Computational Methods For Identification Of Cyclic Peptides Using Mass Spectrometry. Julio Ng Bioinformatics Program, UCSD March, 26 th 2010

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1 Computational Methods or Identification Of Cyclic Peptides Using Mass Spectrometry Julio Ng Bioinformatics Program, UCSD March, 26 th 2010

2 Outline Importance of natural products Mass spectrometry on cyclic peptides Computational methods to analyze MS data Demo

3 Natural Products In 1928, A. leming discovered antibiotic activity of penicillin The beginning of the modern era of drug discovery Alexander leming

4 Natural Products Chemical compound biological activity Antibiotics (colistin) Immunosuppressors (cyclosporin) Antiviral agents (luzopeptin A) Antitumor agents (phakellistatin) Toxins (amanitin)

5 Natural Products not necessarily the final drug entity, is still alive and well. Thus, in the area of cancer, over the time frame from around the 1940s to date, of the 155 small molecules, 73% are other than S (synthetic), with 47% actually being either natural products or directly derived therefrom. In other areas, the influence of natural product structures is quite marked,

6 Natural Products Searching for natural products Plants Micro-organisms Marine organisms Animal A large subclass of natural products are nonribosomal peptides

7 Central Dogma of Biology NRP

8 Non-ribosomal Protein Synthetase (NRPS) ure 2. Surfactin assembly line. The multienzyme complex consists of seven modules (grey and red) which are spec Sieber and Marahiel 2005

9 Non-ribosomal Protein Synthetase (NRPS) Sieber and Marahiel 2005

10 Thioesterase Domain Sieber and Marahiel 2005

11 Special Characteristics Heterocyclic elements D-amino acids Glycosylated residues Cyclic backbone N-methylated residues Non-standard amino acids gure 1. Natural peptidic products. A selection of nonribosomally synthesized peptides. Characteristic structural fea

12 Cyclic Peptides out of 1122 entries in the database * *Caboche et al, 2008

13 Mass Spectrometer Measures m/z

14 Sample Preparation (Protein Analysis) Enzymatic Digestion and ractionation

15 Multi-Stage Mass Spectrometry Mass Spectrometer Secondary ragmentation Ionized parent peptide

16 ragmentation H + H...-HN-CH-CO-NH-CH-CO-NH-CH-CO- OH N-terminus R i-1 R i R i+1 C-terminus AA residue i-1 AA residue i AA residue i+1

17 Identification of Linear Mass Spectra Database search Database of known peptides : b y: MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT, HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE, ALKIIMNVRT, LARGE, HEWAIL, GHNLWAMNAC, GVGSVLRA, EKLNKAATYIN.. LARGE PM MS/MS spectrum E L K Y M E R A N G L E De novo sequencing

18 Challenges in Identification of Cyclic Peptide MS Extensively modified amino acids Non-standard amino acids Cyclic backbone Databases cannot be readily derived from genomic data

19 Cyclic Peptide Mass Spectrum MS1 Mass of the intact cyclic peptide MS2 Mass of the intact linear peptides MS3 Masses of the peptide fragments

20 %" Ms Mixture & '()! " % $ # Seglitide: somatostatin receptor antagonist, used experimentally to treat Alzheimer s disease $" %! " # $ $ % & '()! " # # $ % & '()! " " # $ % & '()!! " # $ % & '()! " # $ % & '() & '() #"!"

21 Identification of Cyclic Mass Spectra NRP-Dereplication Cyclic Mass Spectrum Cyc(A +14 YWKV) $" %! " # $ $ % & '()! " # # $ % & '()! " " # $ % & '()!! " # $ % & '()! " # $ % & '() & '() #"!" NRP-Tagging NRP-Sequencing

22 NRP-Dereplication Case 1: There is a peptide in the database that matches the precursor mass of the spectrum. Is this peptide a good match for the spectrum? Case 2: No peptide in the database matches the precursor mass of the spectrum Can we change a peptide in the database so it becomes a good match for the given spectrum?

23 Simplified Dereplication Problem ormulation PEPTIDE Input: MS3 spectrum, a Peptide Sequence and parameter k Output: A new Peptide Sequence with k mutations away from the original Peptide Sequence such that the new peptide explains best the experimental spectrum. In reality there many peptides in the database, so the dereplication needs to be done for each peptide

24 Tyrocidine amily (Bacillus brevis) Tyrocidine A 99, 114, 113, 147, 97, 147, 147, 114, 128, 163 Tyrocidine A1 99, 128, 113, 147, 97, 147, 147, 114, 128, 163 Tyrocidine B 99, 114, 113, 147, 97, 186, 147, 114, 128, 163 Tyrocidine B1 99, 128, 113, 147, 97, 186, 147, 114, 128, 163 Tyrocidine C 99, 114, 113, 147, 97, 186, 186, 114, 128, 163 Tyrocidine C1 99, 128, 113, 147, 97, 186, 186, 114, 128, 163

25 Dereplication (k = 1) A B C D E

26 NRP-Dereplication (k = 1) A AB A B C D E

27 NRP-Dereplication (k = 1) Δ A AB ABC-Δ ABCD-Δ ABCDE-Δ A B C D E

28 NRP-Dereplication (k = 1) Δ A AB ~DE ~E ~ A B C D E

29 NRP-Dereplication (k = 1) Δ A AB ~DE ~E ~ A B C D E A E A B D E A B C D E

30 Dereplicating tyrocidine C and C1 a) P W W L 32 N O V Y Q Experimental spectrum: Tyrocidine C1 c) b) Coverage Sequence: Tyrocidine C VOLPWWNQY Offset: 14 Daltons (O -> K) 0 V-1 O-2 L-3-4 P-5 W-6 W-7 N-8 Q-9 Y-10

31 Dereplication Results

32 NRP-Dereplication Results on NORINE Compound Top Match(es) Dereplicated Compound Score Destruxin A[+14] Pro, Ile, NMe-Val, NMe-Ala, bala, C4:1(3)-OH(2)[+14] 0.45 HydroxyDestruxin B[-18] Pro, Ile, NMe-Val, NMe-Ala, bala, ic5:0-oh(2.3)[-18] 0.45 Destruxin D[-32] Pro, Ile, NMe-Val, NMe-Ala, bala, ic5:0-oh(2)-ca(4)[-32] 0.45 Destruxin E diol[-20] Pro, Ile, NMe-Val, NMe-Ala, bala, C4:0-OH(2.3.4)[-20] 0.45 Destruxin A Destruxin C[-18] Pro, Ile, NMe-Val, NMe-Ala, bala, ic5:0-oh(2.4)[-18] 0.45 Destruxin [-4] Pro, Ile, NMe-Val, NMe-Ala, bala, C4:0-OH(2.3)[-4] 0.45 Destruxin B[-2] Pro, Ile, NMe-Val, NMe-Ala, bala, Hiv[-2] 0.45 Destruxin E[-2] Pro, Ile, NMe-Val, NMe-Ala, bala, C4:0-OH(2)-Ep(3)[-2] 0.45 Destruxin E chlorohydrin[-38] Pro, Ile, NMe-Val, NMe-Ala, bala, C4:0-OH(2.3)-Cl(4)[-38] 0.45 Tyrocidine C D-Phe, Pro, Trp, D-Trp, Asn, Gln, Tyr, Val, Orn, Leu 0.45 Tyrocidine C Tyrocidine B[+39] D-Phe, Pro, Trp, D-Phe[+39], Asn, Gln, Tyr, Val, Orn, Leu 0.45 Tyrocidine D[-23] D-Phe, Pro, Trp, D-Trp, Asn, Gln, Trp[-23], Val, Orn, Leu 0.45 Tyrocidine B1 Tyrocidine B[+14] D-Phe, Pro, Trp, D-Phe, Asn, Gln, Tyr, Val, Orn[+14], Leu 0.44 Tyrocidine C1 Tyrocidine C[+14] D-Phe, Pro, Trp, D-Trp, Asn, Gln, Tyr, Val, Orn[+14], Leu 0.40 Tyrocidine A1 Tyrocidine A[+14] D-Phe, Pro, Phe, D-Phe, Asn, Gln, Tyr, Val, Orn[+14], Leu 0.37 Tyrocidine B D-Phe, Pro, Trp, D-Phe, Asn, Gln, Tyr, Val, Orn, Leu 0.37 Tyrocidine B Tyrocidine A[+39] D-Phe, Pro, Phe[+39], D-Phe, Asn, Gln, Tyr, Val, Orn, Leu 0.37 Tyrocidine C[-39] D-Phe, Pro, Trp, D-Trp[-39], Asn, Gln, Tyr, Val, Orn, Leu 0.37 Tyrocidine A Tyrocidine A D-Phe, Pro, Phe, D-Phe, Asn, Gln, Tyr, Val, Orn, Leu 0.33 Tyrocidine B[-39] D-Phe, Pro, Trp[-39], D-Phe, Asn, Gln, Tyr, Val, Orn, Leu 0.33 Compound 879 Neoviridogrisein (Thr+Hpa), NMe-Ph-Gly, Ala, NMe-bMe-Leu, NMe-Gly, D-4OH-Pro, D-Leu 0.28 H8405 Beauverolide Ka[-18] C10:0-Me(4)-OH(3), Trp, Phe[-18], D-aIle 0.27 BQ123 Halipeptin B[-20] C10:0-Me(2.2.4)-OH(3.7), Ala, ame-cys[-20], NMe-OH-Ile, Ala 0.26 H3526 Cyanopeptide X Microcystin LR hymenistatin I Pro, Tyr, Val, Pro, Leu, Ile, Ile, Pro 0.25 hymenamide G Pro, Tyr, Val, Pro, Leu, Ile, Leu, Pro 0.25 Majusculamide C[-30] Map, Ala, Ibu, NMe-OMe-Tyr[-30], NMe-Val, Gly, NMe-Ile, Gly, Hmp 0.23 Dolastatin 11[-30] Gly, NMe-Val, NMe-OMe-Tyr[-30], Ibu, Ala, Map, Hmp, Gly, NMe-Leu 0.23 Microcystin LR D-Ala, Leu, D-bMe-Asp, Arg, Adda, D-Glu, NMe-Dha 0.20 [Dha7]microcystin-LR[+14] D-Ala[+14], Leu, D-bMe-Asp, Arg, Adda, D-Glu, dh-ala 0.20 Microcystin LAib[+71] D-Ala, Leu, D-bMe-Asp[+71], Aib, Adda, D-Glu, NMe-Dha 0.19 Seglitide Microsclerodermin [-3] C12:3(7.9.11)-Me(6)-OH(2.4.5)-NH2(3)-Ph(12), Pyr[-3], NMe-Gly, D-Trp, Gly, OH-4Abu 0.13 Cyclomarin C Aureobasin C[-60] D-Hmp, NMe-Val, Phe, NMe-Phe, Pro, Val, NMe-Val, Leu, boh-nme-val[-60] 0.13 Cyclomarin A Aureobasidin [-44] D-Hmp, NMe-Val[-44], Phe, NMe-Phe, Pro, aile, Val, Leu, boh-nme-val 0.12 Dehydrocyclomarin A Hymenamide J[-74] Pro, Tyr, Asp, Phe, Trp[-74], Lys, Val, Tyr 0.12 Dehydrocyclomarin C P1022E[+44] D-Lac, NMe-Leu, 4OH-D-Ph-Lac, NMe-Leu, D-Lac, NMe-Leu[+44], D-Ph-Lac, NMe-Leu 0.11

33 NRP-Dereplication Compound 879 was thought to be novel, but the compound neoviridogrisein was in NORINE* Cyanopeptide X was unknown in 2007, but majusculamide C was in the NORINE*. The compound was desmethoxymajusculamide C *Caboche et al, 2008

34 Cyclic Peptide Identification Problem (De novo reconstruction) Input: MS3 spectrum of a cyclic peptide Output: A ranked list of peptide reconstructions sorted by a scoring Similar to the Partial Digest Problem described by Skiena et al Shown to be NP-Hard for noisy inputs (Cielebak et al 2005) Similar to the problem of sequencing linear peptides with internal fragments. Shown to be NP-Hard (Xu and Ma 2006)

35 Tag Generation Problem NRP-Tagging Input: MS3 spectrum of a cyclic peptide Output: A ranked list of gapped sequences that explains the MS3 spectrum, sorted by a scoring function 99, 114, 113, 147, 97, 147, 147, 114, 128, , 114, [ ], [97+147], 147, 114, 128, , 114, 260, 244, 147, 114, 128, 163

36 NRP-Tagging

37 NRP-Tagging A B C D E A B C D E E A B C D

38 NRP-Tagging A B C D E A B C D E E A B C D

39 Tag NRP-Tagging Generation A B C D E A B C D E E E A B C D

40 NRP-Tagging A B C D E A B C D E E E A B C D A B C D E

41 Single Self-Convolution Input: A mass spectrum Output: A histogram of mass difference counts for a range of masses bins = [] or each peak Pi or each peak P j (i < j) peak_diff = P j - P i bins[peak_diff]++ Pevzner et al 2001

42 Double Self-Convolution Input: A mass spectrum Output: A histogram of 2 consecutive mass differences counts for a range of masses bins = [] or each peak Pi or each peak P j (i < j) or each peak P k (j < k) peak_diff_1 = P j - P i peak_diff_2 = P k - P j bins[peak_diff_1, peak_diff_2]++

43 NRP-Tagging Self Double Convolution keeping track of the starting peak of each peak triplet A B C D E bins[b, C] = 3 A B C D E E E A B C D

44 NRP-Tagging bins = double_convolution(s) for m_a, m_b in bins starts = starting positions of bin[m_a, m_b] for all combinations such that it is a subset of starts m_1 = c_1 m_i = c_i - c_j (j = i - 1) r = parent - c_n - m_a - m_b tag = [m_1,... m_n, m_a, m_b, r] score(tag), store(tag) E A B C D

45 NRP-Tagging bins = double_convolution(s) for m_a, m_b in bins starts = starting positions of bin[m_a, m_b] for all combinations such that it is a subset of starts m_1 = c_1 m_i = c_i - c_j (j = i - 1) r = parent - c_n - m_a - m_b tag = [m_1,... m_n, m_a, m_b, r] score(tag), store(tag) m_1 c_1 c_2 c_3 parent m_2 m_3 m_a m_b r

46 Gap Closing A B C D E E A B C D D E A B C D E A B1 B2 C

47 NRP-Tagging Input: MS 3 spectrum S of an (unknown) cyclic peptide, a minimum tag frequency, a recursion depth, and a scoring function score(s, peptide). Output: Ranked list of candidate gapped peptides 1. ind all tags in S: tags(x, y) ={} for all 0 x, y 200 for all s, s,s S such that s i s j s k do mass 1 = s s mass 2 = s s add s to tags(mass 1, mass 2 ) end for 2. Generate gapped peptides from frequent tags: gappedpeptides = {} for all mass 1, mass 2 with tags(mass 1, mass 2 ) > frequency do for all {0 s 1... s n mass(s) mass 1 mass 2 } tags(mass 1, mass 2 ) do gappedpeptide =[m 1,..., m n, mass 1, mass 2,m n+1 ] where m i = s i s i 1, for 2 i n, m 1 = s 1 and m n+1 = mass(s) mass 1 mass 2 s n Add gappedpeptide to gappedpeptides end for end for 3. Iteratively attempt to split masses larger than 200 Da: results = depth top-scoring peptides from gappedpeptides candidates = results repeat sequences = {} for all gappedpeptide in candidates do intermediates = {} for all mass > 200 Da in gappedpeptide do for all mass 1 such that 0 mass Da do split mass in gappedpeptide into (mass 1, mass mass 1 ) and add the resulting peptide to intermediates end for end for add depth top-scoring peptides from intermediates to sequences end for candidates = sequences Add sequences to results until sequences is empty return results

48 NRP-Tagging Results Compound Best reconstruction Rank Tyrocidine A 99, 114, 113, 147, 97, 147, 147, 114, 128, Tyrocidine A1 99, 128, 113, 147, 97, 147, 147, 114, 128, Tyrocidine B 99, 114, 113, 147, 97, 186, 147, 114, 128, Tyrocidine B1 99, 128, 113, 147, 97, 186, 147, 114, 128, Tyrocidine C 99, 114, 113, 147, 97, 186, 186, 114, 128, Tyrocidine C1 99, 128, 113, 147, 97, 186, 186, 114, 128, Seglitide 85, 163, 186, 128, 99, Cyanopeptide X 57, 113, 161, 141, 71, 113, [114+57], BQ , 186, 115, 97, 99 2 Destruxin A 113, 113, 85, 71, [98+97] 2 H , 97, 163, 99, {97+1}, 113, {113-1}, H , 71, 113, 113, Microcystin LR {[83+71]+1}, {113-1}, {129-1}, {156+1}, 313, Compound , 113, < : 100,104>, {147+18}, 71, 141, 71 7 Cyclomarin A 127, 139, <286 : 129,157>, 143, 71, [177+99] 10 Dehydrocyclomarin A 127, 139, 268, 143, 71, 177, Cyclomarin C 127, 139, 270, {143+32}, {[71+177]-32}, 99 >40 Dehydrocyclomarin C Not generated -

49 NRP-Sequencing De novo sequencing of cyclic peptide spectra using self-alignment

50 A +14 Y W K V Y W K V A +14 W K V A +14 Y K V A +14 Y W V A +14 Y W K A +14 Y W K V A +14 Y W K V 6 linear theoretical spectra of seglitide

51 A +14 Y W K V Y W K V A +14 A +14 Y W K V A +14 Y W K V A +14 Y W V A +14 Y W K K A +14 Y W K V V A +14 Y W K V Prefixes are horizontal lines Suffixes are vertical lines

52 A +14 Y W K V Y W K V A +14 A +14 Y W K V A +14 Y W K V A +14 Y W V A +14 Y W K K A +14 Y W K V V A +14 Y W K V Theoretical spectrum without annotations

53 A +14 Y W K V Y W K V A +14 A +14 Y W K V A +14 Y W K V A +14 Y W V A +14 Y W K K A +14 Y W K V V A +14 Y W K V Y W K V Offset: 85 V K W Y

54 De novo sequence (anti symmetric path: Chen et al 2001)

55 NRP-Sequencing Self-alignment of spectrum using the highest scoring self-convolution value Use standard de novo reconstruction algorithms for linear peptide sequencing Rescore candidate reconstructions using MS n data

56 NRP-Sequencing Results Compound Best reconstruction Rank Tyrocidine A [163+99], 114, [ ], [ ], 147, [ ] 1 Tyrocidine A1 [163+99], 128, [ ], [ ], 147, [ ] 1 Tyrocidine B [163+99], 114, [ ], 97, [ ], 114, Tyrocidine B1 99, 128, [ ], [97+186], 147, [ ] 1 Tyrocidine C 113, 147, 97, 186, 186, 114, [ ], [99+114] 125 Tyrocidine C1 [163+99], [ ], 147, [97+186], 186, [ ] 1 Seglitide 85, [ ], 128, 99, Cyanopeptide X 57, 113, 161, 141, 71, [ ], BQ , 186, 115, [97+99] 1 H , [97+163], 99, [97+113], 113, H , 71, 113, 113, 186 1

57 De novo Reconstructions Conclusions

58 A de Conclusions novo Reconstruction

59 Combining Conclusions Reconstructions

60 Acknowledgments Computer Science Department, UCSD: Nuno Bandeira and Pavel Pevzner Department of Chemistry and Biochemistry, UCSD: Wei-Ting Liu, Dario Meluzzi, Majid Ghassemian and Pieter Dorrestein Scripps Institution of Oceanography, UCSD: Marcelino Gutierrez, Thomas Simmons, Andrew Schultz, Bradley Moore, William Gerwick, William enical and Katherine Maloney. Skaggs School of Pharmacy and Pharmaceutical Sciences, UCSD: Bradley Moore, William Gerwick and Pieter Dorrestein. Department of Chemistry, UCSC: Roger Linington Computer Science Laboratory of Lille, USTL: Gregory Kucherov and the NORINE team

61 Demo (annotation only) (annotation and identification) (alpha site)