Receptor- and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations

Design and Evaluation of Nanoscale Materials with Programmed Responsivity towards Epigenetic Enzymes

Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing 'intelligent' encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly (acetyl L-lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles' hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We further demonstrated the biocompatibility and cellular effects of these materials and conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs.

Combined Machine Learning, Computational, and Experimental Analysis of the Iridium(III) Complexes with Red to Near-Infrared Emission

Various coordination complexes have been the subject of experimental and theoretical studies in recent decades because of their fascinating photophysical properties. In this work, a combined experimental and computational approach was applied to investigate the optical properties of monocationic Ir(III) complexes. An interpretative machine learning-based quantitative structure-property relationship (ML/QSPR) model was successfully developed that could reliably predict the emission wavelength of the Ir(III) complexes and provide a foundation for the theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to explain the differences in the emission wavelengths between structurally different individual Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values of 0.84 and 0.87 for the training and test sets, respectively. It is worth noting that a relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths is detected. This effect is most probably associated with a degree of distortion in the octahedral geometry of the complexes, resulting in a perturbed ligand field. This combined experimental and computational approach shows great potential for the rational design of new Ir(III) complexes with the desired optical properties. Moreover, the developed methodology could be extended to other transition-metal complexes.

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives

Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)-as a known target of toxins in fathead minnows and Daphnia magna, causing the inhibition of AChE-was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure-activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions.

Fragmented blind docking: a novel protein-ligand binding prediction protocol

In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.

Synergistic Behavior of Plant Proteins and Biobased Latexes in Bioplastic Food Packaging Materials: Experimental and Machine Learning Study

Plant-based proteins are attractive components which may serve as sustainable alternatives to current petrochemical products. Both soy protein and major corn protein, zein, are of interest in food packaging applications due to their sustainability, biodegradation properties, and inherent physicochemical properties. This study discusses the development of bioplastic materials, where it explores the effects of combining zein, soy protein, and plasticizing latexes derived from plant oil-based monomers (POBMs) on properties of resulting bioplastic films. By looking for synergistic effects of soy protein's inherent film formation ability and zein's higher strength, we prepare strong yet flexible soy-zein films as materials, called proteoposites. Incorporation of natural additive POBM-latexes helps to plasticize and hydrophobize the bioplastic films and thus to improve mechanical and barrier properties. Variation of the POBM-latexes' particle size further aims to enhance the performance of resulting bioplastic films. As a result, modified soy-zein proteoposite films with improved moisture resistance, enhanced mechanical behavior, and greater barrier properties were developed. Machine learning-based computational models were utilized in order to find main structural factors affecting the bioplastic's properties and develop a quantitative structure-property relationship model between the physicochemical properties of the film components and the resulted bioplastics' properties and performance. The developed model effectively predicts experimental outcomes with >85% (R²: 0.85) accuracy. The newly synthesized proteoposites confirmed the machine learning model predictions. As a result, proteoposite films made of two plant proteins and modified with POBM-latexes can be considered as an attractive and viable replacement for petrochemical food packaging products.

How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases

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Five proteins related to diabetic disease were selected from Protein Data Bank.

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Binding scores were calculated for five proteins with 169 fullerene derivatives.

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Correlation between drug-like descriptors and binding scores activity was examined.

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The contribution of descriptors to protein-ligand binding was demonstrated.

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The QSARs models for prediction of binding scores activity were built.

Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein–ligand binding in order to identify the key binding characteristics of FDs.

Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction.

The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles.

All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity.

A partial least squares and artificial neural network study for a series of arylpiperazines as antidepressant agents

Depression affects more than 300 million people around the world and can lead to suicide. About 30% of patients on treatment for depression drop out of therapy due to side effects or to latency time associated to therapeutic effects. 5-HT receptor, known as serotonin, is considered the key in depression treatment. Arylpiperazine compounds are responsible for several pharmacological effects and are considered as ligands in serotonin receptors, such as the subtype 5-HT_2a. Here, in silico studies were developed using partial least squares (PLSs) and artificial neural networks (ANNs) to design new arylpiperazine compounds that could interact with the 5-HT_2a receptor. First, molecular and electronic descriptors were calculated and posteriorly selected from correlation matrixes and genetic algorithm (GA). Then, the selected descriptors were used to construct PLS and ANN models that showed to be robust and predictive. Lastly, new arylpiperazine compounds were designed and their biological activity values were predicted by both PLS and ANN models. It is worth to highlight compounds G5 and G7 (predicted by the PLS model) and G3 and G15 (predicted by the ANN model), whose predicted pIC₅₀ values were as high as the three highest values from the arylpiperazine original set studied here. Therefore, it can be asserted that the two models (PLS and ANN) proposed in this work are promising for the prediction of the biological activity of new arylpiperazine compounds and may significantly contribute to the design of new drugs for the treatment of depression.

DFT and molecular docking study of chloroquine derivatives as antiviral to coronavirus COVID-19

The recently emerged COVID-19 virus caused hundreds of thousands of deaths and instigated a widespread fear, threatening the world’s most advanced health security. In 2020, chloroquine derivatives are among the drugs tested against the coronavirus pandemic and showed an apparent efficacy. In the present work, the chloroquine and the chloroquine phosphate molecules have been proposed as potential antiviral for the treatment of COVID-19 diseases combining DFT and molecular docking calculations. Molecular geometries, electronic properties and molecular electrostatic potential were investigated using density functional theory (DFT) at the B3LYP/6-31G* method. As results, we found a good agreement between the theoretical and the experimental geometrical parameters (bond lengths and bond angles). The frontier orbitals analysis has been calculated at the same level of theory to determine the charge transfer within the molecule. In order to perform a better description of the FMOs, the density of states was determined. The molecular electrostatic potential maps were calculated to provide information on the chemical reactivity of molecule and also to describe the intermolecular interactions. All these studies help us a lot in determining the reactivity of the mentioned compounds. Finally, docking calculations were carried out to determine the pharmaceutical activities of the chloroquine derivatives against coronavirus diseases. The choice of these ligands was based on their antiviral activities.

Fullerene C60 containing porphyrin-like metal center as drug delivery system for ibuprofen drug

Today, drug delivery systems based on nanostructures have become the most efficient to be studied. Recent studies revealed that the fullerenes can be used as drug carriers and transport drugs in a target cell. The aim of the present work is to study the interaction of C60 fullerene containing porphyrin-like transition metal-N4 clusters (TMN4C55, TM = Fe, Co, and Ni) with a non-steroidal anti-inflammatory drug (ibuprofen (Ibp)) by employing the method of the density functional theory. Results showed that the C60 fullerene with TMN4 clusters could significantly enhance the tendency of C60 for adsorption of ibuprofen drug. Also, our ultraviolet-visible results show that the electronic spectra of Ibp/TMN4C55 complexes exhibit a blue shift toward lower wavelengths (higher energies). It was found that the NiN4C55 fullerene had high chemical reactivity, which was important for binding of the drug onto the carrier surface. In order to gain insight into the binding features of Ibp/TMN4C55 complexes, the atoms in molecules analysis was also performed. Our results exhibit the electrostatic features of the Ibp/TMN4C55 bonding. Consequently, this study demonstrated that the TMN4C55 fullerenes could be used as potential carriers for delivery of Ibp drug in the nanomedicine domain. Graphical Abstract The TMN4C55 (TM=Fe, Co, and Ni) fullerenes could be used as potential carriers for delivery of ibuprofen drug in the nanomedicine domain.

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.

Characterization of antimicrobial and hemolytic properties of short synthetic cationic lipopeptides based on QSAR/QSTR approach

In this study, we investigated the influence of molecular descriptors of cationic lipopeptides on their antimicrobial activity and hemolytic properties. The quantitative structure-activity relationship and quantitative structure-property relationship models were constructed. The antimicrobial, hemolytic and retention data were used as dependent variable and structural parameters as the independent ones. The obtained results suggest that the chromatographic indexes can be employed for prediction of antibacterial activity and that lipopeptides present nonspecific interaction between erythrocytes and bacterial membranes.

Inhibitors or toxins? Large library target-specific screening of fullerene-based nanoparticles for drug design purpose

Fullerene-based nanoparticles have been the subject of vital interest due to their unique properties and potential application in many areas, including medicine. Here we explore their characteristics that could make them prospective leads for known disease-related proteins. High-throughput virtual screening supported by comprehensive multi-software protein-ligand docking simulation and cheminformatics approaches has been applied in investigation of interactions of 1117 proteins with a 169 fullerene nanoparticles decorated with different small molecules. Moreover, obtained docking results were confirmed by the series of unrestricted all-atom molecular dynamics (MD) simulations. Hydrophobicity of fullerene core along with hydrophilic interaction of side chains plays a key role in binding with the studied proteins. We identified a series of nanoparticles that can lead to development of robust drugs for target proteins and another series that can behave as a highly toxic agent. The structure-activity relationship analysis revealed two significant molecular properties responsible for the binding score values. The application of carefully selected computational techniques and described outcome of the study facilitate development of functional fullerene nanoparticles for drug-like and drug delivery applications.

Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures

The development of quantitative structure–activity relationship (QSAR) methods is going very fast for the last decades. OSAR approach already plays an important role in lead structure optimization, and nowadays, with development of big data approaches and computer power, it can even handle a huge amount of data associated with combinatorial chemistry. One of the recent developments is a three-dimensional QSAR, i.e., 3D QSAR. For the last two decades, 3D-OSAR has already been successfully applied to many datasets, especially of enzyme and receptor ligands. Moreover, quite often 3D QSAR investigations are going together with protein–ligand docking studies and this combination works synergistically. In this review, we outline recent advances in development and applications of 3D QSAR and protein–ligand docking approaches, as well as combined approaches for conventional organic compounds and for nanostructured materials, such as fullerenes and carbon nanotubes.

Polymer Coating Materials and Their Fouling Release Activity: A Cheminformatics Approach to Predict Properties

A novel cheminformatics-based approach has been employed to investigate a set of polymer coating materials designed to mitigate the accumulation of marine biofouling on surfaces immersed in the sea. Specifically, a set of 27 nontoxic, amphiphilic polysiloxane-based polymer coatings was synthesized using a combinatorial, high-throughput approach and characterized for fouling-release (FR) activity toward a number of relevant marine fouling organisms, including bacteria, microalgae, and adult barnacles. In order to model these complex systems adequately, a new computational technique was used in which all investigated polymer-based coating materials were considered as mixture systems comprising several compositional variables at a range of concentrations. By applying a combination of methodologies for mixture systems and a quantitative structure-activity relationship approach (QSAR), seven unique QSAR models were developed that were able to successfully predict the desired FR properties. Furthermore, the developed models identified several significant descriptors responsible for FR activity of investigated polymer-based coating materials, with correlation coefficients ranging from r_test² = 0.63 to 0.94. The computational models derived from this study may serve as a powerful set of tools to predict optimal combinations of source components to produce amphiphilic polysiloxane-based coating systems with effective, broad-spectrum FR properties.

Noncovalent interaction assisted fullerene for the transportation of some brain anticancer drugs: A theoretical study

The treatment of brain cancer like glioblastoma multiforme often uses chemotherapeutic drugs like temozolomide, procarbazine, carmustine, and lomustine. Fullerene loaded with these drugs help to cross the blood brain barriers. The adsorptions of the four drug molecules on the surface of the fullerene are studied mostly by using density functional theory (DFT) based method at the M06-2X/6-31G(d) level of calculations. In all four cases, the estimated interactions are noncovalent type and the average adsorption energy lies in between -5 and -11kcal/mol in the gas phase. In the aqueous and protein environment such interactions are weakened further. The binding affinity is further assessed by performing MP2 based calculations to provide interaction energies with a reasonable accuracy. Stabilities and reactivities of the drug adsorbed fullerene complexes are determined from chemical reactivity descriptors. The attached drug molecules increase the polarity of the pristine C₆₀ thus facilitating the drug delivery within the biological systems. The semiconducting behavior of C₆₀ is retained in the C₆₀-drug composite systems. The computed DOS, IR, UV spectra, and molecular orbitals in the vicinity of Fermi level are analyzed to reveal the nature of the noncovalent interactions between C₆₀ and drug molecules. The Wiberg bond order values are used to estimate the strength of the adsorption of the drug molecule on C₆₀. In all four C₆₀-drug interactions, the chemical characteristics of the drug molecule are least perturbed by the C₆₀ moiety thereby suggesting it to be a good carrier for the delivery of these brain anticancer drug molecules to the target cells.

Characterization of New Cationic N,N-Dimethyl[70]fulleropyrrolidinium Iodide Derivatives as Potent HIV-1 Maturation Inhibitors

HIV-1 maturation can be impaired by altering protease (PR) activity, the structure of the Gag-Pol substrate, or the molecular interactions of viral structural proteins. Here we report the synthesis and characterization of new cationic N,N-dimethyl[70]fulleropyrrolidinium iodide derivatives that inhibit more than 99% of HIV-1 infectivity at low micromolar concentrations. Analysis of the HIV-1 life cycle indicated that these compounds inhibit viral maturation by impairing Gag and Gag-Pol processing. Importantly, fullerene derivatives 2a-c did not inhibit in vitro PR activity and strongly interacted with HIV immature capsid protein in pull-down experiments. Furthermore, these compounds potently blocked infectivity of viruses harboring mutant PR that are resistant to multiple PR inhibitors or mutant Gag proteins that confer resistance to the maturation inhibitor Bevirimat. Collectively, our studies indicate fullerene derivatives 2a-c as potent and novel HIV-1 maturation inhibitors.

In silico toxicology: computational methods for the prediction of chemical toxicity

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147-172. doi: 10.1002/wcms.1240 For further resources related to this article, please visit the WIREs website.

Nanoquantitative Structure-Property Relationship Modeling on C42Fullerene Isomers

The interest of scientists in nanostructures has been increased in the last years and proper methods for their assessment are needed.In silicomethods found their usefulness in the replacement of experimental evaluation and are successfully used as efficient alternatives for estimation and prediction of compound’s properties or activities. In this paper, it is shown that a Quantitative Structure-Property Relationship method is proper to be applied also on nanostructures. Based on computational experiment, several models to describe the total strain energy of C42fullerene isomers were obtained and their characteristics are presented. Furthermore, the best performing model obtained on C42fullerene isomers was validated on C40fullerene isomers.

Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure-Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure-Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.

QSAR models for antioxidant activity of new coumarin derivatives

This study presents 37 new antioxidant coumarin derivatives and strategies for structural modification to improve their antioxidant activities, the main ferric-reducing antioxidant power (FRAP) assay used to evaluate their antioxidant properties and the generation of validated quantitative structure-activity (antioxidant activity) relationship (QSAR) models. In an attempt to generate QSAR models, structures of all coumarin derivatives in the data set were fully optimized by semi-empirical PM6 method using SPARTAN 10 software. Descriptors were calculated by DRAGON 6.0 software. Multiple linear regression (MLR) models were developed with different training/test set combinations using QSARINS 2.2.1 software. Robustness, reliability and predictive power of the models were tested by internal and external validations. Applicability domain of the best two-descriptor model (nTR = 30; r(2) = 0.924; RMSETR = 0.213; nTEST = 7; r(2)ext = 0.887; RMSEext = 0.255; CCCext = 0.939) was determined. Descriptors appeared in the model revealed that complexity, H-bond donor and lipophilic character are important parameters in describing the antioxidant activity. Apart from the compounds in the data set, we also designed 31 new antioxidant coumarin derivatives and predicted their antioxidant activity using the best two-descriptor model. Most of these compounds are promising antioxidants.

Amino substituted nitrogen heterocycle ureas as kinase insert domain containing receptor (KDR) inhibitors: Performance of structure–activity relationship approaches

A quantitative structure–activity relationship (QSAR) study was performed on a set of amino-substituted nitrogen heterocyclic urea derivatives. Two novel approaches were applied: (1) the simplified molecular input-line entry systems (SMILES) based optimal descriptors approach; and (2) the fragment-based simplex representation of molecular structure (SiRMS) approach. Comparison with the classic scheme of building up the model and balance of correlation (BC) for optimal descriptors approach shows that the BC scheme provides more robust predictions than the classic scheme for the considered pIC₅₀ of the heterocyclic urea derivatives. Comparison of the SMILES-based optimal descriptors and SiRMS approaches has confirmed good performance of both techniques in prediction of kinase insert domain containing receptor (KDR) inhibitory activity, expressed as a logarithm of inhibitory concentration (pIC50) of studied compounds.

Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach

The knowledge of physico-chemical properties of carbon nanotubes, including behavior in organic solvents is very important for design, manufacturing and utilizing of their counterparts with improved properties. In the present study a quantitative structure-activity/property relationship (QSAR/QSPR) approach was applied to predict the dispersibility of single walled carbon nanotubes (SWNTs) in various organic solvents. A number of additive descriptors and quantum-chemical descriptors were calculated and utilized to build QSAR models. The best predictability is shown by a 4-variable model. The model showed statistically good results (R²_training = 0.797, Q² = 0.665, R²_test = 0.807), with high internal and external correlation coefficients. Presence of the X0Av descriptor and its negative term suggest that small size solvents have better SWCNTs solubility. Mass weighted descriptor ATS6m also indicates that heavier solvents (and small in size) most probably are better solvents for SWCNTs. The presence of the Dipole Z descriptor indicates that higher polarizability of the solvent molecule increases the solubility. The developed model and contributed descriptors can help to understand the mechanism of the dispersion process and predictorganic solvents that improve the dispersibility of SWNTs.

Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models

Causal inference methods are helpful with finding possible biological mechanisms of nanoparticles' toxicity.

From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles

Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were established. A new approach for representation of nanoparticles' structure is presented. For description of the supramolecular structure of nanoparticles the "liquid drop" model was applied. It is expected that a novel, proposed approach could be of general use for predictions related to nanomaterials. In addition, in our study fragmental simplex descriptors and several ligand-metal binding characteristics were calculated. The developed nano-QSAR models were validated and reliably predict the toxicity of all studied metal oxide nanoparticles. Based on the comparative analysis of contributed properties in both models the LDM-based descriptors were revealed to have an almost similar level of contribution to toxicity in both cases, while other parameters (van der Waals interactions, electronegativity and metal-ligand binding characteristics) have unequal contribution levels. In addition, the models developed here suggest different mechanisms of nanotoxicity for these two types of cells.

Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design

The docking methods used in structure-based virtual database screening offer the ability to quickly and cheaply estimate the affinity and binding mode of a ligand for the protein receptor of interest, such as a drug target. These methods can be used to enrich a database of compounds, so that more compounds that are subsequently experimentally tested are found to be pharmaceutically interesting. In addition, like all virtual screening methods used for drug design, structure-based virtual screening can focus on curated libraries of synthesizable compounds, helping to reduce the expense of subsequent experimental verification. In this review, we introduce the protein-ligand docking methods used for structure-based drug design and other biological applications. We discuss the fundamental challenges facing these methods and some of the current methodological topics of interest. We also discuss the main approaches for applying protein-ligand docking methods. We end with a discussion of the challenging aspects of evaluating or benchmarking the accuracy of docking methods for their improvement, and discuss future directions.

Immunotoxicity of nanoparticles: a computational study suggests that CNTs and C60 fullerenes might be recognized as pathogens by Toll-like receptors

Over the last decade, a great deal of attention has been devoted to study the inflammatory response upon exposure to multi/single-walled carbon nanotubes (CNTs) and different fullerene derivatives. In particular, carbon nanoparticles are reported to provoke substantial inflammation in alveolar and bronchial epithelial cells, epidermal keratinocytes, cultured monocyte-macrophage cells, etc. We suggest a hypothetical model providing the potential mechanistic explanation for immune and inflammatory responses observed upon exposure to carbon nanoparticles. Specifically, we performed a theoretical study to analyze CNT and C60 fullerene interactions with the available X-ray structures of Toll-like receptors (TLRs) homo- and hetero-dimer extracellular domains. This assumption was based on the fact that similar to the known TLR ligands both CNTs and fullerenes induce, in cells, the secretion of certain inflammatory protein mediators, such as interleukins and chemokines. These proteins are observed within inflammation downstream processes resulted from the ligand molecule dependent inhibition or activation of TLR-induced signal transduction. Our computational studies have shown that the internal hydrophobic pockets of some TLRs might be capable of binding small-sized carbon nanostructures (5,5 armchair SWCNTs containing 11 carbon atom layers and C60 fullerene). High binding scores and minor structural alterations induced in TLR ectodomains upon binding C60 and CNTs further supported our hypothesis. Additionally, the proposed hypothesis is strengthened by the indirect experimental findings indicating that CNTs and fullerenes induce an excessive expression of specific cytokines and chemokines (i.e. IL-8 and MCP1).

Aconitum and delphinium diterpenoid alkaloids of local anesthetic activity: comparative QSAR analysis based on GA-MLRA/PLS and optimal descriptors approach

The duration of anesthesia (related to protein binding of a drug) and the onset time (determined by the pKa) are important characteristics in assessment of local anesthetic agents. They are known to be affected by a number of factors. Early studies of antiarrhythmic diterpenoid alkaloids from plants Aconitum and Delphinium suggested that they possess local anesthetic activity due to their ability to suppress sodium currents of excited membranes. In this study we utilized toxicity, duration, and onset of action as endpoints to construct Quantitative Structure-Activity Relationship (QSAR) models for the series of 34 diterpenoid alkaloids characterized by local anesthetic activity using genetic algorithm-based multiple linear regression analysis/partial least squares and simplified molecular input line entry system (SMILES)-based optimal descriptors approach. The developed QSAR models correctly reflected factors that determine three endpoints of interest. Toxicity correlates with descriptors describing partition and reactivity of compounds. The duration of anesthesia was encoded by the parameters defining the ability of a compound to bind at the receptor site. The size and number of H-bond acceptor atoms were found not to favor the speed of onset, while topographic electronic descriptor demonstrated strong positive effect on it. SMILES-based optimal descriptors approach resulted in overall improvement of models. This approach was shown to be more sensitive to structural peculiarities of molecules than regression methods. The results clearly indicate that obtained QSARs are able to provide distinct rationales for compounds optimization with respect to particular endpoint.