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  1. Unclassified
  2. Q13291

  • Unclassified
    • A0FGR9
    • A0PK11
    • A6NC51
    • A6ND01
    • A6NDP7
    • A6NDV4
    • A6NFA1
    • A6NFX1
    • A6NGU5
    • A6NHS7
    • A6NIM6
    • A6NKB5
    • A7MBM2
    • A8MVS5
    • A8MVW0
    • A8MVW5
    • A8MXK1
    • B3SHH9
    • B4DS77
    • B6A8C7
    • B8ZZ34
    • O00526
    • O00592
    • O14511
    • O14525
    • O14788
    • O14944
    • O15165
    • O43291
    • O43490
    • O43493
    • O43921
    • O43934
    • O60279
    • O60500
    • O60609
    • O75121
    • O75129
    • O75443
    • O75445
    • O75487
    • O75882
    • O94779
    • O95150
    • O95196
    • O95274
    • O95497
    • O95498
    • O95866
    • O95867
    • O95868
    • P0CG37
    • P0DP58
    • P0DPA2
    • P08F94
    • P01135
    • P01730
    • P01732
    • P04156
    • P04233
    • P04921
    • P05067
    • P05362
    • P05538
    • P06729
    • P07204
    • P07911
    • P09326
    • P09564
    • P09603
    • P09693
    • P09758
    • P10747
    • P10966
    • P11717
    • P11912
    • P13385
    • P13598
    • P13726
    • P14207
    • P15328
    • P15391
    • P15514
    • P15529
    • P15941
    • P16070
    • P16150
    • P16284
    • P16410
    • P16422
    • P17643
    • P17813
    • P18627
    • P19256
    • P19320
    • P19440
    • P20023
    • P20645
    • P20827
    • P21583
    • P21754
    • P22303
    • P22794
    • P23510
    • P24071
    • P28906
    • P29965
    • P30203
    • P32970
    • P32971
    • P33681
    • P34910
    • P35070
    • P35613
    • P37088
    • P40200
    • P40259
    • P40967
    • P41597
    • P42658
    • P43121
    • P43307
    • P47871
    • P48023
    • P48060
    • P49768
    • P49771
    • P49810
    • P51168
    • P51170
    • P51172
    • P51674
    • P51681
    • P51693
    • P52797
    • P52798
    • P52803
    • P53801
    • P55082
    • P55259
    • P58335
    • P58418
    • P58658
    • P60201
    • P60852
    • P78348
    • P78423
    • Q0P6H9
    • Q1HG43
    • Q2KHT4
    • Q2M385
    • Q3KNS1
    • Q3KNT9
    • Q3ZCQ3
    • Q4G0T1
    • Q5DID0
    • Q5FWE3
    • Q5HYA8
    • Q5JRV8
    • Q5SQ64
    • Q5SSG8
    • Q5SZK8
    • Q5T4F4
    • Q5VU65
    • Q5VUB5
    • Q5VV43
    • Q5VV63
    • Q5VX71
    • Q5VZ72
    • Q6GTX8
    • Q6GV28
    • Q6MZM0
    • Q6N075
    • Q6NUS6
    • Q6P1J6
    • Q6P4Q7
    • Q6P9G4
    • Q6P995
    • Q6PCB8
    • Q6PIZ9
    • Q6PJF5
    • Q6UVK1
    • Q6UW56
    • Q6UW88
    • Q6UWB1
    • Q6UWJ1
    • Q6UWL2
    • Q6UWN5
    • Q6UX01
    • Q6UX71
    • Q6UX82
    • Q6UXB8
    • Q6UXC1
    • Q6UXD5
    • Q6UXU4
    • Q6UXV0
    • Q6UXZ0
    • Q6ZMB5
    • Q6ZMJ2
    • Q6ZNA5
    • Q6ZP29
    • Q6ZP80
    • Q6ZRH7
    • Q6ZSS7
    • Q6ZTQ4
    • Q6ZUK4
    • Q6ZVL6
    • Q6ZVN8
    • Q6ZW05
    • Q7RTM1
    • Q7Z2K6
    • Q7Z3B1
    • Q7Z3C6
    • Q7Z3D4
    • Q7Z3F1
    • Q7Z6A9
    • Q7Z7M0
    • Q7Z7N9
    • Q7Z402
    • Q7Z553
    • Q8IUH8
    • Q8IUK5
    • Q8IUW5
    • Q8IW00
    • Q8IWD5
    • Q8IWV2
    • Q8IYR6
    • Q8IZF0
    • Q8J025
    • Q8N0Z9
    • Q8N1N2
    • Q8N2G4
    • Q8N3F9
    • Q8N7C4
    • Q8N7P1
    • Q8N7X8
    • Q8N8F7
    • Q8N8Z6
    • Q8N131
    • Q8N271
    • Q8N387
    • Q8N441
    • Q8N608
    • Q8NA29
    • Q8NAU1
    • Q8NBL3
    • Q8NBM4
    • Q8NBN3
    • Q8NBR0
    • Q8NBT3
    • Q8NC42
    • Q8NC54
    • Q8NC67
    • Q8NCG7
    • Q8NCL8
    • Q8NCW0
    • Q8ND94
    • Q8NE01
    • Q8NE79
    • Q8NEA5
    • Q8NET5
    • Q8NFP4
    • Q8NFT8
    • Q8NFZ8
    • Q8NGA4
    • Q8NH89
    • Q8NI32
    • Q8TB96
    • Q8TBE3
    • Q8TBP5
    • Q8TCT9
    • Q8TCW7
    • Q8TDF5
    • Q8TDQ0
    • Q8TEB7
    • Q8TEM1
    • Q8TEQ8
    • Q8WTR4
    • Q8WV15
    • Q8WVN6
    • Q8WVP7
    • Q8WWF5
    • Q8WWG1
    • Q8WXI7
    • Q8WZ71
    • Q9BQ51
    • Q9BQS7
    • Q9BRK3
    • Q9BSN7
    • Q9BWQ8
    • Q9BX67
    • Q9BX97
    • Q9BXJ7
    • Q9BY79
    • Q9BYF1
    • Q9BZV3
    • Q9BZW8
    • Q9BZZ2
    • Q9C0I4
    • Q9H0V9
    • Q9H1E5
    • Q9H1U4
    • Q9H3R2
    • Q9H5I5
    • Q9H5V8
    • Q9H6D8
    • Q9H6L2
    • Q9H6X2
    • Q9H6Y7
    • Q9H8M5
    • Q9H9P2
    • Q9H195
    • Q9H295
    • Q9H330
    • Q9H665
    • Q9HBG7
    • Q9HBV2
    • Q9HC73
    • Q9HCC8
    • Q9HCJ1
    • Q9HCN6
    • Q9NPF0
    • Q9NPR9
    • Q9NPY3
    • Q9NQ25
    • Q9NQ34
    • Q9NQ60
    • Q9NR16
    • Q9NRR2
    • Q9NS62
    • Q9NS93
    • Q9NU53
    • Q9NUM4
    • Q9NUN5
    • Q9NV12
    • Q9NX61
    • Q9NY35
    • Q9NY37
    • Q9NYX4
    • Q9NZ53
    • Q9NZQ7
    • Q9NZV1
    • Q9P0T7
    • Q9P0V8
    • Q9P2B2
    • Q9P121
    • Q9P232
    • Q9UBS9
    • Q9UGT4
    • Q9UHC9
    • Q9UIB8
    • Q9UIK5
    • Q9UJ14
    • Q9UJ42
    • Q9UJQ1
    • Q9UKB5
    • Q9UKJ0
    • Q9UKJ1
    • Q9UKY0
    • Q9ULC0
    • Q9ULI3
    • Q9ULK6
    • Q9UM73
    • Q9UMF0
    • Q9UNN8
    • Q9UPI3
    • Q9UPZ6
    • Q9UQ52
    • Q9UQC9
    • Q9Y3P8
    • Q9Y4D2
    • Q9Y5F6
    • Q9Y5F7
    • Q9Y5G9
    • Q9Y5H2
    • Q9Y5I4
    • Q9Y5Y0
    • Q9Y5Y7
    • Q9Y6W8
    • Q9Y275
    • Q9Y487
    • Q9Y493
    • Q9Y625
    • Q9Y639
    • Q14CN2
    • Q14CZ8
    • Q17R55
    • Q17RY6
    • Q53EL9
    • Q68D85
    • Q68DH5
    • Q68DV7
    • Q75T13
    • Q86SP6
    • Q86SU0
    • Q86T13
    • Q86TG1
    • Q86UK5
    • Q86UP6
    • Q86UW1
    • Q86UW2
    • Q86V40
    • Q86V85
    • Q86VB7
    • Q86W33
    • Q86WC4
    • Q86WI1
    • Q86XM0
    • Q86XR5
    • Q86XT9
    • Q86XX4
    • Q86YD3
    • Q86YD5
    • Q96A25
    • Q96A28
    • Q96AP7
    • Q96BF3
    • Q96D42
    • Q96DD7
    • Q96DU3
    • Q96F05
    • Q96F81
    • Q96FE7
    • Q96FL8
    • Q96J42
    • Q96K49
    • Q96L08
    • Q96MU8
    • Q96N19
    • Q96NR3
    • Q96PB1
    • Q96PD2
    • Q96PJ5
    • Q96RD6
    • Q96RD7
    • Q96RD9
    • Q96RV3
    • Q685J3
    • Q969N2
    • Q969W9
    • Q01151
    • Q02246
    • Q02297
    • Q02505
    • Q03167
    • Q04900
    • Q05996
    • Q06481
    • Q08722
    • Q10589
    • Q12770
    • Q12836
    • Q12860
    • Q12907
    • Q13145
    • Q13286
    • Q13291
    • Q13449
    • Q13488
    • Q13491
    • Q13586
    • Q13740
    • Q14118
    • Q14773
    • Q14956
    • Q14982
    • Q15116
    • Q16553
    • Q16651
    • Q16653
    • Q30201
    • Q92508
    • Q92542
    • Q92824
    • Q92838
    • Q95460
    • Q99075
    • Q99102

  • Unclassified

On this page

  • General information
  • AlphaFold model
  • Surface representation - binding sites
  • All detected seeds aligned
  • Seed scores per sites
  • Binding site metrics
  • Binding site sequence composition
  • Download
  1. Unclassified
  2. Q13291

Q13291

Author

Hamed Khakzad

Published

August 10, 2024

General information

Code
import requests
import urllib3
urllib3.disable_warnings()

def fetch_uniprot_data(uniprot_id):
    url = f"https://rest.uniprot.org/uniprotkb/{uniprot_id}.json"
    response = requests.get(url, verify=False)  # Disable SSL verification
    response.raise_for_status()  # Raise an error for bad status codes
    return response.json()

def display_uniprot_data(data):
    primary_accession = data.get('primaryAccession', 'N/A')
    protein_name = data.get('proteinDescription', {}).get('recommendedName', {}).get('fullName', {}).get('value', 'N/A')
    gene_name = data.get('gene', [{'geneName': {'value': 'N/A'}}])[0]['geneName']['value']
    organism = data.get('organism', {}).get('scientificName', 'N/A')
    
    function_comment = next((comment for comment in data.get('comments', []) if comment['commentType'] == "FUNCTION"), None)
    function = function_comment['texts'][0]['value'] if function_comment else 'N/A'

    # Printing the data
    print(f"UniProt ID: {primary_accession}")
    print(f"Protein Name: {protein_name}")
    print(f"Organism: {organism}")
    print(f"Function: {function}")

# Replace this with the UniProt ID you want to fetch
uniprot_id = "Q13291"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: Q13291
Protein Name: Signaling lymphocytic activation molecule
Organism: Homo sapiens
Function: Self-ligand receptor of the signaling lymphocytic activation molecule (SLAM) family. SLAM receptors triggered by homo- or heterotypic cell-cell interactions are modulating the activation and differentiation of a wide variety of immune cells and thus are involved in the regulation and interconnection of both innate and adaptive immune response. Activities are controlled by presence or absence of small cytoplasmic adapter proteins, SH2D1A/SAP and/or SH2D1B/EAT-2. SLAMF1-induced signal-transduction events in T-lymphocytes are different from those in B-cells. Two modes of SLAMF1 signaling seem to exist: one depending on SH2D1A (and perhaps SH2D1B) and another in which protein-tyrosine phosphatase 2C (PTPN11)-dependent signal transduction operates. Initially it has been proposed that association with SH2D1A prevents binding to inhibitory effectors including INPP5D/SHIP1 and PTPN11/SHP-2 (PubMed:11806999). However, signaling is also regulated by SH2D1A which can simultaneously interact with and recruit FYN which subsequently phosphorylates and activates SLAMF1 (PubMed:12458214). Mediates IL-2-independent proliferation of activated T-cells during immune responses and induces IFN-gamma production (By similarity). Downstreaming signaling involves INPP5D, DOK1 and DOK2 leading to inhibited IFN-gamma production in T-cells, and PRKCQ, BCL10 and NFKB1 leading to increased T-cell activation and Th2 cytokine production (By similarity). Promotes T-cell receptor-induced IL-4 secretion by CD4(+) cells (By similarity). Inhibits antigen receptor-mediated production of IFN-gamma, but not IL-2, in CD4(-)/CD8(-) T-cells (By similarity). Required for IL-4 production by germinal centers T follicular helper (T(Fh))cells (By similarity). May inhibit CD40-induced signal transduction in monocyte-derived dendritic cells (PubMed:16317102). May play a role in allergic responses and may regulate allergen-induced Th2 cytokine and Th1 cytokine secretion (By similarity). In conjunction with SLAMF6 controls the transition between positive selection and the subsequent expansion and differentiation of the thymocytic natural killer T (NKT) cell lineage. Involved in the peripheral differentiation of indifferent natural killer T (iNKT) cells toward a regulatory NKT2 type (By similarity). In macrophages involved in down-regulation of IL-12, TNF-alpha and nitric oxide in response to lipopolysaccharide (LPS) (By similarity). In B-cells activates the ERK signaling pathway independently of SH2D1A but implicating both, SYK and INPP5D, and activates Akt signaling dependent on SYK and SH2D1A (By similarity). In B-cells also activates p38 MAPK and JNK1 and JNK2 (PubMed:20231852). In conjunction with CD84/SLAMF5 and SLAMF6 may be a negative regulator of the humoral immune response (By similarity). Involved in innate immune response against Gram-negative bacteria in macrophages; probably recognizes OmpC and/or OmpF on the bacterial surface, regulates phagosome maturation and recruitment of the PI3K complex II (PI3KC3-C2) leading to accumulation of PdtIns(3)P and NOX2 activity in the phagosomes (PubMed:20818396)

More information:   

AlphaFold model

Surface representation - binding sites

The computed point cloud for pLDDT > 0.6. Each atom is sampled on average by 10 points.

To see the predicted binding interfaces, you can choose color theme “uncertainty”.

  • Go to the “Controls Panel”

  • Below “Components”, to the right, click on “…”

  • “Set Coloring” by “Atom Property”, and “Uncertainty/Disorder”

All detected seeds aligned

Seed scores per sites

Code
import re
import pandas as pd
import os
import plotly.express as px

ID = "Q13291"
data_list = []

name_pattern = re.compile(r'name: (\S+)')
score_pattern = re.compile(r'score: (\d+\.\d+)')
desc_dist_score_pattern = re.compile(r'desc_dist_score: (\d+\.\d+)')

directory = f"/Users/hamedkhakzad/Research_EPFL/1_postdoc_project/Surfaceome_web_app/www/Surfaceome_top100_per_site/{ID}_A"

for filename in os.listdir(directory):
    if filename.startswith("output_sorted_") and filename.endswith(".score"):
        filepath = os.path.join(directory, filename)
        with open(filepath, 'r') as file:
            for line in file:
                name_match = name_pattern.search(line)
                score_match = score_pattern.search(line)
                desc_dist_score_match = desc_dist_score_pattern.search(line)
                
                if name_match and score_match and desc_dist_score_match:
                    name = name_match.group(1)
                    score = float(score_match.group(1))
                    desc_dist_score = float(desc_dist_score_match.group(1))
                    
                    simple_filename = filename.replace("output_sorted_", "").replace(".score", "")
                    data_list.append({
                        'name': name[:-1],
                        'score': score,
                        'desc_dist_score': desc_dist_score,
                        'file': simple_filename
                    })

data = pd.DataFrame(data_list)

fig = px.scatter(
    data,
    x='score',
    y='desc_dist_score',
    color='file',
    title='Score vs Desc Dist Score',
    labels={'score': 'Score', 'desc_dist_score': 'Desc Dist Score'},
    hover_data={'name': True}
)

fig.update_layout(
    legend_title_text='File',
    legend=dict(
        yanchor="top",
        y=0.99,
        xanchor="left",
        x=1.05
    )
)

fig.show()

Binding site metrics

Code
import pandas as pd
pd.options.mode.chained_assignment = None
import plotly.express as px

df_total = pd.read_csv('/Users/hamedkhakzad/Research_EPFL/1_postdoc_project/Surfaceome_web_app/www/database/df_flattened.csv')
df_plot = df_total[df_total['acc_flat'] == ID]
df_plot ['Total seeds'] = df_plot.loc[:,['seedss_a','seedss_b']].sum(axis=1)
df_plot.loc[:, ["acc_flat", "main_classs", "sub_classs", "seedss_a", "seedss_b", "areass", "bsss", "hpss"]]
acc_flat main_classs sub_classs seedss_a seedss_b areass bsss hpss
4905 Q13291 Unclassified Unclassified 0 3 2050.222564 133 19.9
4906 Q13291 Unclassified Unclassified 143 613 2054.245936 83 -4.8
Code
import math
import matplotlib.pyplot as plt

features = ['seedss_a', 'seedss_b', 'areass', 'hpss']
titles = ['Alpha seeds', 'Beta seeds', 'Area', 'Hydrophobicity']
num_features = len(features)

if len(df_plot) > 8:
    num_rows = 2
    num_cols = 2
else:
    num_rows = 1
    num_cols = 4

fig, axes = plt.subplots(nrows=num_rows, ncols=num_cols, figsize=(9, num_rows * 5))

axes = axes.flatten()
positions = range(1, len(df_plot) + 1)

for i, feature in enumerate(features):
    title = titles[i]
    axes[i].bar(positions, df_plot[feature], color=['blue', 'orange', 'green', 'red', 'purple', 'brown'])
    axes[i].set_title(title, fontsize=13)
    axes[i].set_xticks(positions)
    axes[i].set_xticklabels(df_plot['bsss'], rotation=90)
    axes[i].set_xlabel("Center residues", fontsize=13)
    axes[i].set_ylabel(title, fontsize=13)

for j in range(len(features), len(axes)):
    fig.delaxes(axes[j])

plt.tight_layout()
plt.show()

Binding site sequence composition

Code
amino_acid_map = {
    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D', 'CYS': 'C',
    'GLN': 'Q', 'GLU': 'E', 'GLY': 'G', 'HIS': 'H', 'ILE': 'I',
    'LEU': 'L', 'LYS': 'K', 'MET': 'M', 'PHE': 'F', 'PRO': 'P',
    'SER': 'S', 'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V'
}

from collections import Counter
from ast import literal_eval
from matplotlib.gridspec import GridSpec
import warnings
warnings.filterwarnings("ignore", message="Attempting to set identical low and high xlims")

def convert_to_single_letter(aa_list):
    if type(aa_list) == str:
        aa_list = literal_eval(aa_list)
    return [amino_acid_map[aa] for aa in aa_list]

def create_sequence_visualizations(df, max_letters_per_row=20):
    for idx, row in df.iterrows():
        bsss = row['bsss']
        AAss = row['AAss']
        single_letter_sequence = convert_to_single_letter(AAss)
        
        freq_counter = Counter(single_letter_sequence)
        total_aa = len(single_letter_sequence)
        frequencies = {aa: freq / total_aa for aa, freq in freq_counter.items()}
        
        cmap = plt.get_cmap('viridis')
        norm = plt.Normalize(0, max(frequencies.values()) if frequencies else 1)
        
        n_rows = (len(single_letter_sequence) + max_letters_per_row - 1) // max_letters_per_row
        fig = plt.figure(figsize=(max_letters_per_row * 0.6, n_rows * 1.2 + 0.5))
        
        gs = GridSpec(n_rows + 1, 1, height_ratios=[1] * n_rows + [0.1], hspace=0.3)
        
        for row_idx in range(n_rows):
            start_idx = row_idx * max_letters_per_row
            end_idx = min((row_idx + 1) * max_letters_per_row, len(single_letter_sequence))
            ax = fig.add_subplot(gs[row_idx, 0])
            ax.set_xlim(0, max_letters_per_row)
            ax.set_ylim(0, 1)
            ax.axis('off')
            
            for i, aa in enumerate(single_letter_sequence[start_idx:end_idx]):
                freq = frequencies[aa]
                color = cmap(norm(freq))
                ax.text(i + 0.5, 0.5, aa, ha='center', va='center', fontsize=24, color=color, fontweight='bold')
        
        cbar_ax = fig.add_subplot(gs[-1, 0])
        sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
        sm.set_array([])
        cbar = plt.colorbar(sm, cax=cbar_ax, orientation='horizontal')
        cbar.set_label('Frequency', fontsize=12)
        cbar.ax.tick_params(labelsize=12)
        
        plt.suptitle(f"Center residue {bsss}", fontsize=14)
        plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
        plt.show()
            
create_sequence_visualizations(df_plot)

Download

To download all the seeds and score files for this entry Click Here!

Q13286
Q13449