Slipknot or Crystallographic Error: A Computational Analysis of the Plasmodium falciparum DHFR Structural Folds.
Rolland Bantar TataAli F AlsulamiOlivier Sheik AmamuddyTom L BlundellOzlem Tastan BishopPublished in: International journal of molecular sciences (2022)
The presence of protein structures with atypical folds in the Protein Data Bank (PDB) is rare and may result from naturally occurring knots or crystallographic errors. Proper characterisation of such folds is imperative to understanding the basis of naturally existing knots and correcting crystallographic errors. If left uncorrected, such errors can frustrate downstream experiments that depend on the structures containing them. An atypical fold has been identified in P. falciparum dihydrofolate reductase ( Pf DHFR) between residues 20-51 (loop 1) and residues 191-205 (loop 2). This enzyme is key to drug discovery efforts in the parasite, necessitating a thorough characterisation of these folds. Using multiple sequence alignments (MSA), a unique insert was identified in loop 1 that exacerbates the appearance of the atypical fold-giving it a slipknot-like topology. However, Pf DHFR has not been deposited in the knotted proteins database, and processing its structure failed to identify any knots within its folds. The application of protein homology modelling and molecular dynamics simulations on the DHFR domain of P. falciparum and those of two other organisms ( E. coli and M. tuberculosis ) that were used as molecular replacement templates in solving the Pf DHFR structure revealed plausible unentangled or open conformations of these loops. These results will serve as guides for crystallographic experiments to provide further insights into the atypical folds identified.
Keyphrases
- plasmodium falciparum
- molecular dynamics simulations
- adverse drug
- drug discovery
- protein protein
- amino acid
- patient safety
- transcription factor
- escherichia coli
- high resolution
- mycobacterium tuberculosis
- binding protein
- molecular docking
- minimally invasive
- electronic health record
- small molecule
- single cell
- hiv aids
- artificial intelligence
- machine learning
- pulmonary tuberculosis
- antiretroviral therapy
- hiv infected
- toxoplasma gondii
- single molecule