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| import copy | |
| import gradio as gr | |
| from gradio_molecule3d import Molecule3D | |
| import Bio | |
| import Bio.SeqUtils | |
| from utils.util_functions import merge_ranges | |
| from predict import model_predict | |
| from constants import * | |
| def update_reps_based_on_radio(*args): | |
| struct, text = args[0], args[1] | |
| background, model, active_sites = args[2:4], args[4], args[5:] | |
| predicted_sites, confs, sequence = model_predict(model, struct, text) | |
| merged_sites = merge_ranges(predicted_sites, max_value=len(sequence)) | |
| confidence_details = [] | |
| new_reps = [] | |
| # 1. cal summary | |
| summary_text = [] | |
| for k, v in predicted_sites.items(): | |
| if len(v) > 0: | |
| summary_text.append(f"{len(v)} {no_cat_dict[k]} site(s)") | |
| if len(summary_text) == 0: | |
| summary_text = ["No active sites identified."] | |
| summary_text = '; '.join(summary_text) | |
| # 2. cal dataframe | |
| detail_predicted_sites = {'b':[], '0':[], '1':[], '2':[], '3':[], '4':[], '5':[]} | |
| ass = [] | |
| for k, v in predicted_sites.items(): | |
| for vv in v: | |
| detail_predicted_sites[k].append( | |
| {'residue_type': sequence[vv-1], 'number': vv, 'confidence': confs[vv-1]} | |
| ) | |
| ass.append(vv) | |
| for i in range(len(sequence)): | |
| if i+1 not in ass: | |
| detail_predicted_sites['b'].append( | |
| {'residue_type': sequence[i], 'number': i+1, 'confidence': confs[i]} | |
| ) | |
| # 2.1 处理背景 | |
| backgrounds = detail_predicted_sites.get('b', []) | |
| for r in backgrounds: | |
| confidence_details.append([ | |
| 'Background', | |
| Bio.SeqUtils.seq3(r['residue_type']).upper(), | |
| r['number'], | |
| r.get('confidence', 'N/A') | |
| ]) | |
| # 2.2 处理活性位点 | |
| for i in range(0, len(active_sites), 2): | |
| x, y = active_sites[i], active_sites[i+1] | |
| site_key = str(i//2) | |
| sites = detail_predicted_sites.get(site_key, []) | |
| for s in sites: | |
| confidence_details.append([ | |
| no_cat_dict[site_key], | |
| Bio.SeqUtils.seq3(s['residue_type']).upper(), | |
| s['number'], | |
| s.get('confidence', 'N/A') | |
| ]) | |
| # 3. cal reps | |
| # 3.1 background | |
| ranges = merged_sites['b'] | |
| for r in ranges: | |
| old_reps = copy.deepcopy(default_reps)[0] | |
| old_reps['style'] = background[0][0].lower() + background[0][1:] | |
| old_reps['color'] = background[1][0].lower() + background[1][1:] + "Carbon" | |
| old_reps['residue_range'] = r | |
| new_reps.append(old_reps) | |
| # 3.2 active sites | |
| for i in range(0, len(active_sites), 2): | |
| x, y = active_sites[i], active_sites[i+1] | |
| ranges = merged_sites[str(i//2)] | |
| for r in ranges: | |
| old_reps = copy.deepcopy(default_reps)[0] | |
| old_reps['style'] = x[0].lower() + x[1:] | |
| old_reps['color'] = y[0].lower() + y[1:] + "Carbon" | |
| old_reps['residue_range'] = r | |
| new_reps.append(old_reps) | |
| return summary_text, confidence_details, Molecule3D(label="Identified Functional Sites", reps=new_reps) | |
| def disable_fn(*x): | |
| return [gr.update(interactive=False)] * len(x) | |
| def able_tip(): | |
| return gr.update(visible=True) | |
| def check_input(input): | |
| if input is not None: | |
| return gr.update(interactive=True) | |
| return gr.update(interactive=False) | |
| with gr.Blocks(title="M3Site-app", theme=gr.themes.Default()) as demo: | |
| gr.Markdown("# M<sup>3</sup>Site: Leveraging Multi-Class Multi-Modal Learning for Accurate Protein Active Site Identification and Classification") | |
| gr.Markdown(""" | |
| ## Overview | |
| **M<sup>3</sup>Site** is an advanced tool designed to accurately identify and classify protein active sites using a multi-modal learning approach. By integrating protein sequences, structural data, and functional annotations, M<sup>3</sup>Site provides comprehensive insights into protein functionality, aiding in drug design, synthetic biology, and understanding protein mechanisms. | |
| """) | |
| gr.Markdown(""" | |
| ## How to Use | |
| 1. **Select the Model**: Choose the pre-trained model for site prediction from the dropdown list. | |
| 2. **Adjust Visual Settings**: Customize the visual style and color for background and active sites. | |
| 3. **Upload Protein Structure**: Provide the 3D structure of the protein. You can upload from local or download from PDB Assym. Unit, PDB BioAssembly, AlphaFold DB, or ESMFold DB. | |
| 4. **Enter Function Prompt**: Optionally provide a text description of the protein's function. If unsure, leave it blank. | |
| 5. **Click "Predict"**: Hit the 'Predict' button to initiate the prediction. The predicted active sites will be highlighted in the structure visualization. | |
| 6. **View Results**: The detailed results will be displayed below, including the identified active sites, their types, and confidence scores. | |
| """) | |
| with gr.Accordion("General Settings (Set before prediction)"): | |
| with gr.Row(): | |
| model_drop = gr.Dropdown(model_list, label="Model Selection", value=model_list[0]) | |
| gr.Markdown("") | |
| gr.Markdown("") | |
| with gr.Row(): | |
| with gr.Row(): | |
| style_dropb = gr.Dropdown(style_list, label="Style (Background)", value=style_list[0], min_width=1) | |
| color_dropb = gr.Dropdown(color_list, label="Color (Background)", value=color_list[0], min_width=1) | |
| with gr.Row(): | |
| style_drop1 = gr.Dropdown(style_list, label="Style (CRI)", value=style_list[1], min_width=1) | |
| color_drop1 = gr.Dropdown(color_list, label="Color (CRI)", value=color_list[1], min_width=1) | |
| with gr.Row(): | |
| style_drop2 = gr.Dropdown(style_list, label="Style (SCI)", value=style_list[1], min_width=1) | |
| color_drop2 = gr.Dropdown(color_list, label="Color (SCI)", value=color_list[2], min_width=1) | |
| with gr.Row(): | |
| style_drop3 = gr.Dropdown(style_list, label="Style (PI)", value=style_list[1], min_width=1) | |
| color_drop3 = gr.Dropdown(color_list, label="Color (PI)", value=color_list[3], min_width=1) | |
| with gr.Row(): | |
| with gr.Row(): | |
| style_drop4 = gr.Dropdown(style_list, label="Style (PTCR)", value=style_list[1], min_width=1) | |
| color_drop4 = gr.Dropdown(color_list, label="Color (PTCR)", value=color_list[4], min_width=1) | |
| with gr.Row(): | |
| style_drop5 = gr.Dropdown(style_list, label="Style (IA)", value=style_list[1], min_width=1) | |
| color_drop5 = gr.Dropdown(color_list, label="Color (IA)", value=color_list[5], min_width=1) | |
| with gr.Row(): | |
| style_drop6 = gr.Dropdown(style_list, label="Style (SSA)", value=style_list[1], min_width=1) | |
| color_drop6 = gr.Dropdown(color_list, label="Color (SSA)", value=color_list[6], min_width=1) | |
| with gr.Row(): | |
| gr.Markdown("") | |
| gr.Markdown(''' | |
| *NOTE:* CRI indicates Covalent Reaction Intermediates, SCI indicates Sulfur-containing Covalent Intermediates, PI indicates Phosphorylated Intermediates, | |
| PTCR indicates Proton Transfer & Charge Relay Systems, IA indicates Isomerization Activity, SSA indicates Substrate-specific Activities. | |
| ''') | |
| with gr.Row(): | |
| gr.Markdown("<center><font size=5><b>Input Structure</b></font></center>") | |
| gr.Markdown("<center><font size=5><b>Output Predictions</b></font></center>") | |
| with gr.Row(equal_height=True): | |
| input_struct = Molecule3D(label="Input Protein Structure (Default Style)", reps=reps1) | |
| output_struct = Molecule3D(label="Output Protein Structure", reps=[]) | |
| with gr.Row(equal_height=True): | |
| input_text = gr.Textbox(lines=1, label="Function Prompt", scale=16, min_width=1, placeholder="I don't know the function of this protein.") | |
| btn = gr.Button("Predict", variant="primary", scale=1, min_width=1, interactive=False) | |
| summary_output = gr.Label(label="", scale=18, min_width=1, show_label=False, elem_classes="info") | |
| gr.Markdown("### Result Details") | |
| confidence_output = gr.DataFrame(headers=["Active Site Type", "Residue Type", "Residue Number", "Confidence"]) | |
| option_list = [ | |
| style_dropb, color_dropb, model_drop, | |
| style_drop1, color_drop1, | |
| style_drop2, color_drop2, | |
| style_drop3, color_drop3, | |
| style_drop4, color_drop4, | |
| style_drop5, color_drop5, | |
| style_drop6, color_drop6 | |
| ] | |
| tips = gr.Markdown("### *Tips: Please refresh the page to make a new prediction.*", visible=False) | |
| # gr.Markdown("## Citation") | |
| # gr.Markdown("If you find this tool helpful, please consider citing the following papers:") | |
| # with gr.Accordion("Citations", open=False): | |
| # gr.Markdown('''``` | |
| # @inproceedings{ouyangmmsite, | |
| # title={MMSite: A Multi-modal Framework for the Identification of Active Sites in Proteins}, | |
| # author={Ouyang, Song and Cai, Huiyu and Luo, Yong and Su, Kehua and Zhang, Lefei and Du, Bo}, | |
| # booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems} | |
| # } | |
| # @article{ouyangm3site, | |
| # title={M3Site: Leveraging Multi-Class Multi-Modal Learning for Accurate Protein Active Site Iden-tification and Classification}, | |
| # author={Ouyang, Song and Luo, Yong and Su, Kehua and Zhang, Lefei and Du, Bo}, | |
| # journal={xxxx}, | |
| # year={xxxx}, | |
| # } | |
| # ```''') | |
| # 绑定事件 | |
| input_struct.change(check_input, inputs=input_struct, outputs=btn) | |
| btn.click( | |
| fn=able_tip, | |
| inputs=[], | |
| outputs=tips | |
| ).then( | |
| fn=disable_fn, | |
| inputs=option_list, | |
| outputs=option_list | |
| ).then( | |
| fn=update_reps_based_on_radio, | |
| inputs=[input_struct, input_text] + option_list, | |
| outputs=[summary_output, confidence_output, output_struct] | |
| ).then( | |
| fn=lambda x: x, | |
| inputs=[input_struct], | |
| outputs=[output_struct] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(share=True, debug=True) |