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import os |
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import torch |
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import transformers |
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from transformers import GenerationConfig, pipeline, AutoTokenizer, AutoModelForCausalLM, EsmForProteinFolding |
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import os |
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import tempfile |
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import subprocess |
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import pandas as pd |
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import numpy as np |
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import gradio as gr |
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from time import time |
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import requests |
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model_id = "Esperanto/Protein-Phi-3-mini" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.float16, |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True) |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = "left" |
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer) |
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esm_model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1") |
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esm_tokenizer = AutoTokenizer.from_pretrained("facebook/esmfold_v1") |
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def clean_protein_sequence(protein_seq): |
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valid_amino_acids = "ACDEFGHIKLMNPQRSTVWY" |
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cleaned_seq = ''.join([char for char in protein_seq if char in valid_amino_acids]) |
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return cleaned_seq |
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def modify_b_factors(pdb_content, multiplier): |
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modified_pdb = [] |
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for line in pdb_content.split('\n'): |
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if line.startswith("ATOM"): |
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b_factor = float(line[60:66].strip()) |
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new_b_factor = b_factor * multiplier |
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new_line = f"{line[:60]}{new_b_factor:6.2f}{line[66:]}" |
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modified_pdb.append(new_line) |
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else: |
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modified_pdb.append(line) |
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return "\n".join(modified_pdb) |
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def save_pdb(input_sequence): |
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inputs = esm_tokenizer([input_sequence], return_tensors="pt", add_special_tokens=False) |
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with torch.no_grad(): |
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outputs = esm_model(**inputs) |
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pdb_string_unscaled = esm_model.output_to_pdb(outputs)[0] |
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pdb_string = modify_b_factors(pdb_string_unscaled, 100) |
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plddt_values = outputs.plddt.tolist()[0][0] |
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plddt_values = [round(value * 100, 2) for value in plddt_values] |
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file_path = os.path.join('Protein-Llama-3-8B-Gradio/temporary_folder', f"protein.pdb") |
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os.makedirs(os.path.dirname(file_path), exist_ok=True) |
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with open(file_path, "w") as f: |
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f.write(pdb_string) |
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return np.mean(plddt_values) |
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def read_prot(molpath): |
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with open(molpath, "r") as fp: |
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lines = fp.readlines() |
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mol = "" |
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for l in lines: |
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mol += l |
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return mol |
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def get_cov2_pdb(): |
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pdb_id = '6vxx' |
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url = f'https://files.rcsb.org/download/{pdb_id}.pdb' |
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response = requests.get(url) |
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mol = response.text |
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return mol |
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def protein_visual_html(input_pdb): |
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if input_pdb == 'cov2': |
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mol = get_cov2_pdb() |
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else: |
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mol = read_prot(input_pdb) |
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x = ( |
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"""<!DOCTYPE html> |
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<html> |
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<head> |
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<meta http-equiv="content-type" content="text/html; charset=UTF-8" /> |
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<style> |
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body{ |
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font-family:sans-serif |
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} |
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.mol-container { |
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width: 100%; |
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height: 600px; |
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position: relative; |
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} |
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.mol-container select{ |
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background-image:None; |
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} |
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</style> |
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<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js" integrity="sha512-STof4xm1wgkfm7heWqFJVn58Hm3EtS31XFaagaa8VMReCXAkQnJZ+jEy8PCC/iT18dFy95WcExNHFTqLyp72eQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script> |
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<script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script> |
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</head> |
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<body> |
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<div id="container" class="mol-container"></div> |
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<script> |
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let pdb = `""" + mol + """` |
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$(document).ready(function () { |
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let element = $("#container"); |
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let config = { backgroundColor: "white" }; |
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let viewer = $3Dmol.createViewer(element, config); |
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viewer.addModel(pdb, "pdb"); |
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viewer.getModel(0).setStyle({}, { cartoon: { color:"spectrum" } }); |
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viewer.zoomTo(); |
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viewer.render(); |
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viewer.zoom(0.8, 2000); |
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}) |
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</script> |
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</body></html>""" |
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) |
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return f"""<iframe style="width: 100%; height: 600px" name="result" allow="midi; geolocation; microphone; camera; |
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display-capture; encrypted-media;" sandbox="allow-modals allow-forms |
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allow-scripts allow-same-origin allow-popups |
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allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" |
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allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>""" |
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def predict_structure(input_sequence): |
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if input_sequence == 'SNASADAQSFLNRVCGVSAARLTPCGTGTSTDVVYRAFDIYNDKVAGFAKFLKTNCCRFQEKDEDDNLIDSYFVVKRHTFSNYQHEETIYNLLKDCPAVAKHDFFKFRIDGDMVPHISRQRLTKYTMADLVYALRHFDEGNCDTLKEILVTYNCCDDDYFNKKDWYDFVENPDILRVYANLGERVRQALLKTVQFCDAMRNAGIVGVLTLDNQDLNGNWYDFGDFIQTTPGSGVPVVDSYYSLLMPILTLTRALTAESHVDTDLTKPYIKWDLLKYDFTEERLKLFDRYFKYWDQTYHPNCVNCLDDRCILHCANFNVLFSTVFPPTSFGPLVRKIFVDGVPFVVSTGYHFRELGVVHNQDVNLHSSRLSFKELLVYAADPAMHAASGNLLLDKRTTCFSVAALTNNVAFQTVKPGNFNKDFYDFAVSKGFFKEGSSVELKHFFFAQDGNAAISDYDYYRYNLPTMCDIRQLLFVVEVVDKYFDCYDGGCINANQVI': |
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return protein_visual_html('Protein-Llama-3-8B-Gradio/sars_cov.pdb') |
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else: |
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plddt = save_pdb(input_sequence) |
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pdb_path = 'cov2' |
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return protein_visual_html(pdb_path) |
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def generate_protein_sequence(sequence, seq_length, property=''): |
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enzymes = ["Non-Hemolytic", "Soluble", "Oxidoreductase", "Transferase", "Hydrolase", "Lyase", "Isomerase", "Ligase", "Translocase"] |
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start_time = time() |
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if property is None: |
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input_prompt = 'Seq=<' + sequence |
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elif property == 'SARS-CoV-2 Spike Protein (example)': |
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cleaned_seq = 'SNASADAQSFLNRVCGVSAARLTPCGTGTSTDVVYRAFDIYNDKVAGFAKFLKTNCCRFQEKDEDDNLIDSYFVVKRHTFSNYQHEETIYNLLKDCPAVAKHDFFKFRIDGDMVPHISRQRLTKYTMADLVYALRHFDEGNCDTLKEILVTYNCCDDDYFNKKDWYDFVENPDILRVYANLGERVRQALLKTVQFCDAMRNAGIVGVLTLDNQDLNGNWYDFGDFIQTTPGSGVPVVDSYYSLLMPILTLTRALTAESHVDTDLTKPYIKWDLLKYDFTEERLKLFDRYFKYWDQTYHPNCVNCLDDRCILHCANFNVLFSTVFPPTSFGPLVRKIFVDGVPFVVSTGYHFRELGVVHNQDVNLHSSRLSFKELLVYAADPAMHAASGNLLLDKRTTCFSVAALTNNVAFQTVKPGNFNKDFYDFAVSKGFFKEGSSVELKHFFFAQDGNAAISDYDYYRYNLPTMCDIRQLLFVVEVVDKYFDCYDGGCINANQVI' |
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end_time = time() |
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max_memory_used = 0 |
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return cleaned_seq, end_time - start_time, max_memory_used, 0 |
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elif property in enzymes: |
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input_prompt = '[Generate ' + property.lower() + ' protein] ' + 'Seq=<' + sequence |
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else: |
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input_prompt = '[Generate ' + property + ' protein] ' + 'Seq=<' + sequence |
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start_time = time() |
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protein_seq = generator(input_prompt, temperature=0.5, |
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top_k=40, |
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top_p=0.9, |
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do_sample=True, |
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repetition_penalty=1.2, |
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max_new_tokens=seq_length, |
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num_return_sequences=1)[0]["generated_text"] |
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end_time = time() |
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start_idx = protein_seq.find('Seq=<') |
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end_idx = protein_seq.find('>', start_idx) |
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protein_seq = protein_seq[start_idx:end_idx] |
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cleaned_seq = clean_protein_sequence(protein_seq) |
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tokens = tokenizer.encode(cleaned_seq, add_special_tokens=False) |
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tokens_per_second = len(tokens) / (end_time - start_time) |
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return cleaned_seq, end_time - start_time, tokens_per_second |
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with gr.Blocks() as demo: |
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gr.Markdown(''' |
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### Interactive protein sequence generation and visualization. |
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Generating novel protein sequences possessing desired properties, termed as protein engineering, is crucial for industries like drug development and chemical synthesis. |
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This model supports two types of generation, uncontrollable and controllable. Uncontrollable generation refers to generating any viable protein sequence, whereas controllable refers to generating proteins having a desired property or a characteristic. |
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### Usage |
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For uncontrollable generation, input any starting amino acids and press 'Submit' without choosing a property. For controllable generation, choose any of the ten properties supported by this model before pressing 'Submit'. The important inference metrics will be displayed along with the generated output. |
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### Example |
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As an example, the protein sequence corresponding to the SARS-CoV-2 Spike Protein is given. This example does not run inference on the model as the sequence is hard-coded, however it showcases the flow of interacting with the demo. |
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''') |
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with gr.Row(): |
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input_text = gr.Textbox(label="Enter starting amino acids for protein sequence generation", placeholder="Example input: MK") |
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with gr.Row(): |
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seq_length = gr.Slider(2, 200, value=30, step=1, label="Length", info="Choose the number of tokens to generate") |
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classes = ["SARS-CoV-2 Spike Protein (example)", 'Tetratricopeptide-like helical domain superfamily', 'CheY-like superfamily', 'S-adenosyl-L-methionine-dependent methyltransferase superfamily', 'Thioredoxin-like superfamily', "Non-Hemolytic" ,"Soluble", "Oxidoreductase", "Transferase", "Hydrolase", "Lyase", "Isomerase", "Ligase", "Translocase"] |
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protein_property = gr.Dropdown(classes, label="Class") |
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with gr.Row(): |
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btn = gr.Button("Submit") |
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with gr.Row(): |
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output_text = gr.Textbox(label="Generated protein sequence will appear here") |
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with gr.Row(): |
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infer_time = gr.Number(label="Inference Time (s)", precision=2) |
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tokens_per_sec = gr.Number(label="Tokens/sec", precision=2) |
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btn.click(generate_protein_sequence, inputs=[input_text, seq_length, protein_property], outputs=[output_text, infer_time, tokens_per_sec]) |
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demo.launch() |
