Alignment

from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import SeqIO, AlignIO
import pandas as pd

Prepare fasta file for clustalo

kd=pd.read_excel('out/uniprot_kd.xlsx')

kd

	kd_ID	Uniprot	Entry Name	Protein names	Gene Names	Gene Names (primary)	Organism	kd_note	kd_evidence	kd_start	...	Reactome	ComplexPortal	Subcellular location [CC]	Gene Ontology (biological process)	Tissue specificity	Interacts with	Subunit structure	Function [CC]	Activity regulation	full_seq
0	A0A075F7E9_LERK1_ORYSI_KD1	A0A075F7E9	LERK1_ORYSI	G-type lectin S-receptor-like serine/threonine...	LECRK1 LECRK OsI_14840	LECRK1	Oryza sativa subsp. indica (Rice)	Protein kinase	ECO:0000255|PROSITE-ProRule:PRU00159	523	...	NaN	NaN	SUBCELLULAR LOCATION: Membrane {ECO:0000255}; ...	defense response [GO:0006952]; response to oth...	TISSUE SPECIFICITY: Expressed in plumules, rad...	NaN	SUBUNIT: Interacts (via kinase domain) with AD...	FUNCTION: Involved in innate immunity. Require...	NaN	MVALLLFPMLLQLLSPTCAQTQKNITLGSTLAPQGPASSWLSPSGD...
1	A0A078BQP2_GCY25_CAEEL_KD1	A0A078BQP2	GCY25_CAEEL	Receptor-type guanylate cyclase gcy-25 (EC 4.6...	gcy-25 Y105C5B.2	gcy-25	Caenorhabditis elegans	Protein kinase	ECO:0000255|PROSITE-ProRule:PRU00159	464	...	R-CEL-2514859;	NaN	SUBCELLULAR LOCATION: Cell membrane {ECO:00003...	cGMP biosynthetic process [GO:0006182]; intrac...	TISSUE SPECIFICITY: Expressed in AQR, PQR and ...	NaN	NaN	FUNCTION: Guanylate cyclase involved in the pr...	NaN	MLLLLLLLKISTFVDSFQIGHLEFENSNETRILEICMKNAGSWRDH...
2	A0A078CGE6_M3KE1_BRANA_KD1	A0A078CGE6	M3KE1_BRANA	MAP3K epsilon protein kinase 1 (BnM3KE1) (EC 2...	M3KE1 BnaA03g30290D GSBRNA2T00111755001	M3KE1	Brassica napus (Rape)	Protein kinase	ECO:0000255|PROSITE-ProRule:PRU00159	20	...	NaN	NaN	SUBCELLULAR LOCATION: Cytoplasm, cytoskeleton,...	cell division [GO:0051301]; protein autophosph...	TISSUE SPECIFICITY: Expressed in both the spor...	NaN	NaN	FUNCTION: Serine/threonine-protein kinase invo...	NaN	MARQMTSSQFHKSKTLDNKYMLGDEIGKGAYGRVYIGLDLENGDFV...
3	A0A0G2K344_PK3CA_RAT_KD1	A0A0G2K344	PK3CA_RAT	Phosphatidylinositol 4,5-bisphosphate 3-kinase...	Pik3ca	Pik3ca	Rattus norvegicus (Rat)	PI3K/PI4K catalytic	ECO:0000255|PROSITE-ProRule:PRU00269	765	...	R-RNO-109704;R-RNO-112399;R-RNO-114604;R-RNO-1...	NaN	NaN	actin cytoskeleton organization [GO:0030036]; ...	TISSUE SPECIFICITY: Detected in the hypothalam...	NaN	SUBUNIT: Heterodimer of a catalytic subunit PI...	FUNCTION: Phosphoinositide-3-kinase (PI3K) pho...	NaN	MPPRPSSGELWGIHLMPPRILVECLLPNGMIVTLECLREATLVTIK...
4	A0A0H2ZM62_HK06_STRP2_KD1	A0A0H2ZM62	HK06_STRP2	Sensor histidine protein kinase HK06 (EC 2.7.1...	hk06 SPD_2019	hk06	Streptococcus pneumoniae serotype 2 (strain D3...	Histidine kinase	ECO:0000255|PROSITE-ProRule:PRU00107	239	...	NaN	NaN	SUBCELLULAR LOCATION: Cell membrane {ECO:00003...	NaN	NaN	NaN	NaN	FUNCTION: Member of the two-component regulato...	NaN	MIKNPKLLTKSFLRSFAILGGVGLVIHIAIYLTFPFYYIQLEGEKF...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5531	W0LYS5_CAMKI_MACNP_KD1	W0LYS5	CAMKI_MACNP	Calcium/calmodulin-dependent protein kinase ty...	CaMKI	CaMKI	Macrobrachium nipponense (Oriental river shrim...	Protein kinase	ECO:0000255|PROSITE-ProRule:PRU00159	31	...	NaN	NaN	NaN	molting cycle process [GO:0022404]	TISSUE SPECIFICITY: Highly expressed in hepato...	NaN	NaN	FUNCTION: Calcium/calmodulin-dependent protein...	ACTIVITY REGULATION: Activated by Ca(2+)/calmo...	MPLFGSKKETAKKSSKKDKDEGKMPAVEDKYILKDLLGTGAFSQVR...
5532	W0T9X4_ATG1_KLUMD_KD1	W0T9X4	ATG1_KLUMD	Serine/threonine-protein kinase ATG1 (EC 2.7.1...	ATG1 KLMA_30321	ATG1	Kluyveromyces marxianus (strain DMKU3-1042 / B...	Protein kinase	ECO:0000255|PROSITE-ProRule:PRU00159	21	...	NaN	NaN	SUBCELLULAR LOCATION: Cytoplasm {ECO:0000250|U...	autophagosome assembly [GO:0000045]; autophagy...	NaN	W0TA43	SUBUNIT: Homodimer (By similarity). Dimerizati...	FUNCTION: Serine/threonine protein kinase invo...	NaN	MSSESHGKAVAKAIRLPTENYTVEKEIGKGSFAIVYKGVSLRDGRN...
5533	W7JX98_KGP_PLAFO_KD1	W7JX98	KGP_PLAFO	cGMP-dependent protein kinase (EC 2.7.11.12)	PKG PFNF54_05395	PKG	Plasmodium falciparum (isolate NF54)	Protein kinase	ECO:0000255|PROSITE-ProRule:PRU00159	541	...	NaN	NaN	SUBCELLULAR LOCATION: Cytoplasm {ECO:0000250|U...	protein phosphorylation [GO:0006468]	NaN	NaN	SUBUNIT: Monomer. {ECO:0000250|UniProtKB:Q8I719}.	FUNCTION: Serine/threonine protein kinase whic...	ACTIVITY REGULATION: Activated by cGMP. Not ac...	MEEDDNLKKGNERNKKKAIFSNDDFTGEDSLMEDHLELREKLSEDI...
5534	X5M5N0_WNK_CAEEL_KD1	X5M5N0	WNK_CAEEL	Serine/threonine-protein kinase WNK (EC 2.7.11...	wnk-1 C46C2.1	wnk-1	Caenorhabditis elegans	Protein kinase	ECO:0000255|PROSITE-ProRule:PRU00159	334	...	NaN	NaN	SUBCELLULAR LOCATION: Cytoplasm {ECO:0000250|U...	cell volume homeostasis [GO:0006884]; cellular...	TISSUE SPECIFICITY: Expressed in pharynx, nerv...	G5EEN4	SUBUNIT: Interacts with gck-3 (via C-terminus)...	FUNCTION: Serine/threonine-protein kinase comp...	ACTIVITY REGULATION: Activated in response to ...	MPDSITNGGRPPAPPSSVSSTTASTTGNFGTRRRLVNRIKKVDELH...
5535	X5M8U1_GCY17_CAEEL_KD1	X5M8U1	GCY17_CAEEL	Receptor-type guanylate cyclase gcy-17 (EC 4.6...	gcy-17 W03F11.2	gcy-17	Caenorhabditis elegans	Protein kinase	ECO:0000255|PROSITE-ProRule:PRU00159	535	...	R-CEL-2514859;	NaN	SUBCELLULAR LOCATION: Cell membrane {ECO:00003...	cGMP biosynthetic process [GO:0006182]; intrac...	TISSUE SPECIFICITY: Expressed in PHA sensory n...	NaN	NaN	FUNCTION: Guanylate cyclase involved in the pr...	NaN	MLFLRLFIFTPFLILANCQARRTIKVGLLFVQNVSSLQVGIGYRTS...

5536 rows × 27 columns

def get_fasta(df,seq_col='kd_seq',id_col='kd_ID',path='out.fasta'):
    "Generate fasta file from sequences."
    records = [
        SeqRecord(Seq(row[seq_col]), id=row[id_col], description="")
        for _, row in df.iterrows()
    ]
    SeqIO.write(records, path, "fasta")
    print(len(records))

get_fasta(kd,path='raw/kinase_domains.fasta')

5536

For human only:

human = kd[kd.Organism=='Homo sapiens (Human)']

get_fasta(human,path="raw/human_kinase_domains.fasta")

539

for PI3K catalytic domain

pi3k = kd[kd.kd_note=='PI3K/PI4K catalytic']

get_fasta(pi3k,path="raw/pi3k_kinase_domains.fasta")

168

Run clustalo

sudo apt-get update
sudo apt-get install clustalo
clustalo -i kinase_domains.fasta -o kinase_domains.aln --force --outfmt=clu

# import subprocess
# subprocess.run([
#     "clustalo", "-i", "kinase_domains.fasta", 
#     "-o", "kinase_domains.aln", "--force", "--outfmt=clu"
# ])

Analyze alignment

alignment = AlignIO.read("raw/kinase_domains.aln", "clustal")

Turns in to dataframe:

alignment_array = [list(str(record.seq)) for record in alignment]
df = pd.DataFrame(alignment_array)

df.columns = df.columns+1

df.index=kd.kd_ID

# df.to_parquet('output/uniprot_kd_align.parquet')

df.head()

	1	2	3	4	5	6	7	8	9	10	...	3425	3426	3427	3428	3429	3430	3431	3432	3433	3434
kd_ID
A0A075F7E9_LERK1_ORYSI_KD1	-	-	-	-	-	-	-	-	-	-	...	-	-	-	-	-	-	-	-	-	-
A0A078BQP2_GCY25_CAEEL_KD1	-	-	-	-	-	-	-	-	-	-	...	-	-	-	-	-	-	-	-	-	-
A0A078CGE6_M3KE1_BRANA_KD1	-	-	-	-	-	-	-	-	-	-	...	-	-	-	-	-	-	-	-	-	-
A0A0G2K344_PK3CA_RAT_KD1	-	-	-	-	-	-	-	-	-	-	...	-	-	-	-	-	-	-	-	-	-
A0A0H2ZM62_HK06_STRP2_KD1	-	-	-	-	-	-	-	-	-	-	...	-	-	-	-	-	-	-	-	-	-

5 rows × 3434 columns

# Compute residue frequency at each position
counts_df = df.apply(lambda col: col.value_counts(), axis=0).fillna(0)
freq_df = counts_df.div(counts_df.sum(axis=0), axis=1)

freq_df.head()

	1	2	3	4	5	6	7	8	9	10	...	3425	3426	3427	3428	3429	3430	3431	3432	3433	3434
-	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819	...	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819	0.999819
A	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
C	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
D	0.000000	0.000181	0.000000	0.000000	0.000181	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000181	0.000181	0.000000	0.000000	0.000000	0.000181	0.000000	0.000000	0.000000
E	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000181	0.000000	0.000000

5 rows × 3434 columns

# remove '-' first line
max_series=freq_df.iloc[1:,:]

freq_max = pd.concat([max_series.idxmax(),max_series.max()],axis=1)

freq_max.columns = ['aa','max_value']

freq_max = freq_max.sort_values('max_value',ascending=False).reset_index(names='position')

out = freq_max[freq_max.max_value>0.1]

# out.to_csv('out/align_freq_max_aa.csv',index=False)

out

	position	aa	max_value
0	1549	N	0.815390
1	2618	D	0.809429
2	1724	D	0.800759
3	1525	D	0.791004
4	1730	G	0.775470
...	...	...	...
214	193	E	0.101879
215	640	G	0.101879
216	922	L	0.101337
217	603	R	0.101156
218	2581	G	0.100614

219 rows × 3 columns

Locate active motif & save

• 1549 N is after HRD motif
• 2618 D is around D[IV]WS motif
• 1724 D is DFG motif
• 1525 D is HRD motif

kd['D1']=(df[1525]=='D').astype(int) # HRD

kd['D2'] = (df[1724]=='D').astype(int) #DFG

kd['D3']=(df[2618]=='D').astype(int)

kd['N1'] = (df[1549]=='N').astype(int)

active_col = ['D1','D2']

kd['active_D1_D2'] = (kd[active_col].sum(1)==len(active_col)).astype(int)

active_all = kd[kd.active_D1_D2==1]

active_all.shape

(4209, 32)

# active_all.to_excel('out/uniprot_kd_active_D1_D2.xlsx',index=False)

# kd.to_excel('out/uniprot_kd_motif_labeled.xlsx',index=False)

Take a look of their identity:

kd[kd.active_D1_D2==1].kd_note.value_counts()

kd_note
Protein kinase               4005
PI3K/PI4K catalytic            71
Protein kinase 2               61
Protein kinase 1               37
Histidine kinase               34
Alpha-type protein kinase       1
Name: count, dtype: int64

kd[kd.active_D1_D2==0].kd_note.value_counts()

kd_note
Histidine kinase                  591
Protein kinase                    525
PI3K/PI4K catalytic                97
Protein kinase 1                   36
Alpha-type protein kinase          21
Protein kinase; inactive           13
Protein kinase 2                   12
HWE histidine kinase domain        11
Guanylate kinase-like               5
Histidine kinase 2                  4
Histidine kinase 1                  4
Amino-acid kinase domain (AAK)      3
Kinase domain                       2
Histidine kinase; first part        1
Histidine kinase; second part       1
Protein kinase; truncated           1
Name: count, dtype: int64

Merge with human PSPA

human = kd[kd.Organism=='Homo sapiens (Human)']

human.active_D1_D2.value_counts()

active_D1_D2
1    444
0     95
Name: count, dtype: int64

human_active = human[human.active_D1_D2==1]

human_active[human_active.Uniprot.duplicated(keep=False)]

	kd_ID	Uniprot	Entry Name	Protein names	Gene Names	Gene Names (primary)	Organism	kd_note	kd_evidence	kd_start	...	Interacts with	Subunit structure	Function [CC]	Activity regulation	full_seq	D1	D2	D3	N1	active_D1_D2
988	O75582_KS6A5_HUMAN_KD1	O75582	KS6A5_HUMAN	Ribosomal protein S6 kinase alpha-5 (S6K-alpha...	RPS6KA5 MSK1	RPS6KA5	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	49	...	P67870; Q9UI47-2; Q9Y4C1; Q9NYL2; Q16539; Q9Y4C1	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGI...	1	1	1	1	1
989	O75582_KS6A5_HUMAN_KD2	O75582	KS6A5_HUMAN	Ribosomal protein S6 kinase alpha-5 (S6K-alpha...	RPS6KA5 MSK1	RPS6KA5	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	P67870; Q9UI47-2; Q9Y4C1; Q9NYL2; Q16539; Q9Y4C1	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGI...	1	1	1	1	1
990	O75676_KS6A4_HUMAN_KD1	O75676	KS6A4_HUMAN	Ribosomal protein S6 kinase alpha-4 (S6K-alpha...	RPS6KA4 MSK2	RPS6KA4	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	33	...	Q16539; O14901	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGK...	1	1	1	1	1
991	O75676_KS6A4_HUMAN_KD2	O75676	KS6A4_HUMAN	Ribosomal protein S6 kinase alpha-4 (S6K-alpha...	RPS6KA4 MSK2	RPS6KA4	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	411	...	Q16539; O14901	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGK...	1	1	1	1	1
1918	P51812_KS6A3_HUMAN_KD1	P51812	KS6A3_HUMAN	Ribosomal protein S6 kinase alpha-3 (S6K-alpha...	RPS6KA3 ISPK1 MAPKAPK1B RSK2	RPS6KA3	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	68	...	P46379-2; P67870; P09471; P08238; O14901; P284...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVS...	1	1	1	1	1
1919	P51812_KS6A3_HUMAN_KD2	P51812	KS6A3_HUMAN	Ribosomal protein S6 kinase alpha-3 (S6K-alpha...	RPS6KA3 ISPK1 MAPKAPK1B RSK2	RPS6KA3	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	422	...	P46379-2; P67870; P09471; P08238; O14901; P284...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVS...	1	1	1	1	1
2560	Q15349_KS6A2_HUMAN_KD1	Q15349	KS6A2_HUMAN	Ribosomal protein S6 kinase alpha-2 (S6K-alpha...	RPS6KA2 MAPKAPK1C RSK3	RPS6KA2	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	59	...	P05067; P15056; P67870; O14901; P28482; Q02156...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHH...	1	1	1	1	1
2561	Q15349_KS6A2_HUMAN_KD2	Q15349	KS6A2_HUMAN	Ribosomal protein S6 kinase alpha-2 (S6K-alpha...	RPS6KA2 MAPKAPK1C RSK3	RPS6KA2	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	415	...	P05067; P15056; P67870; O14901; P28482; Q02156...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHH...	1	1	1	1	1
2563	Q15418_KS6A1_HUMAN_KD1	Q15418	KS6A1_HUMAN	Ribosomal protein S6 kinase alpha-1 (S6K-alpha...	RPS6KA1 MAPKAPK1A RSK1	RPS6KA1	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	62	...	O43823; Q16543; P46527; P08238; P28482; P04271...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLKEPWPLMELVPLDPENGQTSGEEAGLQPSKDEGVLKEISI...	1	1	1	1	1
2564	Q15418_KS6A1_HUMAN_KD2	Q15418	KS6A1_HUMAN	Ribosomal protein S6 kinase alpha-1 (S6K-alpha...	RPS6KA1 MAPKAPK1A RSK1	RPS6KA1	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	418	...	O43823; Q16543; P46527; P08238; P28482; P04271...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLKEPWPLMELVPLDPENGQTSGEEAGLQPSKDEGVLKEISI...	1	1	1	1	1
2568	Q15772_SPEG_HUMAN_KD1	Q15772	SPEG_HUMAN	Striated muscle preferentially expressed prote...	SPEG APEG1 KIAA1297	SPEG	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	1601	...	Q99873; O75031; Q6FHY5; Q9NRD5; Q99873-3	SUBUNIT: Interacts with MTM1. Isoform 3 is fou...	FUNCTION: Isoform 3 may have a role in regulat...	NaN	MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGGGAPVAVAGAPVFL...	1	1	1	1	1
2569	Q15772_SPEG_HUMAN_KD2	Q15772	SPEG_HUMAN	Striated muscle preferentially expressed prote...	SPEG APEG1 KIAA1297	SPEG	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	2966	...	Q99873; O75031; Q6FHY5; Q9NRD5; Q99873-3	SUBUNIT: Interacts with MTM1. Isoform 3 is fou...	FUNCTION: Isoform 3 may have a role in regulat...	NaN	MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGGGAPVAVAGAPVFL...	1	1	1	1	1
3480	Q5VST9_OBSCN_HUMAN_KD1	Q5VST9	OBSCN_HUMAN	Obscurin (EC 2.7.11.1) (Obscurin-RhoGEF) (Obsc...	OBSCN KIAA1556 KIAA1639	OBSCN	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	6468	...	Q8WZ42; P16157; P16157-17	SUBUNIT: Interacts (via protein kinase domain ...	FUNCTION: Structural component of striated mus...	NaN	MDQPQFSGAPRFLTRPKAFVVSVGKDATLSCQIVGNPTPQVSWEKD...	1	1	1	1	1
3481	Q5VST9_OBSCN_HUMAN_KD2	Q5VST9	OBSCN_HUMAN	Obscurin (EC 2.7.11.1) (Obscurin-RhoGEF) (Obsc...	OBSCN KIAA1556 KIAA1639	OBSCN	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	7672	...	Q8WZ42; P16157; P16157-17	SUBUNIT: Interacts (via protein kinase domain ...	FUNCTION: Structural component of striated mus...	NaN	MDQPQFSGAPRFLTRPKAFVVSVGKDATLSCQIVGNPTPQVSWEKD...	1	1	1	1	1
5338	Q9UK32_KS6A6_HUMAN_KD1	Q9UK32	KS6A6_HUMAN	Ribosomal protein S6 kinase alpha-6 (S6K-alpha...	RPS6KA6 RSK4	RPS6KA6	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	73	...	Q7Z698	SUBUNIT: Forms a complex with MAPK3/ERK1 but n...	FUNCTION: Constitutively active serine/threoni...	ACTIVITY REGULATION: Constitutively activated ...	MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSC...	1	1	1	1	1
5339	Q9UK32_KS6A6_HUMAN_KD2	Q9UK32	KS6A6_HUMAN	Ribosomal protein S6 kinase alpha-6 (S6K-alpha...	RPS6KA6 RSK4	RPS6KA6	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	Q7Z698	SUBUNIT: Forms a complex with MAPK3/ERK1 but n...	FUNCTION: Constitutively active serine/threoni...	ACTIVITY REGULATION: Constitutively activated ...	MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSC...	1	1	1	1	1

16 rows × 32 columns

from katlas.data import *

info=Data.get_kinase_info()

kinase_map = info[['kinase','uniprot']]

kinase_map = kinase_map[~kinase_map.kinase.str.contains('_b')]

kinase_map = kinase_map.set_index('kinase')['uniprot']

kinase_map = kinase_map['uniprot']

kinase_map = kinase_map.drop_duplicates()

# kinase_map.to_csv('out/kinase_map.csv')

kinase_map

kinase
AAK1      Q2M2I8
ABL1      P00519
ABL2      P42684
TNK2      Q07912
ACVR2A    P27037
           ...  
YSK1      O00506
ZAK       Q9NYL2
ZAP70     P43403
EEF2K     O00418
FAM20C    Q8IXL6
Name: uniprot, Length: 509, dtype: object

pspa = Data.get_pspa_all_norm()

pspa.index.duplicated().sum()

0

idx = pspa.index.str.split('_').str[0]

idx.map(kinase_map).isna().sum()

0

pspa = pspa.reset_index()

uniprot = pd.DataFrame(idx.map(kinase_map))

uniprot.columns=['uniprot']

pspa = pd.concat([uniprot,pspa],axis=1)

# pspa.to_csv('out/pspa_uniprot.csv',index=False)

PSPA with duplicated uniprot:

pspa[pspa.uniprot.duplicated(keep=False)].sort_values('uniprot')

	uniprot	kinase	-5P	-5G	-5A	-5C	-5S	-5T	-5V	-5I	...	5H	5K	5R	5Q	5N	5D	5E	5s	5t	5y
22	Q13873	BMPR2	0.0558	0.0621	0.0638	0.0716	0.0571	0.0571	0.0597	0.0571	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
309	Q13873	BMPR2_TYR	0.0580	0.0617	0.0603	0.0661	0.0613	0.0613	0.0566	0.0580	...	0.0660	0.0665	0.0766	0.0607	0.0619	0.0648	0.0649	0.0702	0.0702	0.0653
212	Q15118	PDHK1	0.0451	0.0697	0.0594	0.0625	0.0594	0.0594	0.0573	0.0590	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
369	Q15118	PDHK1_TYR	0.0590	0.0608	0.0528	0.0620	0.0608	0.0608	0.0439	0.0528	...	0.0676	0.0490	0.0539	0.0620	0.0647	0.0949	0.0797	0.0633	0.0633	0.0820
213	Q16654	PDHK4	0.0452	0.0645	0.0665	0.0672	0.0622	0.0622	0.0515	0.0619	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
371	Q16654	PDHK4_TYR	0.0697	0.0690	0.0545	0.0772	0.0642	0.0642	0.0426	0.0483	...	0.0642	0.0486	0.0498	0.0755	0.0661	0.0836	0.0830	0.0686	0.0686	0.0892
221	Q9BXM7	PINK1	0.0516	0.0474	0.0612	0.0570	0.0523	0.0523	0.0474	0.0507	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
372	Q9BXM7	PINK1_TYR	0.0569	0.0585	0.0572	0.0643	0.0579	0.0579	0.0546	0.0555	...	0.0666	0.0818	0.1049	0.0636	0.0635	0.0525	0.0508	0.0516	0.0516	0.0611

8 rows × 238 columns

pspa_unique_uniprot = pspa[~pspa.uniprot.duplicated()]

Also remove other TYR due to their overall low specificity

pspa_no_TYR = pspa_unique_uniprot[~pspa_unique_uniprot.kinase.str.contains('_TYR')]

pspa_no_TYR = pspa_no_TYR.dropna(axis=1)

# pspa_no_TYR.to_csv('out/pspa_uniprot_unique_no_TYR.csv',index=False)

active_kd = pd.read_excel('out/uniprot_kd_active_D1_D2.xlsx')

human_active[human_active.Uniprot.duplicated()]

	kd_ID	Uniprot	Entry Name	Protein names	Gene Names	Gene Names (primary)	Organism	kd_note	kd_evidence	kd_start	...	Interacts with	Subunit structure	Function [CC]	Activity regulation	full_seq	D1	D2	D3	N1	active_D1_D2
989	O75582_KS6A5_HUMAN_KD2	O75582	KS6A5_HUMAN	Ribosomal protein S6 kinase alpha-5 (S6K-alpha...	RPS6KA5 MSK1	RPS6KA5	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	P67870; Q9UI47-2; Q9Y4C1; Q9NYL2; Q16539; Q9Y4C1	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGI...	1	1	1	1	1
991	O75676_KS6A4_HUMAN_KD2	O75676	KS6A4_HUMAN	Ribosomal protein S6 kinase alpha-4 (S6K-alpha...	RPS6KA4 MSK2	RPS6KA4	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	411	...	Q16539; O14901	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGK...	1	1	1	1	1
1919	P51812_KS6A3_HUMAN_KD2	P51812	KS6A3_HUMAN	Ribosomal protein S6 kinase alpha-3 (S6K-alpha...	RPS6KA3 ISPK1 MAPKAPK1B RSK2	RPS6KA3	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	422	...	P46379-2; P67870; P09471; P08238; O14901; P284...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVS...	1	1	1	1	1
2561	Q15349_KS6A2_HUMAN_KD2	Q15349	KS6A2_HUMAN	Ribosomal protein S6 kinase alpha-2 (S6K-alpha...	RPS6KA2 MAPKAPK1C RSK3	RPS6KA2	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	415	...	P05067; P15056; P67870; O14901; P28482; Q02156...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHH...	1	1	1	1	1
2564	Q15418_KS6A1_HUMAN_KD2	Q15418	KS6A1_HUMAN	Ribosomal protein S6 kinase alpha-1 (S6K-alpha...	RPS6KA1 MAPKAPK1A RSK1	RPS6KA1	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	418	...	O43823; Q16543; P46527; P08238; P28482; P04271...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLKEPWPLMELVPLDPENGQTSGEEAGLQPSKDEGVLKEISI...	1	1	1	1	1
2569	Q15772_SPEG_HUMAN_KD2	Q15772	SPEG_HUMAN	Striated muscle preferentially expressed prote...	SPEG APEG1 KIAA1297	SPEG	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	2966	...	Q99873; O75031; Q6FHY5; Q9NRD5; Q99873-3	SUBUNIT: Interacts with MTM1. Isoform 3 is fou...	FUNCTION: Isoform 3 may have a role in regulat...	NaN	MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGGGAPVAVAGAPVFL...	1	1	1	1	1
3481	Q5VST9_OBSCN_HUMAN_KD2	Q5VST9	OBSCN_HUMAN	Obscurin (EC 2.7.11.1) (Obscurin-RhoGEF) (Obsc...	OBSCN KIAA1556 KIAA1639	OBSCN	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	7672	...	Q8WZ42; P16157; P16157-17	SUBUNIT: Interacts (via protein kinase domain ...	FUNCTION: Structural component of striated mus...	NaN	MDQPQFSGAPRFLTRPKAFVVSVGKDATLSCQIVGNPTPQVSWEKD...	1	1	1	1	1
5339	Q9UK32_KS6A6_HUMAN_KD2	Q9UK32	KS6A6_HUMAN	Ribosomal protein S6 kinase alpha-6 (S6K-alpha...	RPS6KA6 RSK4	RPS6KA6	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	Q7Z698	SUBUNIT: Forms a complex with MAPK3/ERK1 but n...	FUNCTION: Constitutively active serine/threoni...	ACTIVITY REGULATION: Constitutively activated ...	MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSC...	1	1	1	1	1

8 rows × 32 columns

pspa_no_TYR.uniprot.isin(human_active.Uniprot).value_counts()

uniprot
True     368
False     13
Name: count, dtype: int64

PSPA not in human uniprot active kd:

pspa_no_TYR[~pspa_no_TYR.uniprot.isin(human_active.Uniprot)]

	uniprot	kinase	-5P	-5G	-5A	-5C	-5S	-5T	-5V	-5I	...	4E	4s	4t	4y	0s	0t	0y	0S	0T	0Y
8	Q96QP1	ALPHAK3	0.0571	0.0478	0.0253	0.0384	0.0571	0.0571	0.0586	0.0602	...	0.0747	0.1142	0.1142	0.1744	0.1319	1.0000	0.0	0.1319	1.0000	0.0
18	O14874	BCKDK	0.0482	0.0672	0.0598	0.0694	0.0566	0.0566	0.0517	0.0467	...	0.0563	0.0401	0.0401	0.0859	1.0000	0.2348	0.0	1.0000	0.2348	0.0
26	O43683	BUB1	0.0899	0.0222	0.0249	0.0470	0.0286	0.0286	0.0319	0.0659	...	0.0250	0.0261	0.0261	0.0273	0.5413	1.0000	0.0	0.5413	1.0000	0.0
59	Q96QT4	CHAK1	0.0649	0.0823	0.0686	0.0995	0.0608	0.0608	0.0431	0.0313	...	0.0330	0.0400	0.0400	0.0318	1.0000	0.7003	0.0	1.0000	0.7003	0.0
60	Q9BX84	CHAK2	0.0532	0.0844	0.0761	0.0626	0.0588	0.0588	0.0439	0.0406	...	0.0584	0.0610	0.0610	0.0538	1.0000	0.5794	0.0	1.0000	0.5794	0.0
93	O00418	EEF2K	0.0603	0.0627	0.0635	0.0586	0.0602	0.0602	0.0584	0.0597	...	0.0379	0.0620	0.0620	0.0434	0.2741	1.0000	0.0	0.2741	1.0000	0.0
98	Q8IXL6	FAM20C	0.0496	0.0620	0.0669	0.0649	0.0564	0.0564	0.0564	0.0442	...	0.1123	0.1801	0.1801	0.1610	1.0000	0.1276	0.0	1.0000	0.1276	0.0
111	Q8TF76	HASPIN	0.0775	0.0522	0.0492	0.0495	0.0522	0.0522	0.0578	0.1577	...	0.0237	0.0381	0.0381	0.0347	0.3998	1.0000	0.0	0.3998	1.0000	0.0
212	Q15118	PDHK1	0.0451	0.0697	0.0594	0.0625	0.0594	0.0594	0.0573	0.0590	...	0.0535	0.0548	0.0548	0.0575	1.0000	0.4886	0.0	1.0000	0.4886	0.0
213	Q16654	PDHK4	0.0452	0.0645	0.0665	0.0672	0.0622	0.0622	0.0515	0.0619	...	0.0608	0.0848	0.0848	0.0628	1.0000	0.4640	0.0	1.0000	0.4640	0.0
270	Q96SB4	SRPK1	0.0594	0.0753	0.0889	0.0814	0.0525	0.0525	0.0517	0.0468	...	0.0521	0.0701	0.0701	0.0622	1.0000	0.2897	0.0	1.0000	0.2897	0.0
271	P78362	SRPK2	0.0446	0.0660	0.0596	0.0694	0.0491	0.0491	0.0452	0.0349	...	0.0440	0.0562	0.0562	0.0559	1.0000	0.1949	0.0	1.0000	0.1949	0.0
272	Q9UPE1	SRPK3	0.0435	0.0618	0.0556	0.0622	0.0541	0.0541	0.0527	0.0541	...	0.0394	0.0514	0.0514	0.0544	1.0000	0.4323	0.0	1.0000	0.4323	0.0

13 rows × 215 columns

pspa_active_kd = pspa_no_TYR[pspa_no_TYR.uniprot.isin(human_active.Uniprot)]

# some category test _TYR and have double annotation, split by _
info['pspa_category'] = info.pspa_category_big.str.split('_').str[0]

info['pspa_category_detail']= info.pspa_category_small.str.split('_').str[0]

info = info[info.pseudo=='0']

category = info[['uniprot','kinase','group','pspa_category','pspa_category_detail']]

head = pd.DataFrame(pspa_active_kd.uniprot).merge(category)

pspa_active_kd = pspa_active_kd.reset_index(drop=True)

head

	uniprot	kinase	group	pspa_category	pspa_category_detail
0	Q2M2I8	AAK1	Other	NAK	NAK
1	P27037	ACVR2A	TKL	acidophilic	TGFBR
2	Q13705	ACVR2B	TKL	acidophilic	TGFBR
3	P31749	AKT1	AGC	basophilic	AKT/ROCK
4	P31751	AKT2	AGC	basophilic	AKT/ROCK
...	...	...	...	...	...
363	P17948	FLT1	TK	FGF and VEGF receptors	FGF and VEGF receptors
364	P35968	KDR	TK	FGF and VEGF receptors	FGF and VEGF receptors
365	P35916	FLT4	TK	FGF and VEGF receptors	FGF and VEGF receptors
366	P07947	YES1	TK	SRC	SRC
367	P43403	ZAP70	TK	SYK and FAK	SYK and FAK

368 rows × 5 columns

pspa_active_kd = pd.concat([head,pspa_active_kd.iloc[:,2:]],axis=1)

# pspa_active_kd.to_csv('out/pspa_uniprot_unique_no_TYR_category.csv',index=False)

pspa_active_kd

	uniprot	kinase	group	pspa_category	pspa_category_detail	-5P	-5G	-5A	-5C	-5S	...	4E	4s	4t	4y	0s	0t	0y	0S	0T	0Y
0	Q2M2I8	AAK1	Other	NAK	NAK	0.0720	0.0245	0.0284	0.0456	0.0425	...	0.0457	0.0251	0.0251	0.0270	0.1013	1.0000	0.0	0.1013	1.0000	0.0
1	P27037	ACVR2A	TKL	acidophilic	TGFBR	0.0415	0.0481	0.0584	0.0489	0.0578	...	0.0640	0.0703	0.0703	0.0589	0.9833	1.0000	0.0	0.9833	1.0000	0.0
2	Q13705	ACVR2B	TKL	acidophilic	TGFBR	0.0533	0.0517	0.0566	0.0772	0.0533	...	0.0697	0.0761	0.0761	0.0637	0.9593	1.0000	0.0	0.9593	1.0000	0.0
3	P31749	AKT1	AGC	basophilic	AKT/ROCK	0.0603	0.0594	0.0552	0.0605	0.0516	...	0.0312	0.0393	0.0393	0.0263	1.0000	0.6440	0.0	1.0000	0.6440	0.0
4	P31751	AKT2	AGC	basophilic	AKT/ROCK	0.0602	0.0617	0.0643	0.0582	0.0534	...	0.0350	0.0548	0.0548	0.0417	1.0000	0.6077	0.0	1.0000	0.6077	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
363	P17948	FLT1	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0642	0.0688	0.0597	0.0618	0.0614	...	0.0510	0.0677	0.0677	0.0380	0.0000	0.0000	1.0	0.0000	0.0000	1.0
364	P35968	KDR	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0634	0.0672	0.0556	0.0517	0.0541	...	0.0338	0.0300	0.0300	0.0292	0.0000	0.0000	1.0	0.0000	0.0000	1.0
365	P35916	FLT4	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0457	0.0531	0.0488	0.0553	0.0512	...	0.0497	0.0445	0.0445	0.0500	0.0000	0.0000	1.0	0.0000	0.0000	1.0
366	P07947	YES1	TK	SRC	SRC	0.0677	0.0571	0.0537	0.0530	0.0527	...	0.0492	0.0371	0.0371	0.0467	0.0000	0.0000	1.0	0.0000	0.0000	1.0
367	P43403	ZAP70	TK	SYK and FAK	SYK and FAK	0.0602	0.0880	0.0623	0.0496	0.0471	...	0.0558	0.0440	0.0440	0.0318	0.0000	0.0000	1.0	0.0000	0.0000	1.0

368 rows × 218 columns

id_map = human_active[['Uniprot','kd_ID']]
id_map.columns=['uniprot','kd_ID']

active_id = id_map[id_map.uniprot.isin(pspa_active_kd.uniprot)]

Uniprot in pspa with two kinase domains

pspa_2kd = active_id[active_id.uniprot.duplicated(keep=False)].sort_values('uniprot')

pspa_2kd

	uniprot	kd_ID
988	O75582	O75582_KS6A5_HUMAN_KD1
989	O75582	O75582_KS6A5_HUMAN_KD2
990	O75676	O75676_KS6A4_HUMAN_KD1
991	O75676	O75676_KS6A4_HUMAN_KD2
1918	P51812	P51812_KS6A3_HUMAN_KD1
1919	P51812	P51812_KS6A3_HUMAN_KD2
2560	Q15349	Q15349_KS6A2_HUMAN_KD1
2561	Q15349	Q15349_KS6A2_HUMAN_KD2
2563	Q15418	Q15418_KS6A1_HUMAN_KD1
2564	Q15418	Q15418_KS6A1_HUMAN_KD2
5338	Q9UK32	Q9UK32_KS6A6_HUMAN_KD1
5339	Q9UK32	Q9UK32_KS6A6_HUMAN_KD2

pspa_2kd.uniprot.unique()

array(['O75582', 'O75676', 'P51812', 'Q15349', 'Q15418', 'Q9UK32'],
      dtype=object)

pspa_active_kd_remove2kd = pspa_active_kd[~pspa_active_kd.uniprot.isin(pspa_2kd.uniprot.unique())]

pspa_active_kd_remove2kd = pspa_active_kd_remove2kd.reset_index(drop=True)

head = pd.DataFrame(pspa_active_kd_remove2kd.uniprot).merge(id_map)

head

	uniprot	kd_ID
0	Q2M2I8	Q2M2I8_AAK1_HUMAN_KD1
1	P27037	P27037_AVR2A_HUMAN_KD1
2	Q13705	Q13705_AVR2B_HUMAN_KD1
3	P31749	P31749_AKT1_HUMAN_KD1
4	P31751	P31751_AKT2_HUMAN_KD1
...	...	...
357	P17948	P17948_VGFR1_HUMAN_KD1
358	P35968	P35968_VGFR2_HUMAN_KD1
359	P35916	P35916_VGFR3_HUMAN_KD1
360	P07947	P07947_YES_HUMAN_KD1
361	P43403	P43403_ZAP70_HUMAN_KD1

362 rows × 2 columns

pspa_active_kd_remove2kd = pd.concat([head,pspa_active_kd_remove2kd.iloc[:,1:]],axis=1)

# pspa_active_kd_remove2kd.to_csv('out/pspa_uniprot_unique_no_TYR_category_remove2kd.csv')

pspa_active_kd_remove2kd

	uniprot	kd_ID	kinase	group	pspa_category	pspa_category_detail	-5P	-5G	-5A	-5C	...	4E	4s	4t	4y	0s	0t	0y	0S	0T	0Y
0	Q2M2I8	Q2M2I8_AAK1_HUMAN_KD1	AAK1	Other	NAK	NAK	0.0720	0.0245	0.0284	0.0456	...	0.0457	0.0251	0.0251	0.0270	0.1013	1.0000	0.0	0.1013	1.0000	0.0
1	P27037	P27037_AVR2A_HUMAN_KD1	ACVR2A	TKL	acidophilic	TGFBR	0.0415	0.0481	0.0584	0.0489	...	0.0640	0.0703	0.0703	0.0589	0.9833	1.0000	0.0	0.9833	1.0000	0.0
2	Q13705	Q13705_AVR2B_HUMAN_KD1	ACVR2B	TKL	acidophilic	TGFBR	0.0533	0.0517	0.0566	0.0772	...	0.0697	0.0761	0.0761	0.0637	0.9593	1.0000	0.0	0.9593	1.0000	0.0
3	P31749	P31749_AKT1_HUMAN_KD1	AKT1	AGC	basophilic	AKT/ROCK	0.0603	0.0594	0.0552	0.0605	...	0.0312	0.0393	0.0393	0.0263	1.0000	0.6440	0.0	1.0000	0.6440	0.0
4	P31751	P31751_AKT2_HUMAN_KD1	AKT2	AGC	basophilic	AKT/ROCK	0.0602	0.0617	0.0643	0.0582	...	0.0350	0.0548	0.0548	0.0417	1.0000	0.6077	0.0	1.0000	0.6077	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
357	P17948	P17948_VGFR1_HUMAN_KD1	FLT1	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0642	0.0688	0.0597	0.0618	...	0.0510	0.0677	0.0677	0.0380	0.0000	0.0000	1.0	0.0000	0.0000	1.0
358	P35968	P35968_VGFR2_HUMAN_KD1	KDR	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0634	0.0672	0.0556	0.0517	...	0.0338	0.0300	0.0300	0.0292	0.0000	0.0000	1.0	0.0000	0.0000	1.0
359	P35916	P35916_VGFR3_HUMAN_KD1	FLT4	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0457	0.0531	0.0488	0.0553	...	0.0497	0.0445	0.0445	0.0500	0.0000	0.0000	1.0	0.0000	0.0000	1.0
360	P07947	P07947_YES_HUMAN_KD1	YES1	TK	SRC	SRC	0.0677	0.0571	0.0537	0.0530	...	0.0492	0.0371	0.0371	0.0467	0.0000	0.0000	1.0	0.0000	0.0000	1.0
361	P43403	P43403_ZAP70_HUMAN_KD1	ZAP70	TK	SYK and FAK	SYK and FAK	0.0602	0.0880	0.0623	0.0496	...	0.0558	0.0440	0.0440	0.0318	0.0000	0.0000	1.0	0.0000	0.0000	1.0

362 rows × 219 columns

Onehot encode

freq_max

	position	aa	max_value
0	1549	N	0.815390
1	2618	D	0.809429
2	1724	D	0.800759
3	1525	D	0.791004
4	1730	G	0.775470
...	...	...	...
3429	20	G	0.000181
3430	21	I	0.000181
3431	22	D	0.000181
3432	23	A	0.000181
3433	3363	H	0.000181

3434 rows × 3 columns

onehot_col = freq_max[freq_max.max_value>0.05].position.sort_values().tolist()

onehot = df[onehot_col]

onehot_pspa = onehot.loc[pspa_active_kd_remove2kd.kd_ID]

onehot_pspa.shape

(362, 373)

from sklearn.preprocessing import OneHotEncoder

def get_onehot(df):
    df=df.copy()
    encoded_df = pd.DataFrame(index=df.index)
    
    encoder = OneHotEncoder(sparse_output=False, dtype=int, handle_unknown='ignore')
    
    for col in df.columns:
        reshaped = df[[col]]  # keep as DataFrame
        encoded = encoder.fit_transform(reshaped)
        aa_labels = encoder.categories_[0]
        new_col_names = [f"{col}_{aa}" for aa in aa_labels]
        encoded_subdf = pd.DataFrame(encoded,index=df.index)
        encoded_subdf.columns=new_col_names
        encoded_df = pd.concat([encoded_df, encoded_subdf], axis=1)
    return encoded_df

encoded_all=get_onehot(onehot)

encoded_all.shape

(5536, 7567)

encoded_all.head()

	65_-	65_A	65_C	65_D	65_E	65_F	65_G	65_H	65_I	65_K	...	3192_M	3192_N	3192_P	3192_Q	3192_R	3192_S	3192_T	3192_V	3192_W	3192_Y
kd_ID
A0A075F7E9_LERK1_ORYSI_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
A0A078BQP2_GCY25_CAEEL_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
A0A078CGE6_M3KE1_BRANA_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
A0A0G2K344_PK3CA_RAT_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
A0A0H2ZM62_HK06_STRP2_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

5 rows × 7567 columns

from katlas.feature import *
from katlas.plot import *

reduce_feature?

Signature:
reduce_feature(
    df: pandas.core.frame.DataFrame,
    method: str = 'pca',
    complexity: int = 20,
    n: int = 2,
    load: str = None,
    save: str = None,
    seed: int = 123,
    **kwargs,
)
Docstring: Reduce the dimensionality given a dataframe of values
File:      ~/katlas/katlas/feature.py
Type:      function

umap2d_all = reduce_feature(encoded_all,
                        'umap',
                        complexity=15,
                       min_dist=0.7
                       )

/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(

set_sns()

import seaborn as sns
from matplotlib import pyplot as plt

def plot_umap(embedding_df,box_title,hue=None,palette='tab20',alpha=0.7,**kwargs):
    'Plot umap.'
    x_col, y_col = embedding_df.columns 
    
    g = sns.relplot(data=embedding_df, x=x_col, y=y_col, hue=hue,palette=palette,alpha=alpha,**kwargs)
    g._legend.set_title(box_title)
    plt.xticks([])
    plt.yticks([])

hue_is_PSPA=umap2d_all.index.isin(pspa_active_kd_remove2kd.kd_ID)

TF_palette = {True: 'red', False: 'gray'}

plot_umap(umap2d_all,
          box_title='Known PSPA',alpha=0.5,
          hue = hue_is_PSPA,hue_order=[True, False],
          s=9,
          palette=TF_palette)

PSPA kd plot

encoded_df = get_onehot(onehot_pspa)

encoded_df

	65_-	65_A	65_C	65_E	65_F	65_I	65_K	65_L	65_N	65_Q	...	3192_K	3192_L	3192_M	3192_Q	3192_R	3192_S	3192_T	3192_V	3192_W	3192_Y
kd_ID
Q2M2I8_AAK1_HUMAN_KD1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P27037_AVR2A_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
Q13705_AVR2B_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P31749_AKT1_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P31751_AKT2_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
P17948_VGFR1_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	1	0	0	0	0	0	0	0	0
P35968_VGFR2_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P35916_VGFR3_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	1	0	0	0	0	0	0	0	0
P07947_YES_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P43403_ZAP70_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1

362 rows × 5121 columns

umap2d = reduce_feature(encoded_df,
                        'umap',
                        complexity=15,
                       min_dist=0.7
                       )

/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(

hue_pspa = pspa_active_kd_remove2kd.set_index('kd_ID')['pspa_category']
hue_group = pspa_active_kd_remove2kd.set_index('kd_ID')['group']

enrich_group = hue_pspa.value_counts()[hue_pspa.value_counts()>10].index

hue_pspa_partial = hue_pspa[hue_pspa.isin(enrich_group)]

group_color = pd.read_csv('raw/group_color.csv')

group_color = group_color.set_index('Label')['Color'].to_dict()

plot_umap(umap2d,box_title='Group',hue=hue_group, palette=group_color)

pspa_color = get_color_dict(hue_pspa_partial.unique())

def rgb_to_hex(rgb_dict):
    """
    Convert a dictionary with RGB values in [0, 1] to hex color codes.
    """
    hex_dict = {
        key: '#{:02X}{:02X}{:02X}'.format(
            int(r * 255), int(g * 255), int(b * 255)
        )
        for key, (r, g, b) in rgb_dict.items()
    }
    return hex_dict

pspa_color = rgb_to_hex(pspa_color)

pspa_color = pd.DataFrame.from_dict(pspa_color,orient='index').reset_index()

pspa_color.columns=['Label','Color']

# pspa_color.to_csv('raw/pspa_color.csv',index=False)

pspa_color=pd.read_csv('raw/pspa_color.csv')

pspa_color = pspa_color.set_index('Label')['Color'].to_dict()

plot_umap(umap2d,box_title='PSPA category',hue=hue_pspa_partial,palette=pspa_color)