This thesis focuses on molecular recognition in chymosin complexes using various computational approaches used for studying protein-ligand systems. Three computational investigations are presented in this thesis. The first research project, titled, 'Allosteric-Activation Mechanism Of BovineChymosin', is presented in chapter 5. The study investigates the aspartic protease, bovine chymosin, which catalyses the proteolysis of k-casein proteins in milk. The research presented in this chapter employed two computational techniques, molecular dynamics and bias exchange metadynamics simulations, to study the mechanism of allosteric-activation and to compute the free energy surface for the process. The simulations reveal that allosteric activation is initiated by interactions between the HPHPH sequence of k-casein and a small a-helical region of chymosin (residues 112-116). A small conformational change in the a-helix causes the side chain of Phe114 to vacate a pocket that may then be occupied by the sidechain of Tyr77. The free energy surface for the self-inhibited to open transition is significantly altered by the presence of the HPHPH sequence of k-casein. The second research project, named, 'Effect of Mutations in Bovine or Camel Chymosin on the Thermodynamics of Binding k-Caseins', is presented in chapter 6. Both bovine and camel chymosin catalyse the proteolysis of a milk protein, k-casein, which helps to initiate milk coagulation. The research in this chapter reports computational alanine scanning calculations in four chymosin k-casein complexes, helping to elucidate the influence that individual residues have on the protein-ligand binding thermodynamics.Of the 12 sequence differences in the binding sites of bovine and camel chymosin, eight are shown to be particularly important for understanding differences in the binding thermodynamics (Asp112Glu, Lys221Val, Gln242Arg, Gln278Lys. Glu290Asp, His292Asn,Gln294Glu, and Lys295Leu. Residue in bovine chymosin written first). The final research project of this thesis titled, 'Comparative Molecular Field Analysis using Molecular Integral Equation Theory', is delivered in chapter 7. The study reports, and thoroughly benchmarks, a new method for 3D-QSAR that uses a classical statistical mechanics based solvent model combined with machine learning. Recently, Gussregen et al. used solute-solvent distribution functions calculated by the 3D Reference Interaction Site Model (3D-RISM) in a 3D-QSAR model to predict the binding affinities of serine protease inhibitors. The work carried out for this thesis extends this idea by introducing probe atoms into the 3D-RISM solvent model in order to capture other molecular interactions in addition to those related to hydration/dehydration. The CARMa models have been thoroughly benchmarked against other 3D-QSAR methods across six different datasets, demonstrating that CARMa is an extremely robust method, outperforming other field-based QSAR methods.
|Date of Award||1 Oct 2017|
- University Of Strathclyde
|Supervisor||David Palmer (Supervisor) & Tell Tuttle (Supervisor)|