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Olayide A Arodola,1 Mahmoud ES Soliman1,2 1Department of Pharmaceutical Chemistry, University of KwaZulu-Natal, Durban, South Africa; 2Department of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Zagazig University, Egypt Abstract: The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein–ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug–protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry? Keywords: quantum mechanics, drug discovery, drug design, molecular mechanics, molecular dynamics, in silico tools&nbsp

Olayide A Arodola, Mahmoud ES SolimanMolecular Modelling and Drug Design Lab, School of Health Sciences, Westville Campus, University of KwaZulu-Natal, Durban, South AfricaAbstract: Based on experimental data, the anticancer activity of nelfinavir (NFV), a US Food and Drug Administration (FDA)-approved HIV-1 protease inhibitor (PI), was reported. Nevertheless, the mechanism of action of NFV is yet to be verified. It was hypothesized that the anticancer activity of NFV is due to its inhibitory effect on heat shock protein 90 (Hsp90), a promising target for anticancer therapy. Such findings prompted us to investigate the potential anticancer activity of all other FDA-approved HIV-1 PIs against human Hsp90. To accomplish this, “loop docking” – an enhanced in-house developed molecular docking approach – followed by molecular dynamic simulations and postdynamic analyses were performed to elaborate on the binding mechanism and relative binding affinities of nine FDA-approved HIV-1 PIs against human Hsp90. Due to the lack of the X-ray crystal structure of human Hsp90, homology modeling was performed to create its 3D structure for subsequent simulations. Results showed that NFV has better binding affinity (ΔG=−9.2kcal/mol) when compared with other PIs: this is in a reasonable accordance with the experimental data (IC50 3.1µM). Indinavir, saquinavir, and ritonavir have close binding affinity to NFV (ΔG=−9.0, −8.6, and −8.5kcal/mol, respectively). Per-residue interaction energy decomposition analysis showed that hydrophobic interaction (most importantly with Val534 and Met602) played the most predominant role in drug binding. To further validate the docking outcome, 5ns molecular dynamic simulations were performed in order to assess the stability of the docked complexes. To our knowledge, this is the first account of detailed computational investigations aimed to investigate the potential anticancer activity and the binding mechanism of the FDA-approved HIV PIs binding to human Hsp90. Information gained from this study should also provide a route map toward the design, optimization, and further experimental investigation of potential derivatives of PIs to treat HER2+ breast cancer.Keywords: binding free energy, loop docking, molecular dynamics, HIV-1 protease inhibitors, anticancer

Arodola OA, Soliman MES. Quantum mechanics implementation in drug-design workflows: does it really help? Drug Design, Development and Therapy. 2017;11:2551–2564.Figure 3 on page 2557 contains errors. The correct figure is shown.Read the original article