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  1. Ligand
  2. P01909

  • StructuralAndAdhesion
    • A6H8M9
    • A6NMB1
    • B0FP48
    • O00533
    • O14493
    • O14917
    • O15389
    • O15394
    • O15551
    • O43556
    • O43699
    • O60245
    • O60330
    • O60469
    • O60487
    • O75309
    • O75508
    • O75631
    • O75712
    • O75871
    • O94856
    • O94985
    • O95206
    • O95297
    • O95377
    • O95452
    • O95471
    • O95484
    • O95832
    • P06731
    • P08034
    • P12830
    • P13591
    • P13688
    • P17302
    • P19022
    • P20138
    • P20273
    • P20916
    • P22223
    • P25189
    • P29033
    • P31997
    • P32004
    • P32926
    • P33151
    • P35212
    • P40198
    • P40199
    • P50895
    • P54851
    • P55283
    • P55285
    • P55286
    • P55287
    • P55289
    • P55290
    • P55291
    • P56746
    • P56747
    • P56748
    • P56749
    • P56856
    • P56880
    • P57087
    • P78369
    • P82279
    • Q3KPI0
    • Q5IJ48
    • Q5T442
    • Q6PEY0
    • Q6UWV2
    • Q6UY09
    • Q6V0I7
    • Q6V1P9
    • Q6ZMC9
    • Q7Z5N4
    • Q7Z692
    • Q08ET2
    • Q8IXH8
    • Q8N3J6
    • Q8N6F1
    • Q8N6Y1
    • Q8N7P3
    • Q8N126
    • Q8NFK1
    • Q8TAB3
    • Q8TD84
    • Q8TDW7
    • Q9BQT9
    • Q9BT76
    • Q9BUF7
    • Q9BY67
    • Q9BYE9
    • Q9BZA7
    • Q9BZA8
    • Q9H4D0
    • Q9H6B4
    • Q9H159
    • Q9H251
    • Q9HBB8
    • Q9HBT6
    • Q9HC56
    • Q9HCL0
    • Q9NPG4
    • Q9NRJ7
    • Q9NTQ9
    • Q9NYQ8
    • Q9NYZ4
    • Q9P2E7
    • Q9P2J2
    • Q9UJ99
    • Q9UKL4
    • Q9ULB4
    • Q9ULB5
    • Q9UN66
    • Q9UN67
    • Q9UPX0
    • Q9Y5E1
    • Q9Y5E2
    • Q9Y5E3
    • Q9Y5E4
    • Q9Y5E5
    • Q9Y5E6
    • Q9Y5E7
    • Q9Y5E8
    • Q9Y5E9
    • Q9Y5F0
    • Q9Y5F1
    • Q9Y5F2
    • Q9Y5F3
    • Q9Y5G8
    • Q9Y5I7
    • Q9Y6H8
    • Q9Y6N8
    • Q9Y286
    • Q9Y336
    • Q58EX2
    • Q86SJ6
    • Q86UP0
    • Q86VR7
    • Q96JP9
    • Q96JQ0
    • Q96LC7
    • Q96LD1
    • Q96PQ1
    • Q96QU1
    • Q96RL6
    • Q02413
    • Q02487
    • Q08174
    • Q08554
    • Q12864
    • Q13634
    • Q14002
    • Q14126
    • Q14517
    • Q14574
    • Q16585
    • Q16586
    • Q92629
    • Q92823

  • Other
    • A1L157
    • A6NDA9
    • B6SEH8
    • B6SEH9
    • O00241
    • O00478
    • O00481
    • O14817
    • O42043
    • O43155
    • O43300
    • O43657
    • O60635
    • O60636
    • O60637
    • O75144
    • O75325
    • O75954
    • O94898
    • O94933
    • O94991
    • O95857
    • O95858
    • P0C6S8
    • P0C7U0
    • P0DKB5
    • P07359
    • P08247
    • P08962
    • P11049
    • P13224
    • P19075
    • P19397
    • P21926
    • P23942
    • P27701
    • P40197
    • P41732
    • P42081
    • P48509
    • P60507
    • P60508
    • P60509
    • P61550
    • P61565
    • P61566
    • P61570
    • P62079
    • P78324
    • P78410
    • Q3SXY7
    • Q5JXA9
    • Q5R3F8
    • Q5TFQ8
    • Q5VT99
    • Q5ZPR3
    • Q6EMK4
    • Q6N022
    • Q6PJG9
    • Q6UXE8
    • Q6UXG8
    • Q6UXK2
    • Q6UXK5
    • Q6UXM1
    • Q6UY18
    • Q7KYR7
    • Q7L0X0
    • Q7L985
    • Q7Z7D3
    • Q8IW52
    • Q8N7C0
    • Q8N386
    • Q8N967
    • Q8NG11
    • Q8TBG9
    • Q8TF66
    • Q8WUT4
    • Q8WVV5
    • Q9BTN0
    • Q9H3W5
    • Q9H5Y7
    • Q9H9K5
    • Q9H156
    • Q9H756
    • Q9HBL6
    • Q9HBW1
    • Q9HCJ2
    • Q9N2J8
    • Q9N2K0
    • Q9NT68
    • Q9NT99
    • Q9NX77
    • Q9NZM1
    • Q9NZU0
    • Q9NZU1
    • Q9P1W8
    • Q9P2V4
    • Q9P244
    • Q9P273
    • Q9UKH3
    • Q9UKZ4
    • Q9ULH4
    • Q9UM44
    • Q9UQF0
    • Q9Y3B3
    • Q50LG9
    • Q86SJ2
    • Q86UF1
    • Q86VH4
    • Q86VH5
    • Q86WK6
    • Q86WK7
    • Q96FE5
    • Q96FV3
    • Q96JA1
    • Q96KV6
    • Q96NI6
    • Q96PB8
    • Q96PL5
    • Q96PX8
    • Q96S97
    • Q96SJ8
    • Q902F8
    • Q902F9
    • Q12999
    • Q13410
    • Q13641
    • Q14392
    • Q16563
    • Q69384

  • UnkownFunction
    • A0ZSE6
    • A1A5B4
    • A6NM11
    • A6NMS7
    • O14894
    • O15321
    • O60309
    • O94886
    • P11836
    • P30408
    • P48230
    • Q4KMQ2
    • Q5M7Z0
    • Q5T3F8
    • Q5XXA6
    • Q6IEE7
    • Q6IWH7
    • Q6UWL6
    • Q6UX27
    • Q7Z6M3
    • Q7Z7J7
    • Q7Z408
    • Q8IZU9
    • Q8N3T6
    • Q8N5U1
    • Q9BYT9
    • Q9H2W1
    • Q9HD45
    • Q9NQ90
    • Q9NQX7
    • Q9NV96
    • Q9P1W3
    • Q9Y287
    • Q9Y624
    • Q14C87
    • Q14DG7
    • Q24JP5
    • Q75V66
    • Q86WI0
    • Q86XK7
    • Q96CE8
    • Q96IQ7
    • Q96J84
    • Q96PZ7
    • Q96QE4
    • Q495A1
    • Q92544
    • Q99805

  • Ligand
    • O00548
    • O95727
    • O95754
    • P01893
    • P01903
    • P01906
    • P01909
    • P01920
    • P04440
    • P06340
    • P13747
    • P13762
    • P13765
    • P17693
    • P20036
    • P28067
    • P28068
    • P30511
    • P41217
    • P52799
    • P78504
    • P79483
    • P80370
    • P98172
    • Q6UY11
    • Q8N0W4
    • Q8N2Q7
    • Q8NFY4
    • Q8NFZ3
    • Q8NFZ4
    • Q9C0C4
    • Q9H2E6
    • Q9H3S1
    • Q9H3T2
    • Q9H3T3
    • Q9NPR2
    • Q9NR61
    • Q9NTN9
    • Q9NYJ7
    • Q9NZ94
    • Q9P283
    • Q9Y219
    • Q13591
    • Q15768
    • Q29980
    • Q29983
    • Q30154
    • Q92854

  • Miscellaneous

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. Ligand
  2. P01909

P01909

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 = "P01909"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: P01909
Protein Name: HLA class II histocompatibility antigen, DQ alpha 1 chain
Organism: Homo sapiens
Function: Binds peptides derived from antigens that access the endocytic route of antigen presenting cells (APC) and presents them on the cell surface for recognition by the CD4 T-cells. The peptide binding cleft accommodates peptides of 10-30 residues. The peptides presented by MHC class II molecules are generated mostly by degradation of proteins that access the endocytic route, where they are processed by lysosomal proteases and other hydrolases. Exogenous antigens that have been endocytosed by the APC are thus readily available for presentation via MHC II molecules, and for this reason this antigen presentation pathway is usually referred to as exogenous. As membrane proteins on their way to degradation in lysosomes as part of their normal turn-over are also contained in the endosomal/lysosomal compartments, exogenous antigens must compete with those derived from endogenous components. Autophagy is also a source of endogenous peptides, autophagosomes constitutively fuse with MHC class II loading compartments. In addition to APCs, other cells of the gastrointestinal tract, such as epithelial cells, express MHC class II molecules and CD74 and act as APCs, which is an unusual trait of the GI tract. To produce a MHC class II molecule that presents an antigen, three MHC class II molecules (heterodimers of an alpha and a beta chain) associate with a CD74 trimer in the ER to form a heterononamer. Soon after the entry of this complex into the endosomal/lysosomal system where antigen processing occurs, CD74 undergoes a sequential degradation by various proteases, including CTSS and CTSL, leaving a small fragment termed CLIP (class-II-associated invariant chain peptide). The removal of CLIP is facilitated by HLA-DM via direct binding to the alpha-beta-CLIP complex so that CLIP is released. HLA-DM stabilizes MHC class II molecules until primary high affinity antigenic peptides are bound. The MHC II molecule bound to a peptide is then transported to the cell membrane surface. In B-cells, the interaction between HLA-DM and MHC class II molecules is regulated by HLA-DO. Primary dendritic cells (DCs) also to express HLA-DO. Lysosomal microenvironment has been implicated in the regulation of antigen loading into MHC II molecules, increased acidification produces increased proteolysis and efficient peptide loading

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 = "P01909"
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
1203 P01909 Miscellaneous Ligand 0 0 0.0 0 0.0
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!

P01906
P01920