Update README.md

README.md

@@ -125,8 +125,106 @@ Train model
 
 ```python
 hist=model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=128)
+model.save('PRESTO.h5')
 ```
 
+Plots
+```
+from tensorflow.keras.models import load_model
+import itertools
+
+model=load_model('PRESTO.h5')
+y_train_pred = model.predict(X_train)
+y_test_pred = model.predict(X_test)
+plt.title('Sequence-to-Feature Model')
+plt.scatter(y_train,y_train_pred,s=2)
+plt.scatter(y_test,y_test_pred,c='r',s=2)
+plt.xlabel('BSDB ${F_{max}}$ (pN)')
+plt.ylabel('ML ${F_{max}}$ (pN)')
+plt.legend(['training', 'validation'])
+plt.savefig('train_comp.png',dpi=500)
+```
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/3FouBrLR1siS2xScemhke.png)
+
+
+# Optimization examples
+
+Fitness function
+
+```python
+def objective_value(input_seqs,fitness_fcn):
+    tmp=tokenizer.texts_to_sequences(input_seqs)
+    temp=sequence.pad_sequences(tmp, maxlen=max_length)
+    y_pred=fitness_fcn.predict(temp)
+    return y_pred
+
+def iterate_mutate_no_target(sequences, targeta):
+    list_list_seq = []
+    for x in range(len(sequences)):
+        list_list_seq.append(mutate_no_target(sequences[x], targeta))
+    return list_list_seq
+
+def iterate_calc(sequences):
+    list_list_seq = []
+    for x in sequences:
+      list_list_seq.append((objective_value(x,fitness_fcn)*std+mean).flatten())
+    return list_list_seq
+```
+
+Sample sequences
+```python
+prot_2c7w =     'HQRKVVSWIDVYTRATCQPREVVVPLTVELMGTVAKQLVPSCVTVQRCGGCCPDDGLECVPTGQHQVRMQILMIRYPSSQLGEMSLEEHSQCECRPKKK'
+prot_2g38 =     'MSFVITNPEALTVAATEVRRIRDRAIQSDAQVAPMTTAVRPPAADLVSEKAATFLVEYARKYRQTIAAAAVVLEEFAHALTTGADKYATAEADNIKTFS'
+prot_2g38_mut = 'MSFVITNPEALTVAATCVRRIRDRAIQSDAQGAPMTTAVRPCADLVSCGGACCFLGEYACKYGQTIAAAAVVLEEFAHALTTGADKYATAEACNCKTFS'
+prot_1yn4=      'GKHTVPYTISVDGITALHRTYFVFPENKKVLYQEIDSKVKNELASQRGVTTEKINNAQTATYTLTLNDGNKKVVNLKKNDDAKNSIDPSTIKQIQIVVK'
+prot_1kat=      'HHEVVKFMDVYQRSYCHPIETLVDIFQEYPDEIEYIFKPSCVPLMRCGGCCNDEGLECVPTEESNITMQIMRIKPHQGQHIGEMSFLQHNKCECRPKKD'
+prot_1bmp=      'STGSKQRSQNRSKTPKNQEALRMANVAENSSSDQRQACKKHELYVSFRDLGWQDWIIAPEGYAAYYCEGECAFPLNSYMNATNHAIVQTLVHFINPETVPKPCCAPTQLNAISVLYFDDSSNVILKKYRNMVVRACGCH'
+prot_1cdc=      'RDSGTVWGALGHGINLNIPNFQMTDDIDEVRWERGSTLVAEFKRKMKPFLKSGAFEILANGDLKIKNLTRDDSGTYNVTVYSTNGTRILDKALDLRILE'
+```
+```python
+#mutates seq into poly-X (change AA and color)
+ori =           'MNIFEMLRIDEGLRLKIYLDKAIGRNRAALVNLVFQIGETAAAAAAAAAAAAAAAAAAGAAGFTNSLRYLQQKRWDEAAVNFAKSRWYNQTPNRAKRIAAAAAAAAAAAAAAAAAAITVFRTGTWDAYKNL'
+AA='T'
+color='orange'
+multiple_ori = []
+mutated_seqs=[]
+mutated_seqs_fmax=[]
+num=100
+for i in range(num):
+    multiple_ori.append(ori)
+
+mutated_seqs = iterate_mutate_no_target(multiple_ori, AA)
+mutated_seqs_fmax = iterate_calc(mutated_seqs)
+pd.DataFrame(mutated_seqs).to_csv("mutations_"+AA+".csv")
+np.savetxt('mutations_'+AA+'_fmax.csv', mutated_seqs_fmax, delimiter=',')
+
+for y in mutated_seqs_fmax:
+    plt.plot(y,'0.7', zorder=0, linewidth=0.4)
+
+transposed=np.transpose(mutated_seqs_fmax)
+transposed_mean=[]
+transposed_sd=[]
+
+for x in transposed:
+    transposed_mean.append(np.mean(x))
+    transposed_sd.append(np.std(x))
+```
+
+Plot results
+
+```python
+plt.errorbar(range(len(transposed_mean)),transposed_mean,transposed_sd,capsize=2,zorder=50,color=color)
+plt.xlabel('Number of mutations')
+plt.ylabel('${F_{max}}$ (pN)')
+plt.title(str(num)+' 1p3n sequences mutate towards poly-'+AA)
+plt.plot(0,mutated_seqs_fmax[0][0],'or',zorder=100)
+plt.plot(len(mutated_seqs[0])-1,mutated_seqs_fmax[0][len(mutated_seqs_fmax[0])-1],'or',zorder=100)
+plt.savefig('mutations_'+AA+'.png',dpi=500)
+```
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/laM-07m_WywyEmFqV4B1I.png)
+
 # Sample results
 
 ![image](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/L016VkdQ5vDUM1OJLLVyg.png)