Platinum(IV) compounds as potential drugs: a quantitative structure-activity relationship study.

Introduction: Machine learning methods, coupled with a tremendous increase in computer power in recent years, are promising tools in modern drug design and drug repurposing. Methods: Machine learning predictive models, publicly available at chemosophia.com, were used to predict the bioactivity of recently synthesized platinum(IV) complexes against different kinds of diseases and medical conditions. Two novel QSAR models based on the BiS algorithm are developed and validated, capable to predict activities against the SARS-CoV virus and its RNA dependent RNA polymerase. Results: The internal predictive power of the QSAR models was tested by 10-fold cross-validation, giving cross-R2 from 0.863 to 0.903. 38 different activities, ranging from antioxidant, antibacterial, and antiviral activities, to potential anti-inflammatory, anti-arrhythmic and anti-malarial activity were predicted for a series of eighteen platinum(IV) complexes. Conclusion: Complexes 1, 3 and 13 have high generalized optimality criteria and are predicted as potential SARS-CoV RNA dependent RNA polymerase inhibitors.

AlteQ: a new complementarity principle-centered method for the evaluation of docking poses.

Molecular docking is the most popular and widely used method for identifying novel molecules against a target of interest. However, docking procedures and their validation are still under intense development. In the present investigation, we evaluate a quantum free-orbital AlteQ method for evaluating docking complexes generated by taking EGFR complexes as an example. The AlteQ method calculates the electron density using Slater's type atomic contributions in the interspace between the receptor and the ligand. Since the interactions are determined by the overlap of electron clouds, they follow the complementarity principle, and an equation can be obtained that describes these interactions. The AlteQ method evaluates the quality of the interaction between the receptor and the ligand, how complementary the interactions are, and due to this, it is used to reject less realistic structures obtained by docking methods. Here, three different equations were used to determine the quality of the interactions in experimental complexes and docked complexes obtained using AutoDock Vina and AutoDock 4.2.6.Communicated by Ramaswamy H. Sarma.

The design of compounds with desirable properties - The anti-HIV case study.

Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identifying, developing, and optimizing potential drugs. Here, we present novel approach to design new molecules with desired properties. We combined various neural networks and linear regression algorithms to build models for cytotoxicity and anti-HIV activity based on Continual Molecular Interior analysis (CoMIn) and Cinderella's Shoe (CiS) derived molecular descriptors. After validating the reliability of the models, a genetic algorithm was coupled with the Des-Pot Grid algorithm to generate new molecules from a predefined pool of molecular fragments and predict their bioactivity and cytotoxicity. This combination led to the proposal of 16 hit molecules with high anti-HIV activity and low cytotoxicity. The anti-SARS-CoV-2 activity of the hits was predicted.

A systems biology investigation of curcumin potency against TGF-ß-induced EMT signaling in lung cancer.

Curcumin (diferuloylmethane) is bioactive phenolic compound which exerts diverse antimetastatic effect. Several studies have reported the antimetastatic effect of curcumin by its ability to modulate the epithelial-to-mesenchymal transition (EMT) process in different cancers, but underlying molecular mechanism is poorly understood. EMT is a highly conserved biological process in which epithelial cells acquire mesenchymal-like characteristics by losing their cell-cell junctions and polarity. As a consequence, deviation in cellular mechanism leads to cancer metastasis and thereby death. In this perspective, we explored the antimetastatic potential and mechanism of curcumin on the EMT process by establishing in vitro EMT model in lungs cancer (A549) cells induced by TGF-ß1. Our results showed that curcumin mitigates EMT by regulating the expression of crucial mesenchymal markers such as MMP2, vimentin and N-cadherin. Besides, the transcriptional analysis revealed that the curcumin treatment differentially regulated the expression of 75 genes in NanoString nCounter platform. Further protein-protein interaction network and clusters analysis of differentially expressed genes revealed their involvement in essential biological processes that plays a key role during EMT transition. Altogether, the study provides a comprehensive overview of the antimetastatic potential of curcumin in TGF-ß1-induced EMT in lung cancer cells. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03360-7.

Withasomniferol C, a new potential SARS-CoV-2 main protease inhibitor from the Withania somnifera plant proposed by in silico approaches.

Exploring potent herbal medicine candidates is a promising strategy for combating a pandemic in the present global health crisis. In Ayurveda (a traditional medicine system in India), Withania somnifera (WS) is one of the most important herbs and it has been used for millennia as Rasayana (a type of juice) for its wide-ranging health benefits. WS phytocompounds display a broad spectrum of biological activities (such as antioxidant, anticancer and antimicrobial) modulate detoxifying enzymes, and enhance immunity. Inspired by the numerous biological actions of WS phytocompounds, the present investigation explored the potential of the WS phytocompounds against the SARS-CoV-2 main protease (3CLpro). We selected 11 specific withanolide compounds, such as withaphysalin, withasomniferol, and withafastuosin, through manual literature curation against 3CLpro. A molecular similarity analysis showed their similarity with compounds that have an established inhibitory activity against the SARS-CoV-2. In silico molecular docking and molecular dynamics simulations elucidated withasomniferol C (WS11) as a potential candidate against SARS-CoV-2 3CLpro. Additionally, the present work also presents a new method of validating docking poses using the AlteQ method.

Establishment of models for reliability evaluation of 3CLpro ligand-receptor complexes with different binding sites.

Aim: Recent research shows that 3CLpro enzyme of SARS-CoV-2 is a significant target against COVID-19. Drug modeling allows the design of inhibitors of 3CLpro, but the accuracy of those methods remains unclear. Therefore, it is important to determine the trustworthiness of the designed ligand-receptor complexes. Method & materials: The authors built models for the reliability evaluation of 3CLpro complexes with ligands using an in-house developed AlteQ approach and complementarity principles. The models were based on 145 experimentally found 3CLpro complexes with ligands for five different binding sites. Result & conclusion: The obtained models correspond to linear regression with high values of correlation coefficients and can be successfully used to determine the reliability of the docked 3CLpro complexes with ligands.

COVID-19 is currently one of the most significant problems in the world. Research shows that the enzyme 3CLpro of SARS-CoV-2 is a main target for the development of potential anti-COVID-19 therapeutics. Drug modeling methods, especially docking studies, are utilized to design new compounds, however, the approaches have questionable precision. The authors present new models based on calculated electronic characteristics that can be used in drug design for the reliability assessment of SARS-CoV-2 3CLpro receptor­ligand complexes.

A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study.

Coronavirus disease 2019 (COVID-19) is caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2). Its main protease, 3C-like protease (3CLpro), is an attractive target for drug design, due to its importance in virus replication. The analysis of the radial distribution function of 159 3CLpro structures reveals a high similarity index. A study of the catalytic pocket of 3CLpro with bound inhibitors reveals that the influence of the inhibitors is local, perturbing dominantly only residues in the active pocket. A machine learning based model with high predictive ability against SARS-CoV-2 3CLpro is designed and validated. The model is used to perform a drug-repurposing study, with the main aim to identify existing drugs with the highest 3CLpro inhibition power. Among antiviral agents, lopinavir, idoxuridine, paritaprevir, and favipiravir showed the highest inhibition potential. Enzyme - ligand interactions as a key ingredient for successful drug design.

Proposition of a new allosteric binding site for potential SARS-CoV-2 3CL protease inhibitors by utilizing molecular dynamics simulations and ensemble docking.

The SARS-CoV-2 3CL protease (3CLpro) shows a high similarity with 3CL proteases of other beta-coronaviruses, such as SARS and MERS. It is the main enzyme involved in generating various non-structural proteins that are important for viral replication and is one of the most important proteins responsible for SARS-CoV-2 virulence. In this study, we have conducted an ensemble docking of molecules from the DrugBank database using both the crystallographic structure of the SARS-CoV-2 3CLpro, as well as five conformations obtained after performing a cluster analysis of a 300 ns molecular dynamics (MD) simulation. This procedure elucidated the inappropriateness of the active site for non-covalent inhibitors, but it has also shown that there exists an additional, more favorable, allosteric binding site, which could be a better target for non-covalent inhibitors, as it could prevent dimerization and activation of SARS-CoV-2 3CLpro. Two such examples are radotinib and nilotinib, tyrosine kinase inhibitors already in use for treatment of leukemia and which binding to the newly found allosteric binding site was also confirmed using MD simulations. Communicated by Ramaswamy H. Sarma.

The study of EGFR-ligand complex electron property relationship with biological activity.

The present investigation grounded on estimation of electron properties of the structures of EGFR proteins-ligand complexes using our laboratory-developed methodology AlteQ approach, which describes the molecular electron density of the complex in space for a certain point in three-dimensional coordinates. Briefly, the system embodies molecular electron density as a sum of Slater's type atomic increments of the molecular system. Further, using this methodology, we calculated different electron characteristics of selected EGFR protein-ligand complexes and established the relationship between different electron properties with their experimental pharmacological activity value (pIC50). The study suggested that EGFR inhibitory activity has higher correlation with intermolecular contacts of H with pi-system of aromatic ring between protein and ligands. Therefore, this created model has impact to identify and design potential ligands against EGFR in anticancer drug discovery.Communicated by Ramaswamy H. Sarma.

Electronic properties investigation of human dihydrofolate reductase complexes with ligands.

Despite the fact that there are already drugs for cancer, they still show strong toxicity to the human organism. That is why it is necessary to establish the factors affecting activity in order to develop new, more effective drugs aimed at tumor cells, minimizing harm to healthy cells. The present research is based on electronic properties calculation of the complexes using AlteQ approach. In the focus of this study are complexes of human dihydrofolate reductase (hDHFR) with a series of known inhibitors bound in the active site. Further, a statistical analysis was performed to establish the relationships between a myriad electronic characteristics and IC50. The change in total volume and the change of own electrons number of hydrogen atoms in their atomic basins are identified as the descriptors correlating the most with the hDHFR inhibition potency. Additionally, two lipophilic parts of protein (Thr56, Ser59, Ile60 and Ile7, Val8, Ala9) were found, which act as a key factor in decreasing bioactivity. The depth analysis of intermolecular interactions showed that the interactions between water molecules and ligand play a crucial role in hDHFR inhibition. Furthermore, the molecular dynamics simulations were used for deeper understanding of the structural inhibition, each for 50 ns time scale in explicit water conditions. Thus, the AlteQ approach made it possible to determine the factors influencing the activity and evaluate them not only qualitatively, but also quantitatively.Communicated by Ramaswamy H. Sarma.

In silico validation of novel inhibitors of malarial aspartyl protease, plasmepsin V and antimalarial efficacy prediction.

Plasmepsin V (Plm V) is an essential aspartic protease required for survival of the malaria parasite, Plasmodium falciparum (Pf). Plm V is required for cleaving the PEXEL motifs of many Pf proteins and its inhibition leads to a knockout effect, indicating its suitability as potential drug target. To decipher new inhibitors of PfPlm V, molecular docking of four HIV-1 protease inhibitors active against PfPlmV was performed on Glide module of Schrödinger suite that supported saquinavir as a lead drug, and therefore, selected as a control. Saquinavir contains an important hydroxyethylamine (HEA) pharmacophore, which was utilized as backbone coupled with piperazine scaffold to build new library of compounds. Newly designed HEA compounds were screened virtually against Plm V. Molecular docking led to a few hits (1 and 3) with higher docking score over the control drug. Notably, compound 1 showed the highest docking score (-11.90 kcal/mol) and XP Gscore (-11.948 kcal/mol). The Prime MMGBSA binding free energy for compound 1 (-60.88 kcal/mol) and 3 (-50.96 kcal/mol) was higher than saquinavir (-37.51 kcal/mol). The binding free energy for the last frame of molecular dynamic simulation supported compound 1 (-92.88 kcal/mol) as potent inhibitor of PfPlm V over saquinavir (-72.77 kcal/mol), and thus, deserves experimental validations in culture and subsequently in animal models.Communicated by Ramaswamy H. Sarma.

Exploring potential inhibitors against Kyasanur forest disease by utilizing molecular dynamics simulations and ensemble docking.

Kyasanur forest disease (KFD) is a tick-borne, neglected tropical disease, caused by KFD virus (KFDV) which belongs to Flavivirus (Flaviviridae family). This emerging viral disease is a major threat to humans. Currently, vaccination is the only controlling method against the KFDV, and its effectiveness is very low. An effective control strategy is required to combat this emerging tropical disease using the existing resources. In this regard, in silico drug repurposing method offers an effective strategy to find suitable antiviral drugs against KFDV proteins. Drug repurposing is an effective strategy to identify new use for approved or investigational drugs that are outside the scope of their initial usage and the repurposed drugs have lower risk and higher safety compared to de novo developed drugs, because their toxicity and safety issues are profoundly investigated during the preclinical trials in human/other models. In the present work, we evaluated the effectiveness of the FDA approved and natural compounds against KFDV proteins using in silico molecular docking and molecular simulations. At present, no experimentally solved 3D structures for the KFD viral proteins are available in Protein Data Bank and hence their homology model was developed and used for the analysis. The present analysis successfully developed the reliable homology model of NS3 of KFDV, in terms of geometry and energy contour. Further, in silico molecular docking and molecular dynamics simulations successfully presented four FDA approved drugs and one natural compound against the NS3 homology model of KFDV. Communicated by Ramaswamy H. Sarma.

A novel compound active against SARS-CoV-2 targeting uridylate-specific endoribonuclease (NendoU/NSP15): in silico and in vitro investigations.

NendoU (NSP15) is an Mn(2+)-dependent, uridylate-specific enzyme, which leaves 2'-3'-cyclic phosphates 5' to the cleaved bond. Our in-house library was subjected to high throughput virtual screening (HTVS) to identify compounds with potential to inhibit NendoU enzyme, high-rank compounds (those that bound to multiple target structures) were further subjected to 100 nanoseconds MD simulations. Among these, one was found to be bound highly stable within the active site of the NendoU protein structure. Here, we are reporting a derivative of piperazine based '(2S,3S)-3-amino-1-(4-(4-(tert-butyl)benzyl)piperazin-1-yl)-4-phenylbutan-2-ol' (IV) from our in-house libraries having potential efficacy against SARS-CoV-2 in in vitro assays. This compound demonstrated inhibition of viral replication at the same level as Ivermectin, a known SARS-CoV-2 inhibitor, which is not used due to its toxicity at a higher than the currently approved dosage. Compound IV was not toxic to the cell lines up to a 50 µM concentration and exhibited IC50s of 4.97 µM and 8.46 µM in viral entry and spread assay, respectively. Therefore, this novel class of NendoU inhibitor could provide new insights for the development of treatment options for COVID-19.

Probing Adsorption of Dipeptides on Anatase in H2 O and D2 O: Thermodynamics and Molecular Geometry.

Considering the vast importance of peptide and protein interactions with inorganic surfaces, probing hydrogen bonding during their adsorption on metal oxide surfaces is a relevant task that could shed light on the essential features of their interplay. This work is devoted to studying the dipeptides' adsorption on anatase nanoparticles (ANs) in light and heavy water to reveal differences arising upon the change of the major hydrogen bonding carrier. Thermodynamic study of six native dipeptides' adsorption on ANs in both media shows a strong influence of the solvent on the Gibbs free energy and the effect of side-chain mobile protons on the entropy of the process. The adsorption is endothermic irrespective of the medium and is entropy-driven. Computer simulations of peptide adsorption in both media shows similarity in binding via an amino group and demonstrates structural features of protonated and deuterated peptides in obtained complexes. Calculated peptide- anatase nanoparticle (AN) descriptors indicate surface oxygens as points of peptide-nanoparticle contacts.

The Complementarity Principle-One More Step towards Analytical Docking on the Example of Dihydrofolate Reductase Complexes.

New approaches to assessing the "enzyme-ligand" complementarity, taking into account hydrogens, have been proposed. The approaches are based on the calculation of three-dimensional maps of the electron density of the receptor-ligand complexes. The action of complementarity factors, first proposed in this article, has been demonstrated on complexes of human dihydrofolate reductase (DHFR) with ligands. We found that high complementarity is ensured by the formation of the most effective intermolecular contacts, which are provided due to predominantly paired atomic-atomic interactions, while interactions of the bifurcate and more disoriented type are minimized. An analytical docking algorithm based on the proposed receptor-ligand complementarity factors is proposed.

Industrial and wildfire aerosol pollution over world heritage Lake Baikal.

Lake Baikal is the biggest reservoir of fresh water with unique flora and fauna; presently it is negatively affected by climate change, water warming, industrial emissions, shipping, touristic activities, and Siberian forest fires. The assessment of air pollution - related Baikal's ecosystem damage is an unsolved problem. Ship, based expedition exploring the Baikal atmospheric aerosol loading, was performed over the lake area in July 2018. We combine the aerosol near - water and vertical distributions over the Lake Baikal basin with meteorological observations and air mass transportation simulations. Lidar sounding of aerosol fields in the troposphere assesses the atmospheric background in the pristine areas and the pollution during fire-affected periods. Aerosol optical properties (scattering and spectral absorption) converted to the particle number size, black carbon (BC) mass, and Absorption Angstrom Exponent (AAE) provide the inside into aerosol characterization. Transport of industrial emissions from Krasnoyarsk and Irkutsk regions, and wildfire plumes from Republic of Yakutia relates the pollution sources to the increased concentrations of fine particle numbers, PM10 and BC mass over Southern and Northern/Central Baikal, respectively. The highest PM10 and BC are associated to the harbor and touristic areas of intensive shipping and residential biomass burning. Deposition estimates applied to aerosol data exhibit the pollution fluxes to water surface over the whole Baikal area. AAE marks the impact of coal combustion, residential biomass burning, and wildfires indicating the high pollution level of the Lake Baikal ecological system .

Electron Density Analysis of SARS-CoV-2 RNA-Dependent RNA Polymerase Complexes.

The work is devoted to the study of the complementarity of the electronic structures of the ligands and SARS-CoV-2 RNA-dependent RNA polymerase. The research methodology was based on determining of 3D maps of electron densities of complexes using an original quantum free-orbital AlteQ approach. We observed a positive relationship between the parameters of the electronic structure of the enzyme and ligands. A complementarity factor of the enzyme-ligand complexes has been proposed. The console applications of the AlteQ complementarity assessment for Windows and Linux (alteq_map_enzyme_ligand_4_win.exe and alteq_map_enzyme_ligand_4_linux) are available for free at the ChemoSophia webpage.

Use of the Complementarity Principle in Docking Procedures: A New Approach for Evaluating the Correctness of Binding Poses.

Even though the first docking procedures were developed almost 40 years ago, they are still under intense development, alongside with their validation. In this article, we are proposing the use of the quantum free-orbital AlteQ method in evaluating the correctness of ligand binding poses and their ranking. The AlteQ method calculates the electron density in the interspace between the ligand and the receptor, and since their interactions follow the maximum complementarity principle, an equation can be obtained, which describes these interactions. In this way, the AlteQ method evaluates the quality of contacts between the ligand and the receptor, bypasses the drawbacks of using ligand RMSD as a measure of docking quality, and can be considered as an improvement of the "fraction of recovered ligand-receptor contacts" method. Free Windows and Linux versions of the AlteQ program for assessing complementarity between the ligand and the receptor are available for download at www.chemosophia.com.

Complementarity principle in terms of electron density for the study of EGFR complexes.

The complementarity principle is a well-established concept in the field of chemistry and biology. This concept is widely studied as the lock-and-key relationship between two structures, such as enzyme and ligand interactions. These interactions are based on the overlap of electron clouds between two structures. In this study, a mathematical relation determining complementarity of intermolecular contacts in terms of overlaps of electron clouds was examined using a quantum orbital-free AlteQ method developed in-house for 64 EGFR-ligand complexes with experimentally measured binding affinity data. A very high correlation was found between the overlap of ligand and enzyme electron clouds and the calculated terms, providing a good basis for prognosis of bioactivity and for molecular docking studies.

Design, synthesis, antibacterial evaluation, and computational studies of hybrid oxothiazolidin-1,2,4-triazole scaffolds.

Bacterial infections are a serious threat to human health due to the development of resistance against the presently used antibiotics. The problem of growing and widespread antibiotic resistance is only getting worse with the shortage of new classes of antibiotics, creating a substantial unmet medical need in the treatment of serious bacterial infections. Therefore, in the present work, we report 18 novel hybrid thiazolidine-1,2,4-triazole derivatives as DNA gyrase inhibitors. The derivatives were synthesized by multistep organic synthesis and characterized by spectroscopic methods (1 H and 13 C nuclear magnetic resonance and mass spectroscopy). The derivatives were tested for DNA gyrase inhibition, and the result emphasized that the synthesized derivatives have a tendency to inhibit the function of DNA gyrase. Furthermore, the compounds were also tested for antibacterial activity against three Gram-positive (Bacillus subtilis [NCIM 2063], Bacillus cereus [NCIM 2156], Staphylococcus aureus [NCIM 2079]) and two Gram-negative (Escherichia coli [NCIM 2065], Proteus vulgaris [NCIM 2027]) bacteria. The derivatives showed a significant-to-moderate antibacterial activity with noticeable antibiofilm efficacy. Quantitative structure-activity relationship (QSAR), ADME (absorption, distribution, metabolism, elimination) calculation, molecular docking, radial distribution function, and 2D fingerprinting were also performed to elucidate fundamental structural fragments essential for their bioactivity. These studies suggest that the derivatives 10b and 10n have lead antibacterial properties with significant DNA gyrase inhibitory efficacy, and they can serve as a starting scaffold for the further development of new broad-spectrum antibacterial agents.