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| import pandas as pd | |
| from IPython.display import clear_output | |
| import torch | |
| from transformers import EsmForSequenceClassification, AdamW, AutoTokenizer | |
| from torch.utils.data import DataLoader, TensorDataset, random_split | |
| from sklearn.preprocessing import LabelEncoder | |
| from tqdm import tqdm | |
| import numpy as np | |
| import seaborn as sns | |
| from sklearn.model_selection import train_test_split | |
| import matplotlib.pyplot as plt | |
| import pickle | |
| import torch.nn.functional as F | |
| import gradio as gr | |
| import io | |
| from PIL import Image | |
| import Bio | |
| from Bio import SeqIO | |
| import zipfile | |
| import os | |
| # Load the model from the file | |
| with open('family_labels.pkl', 'rb') as filefam: | |
| yfam = pickle.load(filefam) | |
| tokenizerfam = AutoTokenizer.from_pretrained("facebook/esm2_t12_35M_UR50D") #facebook/esm2_t33_650M_UR50D | |
| device = 'cpu' | |
| device | |
| modelfam = EsmForSequenceClassification.from_pretrained("facebook/esm2_t12_35M_UR50D", num_labels=len(yfam.classes_)) | |
| modelfam = modelfam.to('cpu') | |
| modelfam.load_state_dict(torch.load("family.pth", map_location=torch.device('cpu'))) | |
| modelfam.eval() | |
| x_testfam = ["""MAEVLRTLAGKPKCHALRPMILFLIMLVLVLFGYGVLSPRSLMPGSLERGFCMAVREPDH | |
| LQRVSLPRMVYPQPKVLTPCRKDVLVVTPWLAPIVWEGTFNIDILNEQFRLQNTTIGLTV | |
| FAIKKYVAFLKLFLETAEKHFMVGHRVHYYVFTDQPAAVPRVTLGTGRQLSVLEVRAYKR | |
| WQDVSMRRMEMISDFCERRFLSEVDYLVCVDVDMEFRDHVGVEILTPLFGTLHPGFYGSS | |
| REAFTYERRPQSQAYIPKDEGDFYYLGGFFGGSVQEVQRLTRACHQAMMVDQANGIEAVW | |
| HDESHLNKYLLRHKPTKVLSPEYLWDQQLLGWPAVLRKLRFTAVPKNHQAVRNP | |
| """] | |
| encoded_inputfam = tokenizerfam(x_testfam, padding=True, truncation=True, max_length=512, return_tensors="pt") | |
| input_idsfam = encoded_inputfam["input_ids"] | |
| attention_maskfam = encoded_inputfam["attention_mask"] | |
| with torch.no_grad(): | |
| outputfam = modelfam(input_idsfam, attention_mask=attention_maskfam) | |
| logitsfam = outputfam.logits | |
| probabilitiesfam = F.softmax(logitsfam, dim=1) | |
| _, predicted_labelsfam = torch.max(logitsfam, dim=1) | |
| probabilitiesfam[0] | |
| decoded_labelsfam = yfam.inverse_transform(predicted_labelsfam.tolist()) | |
| decoded_labelsfam | |
| #Load donor model from file | |
| tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t12_35M_UR50D") | |
| with open('donor_labels.pkl', 'rb') as file: | |
| label_encoder = pickle.load(file) | |
| # encoded_labels = label_encoder.fit(y) | |
| # labels = torch.tensor(encoded_labels) | |
| model = EsmForSequenceClassification.from_pretrained("facebook/esm2_t12_35M_UR50D", num_labels=len(label_encoder.classes_)) | |
| model = model.to('cpu') | |
| model.load_state_dict(torch.load("best_model_35M_t12_5v5.pth", map_location=torch.device('cpu'))) #model_best_35v2M.pth | |
| model.eval() | |
| x_test = ["""MAEVLRTLAGKPKCHALRPMILFLIMLVLVLFGYGVLSPRSLMPGSLERGFCMAVREPDH | |
| LQRVSLPRMVYPQPKVLTPCRKDVLVVTPWLAPIVWEGTFNIDILNEQFRLQNTTIGLTV | |
| FAIKKYVAFLKLFLETAEKHFMVGHRVHYYVFTDQPAAVPRVTLGTGRQLSVLEVRAYKR | |
| WQDVSMRRMEMISDFCERRFLSEVDYLVCVDVDMEFRDHVGVEILTPLFGTLHPGFYGSS | |
| REAFTYERRPQSQAYIPKDEGDFYYLGGFFGGSVQEVQRLTRACHQAMMVDQANGIEAVW | |
| HDESHLNKYLLRHKPTKVLSPEYLWDQQLLGWPAVLRKLRFTAVPKNHQAVRNP | |
| """] | |
| encoded_input = tokenizer(x_test, padding=True, truncation=True, max_length=512, return_tensors="pt") | |
| input_ids = encoded_input["input_ids"] | |
| attention_mask = encoded_input["attention_mask"] | |
| with torch.no_grad(): | |
| output = model(input_ids, attention_mask=attention_mask) | |
| logits = output.logits | |
| probabilities = F.softmax(logits, dim=1) | |
| _, predicted_labels = torch.max(logits, dim=1) | |
| probabilities[0] | |
| decoded_labels = label_encoder.inverse_transform(predicted_labels.tolist()) | |
| decoded_labels | |
| glycosyltransferase_db = { | |
| "GT31-chsy" : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8 ', 'More Info': 'http://www.cazy.org/GT31.html'}, | |
| "GT2-CesA2" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT43-arath" : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'}, | |
| "GT8-Met1" : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9 ', 'More Info': 'http://www.cazy.org/GT8.html' }, | |
| "GT32-higher" : {'CAZy Name': 'GT32', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT32.html'}, | |
| "GT40" : {'CAZy Name': 'GT40', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT40.html'}, | |
| "GT16" : {'CAZy Name': 'GT16', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '6 ', 'More Info': 'http://www.cazy.org/GT16.html'}, | |
| "GT27" : {'CAZy Name': 'GT27', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '5 ', 'More Info': 'http://www.cazy.org/GT27.html'}, | |
| "GT55" : {'CAZy Name': 'GT55', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '2 ', 'More Info': 'http://www.cazy.org/GT55.html'}, | |
| "GT8-Glycogenin" : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9 ', 'More Info': 'http://www.cazy.org/GT8.html' }, | |
| "GT8-1" : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9 ', 'More Info': 'http://www.cazy.org/GT8.html' }, | |
| "GT25" : {'CAZy Name': 'GT25', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '6 ', 'More Info': 'http://www.cazy.org/GT25.html'}, | |
| "GT2-DPM_like" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '2 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT31-fringe" : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8 ', 'More Info': 'http://www.cazy.org/GT31.html'}, | |
| "GT2-Bact_puta" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT84" : {'CAZy Name': 'GT84', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '1 ', 'More Info': 'http://www.cazy.org/GT84.html'}, | |
| "GT13" : {'CAZy Name': 'GT13', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '6 ', 'More Info': 'http://www.cazy.org/GT13.html'}, | |
| "GT43-cele" : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'}, | |
| "GT2-Bact_LPS1" : {'CAZy Name': 'GT92', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT2-Bact_Oant" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': ' ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT67" : {'CAZy Name': 'GT67', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8 ', 'More Info': 'http://www.cazy.org/GT67.html'}, | |
| "GT2-HAS" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT82" : {'CAZy Name': 'GT82', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '7 ', 'More Info': 'http://www.cazy.org/GT82.html'}, | |
| "GT24" : {'CAZy Name': 'GT24', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9 ', 'More Info': 'http://www.cazy.org/GT24.html'}, | |
| "GT31-plant" : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8 ', 'More Info': 'http://www.cazy.org/GT31.html'}, | |
| "GT81-Bact" : {'CAZy Name': 'GT81', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '2 ', 'More Info': 'http://www.cazy.org/GT81.html'}, | |
| "GT2-Bact_gt25Me": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': ' ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT2-B3GntL" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '4 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT49" : {'CAZy Name': 'GT49', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT49.html'}, | |
| "GT34" : {'CAZy Name': 'GT34', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT34.html'}, | |
| "GT45" : {'CAZy Name': 'GT45', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT45.html'}, | |
| "GT32-lower" : {'CAZy Name': 'GT32', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT32.html'}, | |
| "GT88" : {'CAZy Name': 'GT88', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9 ', 'More Info': 'http://www.cazy.org/GT88.html'}, | |
| "GT21" : {'CAZy Name': 'GT21', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1 ', 'More Info': 'http://www.cazy.org/GT21.html'}, | |
| "GT2-DPG_synt" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '2 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT43-b3gat2" : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'}, | |
| "GT2-Chitin_synt": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '5 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT8-Bact" : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT8.html' }, | |
| "GT8-Met2" : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT8.html' }, | |
| "GT2-Bact_Chlor1": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': ' ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT54" : {'CAZy Name': 'GT54', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '6 ', 'More Info': 'http://www.cazy.org/GT54.html'}, | |
| "GT2-Cel_bre3" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT2-Bact_Rham" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT6" : {'CAZy Name': 'GT6 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT6.html' }, | |
| "GT2-Bact_puta2" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': ' ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT7-1" : {'CAZy Name': 'GT7 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '5 ', 'More Info': 'http://www.cazy.org/GT7.html' }, | |
| "GT2-Csl" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '4 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT2-ExoU" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': ' ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT2-Csl2" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '4 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT64" : {'CAZy Name': 'GT64', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT64.html'}, | |
| "GT2-Bact_Chlor2": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': ' ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT78" : {'CAZy Name': 'GT78', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '2 ', 'More Info': 'http://www.cazy.org/GT78.html'}, | |
| "GT12" : {'CAZy Name': 'GT12', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT12.html'}, | |
| "GT31-gnt" : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8 ', 'More Info': 'http://www.cazy.org/GT31.html'}, | |
| "GT2-Bact_CHS" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '5 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT62" : {'CAZy Name': 'GT62', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '3 ', 'More Info': 'http://www.cazy.org/GT62.html'}, | |
| "GT8-Met_Pla" : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT8.html' }, | |
| "GT15" : {'CAZy Name': 'GT15', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '8 ', 'More Info': 'http://www.cazy.org/GT15.html'}, | |
| "GT43-b3gat1" : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'}, | |
| "GT31-b3glt" : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8 ', 'More Info': 'http://www.cazy.org/GT31.html'}, | |
| "GT2-CesA1" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT60" : {'CAZy Name': 'GT60', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '5 ', 'More Info': 'http://www.cazy.org/GT60.html'}, | |
| "GT14" : {'CAZy Name': 'GT14', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '7 ', 'More Info': 'http://www.cazy.org/GT14.html'}, | |
| "GT2-Bact_DPM_sy": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '2 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT17" : {'CAZy Name': 'GT17', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '7 ', 'More Info': 'http://www.cazy.org/GT17.html'}, | |
| "GT2-Bact_LPS2" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '3 ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT77" : {'CAZy Name': 'GT77', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9 ', 'More Info': 'http://www.cazy.org/GT77.html'}, | |
| "GT2-Bact_EpsO" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': ' ', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| "GT43-b3gat3" : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'}, | |
| "GT8-Fun" : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9 ', 'More Info': 'http://www.cazy.org/GT8.html' }, | |
| "GT75" : {'CAZy Name': 'GT75', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT75.html'}, | |
| "GT2-Bact_GlfT" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT2.html' }, | |
| } | |
| def get_family_info(family_name): | |
| family_info = glycosyltransferase_db.get(family_name, {}) | |
| output = "" | |
| for key, value in family_info.items(): | |
| if key == "more_info": | |
| output += "**{}:**".format(key.title().replace("_", " ")) + "\n" | |
| for link in value: | |
| output += "[{}]({}) ".format(link, link) | |
| else: | |
| output += "**{}:** {} ".format(key.title().replace("_", " "), value) | |
| return output | |
| def fig_to_img(fig): | |
| """Converts a matplotlib figure to a PIL Image and returns it""" | |
| buf = io.BytesIO() | |
| fig.savefig(buf, format='png', bbox_inches='tight') | |
| buf.seek(0) | |
| img = Image.open(buf) | |
| return img | |
| def preprocess_protein_sequence(protein_fasta): | |
| lines = protein_fasta.split('\n') | |
| headers = [line for line in lines if line.startswith('>')] | |
| if len(headers) > 1: | |
| return None, "Multiple fasta sequences detected. Please upload a fasta file with only one sequence." | |
| protein_sequence = ''.join(line for line in lines if not line.startswith('>')) | |
| # Check for invalid characters | |
| valid_characters = set("ACDEFGHIKLMNPQRSTVWYacdefghiklmnpqrstvwy") # the 20 standard amino acids | |
| if not set(protein_sequence).issubset(valid_characters): | |
| return None, "Invalid protein sequence. It contains characters that are not one of the 20 standard amino acids. Does your sequence contain gaps?" | |
| return protein_sequence, None | |
| def process_family_sequence(protein_fasta): | |
| protein_sequence, error_msg = preprocess_protein_sequence(protein_fasta) | |
| if error_msg: | |
| return None, None, None, error_msg | |
| encoded_input = tokenizer([protein_sequence], padding=True, truncation=True, max_length=512, return_tensors="pt") | |
| input_idsfam = encoded_input["input_ids"] | |
| attention_maskfam = encoded_input["attention_mask"] | |
| with torch.no_grad(): | |
| outputfam = modelfam(input_idsfam, attention_mask=attention_maskfam) | |
| logitsfam = outputfam.logits | |
| probabilitiesfam = F.softmax(logitsfam, dim=1) | |
| _, predicted_labelsfam = torch.max(logitsfam, dim=1) | |
| decoded_labelsfam = yfam.inverse_transform(predicted_labelsfam.tolist()) | |
| family_info = get_family_info(decoded_labelsfam[0]) | |
| figfam = plt.figure(figsize=(10, 5)) | |
| labelsfam = yfam.classes_ | |
| probabilitiesfam = probabilitiesfam.tolist() | |
| # Convert the nested list to a flat list of probabilities | |
| probabilitiesfam_flat = probabilitiesfam[0] if probabilitiesfam else [] | |
| # Sort labels and probabilities by probability | |
| labels_probsfam = list(zip(labelsfam, probabilitiesfam_flat)) | |
| labels_probsfam.sort(key=lambda x: x[1], reverse=True) | |
| # Select the top 5 fams | |
| labels_probs_top5fam = labels_probsfam[:5] | |
| labels_top5, probabilities_top5 = zip(*labels_probs_top5fam) | |
| y_posfam = np.arange(len(labels_top5)) | |
| plt.barh(y_posfam, [prob*100 for prob in probabilities_top5], align='center', alpha=0.5) | |
| plt.yticks(y_posfam, labels_top5) | |
| plt.xlabel('Probability (%)') | |
| plt.title('Top 5 Family Class Probabilities') | |
| plt.xlim(0, 100) | |
| plt.close(figfam) | |
| img = fig_to_img(figfam) | |
| if len(protein_sequence) < 100: | |
| return decoded_labelsfam[0], img, None, f"**Warning:** The sequence is relatively short. Fragmentary and partial sequences may result in incorrect predictions. \n\n {family_info}" | |
| return decoded_labelsfam[0], img, None, family_info | |
| def process_single_sequence(protein_fasta): #, protein_file | |
| protein_sequence, error_msg = preprocess_protein_sequence(protein_fasta) | |
| if error_msg: | |
| return None, None, None, error_msg | |
| encoded_input = tokenizer([protein_sequence], padding=True, truncation=True, max_length=512, return_tensors="pt") | |
| input_ids = encoded_input["input_ids"] | |
| attention_mask = encoded_input["attention_mask"] | |
| with torch.no_grad(): | |
| output = model(input_ids, attention_mask=attention_mask) | |
| logits = output.logits | |
| dprobabilities = F.softmax(logits, dim=1)[0] | |
| _, predicted_labels = torch.max(logits, dim=1) | |
| decoded_labels = label_encoder.inverse_transform(predicted_labels.tolist()) | |
| family_info = get_family_info(decoded_labels[0]) | |
| fig = plt.figure(figsize=(10, 5)) | |
| labels = label_encoder.classes_ | |
| dprobabilities = dprobabilities.tolist() | |
| # Sort labels and probabilities by probability | |
| labels_probs = list(zip(labels, dprobabilities)) | |
| labels_probs.sort(key=lambda x: x[1], reverse=True) | |
| # Select the top 3 donors | |
| labels_probs_top3 = labels_probs[:3] | |
| labels_top3, probabilities_top3 = zip(*labels_probs_top3) | |
| y_pos = np.arange(len(labels_top3)) | |
| plt.barh(y_pos, [prob*100 for prob in probabilities_top3], align='center', alpha=0.5) | |
| plt.yticks(y_pos, labels_top3) | |
| plt.xlabel('Probability (%)') | |
| plt.title('Top 3 Donor Class Probabilities') | |
| plt.xlim(0, 100) | |
| plt.close(fig) | |
| img = fig_to_img(fig) | |
| if len(protein_sequence) < 100: | |
| return decoded_labels[0], img, None, f"**Warning:** The sequence is relatively short. Fragmentary and partial sequences may result in incorrect predictions. \n\n {family_info}" | |
| return decoded_labels[0], img, None, None | |
| def process_sequence_file(protein_file): # added progress parameter that is displayed in gradio #, progress=gr.Progress() | |
| try: | |
| records = list(SeqIO.parse(protein_file.name, "fasta")) | |
| except Exception as e: | |
| return str(e) | |
| if not os.path.exists('results'): | |
| os.makedirs('results') | |
| total = len(records) | |
| for idx, record in enumerate(records): | |
| protein_sequence = str(record.seq) | |
| valid_characters = set("ACDEFGHIKLMNPQRSTVWYacdefghiklmnpqrstvwy") | |
| if not set(protein_sequence).issubset(valid_characters): | |
| with open(f'results/result_{idx+1}.txt', 'w') as file: | |
| file.write("Invalid protein sequence. It contains characters that are not one of the 20 standard amino acids. Does your sequence contain gaps?") | |
| continue | |
| label, img, _, info = process_single_sequence(protein_sequence) | |
| img.save(f'results/result_{idx+1}.png') | |
| with open(f'results/result_{idx+1}.txt', 'w') as file: | |
| file.write(f'Predicted Donor: {label}\n\n{info}') | |
| # progress(idx/total) # Update the progress bar | |
| # Create a zip file w/ results -- To Do: Figure out how to improve compression for large files | |
| with zipfile.ZipFile('predicted_results.zip', 'w', zipfile.ZIP_DEFLATED) as zipf: | |
| for root, dirs, files in os.walk('results/'): | |
| for file in files: | |
| zipf.write(os.path.join(root, file)) | |
| return 'predicted_results.zip' #Provide indication of how to interpret downloaded zip file? f"**Warning:** The sequence is relatively short. Fragmentary and partial sequences may result in incorrect predictions. | |
| # Function to mask a residue at a particular position | |
| def mask_residue(sequence, position): | |
| return sequence[:position] + 'X' + sequence[position+1:] | |
| def generate_heatmap(protein_fasta): | |
| protein_sequence, error_msg = preprocess_protein_sequence(protein_fasta) | |
| # Tokenize and predict for original sequence | |
| encoded_input = tokenizer([protein_sequence], padding=True, truncation=True, max_length=512, return_tensors="pt") | |
| with torch.no_grad(): | |
| original_output = model(encoded_input["input_ids"], attention_mask=encoded_input["attention_mask"]) | |
| original_probabilities = F.softmax(original_output.logits, dim=1).cpu().numpy()[0] | |
| # Define the size of each group | |
| group_size = 10 # allow user to change this | |
| # Calculate the number of groups | |
| num_groups = len(protein_sequence) // group_size + (len(protein_sequence) % group_size > 0) | |
| # Initialize an array to hold the importance scores | |
| importance_scores = np.zeros((num_groups, len(original_probabilities))) | |
| # Initialize tqdm progress bar | |
| # with tqdm(total=num_groups, desc="Processing groups", position=0, leave=True) as pbar: | |
| # # Loop through each group of residues in the sequence | |
| for i in range(0, len(protein_sequence), group_size): | |
| # Mask the residues in the group at positions [i, i + group_size) | |
| masked_sequence = protein_sequence[:i] + 'X' * min(group_size, len(protein_sequence) - i) + protein_sequence[i + group_size:] | |
| # Tokenize and predict for the masked sequence | |
| encoded_input = tokenizer([masked_sequence], padding=True, truncation=True, max_length=512, return_tensors="pt") | |
| with torch.no_grad(): | |
| masked_output = model(encoded_input["input_ids"], attention_mask=encoded_input["attention_mask"]) | |
| masked_probabilities = F.softmax(masked_output.logits, dim=1).cpu().numpy()[0] | |
| # Calculate the change in probabilities and store it as the importance score | |
| group_index = i // group_size | |
| importance_scores[group_index, :] = np.abs(original_probabilities - masked_probabilities) | |
| progress = (i // group_size + 1) / num_groups * 100 | |
| print(f"Progress: {progress:.2f}%") | |
| figmap, ax = plt.subplots(figsize=(20, 20)) | |
| sns.heatmap(importance_scores, annot=True, cmap="coolwarm", xticklabels=label_encoder.classes_, yticklabels=[f"{i}-{i+group_size-1}" for i in range(0, len(protein_sequence), group_size)], ax=ax) | |
| ax.set_xlabel("Predicted Labels") | |
| ax.set_ylabel("Residue Position Groups") | |
| img = fig_to_img(figmap) | |
| return img | |
| def main_function_single(sequence, show_explanation): | |
| # Process seq, and return outputs for both fam and don | |
| family_label, family_img, _, family_info = process_family_sequence(sequence) | |
| donor_label, donor_img, *_ = process_single_sequence(sequence) | |
| figmap = None | |
| if show_explanation: | |
| figmap = generate_heatmap(sequence) | |
| return family_label, family_img, family_info, donor_label, donor_img, figmap | |
| def main_function_upload(protein_file): #, progress=gr.Progress() | |
| return process_sequence_file(protein_file) #, progress | |
| prediction_imagefam = gr.outputs.Image(type='pil', label="Family prediction graph") | |
| prediction_imagedonor = gr.outputs.Image(type='pil', label="Donor prediction graph") | |
| prediction_explain = gr.outputs.Image(type='pil', label="Donor prediction explanation") | |
| with gr.Blocks() as app: | |
| gr.Markdown("# Glydentify") | |
| with gr.Tab("Single Sequence Prediction"): | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(): | |
| sequence = gr.inputs.Textbox(lines=16, placeholder='Enter Protein Sequence Here...', label="Protein Sequence") | |
| explanation_checkbox = gr.inputs.Checkbox(label="Show Explanation", default=False) | |
| with gr.Column(): | |
| with gr.Accordion("Example:"): | |
| gr.Markdown(""" | |
| \>sp|Q9Y5Z6|B3GT1_HUMAN Beta-1,3-galactosyltransferase 1 OS=Homo sapiens OX=9606 GN=B3GALT1 PE=1 SV=1 | |
| MASKVSCLYVLTVVCWASALWYLSITRPTSSYTGSKPFSHLTVARKNFTFGNIRTRPINPHSFEFLINEPNKCEKNIPFLVILIST | |
| THKEFDARQAIRETWGDENNFKGIKIATLFLLGKNADPVLNQMVEQESQIFHDIIVEDFIDSYHNLTLKTLMGMRWVATFCSK | |
| AKYVMKTDSDIFVNMDNLIYKLLKPSTKPRRRYFTGYVINGGPIRDVRSKWYMPRDLYPDSNYPPFCSGTGYIFSADVAELIYK | |
| TSLHTRLLHLEDVYVGLCLRKLGIHPFQNSGFNHWKMAYSLCRYRRVITVHQISPEEMHRIWNDMSSKKHLRC | |
| """) | |
| family_prediction = gr.outputs.Textbox(label="Predicted family") | |
| donor_prediction = gr.outputs.Textbox(label="Predicted donor") | |
| info_markdown = gr.Markdown() | |
| # Predict and Clear buttons | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(): | |
| predict_button = gr.Button("Predict") | |
| predict_button.click(main_function_single, inputs=[sequence, explanation_checkbox], | |
| outputs=[family_prediction, prediction_imagefam, info_markdown, | |
| donor_prediction, prediction_imagedonor, prediction_explain]) | |
| # Family & Donor Section | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(): | |
| with gr.Accordion("Prediction Bar Graphs:"): | |
| prediction_imagefam.render() # = gr.outputs.Image(type='pil', label="Family prediction graph") | |
| prediction_imagedonor.render() # = gr.outputs.Image(type='pil', label="Donor prediction graph") | |
| # Explain Section | |
| with gr.Column(): | |
| if explanation_checkbox: # Only render if the checkbox is checked | |
| with gr.Accordion("Donor explanation"): | |
| prediction_explain.render() # = gr.outputs.Image(type='pil', label="Donor prediction explaination") | |
| with gr.Tab("Multiple Sequence Prediction"): | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(): | |
| protein_file = gr.inputs.File(label="Upload FASTA file") | |
| with gr.Column(): | |
| result_file = gr.outputs.File(label="Download predictions of uploaded sequences") | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(): | |
| process_button = gr.Button("Process") | |
| process_button.click(main_function_upload, inputs=protein_file, outputs=[result_file]) | |
| with gr.Column(): | |
| clear = gr.Button("Clear") | |
| clear.click(lambda: None) | |
| # clear.click() | |
| app.launch(show_error=True) | |