p53 Analysis Using BioPython

The TP53 gene encodes the regulatory protein p53, which serves as an important regulator. The normal functions of the p53 protein include: the control of cellular growth, regulation of mitochondrial respiration, and response to cellular stresses that may result in the transformation of a normal functioning cell into a tumor cell (Matoba et al, 2006; Soussi, T., & Béroud, C. 2019). Non-functional p53 loses the ability to act as a tumor suppressor, preventing the process of cell cycle arrest, cell death, and cellular senescence, therefore allowing tumors to form (Suzuki, K., & Matsubara, H., 2011). Tumors without nonfunctional p53 are extremely rare- the inactivation of this protein is the most common mutation found in human cancers as cells with the loss of functional p53 are often selected for by the majority of cancer types (Soussi, T., & Béroud, C. 2019).

Using Python, I will go through the process of displaying the structure of p53 seen below after running DNA and protein analysis.

(Click, drag, and zoom)

            

            from Bio import SeqIO
            

            for record in SeqIO.parse('sequence.fasta', 'fasta'):
                print(record)
            

        

            

            ID: U94788.1
            Name: U94788.1
            Description: U94788.1 Human p53 (TP53) gene, complete cds
            Number of features: 0
            Seq('TTCCCATCAAGCCCTAGGGCTCCTCGTGGCTGCTGGGAGTTGTAGTCTGAACGC...TGG')
            

        

            

            p53_record = SeqIO.read('sequence.fasta', 'fasta')
            

        

            

            SeqRecord(seq=Seq('TTCCCATCAAGCCCTAGGGCTCCTCGTGGCTGCTGGGAGTTGTAGTCTGAACGC...TGG'), 
            id='U94788.1', name='U94788.1', description='U94788.1 Human p53 (TP53) gene, 
            complete cds', dbxrefs=[])
            

        

            

            p53_dna = p53_record.seq
            

            p53_mRNA = p53_dna.transcribe()
            

            p53_protein = p53_mRNA.translate()
            

        

            

            p53_AA = p53_protein.split('*')
            

            p53_AA_clean = [str(seq) for seq in p53_AA]
            

        

            

            import pandas as pd
            

            df = pd.DataFrame({'amino_acids':p53_AA_clean})
            

            df['count'] = df['amino_acids'].str.len()
            

            df.head()
            

        

            

            from collections import Counter
            

            df.nlargest(10, 'count')
            

        

            

            Counter(p53_protein).most_common(10)
            

        

            

            [('L', 737),
            ('S', 572),
            ('G', 533),
            ('P', 510),
            ('R', 487),
            ('A', 409),
            ('K', 366),
            ('T', 337),
            ('V', 325),
            ('*', 287)]
            

        

            

            import py3Dmol
            

            view = py3Dmol.view(query='pdb:3TS8')
            view.setStyle({'cartoon': {'color': 'spectrum'}})
            

            view.png()