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  1. StructuralAndAdhesion
  2. P20916

  • 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. StructuralAndAdhesion
  2. P20916

P20916

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 = "P20916"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: P20916
Protein Name: Myelin-associated glycoprotein
Organism: Homo sapiens
Function: Adhesion molecule that mediates interactions between myelinating cells and neurons by binding to neuronal sialic acid-containing gangliosides and to the glycoproteins RTN4R and RTN4RL2 (By similarity). Not required for initial myelination, but seems to play a role in the maintenance of normal axon myelination. Protects motoneurons against apoptosis, also after injury; protection against apoptosis is probably mediated via interaction with neuronal RTN4R and RTN4RL2. Required to prevent degeneration of myelinated axons in adults; this probably depends on binding to gangliosides on the axon cell membrane (By similarity). Negative regulator of neurite outgrowth; in dorsal root ganglion neurons the inhibition is mediated primarily via binding to neuronal RTN4R or RTN4RL2 and to a lesser degree via binding to neuronal gangliosides. In cerebellar granule cells the inhibition is mediated primarily via binding to neuronal gangliosides. In sensory neurons, inhibition of neurite extension depends only partially on RTN4R, RTN4RL2 and gangliosides. Inhibits axon longitudinal growth (By similarity). Inhibits axon outgrowth by binding to RTN4R (By similarity). Preferentially binds to alpha-2,3-linked sialic acid. Binds ganglioside Gt1b (By similarity)

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 = "P20916"
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
2698 P20916 Miscellaneous StructuralAndAdhesion 71 378 1551.591887 433 17.09990
2699 P20916 Miscellaneous StructuralAndAdhesion 17 233 1355.735305 393 1.29999
2700 P20916 Miscellaneous StructuralAndAdhesion 408 208 2422.820107 195 -4.60000
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!

P20273
P22223