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--- |
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dataset_info: |
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features: |
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- name: PDB_ID |
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dtype: string |
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- name: Entity_ID |
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dtype: string |
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- name: Chain |
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dtype: string |
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- name: Length |
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dtype: int16 |
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- name: Fmax_eps-over-A |
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dtype: float32 |
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- name: Fmax_pN |
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dtype: float32 |
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- name: Dmax_A |
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dtype: float32 |
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- name: Lmax_A |
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dtype: float32 |
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- name: Lambda |
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dtype: float32 |
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- name: Sequence |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 3212304 |
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num_examples: 16652 |
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- name: test |
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num_bytes: 21731 |
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num_examples: 124 |
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download_size: 2217065 |
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dataset_size: 3234035 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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--- |
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# PRESTO: Rapid protein mechanical strength prediction with an end-to-end deep learning model |
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Proteins often form biomaterials with exceptional mechanical properties equal or even superior to synthetic materials. Currently, using experimental atomic force microscopy or computational molecular dynamics to evaluate protein mechanical strength remains costly and time-consuming, limiting large-scale de novo protein investigations. Therefore, there exists a growing demand for fast and accurate prediction of protein mechanical strength. To address this challenge, we propose PRESTO, a rapid end-to-end deep learning (DL) model to predict protein resistance to pulling directly from its sequence. By integrating a natural language processing model with simulation-based protein stretching data, we first demonstrate that PRESTO can accurately predict the maximal pulling force, for given protein sequences with unprecedented efficiency, bypassing the costly steps of conventional methods. Enabled by this rapid prediction capacity, we further find that PRESTO can successfully identify specific mutation locations that may greatly influence protein strength in a biologically plausible manner, such as at the center of polyalanine regions. Finally, we apply our method to design de novo protein sequences by randomly mixing two known sequences at varying ratios. Interestingly, the model predicts that the strength of these mixed proteins follows up- or down-opening “banana curves”, constructing a protein strength curve that breaks away from the general linear law of mixtures. By discovering key insights and suggesting potential optimal sequences, we demonstrate the versatility of PRESTO primarily as a screening tool in a rapid protein design pipeline. Thereby our model may offer new pathways for protein material research that requires analysis and testing of large-scale novel protein sets, as a discovery tool that can be complemented with other modeling methods, and ultimately, experimental synthesis and testing. |
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# Data, model and training |
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Load data |
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```python |
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import math |
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import numpy as np |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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from sklearn.preprocessing import StandardScaler |
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from datasets import load_dataset |
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ds = load_dataset("lamm-mit/PRESTO-protein-force", split="train") |
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protein_df = ds.to_pandas() |
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print(protein_df.columns) |
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``` |
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Scale data |
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```python |
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y_data = np.array(protein_df.Fmax_pN.values) |
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plt.scatter(range(y_data.shape[0]),y_data,s=2) |
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plt.xlabel('Sequence Number') |
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plt.ylabel('${F_{max}} (pN)$') |
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plt.savefig('data_view.png',dpi=500) |
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scalar=StandardScaler() |
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y_data_temp=np.reshape(y_data,(y_data.shape[0],1)) |
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fit_data=scalar.fit(y_data_temp) |
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mean=fit_data.mean_[0] |
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std=math.sqrt(fit_data.var_) |
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Y=scalar.fit_transform(y_data_temp) |
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``` |
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Define tokenizer and model |
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```python |
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# maximum length of sequence, longer the sequences, more time we need - |
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max_length = 300 |
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from tensorflow.keras.preprocessing.text import Tokenizer |
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tokenizer = Tokenizer(char_level=True) |
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tokenizer.fit_on_texts(seqs) |
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X = tokenizer.texts_to_sequences(seqs) |
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# if AA seq is longer than max_lenght will be discarded |
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X = sequence.pad_sequences(X, maxlen=max_length) |
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``` |
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Split train/test |
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```python |
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X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=.2,random_state=23) |
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``` |
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Define model |
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```python |
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from tensorflow.keras.models import Sequential |
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from tensorflow.keras.layers import Dense, Conv1D, Flatten |
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from tensorflow.keras.layers import Dropout |
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from tensorflow.keras.layers import Embedding |
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model = Sequential() |
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model.add(Embedding(len(tokenizer.word_index)+1, embedding_dim, input_length=max_length)) |
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model.add(Conv1D(filters=64, kernel_size=3, padding='same', activation='relu')) |
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model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu')) |
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model.add(tf.keras.layers.Bidirectional(keras.layers.LSTM(units=32,return_sequences=True))) |
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model.add(tf.keras.layers.Bidirectional(keras.layers.LSTM(units=16,return_sequences=True))) |
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model.add(Flatten()) |
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model.add(Dropout(0.5)) |
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model.add(Dense(32, activation='relu')) |
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model.add(Dense(1, activation='linear')) |
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model.compile(loss='mean_squared_error', optimizer='adam') |
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``` |
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Train model |
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```python |
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hist=model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=128) |
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model.save('PRESTO.h5') |
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``` |
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Plots |
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``` |
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from tensorflow.keras.models import load_model |
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import itertools |
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model=load_model('PRESTO.h5') |
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y_train_pred = model.predict(X_train) |
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y_test_pred = model.predict(X_test) |
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plt.title('Sequence-to-Feature Model') |
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plt.scatter(y_train,y_train_pred,s=2) |
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plt.scatter(y_test,y_test_pred,c='r',s=2) |
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plt.xlabel('BSDB ${F_{max}}$ (pN)') |
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plt.ylabel('ML ${F_{max}}$ (pN)') |
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plt.legend(['training', 'validation']) |
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plt.savefig('train_comp.png',dpi=500) |
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``` |
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# Optimization examples |
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Fitness function |
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```python |
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def objective_value(input_seqs,fitness_fcn): |
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tmp=tokenizer.texts_to_sequences(input_seqs) |
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temp=sequence.pad_sequences(tmp, maxlen=max_length) |
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y_pred=fitness_fcn.predict(temp) |
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return y_pred |
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def iterate_mutate_no_target(sequences, targeta): |
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list_list_seq = [] |
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for x in range(len(sequences)): |
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list_list_seq.append(mutate_no_target(sequences[x], targeta)) |
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return list_list_seq |
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def iterate_calc(sequences): |
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list_list_seq = [] |
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for x in sequences: |
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list_list_seq.append((objective_value(x,fitness_fcn)*std+mean).flatten()) |
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return list_list_seq |
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``` |
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Sample sequences |
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```python |
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prot_2c7w = 'HQRKVVSWIDVYTRATCQPREVVVPLTVELMGTVAKQLVPSCVTVQRCGGCCPDDGLECVPTGQHQVRMQILMIRYPSSQLGEMSLEEHSQCECRPKKK' |
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prot_2g38 = 'MSFVITNPEALTVAATEVRRIRDRAIQSDAQVAPMTTAVRPPAADLVSEKAATFLVEYARKYRQTIAAAAVVLEEFAHALTTGADKYATAEADNIKTFS' |
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prot_2g38_mut = 'MSFVITNPEALTVAATCVRRIRDRAIQSDAQGAPMTTAVRPCADLVSCGGACCFLGEYACKYGQTIAAAAVVLEEFAHALTTGADKYATAEACNCKTFS' |
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prot_1yn4= 'GKHTVPYTISVDGITALHRTYFVFPENKKVLYQEIDSKVKNELASQRGVTTEKINNAQTATYTLTLNDGNKKVVNLKKNDDAKNSIDPSTIKQIQIVVK' |
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prot_1kat= 'HHEVVKFMDVYQRSYCHPIETLVDIFQEYPDEIEYIFKPSCVPLMRCGGCCNDEGLECVPTEESNITMQIMRIKPHQGQHIGEMSFLQHNKCECRPKKD' |
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prot_1bmp= 'STGSKQRSQNRSKTPKNQEALRMANVAENSSSDQRQACKKHELYVSFRDLGWQDWIIAPEGYAAYYCEGECAFPLNSYMNATNHAIVQTLVHFINPETVPKPCCAPTQLNAISVLYFDDSSNVILKKYRNMVVRACGCH' |
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prot_1cdc= 'RDSGTVWGALGHGINLNIPNFQMTDDIDEVRWERGSTLVAEFKRKMKPFLKSGAFEILANGDLKIKNLTRDDSGTYNVTVYSTNGTRILDKALDLRILE' |
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``` |
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```python |
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#mutates seq into poly-X (change AA and color) |
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ori = 'MNIFEMLRIDEGLRLKIYLDKAIGRNRAALVNLVFQIGETAAAAAAAAAAAAAAAAAAGAAGFTNSLRYLQQKRWDEAAVNFAKSRWYNQTPNRAKRIAAAAAAAAAAAAAAAAAAITVFRTGTWDAYKNL' |
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AA='T' |
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color='orange' |
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multiple_ori = [] |
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mutated_seqs=[] |
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mutated_seqs_fmax=[] |
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num=100 |
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for i in range(num): |
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multiple_ori.append(ori) |
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mutated_seqs = iterate_mutate_no_target(multiple_ori, AA) |
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mutated_seqs_fmax = iterate_calc(mutated_seqs) |
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pd.DataFrame(mutated_seqs).to_csv("mutations_"+AA+".csv") |
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np.savetxt('mutations_'+AA+'_fmax.csv', mutated_seqs_fmax, delimiter=',') |
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for y in mutated_seqs_fmax: |
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plt.plot(y,'0.7', zorder=0, linewidth=0.4) |
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transposed=np.transpose(mutated_seqs_fmax) |
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transposed_mean=[] |
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transposed_sd=[] |
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for x in transposed: |
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transposed_mean.append(np.mean(x)) |
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transposed_sd.append(np.std(x)) |
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``` |
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Plot results |
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```python |
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plt.errorbar(range(len(transposed_mean)),transposed_mean,transposed_sd,capsize=2,zorder=50,color=color) |
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plt.xlabel('Number of mutations') |
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plt.ylabel('${F_{max}}$ (pN)') |
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plt.title(str(num)+' 1p3n sequences mutate towards poly-'+AA) |
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plt.plot(0,mutated_seqs_fmax[0][0],'or',zorder=100) |
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plt.plot(len(mutated_seqs[0])-1,mutated_seqs_fmax[0][len(mutated_seqs_fmax[0])-1],'or',zorder=100) |
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plt.savefig('mutations_'+AA+'.png',dpi=500) |
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``` |
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# Sample results |
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# Reference |
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```bibtex |
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@article{liu2022presto, |
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title = {{PRESTO}: Rapid protein mechanical strength prediction with an end-to-end deep learning model}, |
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author = {Liu, Frank Y. C. and Ni, Bo and Buehler, Markus J.}, |
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journal = {Extreme Mechanics Letters}, |
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volume = {55}, |
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pages = {101803}, |
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year = {2022}, |
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publisher = {Elsevier}, |
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doi = {10.1016/j.eml.2022.101803}, |
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} |
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``` |
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