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The combination of deep learning model and wet experiment is expected to be used for metabolic flux analysis
2022-07-05 08:49:00 【Python code doctor】
background
On the road of protein structure transformation , Need powerful computer programming assistance , In the field of deep learning , Found a powerful tool , So study how to combine with wet experiment .
Tools
DeeplearningApproach Anconda python And various dependent packages Computer Wet experimental data
Realize the idea
1. First , Good configuration DeeplearningApproach, How to configure see another article 《 Deep learning predicts enzyme activity parameters, improves enzyme constraint model construction, and builds ab initio environment 》.
2. Use the mutation sites with existing data to predict , For example, I want to predict the sequence of a protein L59,W60,Y153,R416 Site mutation to alanine (A), So I need to configure my tsv file , I wrote a script for the mutation here , The code is as follows :
import copy
def sort(a,b,c,d):
for n,i in enumerate(a):
if n+1 == b: # Determine whether it is the site I want
d = a[n] # Assign this site of the original sequence to d
a = copy.deepcopy(a) # Here you need a deep copy , Otherwise it will change WT Sequence , Of course, if it is the same site, it can be predicted as others 19 Amino acids can be used without
a[n] = c # This site of the original sequence is replaced by the site I need ,
i = a[n] # Extract the changed amino acids
break
else:
a[n] = i
a = ''.join(a)
# print(' Has already put the first %d Amino acids %s Change it to %s, The output sequence is as follows :%s'%((n+1),d,i,a))
print(' Enter the substrate name '+' Enter the chemical formula of the substrate '+a) # The difference between a plus sign and a comma
def main():
# Manual input
# while True:
# a = list(input(' Please enter the amino acid sequence :'))
# b = int(input(' Please select the amino acid site to be modified :'))
# c = input(' Please select the single letter abbreviation of amino acid to be changed :')
# sort(a,b,c,d=None)
aa_list = ['A','G','V','L','I','P','F','Y','W','S','T','C','M','N','Q','D','E','K','R','H']
pro = ' Input amino acid sequence '
pro_list = []
for p in pro:
pro_list.append(p)
for i in aa_list:
sort(pro_list,231,i,d=None)
if __name__ == '__main__':
main()
3. By extraction WT,L59,W60,Y153,R416 Of Kcat Value to draw a column chart ( Of course, it's OK to look without drawing , Just a few data ), Compare with the existing experimental data :
Deep excavation
1) How to use it DeeplearningApproach characteristic ?
Make one tsv library , This library contains unit point mutations and double site mutations of all amino acids near the active site of the protein , And WT Of Kcat It's worth comparing , Screen beneficial mutations for experiments .
2) Can you make DeeplearningApproach More suitable for a certain exact experiment , Make it Kcat The value is more accurate , And then use it Kcat Do metabolic flux analysis , Maybe we can start from two aspects :
- Adjust the parameters , To optimize the fitting / Under fitting phenomenon , Do some super parameter optimization , Make it more appropriate to the existing experimental results .
- Using a large amount of experimental data ( At least one million ) Retraining the model , Get a model with strong specificity ( It is almost impossible to achieve , Just a guess )
Be careful
The experimental data are fictitious , To introduce the method .
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