Tutorial: Spectral Libraries¶

This notebook introduces functionalities for spectral libraries to developers.

The Base Library Class¶

alphabase.spectral_library.base.SpecLibBase is the base class for spectral libraries. See https://alphabase.readthedocs.io/en/latest/ for details. We recommend users to access spectral library functionalities via alphabase.protein.fasta.SpecLibFasta.

`SpecLibFasta`¶

Almost all DataFrame functionalities to process proteins and peptides have been integrated into alphabase.protein.fasta.SpecLibFasta.

[1]:

from alphabase.protein.fasta import SpecLibFasta

fasta_lib = SpecLibFasta(
    charged_frag_types=['b_z1','y_z1'],
    protease='trypsin',
    fix_mods=['Carbamidomethyl@C'],
    var_mods=['Acetyl@Protein_N-term','Oxidation@M'],
    decoy=None,
)

OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.

Start from fasta/proteins¶

The SpecLibFasta will do following for us:

Load fasta files into a protein_dict
Digest proteins into peptide sequences
Append decoy peptide sequences if self.decoy is not None
Add fixed and variable modifications
[Add special modifications]
[Add peptide labeling]
Add charge states to peptides

Load fasta files into a protein_dict¶

[2]:

# from alphabase.protein.fasta import load_all_proteins
# protein_dict = load_all_proteins(fasta_files)

# For example, the protein_dict is:
protein_dict = {
    'yy': {
        'protein_id': 'yy',
        'full_name': 'yy_yy',
        'gene_name': 'y_y',
        'sequence': 'FGHIKLMNPQR'
    },
    'xx': {
        'protein_id': 'xx',
        'full_name': 'xx_xx',
        'gene_name': 'x_x',
        'sequence': 'MACDESTYKXKFGHIKLMNPQRST'
    },
}

Digest proteins into peptide sequences¶

[3]:

fasta_lib.get_peptides_from_protein_dict(protein_dict)
fasta_lib.precursor_df

[3]:

	sequence	protein_idxes	miss_cleavage	is_prot_nterm	is_prot_cterm	nAA
0	XKFGHIK	1	1	False	False	7
1	LMNPQRST	1	1	False	True	8
2	ACDESTYK	1	0	True	False	8
3	MACDESTYK	1	0	True	False	9
4	ACDESTYKXK	1	1	True	False	10
5	FGHIKLMNPQR	0;1	1	True	True	11
6	MACDESTYKXK	1	1	True	False	11
7	XKFGHIKLMNPQR	1	2	False	False	13
8	FGHIKLMNPQRST	1	2	False	True	13
9	ACDESTYKXKFGHIK	1	2	True	False	15
10	MACDESTYKXKFGHIK	1	2	True	False	16

[4]:

fasta_lib.protein_df

[4]:

	protein_id	full_name	gene_name	sequence
0	yy	yy_yy	y_y	FGHIKLMNPQR
1	xx	xx_xx	x_x	MACDESTYKXKFGHIKLMNPQRST

Append decoy sequences¶

This depends on self.decoy:str, its value can be

protein_reverse: Reverse on target protein sequences
pseudo_reverse: Pseudo-reverse on target peptide sequences
diann: DiaNN-like decoy
None: no decoy.

Let’s take diann as an example:

[5]:

fasta_lib.decoy = 'diann'
fasta_lib.append_decoy_sequence()
fasta_lib.precursor_df.sample(5, random_state=0)

[5]:

	sequence	protein_idxes	miss_cleavage	is_prot_nterm	is_prot_cterm	nAA	decoy
20	MACDESTYKXKFGHIK	1	2	True	False	16	0
10	FGHIKLMNPQR	0;1	1	True	True	11	0
14	FLHIKLMNPQRTT	1	2	False	True	13	1
13	FLHIKLMNPNR	0;1	1	True	True	11	1
1	XLFGHVK	1	1	False	False	7	1

Add modifications¶

add_modifications() will add fixed and variable modifications.

[6]:

fasta_lib.add_modifications()
fasta_lib.precursor_df.sample(5, random_state=0)

[6]:

	sequence	protein_idxes	miss_cleavage	is_prot_nterm	is_prot_cterm	mods	mod_sites	nAA	decoy
35	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M	0;7	11	1
34	FLHIKLMNPNR	0;1	1	True	True			11	1
41	FGHIKLMNPQRST	1	2	False	True	Oxidation@M	7	13	0
27	MACDESTYKXK	1	1	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	11	0
11	MACDESTYK	1	0	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	9	0

Add special modifications¶

Special modifications here refer to some PTMs we want to have more controls on:

We only needs peptides without unmodified forms
GlyGly@K cannot occur on peptide C-term because trypsin cannot cleave Lys with GlyGly
For some special modifications like Phospho@S and HexNAc@S, we would like to limit the number of peptidome forms to control the memory usage.

[7]:

fasta_lib.special_mods = ['GlyGly@K']
fasta_lib.special_mods_cannot_modify_pep_c_term = True
fasta_lib.min_special_mod_num = 1 # exclude the unmodified forms
fasta_lib.max_special_mod_num = 1 # limit the number of
fasta_lib.add_special_modifications()
fasta_lib.precursor_df.sample(10, random_state=0)

[7]:

	sequence	protein_idxes	miss_cleavage	is_prot_nterm	is_prot_cterm	mods	mod_sites	nAA	decoy
45	MACDESTYKXKFGHIK	1	2	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly@K	0;3;9	16	0
33	ASDESTYKXKFGHVK	1	2	True	False	Acetyl@Protein_N-term;GlyGly@K	0;8	15	1
40	MACDESTYKXKFGHIK	1	2	True	False	Oxidation@M;Carbamidomethyl@C;GlyGly@K	1;3;11	16	0
26	FGHIKLMNPQRST	1	2	False	True	GlyGly@K	5	13	0
11	MACDESTYKXK	1	1	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3;9	11	0
2	ACDESTYKXK	1	1	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly@K	0;2;8	10	0
32	ASDESTYKXKFGHVK	1	2	True	False	GlyGly@K	10	15	1
43	MACDESTYKXKFGHIK	1	2	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3;9	16	0
46	MACDESTYKXKFGHIK	1	2	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly@K	0;3;11	16	0
30	XKFGHIKLMNPQR	1	2	False	False	GlyGly@K	7	13	0

Add peptide labeling¶

For example Dimethyl:

[8]:

fasta_lib.labeling_channels = {
    0: ['Dimethyl@K', 'Dimethyl@Any_N-term'],
    4: ['Dimethyl:2H(4)@K', 'Dimethyl:2H(4)@Any_N-term'],
}
fasta_lib.add_peptide_labeling()
fasta_lib.precursor_df.sample(10, random_state=0)

[8]:

	sequence	protein_idxes	miss_cleavage	is_prot_nterm	is_prot_cterm	mods	mod_sites	nAA	decoy	labeling_channel
85	XKFGHIKLMNPQR	1	2	False	False	GlyGly@K;Dimethyl:2H(4)@Any_N-term;Dimethyl:2H...	7;0;2;7	13	0	4
10	MACDESTYKXK	1	1	True	False	Carbamidomethyl@C;GlyGly@K;Dimethyl@Any_N-term...	3;9;0;9;11	11	0	0
75	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term;GlyGly@K;Dimethyl:2H(4)@K	0;5;5	11	1	4
2	ACDESTYKXK	1	1	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly...	0;2;8;8;10	10	0	0
24	XLFGHIKLMNPNR	1	2	False	False	GlyGly@K;Dimethyl@Any_N-term;Dimethyl@K	7;0;7	13	1	0
101	MACDESTYKXKFGHIK	1	2	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly...	0;3;11;9;11;16	16	0	4
109	MLCDESTYKXKFGHVK	1	2	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly...	0;3;11;9;11;16	16	1	4
7	FGHIKLMNPQR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M;GlyGly@K;Dim...	0;7;5;5	11	0	0
16	MLCDESTYKVK	1	1	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C;GlyGly...	0;3;9;9;11	11	1	0
91	ACDESTYKXKFGHIK	1	2	True	False	Carbamidomethyl@C;GlyGly@K;Dimethyl:2H(4)@Any_...	2;10;0;8;10;15	15	0	4

Add charge states¶

[9]:

fasta_lib.add_charge()
fasta_lib.precursor_df.sample(5, random_state=0)

[9]:

	sequence	protein_idxes	miss_cleavage	is_prot_nterm	is_prot_cterm	mods	mod_sites	nAA	decoy	labeling_channel	charge
122	MACDESTYKXKFGHIK	1	2	True	False	Oxidation@M;Carbamidomethyl@C;GlyGly@K;Dimethy...	1;3;11;0;9;11;16	16	0	0	4
66	FLHIKLMNPQRTT	1	2	False	True	GlyGly@K;Dimethyl@Any_N-term;Dimethyl@K	5;0;5	13	1	0	2
142	MLCDESTYKXKFGHVK	1	2	True	False	Oxidation@M;Carbamidomethyl@C;GlyGly@K;Dimethy...	1;3;9;0;9;11;16	16	1	0	3
246	XKFGHIKLMNPQR	1	2	False	False	Oxidation@M;GlyGly@K;Dimethyl:2H(4)@Any_N-term...	9;2;0;2;7	13	0	4	2
146	MLCDESTYKXKFGHVK	1	2	True	False	Oxidation@M;Carbamidomethyl@C;GlyGly@K;Dimethy...	1;3;11;0;9;11;16	16	1	0	4

`import_and_process_protein_dict()` combines all steps¶

Or import_and_process_fasta() for fasta files.

[10]:

fasta_lib.special_mods = []
fasta_lib.labeling_channels = None
fasta_lib.import_and_process_protein_dict(protein_dict)
fasta_lib.protein_df

[10]:

	protein_id	full_name	gene_name	sequence
0	yy	yy_yy	y_y	FGHIKLMNPQR
1	xx	xx_xx	x_x	MACDESTYKXKFGHIKLMNPQRST

[11]:

fasta_lib.precursor_df

[11]:

	sequence	protein_idxes	miss_cleavage	is_prot_nterm	is_prot_cterm	mods	mod_sites	nAA	decoy	charge	precursor_mz
0	LMNPQRST	1	1	False	True	Oxidation@M	2	8	0	2	481.739834
1	LMNPQRST	1	1	False	True			8	0	2	473.742377
2	ACDESTYK	1	0	True	False	Carbamidomethyl@C	2	8	0	2	487.200207
3	ACDESTYK	1	0	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;2	8	0	2	508.205490
4	LLNPQRTT	1	1	False	True			8	1	2	471.771991
5	ASDESTSK	1	0	True	False			8	1	2	412.685247
6	ASDESTSK	1	0	True	False	Acetyl@Protein_N-term	0	8	1	2	433.690529
7	MACDESTYK	1	0	True	False	Oxidation@M;Carbamidomethyl@C	1;3	9	0	2	560.717907
8	MACDESTYK	1	0	True	False	Carbamidomethyl@C	3	9	0	2	552.720450
9	MACDESTYK	1	0	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	9	0	2	581.723190
10	MACDESTYK	1	0	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;3	9	0	2	573.725732
11	MLCDESTSK	1	0	True	False	Oxidation@M;Carbamidomethyl@C	1;3	9	1	2	543.725732
12	MLCDESTSK	1	0	True	False	Carbamidomethyl@C	3	9	1	2	535.728275
13	MLCDESTSK	1	0	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	9	1	2	564.731015
14	MLCDESTSK	1	0	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;3	9	1	2	556.733557
15	ASDESTYKVK	1	1	True	False			10	1	2	564.282586
16	ASDESTYKVK	1	1	True	False	Acetyl@Protein_N-term	0	10	1	2	585.287868
17	FGHIKLMNPQR	0;1	1	True	True	Oxidation@M	7	11	0	2	678.863889
18	FGHIKLMNPQR	0;1	1	True	True	Oxidation@M	7	11	0	3	452.911685
19	FGHIKLMNPQR	0;1	1	True	True			11	0	2	670.866431
20	FGHIKLMNPQR	0;1	1	True	True			11	0	3	447.580046
21	FGHIKLMNPQR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M	0;7	11	0	2	699.869171
22	FGHIKLMNPQR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M	0;7	11	0	3	466.915206
23	FGHIKLMNPQR	0;1	1	True	True	Acetyl@Protein_N-term	0	11	0	2	691.871714
24	FGHIKLMNPQR	0;1	1	True	True	Acetyl@Protein_N-term	0	11	0	3	461.583568
25	MLCDESTYKVK	1	1	True	False	Oxidation@M;Carbamidomethyl@C	1;3	11	1	2	695.323071
26	MLCDESTYKVK	1	1	True	False	Oxidation@M;Carbamidomethyl@C	1;3	11	1	3	463.884473
27	MLCDESTYKVK	1	1	True	False	Carbamidomethyl@C	3	11	1	2	687.325613
28	MLCDESTYKVK	1	1	True	False	Carbamidomethyl@C	3	11	1	3	458.552834
29	MLCDESTYKVK	1	1	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	11	1	2	716.328353
30	MLCDESTYKVK	1	1	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	11	1	3	477.887994
31	MLCDESTYKVK	1	1	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;3	11	1	2	708.330896
32	MLCDESTYKVK	1	1	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;3	11	1	3	472.556356
33	FLHIKLMNPNR	0;1	1	True	True	Oxidation@M	7	11	1	2	699.887364
34	FLHIKLMNPNR	0;1	1	True	True	Oxidation@M	7	11	1	3	466.927335
35	FLHIKLMNPNR	0;1	1	True	True			11	1	2	691.889907
36	FLHIKLMNPNR	0;1	1	True	True			11	1	3	461.595697
37	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M	0;7	11	1	2	720.892646
38	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M	0;7	11	1	3	480.930856
39	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term	0	11	1	2	712.895189
40	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term	0	11	1	3	475.599218
41	FLHIKLMNPQRTT	1	2	False	True	Oxidation@M	7	13	1	2	807.942867
42	FLHIKLMNPQRTT	1	2	False	True	Oxidation@M	7	13	1	3	538.964337
43	FLHIKLMNPQRTT	1	2	False	True	Oxidation@M	7	13	1	4	404.475072
44	FLHIKLMNPQRTT	1	2	False	True			13	1	2	799.945410
45	FLHIKLMNPQRTT	1	2	False	True			13	1	3	533.632699
46	FLHIKLMNPQRTT	1	2	False	True			13	1	4	400.476343
47	FGHIKLMNPQRST	1	2	False	True	Oxidation@M	7	13	0	2	772.903742
48	FGHIKLMNPQRST	1	2	False	True	Oxidation@M	7	13	0	3	515.604920
49	FGHIKLMNPQRST	1	2	False	True			13	0	2	764.906285
50	FGHIKLMNPQRST	1	2	False	True			13	0	3	510.273282

Start from peptides instead of proteins¶

The modularity design of SpecLibFasta allows us to starts from arbitrary types of peptide inputs, meaning that fasta files or protein_dict is not necessary.

For example, we have a list of sequences, and we what to add modifications using SpecLibFasta functionalities:

[12]:

import pandas as pd
pep_lib = SpecLibFasta(
    charged_frag_types=['b_z1','y_z1'],
    fix_mods=['Carbamidomethyl@C'],
    var_mods=['Acetyl@Protein_N-term','Oxidation@M'],
    labeling_channels={
        0: ['Dimethyl@K', 'Dimethyl@Any_N-term'],
        4: ['Dimethyl:2H(4)@K', 'Dimethyl:2H(4)@Any_N-term'],
    },
    decoy=None,
)

pep_lib.precursor_df = pd.DataFrame({
    'sequence': ['ABCDEFG','HIJKLMN','OPQRST','UVWXYZ']
})
pep_lib.process_from_naked_peptide_seqs()
pep_lib.precursor_df

[12]:

	sequence	nAA	is_prot_nterm	is_prot_cterm	mods	mod_sites	labeling_channel	charge	precursor_mz
0	OPQRST	6	False	False	Dimethyl@Any_N-term	0	0	2	427.248152
1	HIJKLMN	7	False	False	Oxidation@M;Dimethyl@Any_N-term;Dimethyl@K	6;0;4	0	2	470.786056
2	HIJKLMN	7	False	False	Dimethyl@Any_N-term;Dimethyl@K	0;4	0	2	462.788599
3	OPQRST	6	False	False	Dimethyl:2H(4)@Any_N-term	0	4	2	429.260705
4	HIJKLMN	7	False	False	Oxidation@M;Dimethyl:2H(4)@Any_N-term;Dimethyl...	6;0;4	4	2	474.811163
5	HIJKLMN	7	False	False	Dimethyl:2H(4)@Any_N-term;Dimethyl:2H(4)@K	0;4	4	2	466.813706

Calculate masses¶

Calculate precursor m/z¶

[13]:

fasta_lib.calc_precursor_mz()
# fasta_lib.calc_precursor_isotope()
fasta_lib.precursor_df

[13]:

	sequence	protein_idxes	miss_cleavage	is_prot_nterm	is_prot_cterm	mods	mod_sites	nAA	decoy	charge	precursor_mz
0	LMNPQRST	1	1	False	True	Oxidation@M	2	8	0	2	481.739834
1	LMNPQRST	1	1	False	True			8	0	2	473.742377
2	ACDESTYK	1	0	True	False	Carbamidomethyl@C	2	8	0	2	487.200207
3	ACDESTYK	1	0	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;2	8	0	2	508.205490
4	LLNPQRTT	1	1	False	True			8	1	2	471.771991
5	ASDESTSK	1	0	True	False			8	1	2	412.685247
6	ASDESTSK	1	0	True	False	Acetyl@Protein_N-term	0	8	1	2	433.690529
7	MACDESTYK	1	0	True	False	Oxidation@M;Carbamidomethyl@C	1;3	9	0	2	560.717907
8	MACDESTYK	1	0	True	False	Carbamidomethyl@C	3	9	0	2	552.720450
9	MACDESTYK	1	0	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	9	0	2	581.723190
10	MACDESTYK	1	0	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;3	9	0	2	573.725732
11	MLCDESTSK	1	0	True	False	Oxidation@M;Carbamidomethyl@C	1;3	9	1	2	543.725732
12	MLCDESTSK	1	0	True	False	Carbamidomethyl@C	3	9	1	2	535.728275
13	MLCDESTSK	1	0	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	9	1	2	564.731015
14	MLCDESTSK	1	0	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;3	9	1	2	556.733557
15	ASDESTYKVK	1	1	True	False			10	1	2	564.282586
16	ASDESTYKVK	1	1	True	False	Acetyl@Protein_N-term	0	10	1	2	585.287868
17	FGHIKLMNPQR	0;1	1	True	True	Oxidation@M	7	11	0	2	678.863889
18	FGHIKLMNPQR	0;1	1	True	True	Oxidation@M	7	11	0	3	452.911685
19	FGHIKLMNPQR	0;1	1	True	True			11	0	2	670.866431
20	FGHIKLMNPQR	0;1	1	True	True			11	0	3	447.580046
21	FGHIKLMNPQR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M	0;7	11	0	2	699.869171
22	FGHIKLMNPQR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M	0;7	11	0	3	466.915206
23	FGHIKLMNPQR	0;1	1	True	True	Acetyl@Protein_N-term	0	11	0	2	691.871714
24	FGHIKLMNPQR	0;1	1	True	True	Acetyl@Protein_N-term	0	11	0	3	461.583568
25	MLCDESTYKVK	1	1	True	False	Oxidation@M;Carbamidomethyl@C	1;3	11	1	2	695.323071
26	MLCDESTYKVK	1	1	True	False	Oxidation@M;Carbamidomethyl@C	1;3	11	1	3	463.884473
27	MLCDESTYKVK	1	1	True	False	Carbamidomethyl@C	3	11	1	2	687.325613
28	MLCDESTYKVK	1	1	True	False	Carbamidomethyl@C	3	11	1	3	458.552834
29	MLCDESTYKVK	1	1	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	11	1	2	716.328353
30	MLCDESTYKVK	1	1	True	False	Acetyl@Protein_N-term;Oxidation@M;Carbamidomet...	0;1;3	11	1	3	477.887994
31	MLCDESTYKVK	1	1	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;3	11	1	2	708.330896
32	MLCDESTYKVK	1	1	True	False	Acetyl@Protein_N-term;Carbamidomethyl@C	0;3	11	1	3	472.556356
33	FLHIKLMNPNR	0;1	1	True	True	Oxidation@M	7	11	1	2	699.887364
34	FLHIKLMNPNR	0;1	1	True	True	Oxidation@M	7	11	1	3	466.927335
35	FLHIKLMNPNR	0;1	1	True	True			11	1	2	691.889907
36	FLHIKLMNPNR	0;1	1	True	True			11	1	3	461.595697
37	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M	0;7	11	1	2	720.892646
38	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term;Oxidation@M	0;7	11	1	3	480.930856
39	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term	0	11	1	2	712.895189
40	FLHIKLMNPNR	0;1	1	True	True	Acetyl@Protein_N-term	0	11	1	3	475.599218
41	FLHIKLMNPQRTT	1	2	False	True	Oxidation@M	7	13	1	2	807.942867
42	FLHIKLMNPQRTT	1	2	False	True	Oxidation@M	7	13	1	3	538.964337
43	FLHIKLMNPQRTT	1	2	False	True	Oxidation@M	7	13	1	4	404.475072
44	FLHIKLMNPQRTT	1	2	False	True			13	1	2	799.945410
45	FLHIKLMNPQRTT	1	2	False	True			13	1	3	533.632699
46	FLHIKLMNPQRTT	1	2	False	True			13	1	4	400.476343
47	FGHIKLMNPQRST	1	2	False	True	Oxidation@M	7	13	0	2	772.903742
48	FGHIKLMNPQRST	1	2	False	True	Oxidation@M	7	13	0	3	515.604920
49	FGHIKLMNPQRST	1	2	False	True			13	0	2	764.906285
50	FGHIKLMNPQRST	1	2	False	True			13	0	3	510.273282

After calc_precursor_mz(), all sequences containing x are removed because x’s mass is very large which is out of the range of fasta_lib.min_precursor_mz and fasta_lib.max_precursor_mz.

[14]:

from alphabase.constants.aa import AA_ASCII_MASS
(
    fasta_lib.min_precursor_mz, fasta_lib.max_precursor_mz,
    f"mass of 'x' is {AA_ASCII_MASS[ord('x')]}"
)

[14]:

(400.0, 2000.0, "mass of 'x' is 100000000.0")

Calculate fragment m/z¶

[15]:

fasta_lib.calc_fragment_mz_df()
fasta_lib.fragment_mz_df

[15]:

	b_z1	y_z1
0	114.091339	849.388306
1	261.126740	702.352905
2	375.169678	588.309998
3	472.222443	491.257233
4	600.281006	363.198669
...	...	...
486	941.502563	588.309998
487	1038.555298	491.257233
488	1166.613892	363.198669
489	1322.714966	207.097549
490	1409.747070	120.065521

491 rows × 2 columns

Use frag_start_idx and frag_stop_idx in precursor_df to locate the corresponding fragments

[16]:

ith_pep = 5
frag_start, frag_stop = fasta_lib.precursor_df.loc[ith_pep,['frag_start_idx','frag_stop_idx']].values
fasta_lib.fragment_mz_df.iloc[frag_start:frag_stop]

[16]:

	b_z1	y_z1
35	72.044388	753.326111
36	159.076416	666.294067
37	274.103363	551.267151
38	403.145966	422.224548
39	490.177979	335.192505
40	591.225647	234.144836
41	678.257690	147.112808

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