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  1. SLC
  2. P58743

  • 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. P58743

P58743

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 = "P58743"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)
UniProt ID: P58743
Protein Name: Prestin
Organism: Homo sapiens
Function: Voltage-sensitive motor protein that drives outer hair cell (OHC) electromotility (eM) and participates in sound amplification in the hearing organ (By similarity). Converts changes in the transmembrane electric potential into mechanical displacements resulting in the coupling of its expansion to movement of a charged voltage sensor across the lipid membrane (By similarity). The nature of the voltage sensor is not completely clear, and two models compete. In the first model, acts as an incomplete transporter where intracellular chloride anion acts as extrinsic voltage sensor that drives conformational change in the protein which is sufficient to produce a length change in the plane of the membrane and hence in the length of the OHC (By similarity). The second model in which multiple charged amino acid residues are distributed at the intracellular and extracellular membrane interfaces that form an intrinsic voltage sensor, whose movement produces the non-linear capacitance (NLC) (PubMed:34390643). However, the effective voltage sensor may be the result of a hybrid voltage sensor, assembled from intrinsic charge (charged residues) and extrinsic charge (bound anion) (By similarity). Notably, binding of anions to the anion-binding pocket partially neutralizes the intrinsic positive charge rather than to form an electrically negative sensor, therefore remaining charge may serve as voltage sensor that, after depolarization, moves from down (expanded state) to up (contracted) conformation, which is accompanied by an eccentric contraction of the intermembrane cross-sectional area of the protein as well as a major increase in the hydrophobic thickness of the protein having as consequences the plasma membrane thickening and the cell contraction after membrane depolarization (PubMed:34390643). The anion-binding pocket transits from the inward-open (Down) state, where it is exposed toward the intracellular solvent in the absence of anion, to the occluded (Up) state upon anion binding (PubMed:34390643). Salicylate competes for the anion-binding site and inhibits the voltage-sensor movement, and therefore inhibits the charge transfer and electromotility by displacing Cl(-) from the anion-binding site and by preventing the structural transitions to the contracted state (PubMed:34390643). In addition, can act as a weak Cl(-)/HCO3(-) antiporter across the cell membrane and so regulate the intracellular pH of the outer hair cells (OHCs), while firstly found as being unable to mediate electrogenic anion transport (By similarity). Moreover, supports a role in cardiac mechanical amplification serving as an elastic element to enhance the actomyosin- based sarcomere contraction system (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 = "P58743"
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
4608 P58743 Transporters SLC 12 124 759.99606 545 12.8999
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!

P57103
P82251