Q96DU3

General information

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 = "Q96DU3"
data = fetch_uniprot_data(uniprot_id)
display_uniprot_data(data)

UniProt ID: Q96DU3
Protein Name: SLAM family member 6
Organism: Homo sapiens
Function: Self-ligand receptor of the signaling lymphocytic activation molecule (SLAM) family. SLAM receptors triggered by homo- or heterotypic cell-cell interactions are modulating the activation and differentiation of a wide variety of immune cells and thus are involved in the regulation and interconnection of both innate and adaptive immune response. Activities are controlled by presence or absence of small cytoplasmic adapter proteins, SH2D1A/SAP and/or SH2D1B/EAT-2. Triggers cytolytic activity only in natural killer cells (NK) expressing high surface densities of natural cytotoxicity receptors (PubMed:11489943, PubMed:16920955). Positive signaling in NK cells implicates phosphorylation of VAV1. NK cell activation seems to depend on SH2D1B and not on SH2D1A (PubMed:16920955). In conjunction with SLAMF1 controls the transition between positive selection and the subsequent expansion and differentiation of the thymocytic natural killer T (NKT) cell lineage (By similarity). Promotes T-cell differentiation into a helper T-cell Th17 phenotype leading to increased IL-17 secretion; the costimulatory activity requires SH2D1A (PubMed:16920955, PubMed:22184727). Promotes recruitment of RORC to the IL-17 promoter (PubMed:22989874). In conjunction with SLAMF1 and CD84/SLAMF5 may be a negative regulator of the humoral immune response. In the absence of SH2D1A/SAP can transmit negative signals to CD4(+) T-cells and NKT cells. Negatively regulates germinal center formation by inhibiting T-cell:B-cell adhesion; the function probably implicates increased association with PTPN6/SHP-1 via ITSMs in absence of SH2D1A/SAP. However, reported to be involved in maintaining B-cell tolerance in germinal centers and in preventing autoimmunity (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

import re
import pandas as pd
import os
import plotly.express as px

ID = "Q96DU3"
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

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
4908	Q96DU3	Unclassified	Unclassified	31	296	705.181861	64	-3.1
4909	Q96DU3	Unclassified	Unclassified	55	549	869.930859	32	12.6

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

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!