In silico site-directed mutagenesis of Acinetobacter Haemolyticus Lipase KV1 for improved alkaline stability

The interest on alkaline-stable lipases by the scientific community is increasing due to its great potential use. As most industrial processes are performed under highly basic conditions, alkaline-stable lipases become hugely valued biocatalysts. In this study, three aspartic acid residues at positions 51, 122 and 247 in the outer loop of LipKV1 from Acinetobacter haemolyticus was computationally mutated into lysine using the SWISS-MODEL program, followed by energy minimization of the protein models. PROCHECK, ERRAT and Verify3D refined models of LipKV1 and Mut-LipKV1 indicated that the Mut-LipKV1 protein conformation is in a good condition. The study found that the overall electrostatic surface potentials and charge distributions of the Mut-LipKV1 model was more stable and better adapted to conditions of elevated pHs (pH 8.0 −10.0). Molecular dynamics (MD) simulation of Lip-KV1 and Mut-LipKV1 protein models under different alkaline pHs using GROMACS version 2018.6 revealed that Mut-LipKV1 was more stable at the high pH 9.0 (RMSD ~0.3 nm, RMSF ~0.05 – 0.2 nm), compared the optimal pH 8.0 of LipKV1 (RMSD 0.3 nm, RMSF 0.05 – 0.20 nm). Molecular docking using AutoDock Vina with tributyrin as the substrate identified detailed changes that occurred post mutation. The highest binding affinity (−4.1 kcal/mol) with Mut-LipKV1 which occurred at pH 9.0 was from a single hydrogen bond with His289. MD simulations showed that configurations which formed between Mut-LipKV1-tributyrin (RMSD 0.3 nm; RMSF 0.05 − 0.3 nm) and the LipKV1-tributyrin complexes (RMSD 0.35 nm; RMSF 0.05 − 0.4 nm) were comparably stable at pH 8.0. Furthermore, MM-PBSA calculation validated that the Mut-LipKV1-tributyrin complex at pH 8.0 (-44.01 kcal/mol) showed comparable binding free energy to LipKV1-tributyrin complex (−43.83 kcal/mol). Whereas the lowest binding free energy for Mut-LipKV1-tributyrin complex was simulated at pH 12.0 (−44.04 kcal/mol). Thus, adaptive strategy of replacing the outer loop surface aspartic acid to lysine in LipKV1 successfully broadened pH stability of Mut-LipKV1 towards higher pH, raising it from pH 8.0 − 11.0 to pH 8.0 − 12.0 in the mutant lipase. In a nutshell, this research offered a considerable insight for further improving the alkaline tolerance of lipases

An in-silico study: Investigating small molecule modulators of bio-molecular interactions

Small molecule inhibitors are commonly used to target protein targets that assist in the spread of diseases such as AIDS, cancer and deadly forms of influenza. Despite drug companies spending millions on R&D, the number of drugs that pass clinical trials is limited due to difficulties in engineering optimal non-covalent interactions. As many protein targets have the ability to rapidly evolve resistance, there is an urgent need for methods that rapidly identify effective new compounds. The thermodynamic driving force behind most biochemical reactions is known as the Gibbs free energy and it contains opposing dynamic and structural components that are known as the entropy (ΔS°) and enthalpy (ΔH°) respectively. ΔG° = ΔH° - TΔS°. Traditionally, drug design focussed on complementing the shape of an inhibitor to the binding cavity to optimise ΔG° favourability. However, this approach neglects the entropic contribution and phenomena such as Entropy-Enthalpy Compensation (EEC) often result in favourable bonding interactions not improving ΔG°, due to entropic unfavorability. Similarly, attempts to optimise inhibitor entropy can also have unpredictable results. Experimental methods such as ITC report on global thermodynamics, but have difficulties identifying the underlying molecular rationale for measured values. However, computational techniques do not suffer from the same limitations. MUP-I can promiscuously bind panels of hydrophobic ligands that possess incremental structural differences. Thus, small perturbations to the system can be studied through various in silico approaches. This work analyses the trends exhibited across these panels by examining the dynamic component via the calculation of per-unit entropies of protein, ligand and solvent. Two new methods were developed to assess the translational and rotational contributions to TΔS°, and a protocol created to study ligand internalisation. Synthesising this information with structural data obtained from spatial data on the binding cavity, intermolecular contacts and H-bond analysis allowed detailed molecular rationale for the global thermodynamic signatures to be derived