Dataset Information

Structural investigations, quantum mechanical studies on proton and metal affinity and biological activity predictions of selpercatinib

ABSTRACT: Cancer of the lungs and thyroid is particularly difficult to manage and treat. Notably, selpercatinib has recently been suggested as an effective drug to combat these diseases. The entire world is currently tackling the pandemic caused by the SARS-CoV-19 virus. Numerous pharmaceuticals have been evaluated for the management of the disease caused by SARS-CoV-19 (i.e., COVID-19). In this study, selpercatinib was proposed as a potential inhibitor of different SARS-CoV-19 proteins. Several intriguing effects of the molecule were found during the conducted computational investigations. Selpercatinib could effectively act as a proton sponge and exhibited high proton affinity in solution. Moreover, it was able to form complexes with metal ions in aqueous solutions. Specifically, the compound displayed high affinity towards zinc ions, which are important for the prevention of virus multiplication inside human cells. However, due to their charge, zinc ions are not able to pass the lipid bilayer and enter the cell. Thus, it was determined that selpercatinib could act as an ionophore, effectively transporting active zinc ions into cells. Furthermore, various quantum mechanical analyses, including energy studies, evaluation of the reactivity parameters, examination of the electron localisation and delocalisation properties, as well as assessment of the nonlinear optical (NLO) properties and information entropy, were conducted herein. The performed docking studies (docking scores ?9.3169, ?9.1002, ?8.1853 and ?8.1222 kcal mol?1) demonstrated that selpercatinib strongly bound with four isolated SARS-CoV-2 proteins. Graphical abstract Unlabelled Image

SUBMITTER: Al-Zaqri N

PROVIDER: S-EPMC7957184 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Conspectus Quantum mechanics (QM) has revolutionized our understanding of the structure and reactivity of small molecular systems. Given the tremendous impact of QM in this research area, it is attractive to believe that this could also be brought into the biological realm where systems of a few thousand atoms and beyond are routine. Applying QM methods to biological problems brings an improved representation to these systems by the direct inclusion of inherently QM effects such as polarization and charge transfer. Because of the improved representation, novel insights can be gleaned from the application of QM tools to biomacromolecules in aqueous solution. To achieve this goal, the computational bottlenecks of QM methods had to be addressed. In semiempirical theory, matrix diagonalization is rate limiting, while in density functional theory or Hartree-Fock theory electron repulsion integral computation is rate-limiting. In this Account, we primarily focus on semiempirical models where the divide and conquer (D&C) approach linearizes the matrix diagonalization step with respect to the system size. Through the D&C approach, a number of applications to biological problems became tractable. Herein, we provide examples of QM studies on biological systems that focus on protein solvation as viewed by QM, QM enabled structure-based drug design, and NMR and X-ray biological structure refinement using QM derived restraints. Through the examples chosen, we show the power of QM to provide novel insights into biological systems, while also impacting practical applications such as structure refinement. While these methods can be more expensive than classical approaches, they make up for this deficiency by the more realistic modeling of the electronic nature of biological systems and in their ability to be broadly applied. Of the tools and applications discussed in this Account, X-ray structure refinement using QM models is now generally available to the community in the refinement package Phenix. While the power of this approach is manifest, challenges still remain. In particular, QM models are generally applied to static structures, so ways in which to include sampling is an ongoing challenge. Car-Parrinello or Born-Oppenheimer molecular dynamics approaches address the short time scale sampling issue, but how to effectively use QM to study phenomenon covering longer time scales will be the focus of future research. Finally, how to accurately and efficiently include electron correlation effects to facilitate the modeling of, for example, dispersive interactions, is also a major hurdle that a broad range of groups are addressing The use of QM models in biology is in its infancy, leading to the expectation that the most significant use of these tools to address biological problems will be seen in the coming years. It is hoped that while this Account summarizes where we have been, it will also help set the stage for future research directions at the interface of quantum mechanics and biology.

Dataset Information

Structural investigations, quantum mechanical studies on proton and metal affinity and biological activity predictions of selpercatinib

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets