Binding Free Energy Calculations of Nine FDA-approved Protease Inhibitors Against HIV-1 Subtype C I36T↑T Containing 100 Amino Acids Per Monomer.
- 1Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
- 2Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, CP 11101, Belém, PA, 66075-110, Brazil.
- 3School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa.
- 4Protein Structure-Function Research Unit, School of Molecular and Cell Biology, University of the Witwatersrand, Wits, 2050, South Africa.
In this work, have investigated the binding affinities of nine FDA-approved protease inhibitor drugs against a new HIV-1 subtype C mutated protease, I36T↑T. Without an X-ray crystal structure, homology modelling was used to generate a three-dimensional model of the protease. This and the inhibitor models were employed to generate the inhibitor/I36T↑T complexes, with the relative positions of the inhibitors being superimposed and aligned using the X-ray crystal structures of the inhibitors/HIV-1 subtype B complexes as a reference. Molecular dynamics simulations were carried out on the complexes to calculate the average binding free energies for each inhibitor using the molecular mechanics generalized Born surface area (MM-GBSA) method. When compared to the binding free energies of the HIV-1 subtype B and subtype C proteases (calculated previously by our group using the same method), it was clear that the I36T↑T proteases mutations and insertion had a significant negative effect on the binding energies of the non-pepditic inhibitors nelfinavir, darunavir and tipranavir. On the other hand, ritonavir, amprenavir and indinavir show improved calculated binding energies in comparison with the corresponding data for wild-type C-SA protease. The computational model used in this study can be used to investigate new mutations of the HIV protease and help in establishing effective HIV drug regimes and may also aid in future protease drug design. © 2015 John Wiley & Sons A/S.
100 amino acids; C-SA HIV PR; I36T↑T; insertion; mutation
- PMID: 26613568
As of January 2016 there were an estimated 36.7 million people living with HIV globally. Southern and Eastern Africa made up an estimated 19 million of that global figure, that’s a staggering 52% of all people living with HIV on earth. The HIV-1 group M variant of the HI virus, the most rampant variant, is divided into subtypes based on their genetic profile. These subtypes also have a geographical locale, with certain subtypes found in certain parts of the world, for instance the common subtype for Southern Africa is the subtype C.
While the subtype C variant of the virus is prominent in Southern Africa and accounts for nearly half the global infection statics, it is the far else prominent subtype B variant that is the basis of all our knowledge of HIV and accounts for most of the drug research and development. The subtype B variant, which makes up a little more than 10% of the global estimate, is predominantly found in Europe and the Americas, two of the largest and wealthiest economies.
Southern Africa also accounts for nearly 60% of people on antiretroviral (ARV) treatment, using some form combination drug therapy. Our question was how effective were the drugs that were developed using a subtype B archetype against the subtype C variant. We focused our question on nine US food and drug administration (FDA) approved protease inhibitors. The HIV-1 protease is an aspartyl enzyme that along with two other HIV enzymes, reverse transcriptase and integrase facilitate the virus’s replication cycle. The protease is responsible for the cleavage of inactive long chain polyproteins into active proteins constituents of an infectious HIV virion. Therefore the inhibition of the protease would halt the replication of the virus in the host.
We decided to base the effectiveness of the inhibitors on their binding energies calculated using computational methods. The computational method allowed us to test all nine inhibitor drugs against not only the South African wild type subtype C (C-SA) protease, but also a new mutation to the C-SA protease, designated I36T↑T. The new mutation contained mutations to the amino acid sequence of the protease but also an insertion of an amino acid, taking the standard 99 amino acids per monomer of the C-SA protease to 100 amino acids per monomer. Figure 1 shows the comparison of the C-SA protease and the I36T↑T mutation.
Figure 1. Ribbon representation of overlay of modelled I36T↑T protease (brown) and wild type HIV-1 subtype C (blue) previously reported. The amino acid mutations (green), the amino acid insertion (red) and the catalytic Asp (yellow) are highlighted as spheres.
We first ran our computational model against the C-SA and compared it to computational results from the subtype B protease and any experimental data on the binding energies of the nine inhibitors. We were satisfied with the results of the study and applied the model to the I36T↑T protease mutation.
As the I36T↑T mutation was so new, there was no X-ray crystallography data on the structure of the protease mutation, so used the the amino acid sequence and homolgy modelling to render a 3D model of the protease to use in the molecular dynamic simulations. Although a 3D model of the C-SA exsisted from X-ray crystallography data, we chose to use the subtype B protease as the template or compariative model because the 3D model of the C-SA was in the open comformation and during drug binding the protease is always in a closed comformation, as was the subtype B template.
With the 3D model of the I36T↑T mutation completed and verified, we began placing each of the nine FDA protease inhibitors in the active site of the protease. Their positions were determined using their relative positions found in X-ray crystallography data of each drug bound to a subtype B protease, this data was found on the RCSB protein data bank. Each drug prtoease model was used in a molecular dynamics simulation using the software package AMBER. The simulations were run for 10ns, which we found to be suffcient for the system to reach equilibrium. The trajectories from the last 2ns of the molecular dynamics simulations were used to calculate the binding free energies for each of the drugs. We used these binding energy results for compare to the calculated binding energies of the subtype B and C-SA from our previous study and we found that the I36T↑T mutations and insertion significantly impacted the binding effectiveness of nonpepditic inhibitors, as well as improved binding effectiveness of other inhibitors compared the C-SA protease.
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