Virtual screening of PEBP1 inhibitors by combining 2D/3D-QSAR analysis, hologram QSAR, homology modeling, molecular docking analysis, and molecular dynamic simulations

Human phosphatidylethanolamine binding protein 1 (hPEBP1) is a novel target affecting many cellular signaling pathways involved in the formation of metastases. It can be used in the treatment of many cases of cancer. For these reasons, pharmaceutical companies use computational approaches, including multi-QSAR (2D, 3D, and hologram QSAR) analysis, homology modeling, molecular docking analysis, and molecular dynamic simulations, to speed up the drug discovery process. In this paper, QSAR modeling was conducted using two quantum chemistry optimization methods (AM1 and DFT levels). As per PLS results, we found that the DFT/B3LYP method presents high predictability according to 2D-QSAR, CoMFA, CoMSIA, and hologram QSAR studies, with Q² of 0.81, 0.67, 0.79, and 0.67, and external power with R²_pred of 0.78, 0.58, 0.66, and 0.56, respectively. This result has been validated by CoMFA/CoMSIA graphics, which suggests that electrostatic fields combined with hydrogen bond donor/acceptor fields are beneficial to the antiproliferative activity. While the hologram QSAR models show the contributions of each fragment in improving the activity. The results from QSAR analyses revealed that ursolic acids with heterocyclic rings could improve the activities. Ramachandran plot validated the modeled PEBP1 protein. Molecular docking and MD simulations revealed that the hydrophobic and hydrogen bond interactions are dominant in the PEBP1's pocket. These results were used to predict in silico structures of three new compounds with potential anticancer activity. Similar molecular docking stability studies and molecular dynamics simulations were conducted.

Correlation Effects in Trimeric Acylphloroglucinols

Trimeric acylphloroglucinols (T-ACPLs) are a subclass of the large class of acylphloroglucinols—derivatives of 1,3,5-trihydroxybenzene containing an R–C=O group. T-ACPL molecules contain three acylphloroglucinol moieties linked by methylene bridges. Many of them are present in natural sources and exhibit biological activities, often better than the corresponding activities of monomeric acylphloroglucinols. All the stable conformers of T-ACPLs contain seven intramolecular hydrogen bonds, which constitute the dominant stabilising factors. A total of 38 different T-ACPLs, including both naturally occurring and model molecules, have been calculated at the HF and DFT/B3LYP levels. The DFT/B3LYP calculations were carried out both without and with Grimme’s dispersion correction, to highlight the dispersion (and, therefore, also electron correlation) effects for these molecules. The roles of dispersion are evaluated considering the effects of Grimme’s correction on the estimation of the conformers’ energies, the description of the characteristics of the individual hydrogen bonds, the conformers’ geometries and other molecular properties. Overall, the results offer a comprehensive overview of the conformational preferences of T-ACPL molecules, their intramolecular hydrogen bond patterns, and the correlation effects on their properties.

Concentration-dependent adsorption of organic contaminants by graphene nanosheets: quantum-mechanical models

Adsorption is the key process in the expression of environmentally relevant physicochemical and toxicological properties of carbon nanomaterials. However, the adsorption of organic contaminants on to nanomaterials is a highly complex phenomenon, owing to the heterogeneity of adsorption sites, for example, on graphene surface as well as due to multiple factors operative during the adsorption, particularly, at the quantum-mechanical level. For predicting the concentration-dependent adsorption coefficients of organic contaminants by carbon nanomaterials, one option has been to rely on the existing linear-solvation energy relationship (LSER) models. The present work on the adsorption of aromatic and aliphatic organic contaminants by graphene nanosheets reveals that the existing LSER models are prone to failure when tested for internal and external validation using an external prediction set of compounds unknown to the model. As an alternative to the LSERs, the present work reports pure quantum-mechanical models developed using computational only quantum-mechanical descriptors. The reliability of the quantum-mechanical models was tested using state-of-the-art validation procedures employing an external prediction set of compounds. The proposed quantum-mechanical models reveal mean polarizability, zero-point vibrational energy, and its electron-correlation contribution to be the key descriptors in the prediction of adsorption coefficients of organic contaminants by graphene nanosheets.

Externally predictive quantum-mechanical models for the adsorption of aromatic organic compounds by graphene-oxide nanomaterials

Graphene oxide is most often chosen as an alternative to graphene in the applications of carbon-based nanomaterials where adsorption is the primary process. However, its adsorption properties are poorly understood. The existing reports on the adsorption mechanism of graphene oxide rely on the linear free-energy/solvation-energy relationship (LFER/LSER) models. This computational work explores the role of quantum mechanical descriptors in the adsorption of aromatic organic compounds by graphene-oxide. For this, externally predictive quantitative models based on quantum-mechanical descriptors are developed and compared with the existing LSERs for the prediction of adsorption coefficients of organic compounds at three different adsorbate concentrations. The predictivity of the models is assessed using an external prediction set of compounds not used for developing the models. Notably, the mean polarizability, but originating from the quantum mechanical exchange interactions (between electrons of parallel spin), is found to be the most significant factor in driving the adsorption on graphene oxide. The present work also proposes quantum-mechanical-LSER models based on a combination of quantum-mechanical and LSER descriptors, which are in fact found to be equally predictive as the existing LSERs. The quantum-mechanical models proposed in this work are further utilized for the prediction of adsorption coefficients of aliphatic compounds.

Quantum-mechanical LSERs for the concentration-dependent adsorption of aromatic organic compounds by activated carbon: Applications and comparison with carbon nanotubes

Carbon nanotubes (CNTs) have taken precedence over activated carbon in various applications where adsorption is the primary process. The adsorption of chemical compounds by CNTs and activated carbon is most often predicted through linear free energy/solvation energy relationships (LFERs/LSERs). This work proposes quantum-mechanical LSER models based on a combination of quantum-mechanical descriptors and solvatochromic descriptors of LSERs for predicting the adsorption of aromatic organic compounds by activated carbon at varying adsorbate concentrations. The models are validated using state-of-the-art procedures employing an external prediction set of compounds. This work reveals the hydrogen bond donating and accepting ability of compounds to be the most influencing - but a negative - factor in the adsorption process of activated carbon. The quantum-mechanical LSERs proposed in this work are analysed and found to be equally reliable as the existing LSERs. These were further used to predict the adsorption of nucleobases, steroid hormones, agrochemicals, endocrine disruptors and pharmaceutical drugs. Notably, agrochemicals and endocrine disruptors are predicted to be adsorbed more strongly by activated carbon when compared with their adsorption by CNTs. However, quantum-mechanical LSERs predict the adsorption strength of biomolecules on activated carbon to be similar to that on the CNTs, which can be used to assess the risk associated with using carbon materials.

Concentration dependent adsorption of aromatic organic compounds by SWCNTs: Quantum-mechanical descriptors for nano-toxicological studies of biomolecules and agrochemicals

For evaluating the environmental risk associated using carbon nanotubes (CNTs), a successful prediction is desired for the adsorption of organic compounds (OCs) by CNTs at different adsorbate concentrations. This is most often achieved through poly-parameter linear solvation energy relationships (LSERs) based on solvatochromic descriptors. This study examines the real predictivity of the existing LSERs for predicting the adsorption of OCs by single-walled CNTs (SWCNTs) while comparing it with that of the models developed in the present work using quantum-mechanical descriptors. The real predictivity of the quantum-mechanical models and existing LSERs is compared using state-of-the-art statistical procedures employing an external prediction set of compounds not used in the model development. The quantum-mechanically computed mean polarizability, but originating from the interactions between electrons of parallel spin, is found to play an essential role in the adsorption of OCs by SWCNTs. Besides the solvatochromic descriptors (McGowan volume and molar excess refractivity), the instantaneous inter-electronic interactions, captured through electron-correlation based quantum-mechanical descriptors, are found to significantly affect the adsorption at varying adsorbate concentration. The models developed using a combination of quantum-mechanical and solvatochromic descriptors are found to be quite reliable. The models proposed were further employed to predict the adsorption of agrochemicals such as insecticides, pesticides, herbicides, as well as adsorption of endocrine disruptors and biomolecules such as nucleobases and steroid hormones. These are predicted to be strongly adsorbed by SWCNTs with Progesterone and Guanine exhibiting maximal interaction with the SWCNTs among biomolecules. The quantum-mechanical descriptors proposed in this work can be used for the risk assessment of SWCNTs in systems where adsorption is the primary process.

Quantum-mechanical parameters for the risk assessment of multi-walled carbon-nanotubes: A study using adsorption of probe compounds and its application to biomolecules

This work forwards new insights into the risk-assessment of multi-walled carbon-nanotubes (MWCNTs) while analysing the role of quantum-mechanical interactions between the electrons in the adsorption of probe compounds and biomolecules by MWCNTs. For this, the quantitative models are developed using quantum-chemical descriptors and their electron-correlation contribution. The major quantum-chemical factors contributing to the adsorption are found to be mean polarizability, electron-correlation energy, and electron-correlation contribution to the absolute electronegativity and LUMO energy. The proposed models, based on only three quantum-chemical factors, are found to be even more robust and predictive than the previously known five or four factors based linear free-energy and solvation-energy relationships. The proposed models are employed to predict the adsorption of biomolecules including steroid hormones and DNA bases. The steroid hormones are predicted to be strongly adsorbed by the MWCNTs, with the order: hydrocortisone > aldosterone > progesterone > ethinyl-oestradiol > testosterone > oestradiol, whereas the DNA bases are found to be relatively less adsorbed but follow the order as: guanine > adenine > thymine > cytosine > uracil. Besides these, the developed electron-correlation based models predict several insecticides, pesticides, herbicides, fungicides, plasticizers and antimicrobial agents in cosmetics, to be strongly adsorbed by the carbon-nanotubes. The present study proposes that the instantaneous inter-electronic interactions may be quite significant in various physico-chemical processes involving MWCNTs, and can be used as a reliable predictor for their risk assessment.