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  1. Unclassified
  2. Q9Y487

  • 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. Q9Y487

Q9Y487

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 = "Q9Y487"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: Q9Y487
Protein Name: V-type proton ATPase 116 kDa subunit a 2
Organism: Homo sapiens
Function: Subunit of the V0 complex of vacuolar(H+)-ATPase (V-ATPase), a multisubunit enzyme composed of a peripheral complex (V1) that hydrolyzes ATP and a membrane integral complex (V0) that translocates protons (By similarity). V-ATPase is responsible for acidifying and maintaining the pH of intracellular compartments and in some cell types, is targeted to the plasma membrane, where it is responsible for acidifying the extracellular environment (By similarity). Essential component of the endosomal pH-sensing machinery (PubMed:16415858). May play a role in maintaining the Golgi functions, such as glycosylation maturation, by controlling the Golgi pH (PubMed:18157129). In aerobic conditions, involved in intracellular iron homeostasis, thus triggering the activity of Fe(2+) prolyl hydroxylase (PHD) enzymes, and leading to HIF1A hydroxylation and subsequent proteasomal degradation (PubMed:28296633)

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 = "Q9Y487"
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
5563 Q9Y487 Unclassified Unclassified 0 13 643.207935 229 16.10000
5564 Q9Y487 Unclassified Unclassified 0 0 1744.996670 485 9.79999
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!

Q9Y275
Q9Y493