Pipeline

Pipeline#

In this tutorial, we show how to generate cumuCCs for amino acid residues at the per-protein level.

Tip

Please make sure that the build-in example dataset has been downloaded before you walk through the tutorial.

Read a sequence#

We begin by reading the sequence of protein 1xqf chain A as shown below.

Please note that seqNetRR is capable of reading sequences from deoxyribonucleic acids (DNA), ribonucleic acids (RNA), and amino acids (proteins). Additionally, it can theoretically process sequences from any biological entity, making it a versatile tool for sequence analysis across multiple molecular types.

from tmkit.sequence import Fasta as sfasta

# read a sequence
sequence = sfasta.get(
    fasta_fpn='./data/fasta/1xqfA.fasta',
)
print(sequence)

AVADKADNAFMMICTALVLFMTIPGIALFYGGLIRGKNVLSMLTQVTVTFALVCILWVVYGYS
LAFGEGNNFFGNINWLMLKNIELTAVMGSIYQYIHVAFQGSFACITVGLIVGALAERIRFSAV
LIFVVVWLTLSYIPIAHMVWGGGLLASHGALDFAGGTVVHINAAIAGLVGAYLPHNLPMVFTG
TAILYIGWFGFNAGSAGTANEIAALAFVNTVVATAAAILGWIFGEWALRGKPSLLGACSGAIA
GLVGVTPACGYIGVGGALIIGVVAGLAGLWGVTMPCDVFGVHGVCGIVGCIMTGIFAASSLGG
VGFAEGVTMGHQLLVQLESIAITIVWSGVVAFIGYKLADLTVGLRVP

Extract residues#

We can next extract all residues separated by a certain separation as shown below. The lower and upper bounds of how far a residue is next to another residue can be regulated by seq_sep_inferior and seq_sep_superior. When they are 0 and None, respectively, all unrepeated residues will be generated.

from tmkit.seqnetrr.combo.Length import length as plength

pos_list = plength(
    seq_sep_inferior=0,
    seq_sep_superior=None,
).tosgl(
    length=len(sequence),
)
print(pos_list[:10])

[[1], [2], [3], [4], [5], [6], [7], [8], [9], [10], ...]

Generate a position list#

By utilizing pos_list, we can enrich residue information with additional details. The following code appends amino acid symbols to their respective positions.

For example, in the output, each array consists of four elements as shown below. For instance, an entry like [1, ‘A’, 1, 0] represents:

Position

Case

Description

1

1

FASTA sequence position

2

A

Amino acid symbol

3

1

Placeholder for PDB position

4

0

Placeholder for an additional feature

This structure allows for flexible annotation and further feature integration.

from tmkit.seqnetrr.combo.Position import Position as pfasta

position = pfasta(
    sequence=sequence,
).single(
    pos_list=pos_list,
)
print(position[:10])

[[1, 'A', 1, 0],
[2, 'V', 2, 0],
[3, 'A', 3, 0],
[4, 'D', 4, 0],
[5, 'K', 5, 0],
[6, 'A', 6, 0],
[7, 'D', 7, 0],
[8, 'N', 8, 0],
[9, 'A', 9, 0],
[10, 'F', 10, 0],
 ...
]

Window setting#

A window with size 5 is placed to extract neighbouring residues of each of central residues.

from tmkit.seqnetrr.window.Single import Single

window_m_ids = Single(
    sequence=sequence,
    position=position,
    window_size=3,
).mid()
print(window_m_ids[:10])

[[None, None, None, 1, 2, 3, 4],
[None, None, 1, 2, 3, 4, 5],
[None, 1, 2, 3, 4, 5, 6],
[1, 2, 3, 4, 5, 6, 7],
[2, 3, 4, 5, 6, 7, 8],
[3, 4, 5, 6, 7, 8, 9],
[4, 5, 6, 7, 8, 9, 10],
[5, 6, 7, 8, 9, 10, 11],
[6, 7, 8, 9, 10, 11, 12],
[7, 8, 9, 10, 11, 12, 13],
 ...
 ]

Generate cumuCCs#

The code below can Generate cumuCCs for residues of interest.

from tmkit.seqnetrr.graph.Cumulative import Cumulative

res = Cumulative(
    sequence=sequence,
    window_size=5,
    window_m_ids=window_m_ids,
    input_kind='freecontact',
).assign(
    list_2d=position,
    L=int(len(sequence)/5),
    fpn='data/rrc/tool/1xqfA.evfold',
)
print(res)

[[1, 'A', 1, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 981.2731380245631, 966.1044130133483, 876.1295995508353, 921.1073699092246],
[2, 'V', 2, 0, 0, 0, 0, 0, 0.0, 0.0, 981.2731380245631, 966.1044130133483, 876.1295995508353, 921.1073699092246, 866.7270683087568],
[3, 'A', 3, 0, 0, 0, 0, 0.0, 0.0, 981.2731380245631, 966.1044130133483, 876.1295995508353, 921.1073699092246, 866.7270683087568, 969.3246187616661],
[4, 'D', 4, 0, 0, 0, 0.0, 0.0, 981.2731380245631, 966.1044130133483, 876.1295995508353, 921.1073699092246, 866.7270683087568, 969.3246187616661, 819.806884450987],
[5, 'K', 5, 0, 0, 0.0, 0.0, 981.2731380245631, 966.1044130133483, 876.1295995508353, 921.1073699092246, 866.7270683087568, 969.3246187616661, 819.806884450987, 754.0407418336364],
[6, 'A', 6, 0, 0.0, 0.0, 981.2731380245631, 966.1044130133483, 876.1295995508353, 921.1073699092246, 866.7270683087568, 969.3246187616661, 819.806884450987, 754.0407418336364, 868.7066033260365],
[7, 'D', 7, 0, 0.0, 981.2731380245631, 966.1044130133483, 876.1295995508353, 921.1073699092246, 866.7270683087568, 969.3246187616661, 819.806884450987, 754.0407418336364, 868.7066033260365, 725.6260121243803],
[8, 'N', 8, 0, 981.2731380245631, 966.1044130133483, 876.1295995508353, 921.1073699092246, 866.7270683087568, 969.3246187616661, 819.806884450987, 754.0407418336364, 868.7066033260365, 725.6260121243803, 697.4153464206792],
[9, 'A', 9, 0, 966.1044130133483, 876.1295995508353, 921.1073699092246, 866.7270683087568, 969.3246187616661, 819.806884450987, 754.0407418336364, 868.7066033260365, 725.6260121243803, 697.4153464206792, 828.2056524269541],
[10, 'F', 10, 0, 876.1295995508353, 921.1073699092246, 866.7270683087568, 969.3246187616661, 819.806884450987, 754.0407418336364, 868.7066033260365, 725.6260121243803, 697.4153464206792, 828.2056524269541, 705.506309006248]],
...
]

Arributes

Arribute

Description

input_kind

feature format that is regulated by a co-evolution method, such ccmpred, freeContact, gdca, or plmc

fpn

path where a covariance matrix (co-evolutionary features) is placed. The covariance matrix should be generated by either CCMPred, FreeContact, GDCA, or plm-DCA. Or, a file that contains three columns (the 1st two for residue pair IDs and the 3rd one for co-evolutionary strengths) is fine.

mode

method to generate cumuCCs. It can be ‘hash’, ‘hash_rf’, ‘hash_ori’, ‘pandas’, or ‘numpy’.

window_size

window size

window_m_ids

list of residues after applying a window

sequence

molecular sequence

L

top-ranked L co-evolutionary strengths that a residue of interest is involved in

See also

Please see here for better understanding the file-naming system.

Mock test#

If you don’t have a file of coevolutionary strengths for a protein, you still want to check what will happen with the function. You can use our built-in function for simulating a covariance matrix by assigning simulate to input_kind and len(sequence) to simu_seq_len and turning off parameter fpn. It will be like this.

from tmkit.seqnetrr.graph.Cumulative import Cumulative

res = Cumulative(
    sequence=sequence,
    window_size=5,
    window_m_ids=window_m_ids,
    input_kind='simulate',
).assign(
    list_2d=position,
    L=int(len(sequence)/5),
    simu_seq_len=len(sequence),
)
print(res[:10])

[[1, 'A', 1, 0, 0, 0, 0, 0, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
[2, 'V', 2, 0, 0, 0, 0, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
[3, 'A', 3, 0, 0, 0, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
[4, 'D', 4, 0, 0, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
[5, 'K', 5, 0, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
[6, 'A', 6, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
[7, 'D', 7, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
[8, 'N', 8, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
[9, 'A', 9, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
[10, 'F', 10, 0, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028, 0.3988919667590028],
...
 ]
Contents