0 0 1 1 * confQ : if ssQ = ssT P ssQT … …. 0 0 1 1 L -0.5 * confQ : if ssQ ≠ ssT A …… 0 1 0 1 T …. … 0 1 0 1 W = 0.5 (new opt value = 0.55) E … .04 .025 0.02 0 1 0 1 12.8 0.9 ZQ,T = (SQ,T - µ) / σ PSIPRED gap sequence profile open, extn nf. H E C co .02 .08 … 0.25 ... 0.02 0 0 1 Q U …… 1 0 0 3.7,0.3 : if ssT = coil …. … GI,GE E 1 0 0 12.8,0.9 : if ssT ≠ coil R …… 1 0 0 … .03 .015 0.05 0 0 1 Y = continuously valued from [0..1]
- 11. Sparks scoring functions • Sparks 2 – Sequence-based profile-to-profile – Secondary structure prediction using PSIPRED (Jones) [+1/-1] • SP3 – Sparks 2 plus… – Residue-depth dependent profile • SP4 – SP3 plus… – Solvent accessibility prediction using SABLE (Adamczak, Porollo, Meller) • Trained (parameterized) – using ProSup (Sippl; 2000) alignments • Tests performed – Fold recognition (FR) + Model building: Lindahl FR set – FR + Model building: LiveBench 8 (MaxSub) – FR + Model building: CASP7 (GDT Z-score) – Alignment: Sali’s test set (200 pairs, 65% overlap, 3.5 Å) (TM overlap)
- 12. HMAP GN2 Sequence-based Sequence-based profile profile (AA) Secondary structure Contact number (CN) Affine gap penalty Secondary structure (SS) Hydrophilicity index (HI) Structure-derived gap penalty Geometric distance (GP = exp (D – 8Å)) Hydrogen bonding Insertions more likely with small CN Deletions beg./end in same SS = impossible (very high GP)
- 13. Log-likelihood ratios from structural alignments SKA Make training alignments Count frequencies Convert to log-likelihood ratios S (i,j) = LLR0 + wAA * LLRAA (i,j) + f structure LLR = log f wSS * LLRSS (i,j) + random wCN * LLRCN (i,j) + wHI * LLRHI (i,j)
- 14. Log-odd substitution matrix for aligning SS-to-predicted SS (PSI-PRED) based on structural alignment (SKA)
- 15. Should we use dot [ aaQ , aaT ] ?
- 16. Construction of a log-odds score based on the cos-angle function between profiles
- 17. CA _ atoms 1 CN = 0.72 ∑ r2
- 18. Construction of a log-odds score based on contact number counts of structural alignments 1 CA _ atoms 1 CA _ atoms 1 N weighted_CN = 20 ∑ ( r / 3 .8 Å ) 2 = 0.72 ∑ r2
- 19. K RE QD N P H ST GY W AMFLVIC hydrophilicity index profile HI = ∑ i HI i
- 20. Construction of a log-odds score based on observed levels of HI agreement btwn the Q&T K RE QD N P H ST GY W AMFLVIC Observed Fitted ( exp exp ( − abs H Q − H T ) ) ⋅ ( .75 + .3 * abs ( H T − .22) ) − 1.8
- 21. Training and Benchmarking Sets
- 22. SCOP 1.71 all vs. all ( skan psd < 0.6, rmsd < 3.5 ) 1M pairs sort pairs by % sid ( from 0%, “devilish set” ) re-order, 7.5% sid on top ( “difficult set” ) filter ( ali len > 80, % sid < 40, ska psd < 0.6 ) 326k pairs no Any more pairs? Done! test set difficult: 995 pairs yes devilish: 913 pairs take next top pair ( lowest % sid in list ) yes Scop family already in benchmark? no add pair to benchmark
- 23. SCOP 1.71 pairs from all vs. all comparison no Any more pairs? Done! yes List of protein pairs yes w/o sequence similarity to test set Blast against test set sequences No e-value < 1? no remove sequence pair make training set… difficult: 238 pairs
- 24. Scop 1.71 Training set results
- 25. Summary of counts: Class: 5 Fold: 102 Superfamily: 120 +148 folds represented once Sequence Identity: 0 - 5% 30 5 - 10% 110 10 - 15% 48 15 - 20% 18 20 - 25% 11 25 - 30% 5 30 - 35% 7 35 - 40% 9 40 - 45% 45 - 100% all: 238 Classes: c 49 d 44 b 32 a 23 e 2
- 26. Shift performance / Training data (238 pairs) / gn2 vs nalign – sparks2 – sp3 – sp4 39/31 54/18 65/19 55/18 3 gn2 alignments with shift > 50
- 27. Qmod performance / Training data (238 pairs) / gn2 vs nalign – sparks2 – sp3 – sp4 129/94 136/85 119/105 110/114
- 28. Overall performance / Training data (238 pairs) / gn2 vs nalign – sparks2 – sp3 – sp4 Scoring function Total shift Residues aligned correctly gn2 1124 21,500 nalign 1179* 21,197 sparks2 1522 20,669 sp3 1607 21,020 sp4 1672 21,299*
- 29. Scop 1.71 Test set results
- 30. Summary of counts: Class: 7 Fold: 341 Superfamily: 460 +230 folds represented once Sequence Identity: 0 - 5% 72 5 - 10% 423 10 - 15% 148 15 - 20% 103 20 - 25% 90 25 - 30% 67 30 - 35% 42 35 - 40% 37 40 - 45% 45 - 100% all: 995 Classes: d 182 c 141 b 137 a 115 e 18 f 18 g 3
- 31. Shift performance / Test data (995 pairs) / gn2 vs nalign – sparks2 – sp3 – sp4 136/111 159/112 174/102 161/111 18 gn2 alignments with shift > 50
- 32. Qmod performance / Test data (995 pairs) / gn2 vs nalign – sparks2 – sp3 – sp4 nalign sparks2 524/342 544/344 sp3 sp4 514/379 489/408
- 33. Overall performance / Test data (995 pairs) / gn2 vs nalign – sparks2 – sp3 – sp4 Scoring function Total shift Residues aligned correctly gn2 4718 110,289* sparks2 4935* 109,328 sp4 5038 111,351 nalign 5071 109,349 sp3 5377 110,172 Total shift Correctly aligned relative to best relative to best (Wilcoxon test) (Wilcoxon test) gn2 0 -1,062 (p < 0.22) sparks2 +217 (p < 5*10-4) -2,023 (p < 5*10-4) sp4 +320 (p < 5*10-4) 0 nalign +353 (p < 5*10-4) -2,002 (p < 1.36*10-2) sp3 +659 (p < 5*10-4) -1,179 (p < 5*10-4)
- 34. Spo0 set results (Q = 1F51, T = Spo0 family)
- 35. 141 ali’s 74/29 Scoring function Total shift Residues aligned correctly gn2 1035 (-22%) 6283 (+13%) nalign 1323 5547
- 36. Remarks Apparent success of the LLR method, but some mysteries Sali test set (next slide) Performance is underestimated in alignments with structural repeats (next slide +1) Need for looking at alternative structural alignments Room for improvement E.g. adding FUGUE-like (Blundell) sequence-structure LLR -or- SABLE/SA prediction -or- IBR potential (Zhu)
- 37. Summary of counts: Class: 7 Fold: 74 Superfamily: 86 NA: 2 Sequence Identity: +38 represented once 0 - 5% 13 5 - 10% 11 10 - 15% 11 15 - 20% 16 20 - 25% 79 25 - 30% 60 30 - 35% 24 35 - 40% 12 40 - 45% 4 45 - 100% 2 all: 239 (note: psid calculated by ska) Classes: b 99 c 95 d 63 a 34 e 4 g 2 f 1 Madhusudhan MS, Marti-Renom MA, Sanchez R, Sali A. Variable gap penalty for protein sequence-structure alignment. Protein Eng Des Sel. 2006 Mar;19(3):129-33
- 38. Caveat (example #1) from Training Set
- 41. What’s a scoring function? a b b c d d d e f g a b b c d d d e f g a b a b - c d - - e f g c a b b c d d d e f g d a e b f c g d e f g a b b c d d d e f g max ∑ S (•) + min ∑ C ( – ) a b - - c d - e f g similarity S cost C < 0 Aims Optimal alignment problem: Native alignment scores best Sampling suboptimal alignments: Native alignment scores best & Poor alignments kept at minimum