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  1. SLC
  2. Q9P2U8

  • Active_transporters
    • O15438
    • O15439
    • O15440
    • O60706
    • O94911
    • O95342
    • O95477
    • P05023
    • P08183
    • P13637
    • P21439
    • P23634
    • P33527
    • P50993
    • P78363
    • Q2M3G0
    • Q4VNC0
    • Q5T3U5
    • Q8IUA7
    • Q8IZY2
    • Q8N139
    • Q8WWZ7
    • Q9BZC7
    • Q9H7F0
    • Q9H172
    • Q9H222
    • Q9HD20
    • Q9NP78
    • Q9NQ11
    • Q86UK0
    • Q86UQ4
    • Q96J65
    • Q01814
    • Q13733
    • Q16720
    • Q92887
    • Q99758

  • AuxillaryTransportUnit
    • A6NFC5
    • O60359
    • O60939
    • P05026
    • P14415
    • P51164
    • P54709
    • P62955
    • P98161
    • Q4KMZ8
    • Q5VU97
    • Q5VXU1
    • Q7Z442
    • Q7Z443
    • Q8IWT1
    • Q8N8D7
    • Q8TDX9
    • Q8WXS4
    • Q8WXS5
    • Q9BXT2
    • Q9NPA1
    • Q9NTG1
    • Q9NY72
    • Q9UBN1
    • Q9UF02
    • Q9UN42
    • Q9Y691
    • Q86W47
    • Q06432
    • Q07699
    • Q16558

  • Channels
    • A5X5Y0
    • A8MPY1
    • O00591
    • O14764
    • O15399
    • O15547
    • O43315
    • O43424
    • O43497
    • O60391
    • O75311
    • O94778
    • O95264
    • O95279
    • P02708
    • P07510
    • P11230
    • P14867
    • P17787
    • P18505
    • P18507
    • P23415
    • P23416
    • P24046
    • P28472
    • P28476
    • P29972
    • P30301
    • P30532
    • P30926
    • P31644
    • P32297
    • P34903
    • P35498
    • P35499
    • P36544
    • P39086
    • P41181
    • P42261
    • P42262
    • P42263
    • P43681
    • P46098
    • P47869
    • P47870
    • P48050
    • P48058
    • P48167
    • P48169
    • P48549
    • P51575
    • P51801
    • P55064
    • P55087
    • P56373
    • P78334
    • Q7Z418
    • Q8N1C3
    • Q8TCU5
    • Q8TDN1
    • Q8TDN2
    • Q8WXA8
    • Q9BSA4
    • Q9C0H2
    • Q9GZU1
    • Q9GZZ6
    • Q9H1D0
    • Q9H313
    • Q9HBA0
    • Q9NQA5
    • Q9NY46
    • Q9P0L9
    • Q9P0X4
    • Q9UBL9
    • Q9UGM1
    • Q9UI33
    • Q9ULK0
    • Q9ULQ1
    • Q9UN88
    • Q9UQD0
    • Q9Y5S1
    • Q9Y5Y9
    • Q70Z44
    • Q96KK3
    • Q96PS8
    • Q401N2
    • Q01118
    • Q04844
    • Q05586
    • Q05901
    • Q07001
    • Q12879
    • Q13002
    • Q13003
    • Q13224
    • Q13563
    • Q13936
    • Q14500
    • Q14524
    • Q14957
    • Q15822
    • Q15825
    • Q15858
    • Q16099
    • Q16445
    • Q16478
    • Q99250
    • Q99571
    • Q99572
    • Q99928

  • Other_transporters
    • A6NH21
    • Q5GH77
    • Q8NFU0
    • Q8NFU1
    • Q9NRX5
    • Q86VE9

  • SLC
    • A0AV02
    • A0PJK1
    • A1A5C7
    • A4IF30
    • A6NNN8
    • G3V0H7
    • O00337
    • O00341
    • O15375
    • O15431
    • O43511
    • O43826
    • O43868
    • O60669
    • O94956
    • O95436
    • O95528
    • O95907
    • P02730
    • P08195
    • P09131
    • P13866
    • P19634
    • P32418
    • P40879
    • P41440
    • P43003
    • P43004
    • P43005
    • P43007
    • P46059
    • P46721
    • P48067
    • P48664
    • P48764
    • P50443
    • P52569
    • P53985
    • P54219
    • P55011
    • P55017
    • P57103
    • P58743
    • P82251
    • Q2Y0W8
    • Q3KNW5
    • Q4U2R8
    • Q5PT55
    • Q6NVV3
    • Q6P5W5
    • Q6PXP3
    • Q6T423
    • Q6U841
    • Q6YBV0
    • Q6ZMD2
    • Q6ZMH5
    • Q6ZQN7
    • Q6ZSM3
    • Q7L0J3
    • Q7LBE3
    • Q7RTT9
    • Q08AI6
    • Q8IWA5
    • Q8IY34
    • Q8IZD6
    • Q8N4M1
    • Q8N130
    • Q8N434
    • Q8N695
    • Q8N697
    • Q8NCS7
    • Q8NDX2
    • Q8NFF2
    • Q8NHS3
    • Q8WUG5
    • Q8WWI5
    • Q8WWT9
    • Q9BXP2
    • Q9BXS9
    • Q9BY07
    • Q9BYT1
    • Q9BZD2
    • Q9BZV2
    • Q9BZW2
    • Q9C0K1
    • Q9H2B4
    • Q9H2H9
    • Q9H2X9
    • Q9H2Y9
    • Q9H015
    • Q9H841
    • Q9HAS3
    • Q9HC58
    • Q9NP94
    • Q9NPD5
    • Q9NRM0
    • Q9NSA0
    • Q9NUM3
    • Q9NY64
    • Q9NYB5
    • Q9P2U7
    • Q9P2U8
    • Q9UBD6
    • Q9UBY0
    • Q9UGH3
    • Q9UHI7
    • Q9UHW9
    • Q9UI40
    • Q9UIG8
    • Q9UKG4
    • Q9ULF5
    • Q9UP95
    • Q9UPR5
    • Q9Y6L6
    • Q9Y6M7
    • Q9Y6R1
    • Q9Y267
    • Q9Y666
    • Q9Y694
    • Q53GD3
    • Q71RS6
    • Q96GZ6
    • Q96JW4
    • Q96N87
    • Q96QE2
    • Q96RN1
    • Q96T83
    • Q495M3
    • Q496J9
    • Q504Y0
    • Q969I6
    • Q01650
    • Q05940
    • Q06495
    • Q07837
    • Q12908
    • Q13183
    • Q13336
    • Q13433
    • Q13621
    • Q14542
    • Q14973
    • Q15758
    • Q15849
    • Q16348
    • Q16572
    • Q92581
    • Q92911
    • Q92959

  • Transporters

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. SLC
  2. Q9P2U8

Q9P2U8

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 = "Q9P2U8"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: Q9P2U8
Protein Name: Vesicular glutamate transporter 2
Organism: Homo sapiens
Function: Multifunctional transporter that transports L-glutamate as well as multiple ions such as chloride, proton, potassium, sodium and phosphate (PubMed:11698620, PubMed:33440152). At the synaptic vesicle membrane, mainly functions as a uniporter which transports preferentially L-glutamate but also, phosphate from the cytoplasm into synaptic vesicles at presynaptic nerve terminals of excitatory neural cells (PubMed:11698620). The L-glutamate or phosphate uniporter activity is electrogenic and is driven by the proton electrochemical gradient, mainly by the electrical gradient established by the vacuolar H(+)-ATPase across the synaptic vesicle membrane (PubMed:11698620). In addition, functions as a chloride channel that allows the chloride permeation through the synaptic vesicle membrane therefore affects the proton electrochemical gradient and promotes synaptic vesicles acidification (By similarity). Moreover, functions as a vesicular K(+)/H(+) antiport allowing to maintain the electrical gradient and to decrease chemical gradient and therefore sustain vesicular glutamate uptake (By similarity). The vesicular H(+)/H(+) antiport activity is electroneutral (By similarity). At the plasma membrane, following exocytosis, functions as a symporter of Na(+) and phosphate from the extracellular space to the cytoplasm allowing synaptic phosphate homeostasis regulation (Probable) (PubMed:10820226). The symporter activity is driven by an inside negative membrane potential and is electrogenic (Probable). Also involved in the regulation of retinal hyaloid vessel regression during postnatal development (By similarity). May also play a role in the endocrine glutamatergic system of other tissues such as pineal gland and pancreas (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 = "Q9P2U8"
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
5546 Q9P2U8 Transporters SLC 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!

Q9P2U7
Q9UBD6