Azelaic acid can efficiently compete for the auxin binding site TIR1, altering auxin polar transport, gravitropic response, and root growth and architecture in Arabidopsisthaliana roots.

The present study investigates the phytotoxic potential of azelaic acid (AZA) on Arabidopsis thaliana roots. Effects on root morphology, anatomy, auxin content and transport, gravitropic response and molecular docking were analysed. AZA inhibited root growth, stimulated lateral and adventitious roots, and altered the root apical meristem by reducing meristem cell number, length and width. The treatment also slowed down the roots' gravitropic response, likely due to a reduction in statoliths, starch-rich organelles involved in gravity perception. In addition, auxin content, transport and distribution, together with PIN proteins' expression and localisation were altered after AZA treatment, inducing a reduction in auxin transport and its distribution into the meristematic zone. Computational simulations showed that AZA has a high affinity for the auxin receptor TIR1, competing with auxin for the binding site. The AZA binding with TIR1 could interfere with the normal functioning of the TIR1/AFB complex, disrupting the ubiquitin E3 ligase complex and leading to alterations in the response of the plant, which could perceive AZA as an exogenous auxin. Our results suggest that AZA mode of action could involve the modulation of auxin-related processes in Arabidopsis roots. Understanding such mechanisms could lead to find environmentally friendly alternatives to synthetic herbicides.

Trans-cinnamaldehyde-related overproduction of benzoic acid and oxidative stress on Arabidopsis thaliana.

Introduction: Trans-cinnamaldehyde is a specialised metabolite that naturally occurs in plants of the Lauraceae family. This study focused on the phytotoxic effects of this compound on the morphology and metabolism of Arabidopsis thaliana seedlings. Material and methods: To evaluate the phytotoxicity of trans-cinnamaldehyde, a dose-response curve was first performed for the root growth process in order to calculate the reference inhibitory concentrations IC50 and IC80 (trans-cinnamaldehyde concentrations inducing a 50% and 80% inhibition, respectively). Subsequently, the structure and ultrastructure of the roots treated with the compound were analysed by light and electron microscopy. Based on these results, the following assays were carried out to in depth study the possible mode of action of the compound: antiauxinic PCIB reversion bioassay, determination of mitochondrial membrane potential, ROS detection, lipid peroxidation content, hormone quantification, in silico studies and gene expression of ALDH enzymes. Results: Trans-cinnamaldehyde IC50 and IC80 values were as low as 46 and 87 µM, reducing the root growth and inducing the occurrence of adventitious roots. At the ultrastructural level, the compound caused alterations to the mitochondria, which were confirmed by detection of the mitochondrial membrane potential. The morphology observed after the treatment (i.e., appearance of adventitious roots) suggested a possible hormonal mismatch at the auxin level, which was confirmed after PCIB bioassay and hormone quantification by GC-MS. The addition of the compound caused an increase in benzoic, salicylic and indoleacetic acid content, which was related to the increased gene expression of the aldehyde dehydrogenase enzymes that can drive the conversion of trans-cinnamaldehyde to cinnamic acid. Also, an increase of ROS was also observed in treated roots. The enzyme-compound interaction was shown to be stable over time by docking and molecular dynamics assays. Discussion: The aldehyde dehydrogenases could drive the conversion of trans-cinnamaldehyde to cinnamic acid, increasing the levels of benzoic, salicylic and indoleacetic acids and causing the oxidative stress symptoms observed in the treated seedlings. This would result into growth and development inhibition of the trans-cinnamaldehyde-treated seedlings and ultimately in their programmed-cell-death.

Similarities on the mode of action of the terpenoids citral and farnesene in Arabidopsis seedlings involve interactions with DNA binding proteins.

The sesquiterpene farnesene and the monoterpene citral are phytotoxic natural compounds characterized by a high similarity in macroscopic effects, suggesting an equal or similar mechanism of action when assayed at IC50 concentration. In the present study, a short-time experiment (24 and 48 h) using an imaging spectrofluorometer allowed us to monitor the in-vivo effects of the two molecules, highlighting that both terpenoids were similarly affecting all PSII parameters, even when the effects of citral were quicker in appearing than those of farnesene. The multivariate, univariate, and pathway analyses, carried out on untargeted-metabolomic data, confirmed a clear separation of the plant metabolome in response to the two treatments, whereas similarity in the affected pathways was observed. The main metabolites affected were amino acids and polyamine, which significantly accumulated in response to both treatments. On the contrary, a reduction in sugar content (i.e. glucose and sucrose) was observed. Finally, the in-silico studies demonstrated a similar mechanism of action for both molecules by interacting with DNA binding proteins, although differences concerning the affinity with the proteins with which they could potentially interact were also highlighted. Despite the similarities in macroscopic effects of these two molecules, the metabolomic and in-silico data suggest that both terpenoids share a similar but not equal mechanism of action and that the similar effects observed on the photosynthetic machinery are more imputable to a side effect of molecules-induced oxidative stress.

Emerging Computational Approaches for Antimicrobial Peptide Discovery.

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

Transcriptome and binding data indicate that citral inhibits single strand DNA-binding proteins.

The mechanism of phytotoxicity of citral was probed in Arabidopsis thaliana using RNA-Seq and in silico binding analyses. Inhibition of growth by 50% by citral downregulated transcription of 9156 and 5541 genes in roots and shoots, respectively, after 1 h. Only 56 and 62 genes in roots and shoots, respectively, were upregulated. In the shoots, the downregulation increased at 3 h (6239 genes downregulated, vs 66 upregulated). Of all genes affected in roots at 1 h (time of greatest effect), 7.69% of affected genes were for nucleic acid binding functions. Genes for single strand DNA binding proteins (SSBP) WHY1, WHY 2 and WHY3 were strongly downregulated in the shoot up until 12 h after citral exposure. Effects were strong in the root at just 1 h after the treatment and then at 12 and 24 h. Similar effects occurred with the transcription factors MYC-2, ANAC and SCR-SHR, which were also significantly downregulated for the first hour of treatment, and downregulation occurred again after 12 and 24 h treatment. Downregulation of ANAC in the first hour of treatment was significantly (P < 0.0001) decreased more than eight times compared to the control. In silico molecular docking analysis suggests binding of citral isomers to the SSBPs WHY1, WHY2, and WHY3, as well as with other transcription factors such as MYC-2, ANAC and SCR-SHR. Such effects could account for the profound and unusual effects of citral on downregulation of gene transcription.

Auxin-like effects of the natural coumarin scopoletin on Arabidopsis cell structure and morphology.

The mode of action and phytotoxic potential of scopoletin, a natural compound belonging to the group of coumarins, has been evaluated in detail. Analysis conducted by light and electron transmission microscopy showed strong cell and tissue abnormalities on treated roots, such as cell wall malformations, multi-nucleated cells, abnormal nuclei and tissue disorganization. Scopoletin compromised root development by inducing wrong microtubule assembling, mitochondrial membrane depolarization and ultimate cell death, in a way similar to auxin herbicides. The structural similarities of the natural compound scopoletin and the auxin herbicide 2,4-D, as well as the ability of scopoletin to fit into the auxin-binding site TIR1, were analyzed, suggesting that the phytotoxic activity of scopoletin matches with that exhibited by auxinic herbicides.

Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A2AAdenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors.

BACKGROUND: Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process. During the application of ensemble methods to virtual screening the proper validation of the models in virtual screening conditions is often neglected. No analysis of the diversity of the ensemble members is performed frequently or no considerations regarding the applicability domain of the base models are being made. METHODS: In this research, we review basic concepts and definitions related to virtual screening. We comment recent applications of ensemble methods to ligand-based virtual screening and highlight their advantages and limitations. RESULTS: Next, we propose a method based on genetic algorithms optimization for the generation of virtual screening tailored ensembles which address the previously identified problems in the current applications of ensemble methods to virtual screening. CONCLUSION: Finally, the proposed methodology is successfully applied to the generation of ensemble models for the ligand-based virtual screening of dual target A2A adenosine receptor antagonists and MAO-B inhibitors as potential Parkinson's disease therapeutics.

Topological sub-structural molecular design (TOPS-MODE): a useful tool to explore key fragments of human A3 adenosine receptor ligands.

Adenosine regulates tissue function by activating four G-protein-coupled adenosine receptors (ARs). Selective agonists and antagonists for A3 ARs have been investigated for the treatment of a variety of immune disorders, cancer, brain, and heart ischemic conditions. We herein present a QSAR study based on a Topological sub-structural molecular design (TOPS-MODE) approach, intended to predict the A3 ARs of a diverse dataset of 124 (94 training set/ 30 prediction set) adenosine derivatives. The final model showed good fit and predictive capability, displaying 85.1 % of the experimental variance. The TOPS-MODE approach afforded a better understanding and interpretation of the developed model based on the useful information extracted from the analysis of the contribution of different molecular fragments to the affinity.

Integrated Ugi-based assembly of functionally, skeletally, and stereochemically diverse 1,4-benzodiazepin-2-ones.

A practical, integrated and versatile U-4CR-based assembly of 1,4-benzodiazepin-2-ones exhibiting functionally, skeletally, and stereochemically diverse substitution patterns is described. By virtue of its convergence, atom economy, and bond-forming efficiency, the methodology documented herein exemplifies the reconciliation of structural complexity and experimental simplicity in the context of medicinal chemistry projects.

Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists.

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.

Synthesis and cytostatic activity of purine nucleosides derivatives of allofuranose.

Several new purine nucleosides derivatives of allofuranose were prepared according to Vorbrüggen method, starting from 1,2,5,6-di-O-isopropylidene-α-D-allofuranose and using 1,2,3,5,6-pentaacetoxy-ß-D-allofuranose as key intermediate. The synthesized allofuranosyl nucleosides, as well as some acetyl derivatives, were evaluated for their cytotoxicity in vitro in three human cancer cell lines (MCF-7, Hela-229 and HL-60). Among the studied compounds the 9-(2,3,5,6-tetra-O-acetyl-ß-D-allofuranosyl)-2,6-dichloropurine (9) was the most potent one on the three cell lines evaluated, being its activity against HL-60 cells similar to cisplatin.

Multidimensional drug design: simultaneous analysis of binding and relative efficacy profiles of N(6)-substituted-4'-thioadenosines A3 adenosine receptor agonists.

Desirability theory (DT) is a well-known multi-criteria decision-making approach. In this work, DT is employed as a prediction model (PM) interpretation tool to extract useful information on the desired trade-offs between binding and relative efficacy of N(6)-substituted-4'-thioadenosines A3 adenosine receptor (A3AR) agonists. At the same time, it was shown the usefulness of a parallel but independent approach providing a feedback on the reliability of the combination of properties predicted as a unique desirability value. The appliance of belief theory allowed the quantification of the reliability of the predicted desirability of a compound according to two inverse and independent but complementary prediction approaches. This information is proven to be useful as a ranking criterion in a ligand-based virtual screening study. The development of a linear PM of the A3AR agonists overall desirability allows finding significant clues based on simple molecular descriptors. The model suggests a relevant role of the type of substituent on the N(6) position of the adenine ring that in general contribute to reduce the flexibility and hydrophobicity of the lead compound. The mapping of the desirability function derived of the PM offers specific information such as the shape and optimal size of the N(6) substituent. The model herein developed allows a simultaneous analysis of both binding and relative efficacy profiles of A3AR agonists. The information retrieved guides the theoretical design and assembling of a combinatorial library suitable for filtering new N(6)-substituted-4'-thioadenosines A3AR agonist candidates with simultaneously improved binding and relative efficacy profiles. The utility of the desirability/belief-based proposed virtual screening strategy was deduced from our training set. Based on the overall results, it is possible to assert that the combined use of desirability and belief theories in computational medicinal chemistry research can aid the discovery of A3AR agonist candidates with favorable balance between binding and relative efficacy profiles.

Variable selection methods in QSAR: an overview.

Variable selection is a procedure used to select the most important features to obtain as much information as possible from a reduced amount of features. The selection stage is crucial. The subsequent design of a quantitative structure-activity relationship (QSAR) model (regression or discriminant) would lead to poor performance if little significant features are selected. In drug design modern era, by the means of combinatorial chemistry and high throughput screening, an unprecedented amount of experimental information has been generated. In addition, many molecular descriptors have been defined in the last two decays. All this information can be analyzed by QSAR techniques using adequate statistical procedures. These techniques and procedures should be fast, automated, and applicable to large data sets of structurally diverse compounds. For that reason, the identification of the best one seems to be a very difficult task in view of the large variable selection techniques existing nowadays. The intention of this review is to summarize some of the present knowledge concerning to variable selection methods applied to some well-known statistical techniques such as linear regression, PLS, kNN, Artificial Neural Networks, etc, with the aim to disseminate the advances of this important stage of the QSAR building model.

New QSAR combined strategy for the design of A1 adenosine receptor agonists.

Combined discriminant and regression analysis was carried out on a series of 167 A1 adenosine receptor agonists to identify the best linear and nonlinear models for the design of new compounds with a better biological profile. On the basis of the best linear discriminant analysis and both linear and nonlinear Multi Layer Perceptron neural networks regression, we have designed and synthesized 14 carbonucleoside analogues of adenosine. Their biological activities were predicted and experimentally measured to demonstrate the capability of our model to avoid the prediction of false positives. A good agreement was found between the calculated and observed biological activity.

Search for new antagonist ligands for adenosine receptors from QSAR point of view. How close are we?

In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of drug biological activity is advisable prior to synthesis and this can be achieved by employing predictive biological property methods. In this sense, Quantitative Structure-Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of QSAR applications to develop adenosine receptor (AR) antagonists is not common as for the case of the antibiotics and anticancer compounds for instance. The intention of this review is to summarize the present knowledge concerning computational predictions of new molecules as adenosine receptor antagonists.

2D-autocorrelation descriptors for predicting cytotoxicity of naphthoquinone ester derivatives against oral human epidermoid carcinoma.

A QSAR study was developed, employing 2D-autocorrelation descriptors and a set of 37 naphthoquinone ester derivatives, in order to model the cytotoxicity of these compounds against oral human epidermoid carcinoma (KB). A comparison with other approaches such as the BCUT, Galvez topological charge indexes, Randic molecular profile, Geometrical, and RDF descriptors was carried out. Mathematical models were obtained by means of the multiple regression analysis (MRA) and the variables were selected using genetic algorithm. Based on the statistical results the 2D-autocorrelation descriptors were considered the best and were able to describe more than 84.2% of the variance in the experimental activity once we controlled for outliers.

QSAR studies using radial distribution function for predicting A1 adenosine receptors agonists.

The radial distribution function (RDF) approach has been applied to the study of the A(1) adenosine receptors agonist effect of 32 adenosine analogues. A model able to describe more than 79% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the three different approaches, including the use of 2D autocorrelations, BCUT and 3D-MORSE descriptors were able to explain more than 72% of the variance in the mentioned property with the same number of variables in the equation. In addition, we established a comparison with other models reported by us for this receptor subtype using this data set, and the RDF descriptors continue getting the best results.

Synthesis and anti-HIV activity of novel cyclopentenyl nucleoside analogues of 8-azapurine.

Novel nucleoside analogues of structure 3-5 were synthesized starting from (+/-)-cis-2-amino-3-cyclopentenylmethanol (1). The chlorine derivative 3 inhibited both HIV-1 and HIV-2 replication in MT-4 cells with IC(50) values of 10.67 microM and of 13.79 microM, respectively.

QSAR studies about cytotoxicity of benzophenazines with dual inhibition toward both topoisomerases I and II: 3D-MoRSE descriptors and statistical considerations about variable selection.

Deoxyribonucleic acid (DNA) topoisomerases are involved in diverse cellular processes, such as replication, transcription, recombination, and chromosome segregation. Searching new compounds that inhibit both topoisomerases I and II is very important due to the deficiency of the specific inhibitors to overcome multidrug resistance (MDR). A QSAR study was developed, employing the 3D-MoRSE descriptors and a set of 64 benzophenazines in order to model the inhibition of the topoisomerases I and II, expressed by the cytotoxicity of these compounds (IC(50)) versus drug-resistant human small cell lung carcinoma line cell H69/LX4. A comparison with other approaches such as the Topological, BCUT, Galvez topological charge indexes, 2D autocorrelations, Randic molecular profile, Geometrical, RDF, and WHIM descriptors was carried out. The mathematical models were obtained by means of the multiple regression analysis (MRA) and the variables were selected using the genetic algorithm. The model relative to the 3D-MoRSE descriptors was considered as the best, taking into account its statistical parameters. It was able to describe more than 82.2% of the variance in the experimental activity once the outliers were extracted.

Quantitative structure activity relationships as useful tools for the design of new adenosine receptor ligands. 1. Agonist.

In order to minimize expensive drug failures it is essential to determine the potential biological activity of new candidates as early as possible. In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of a drugs biological activity is advisable even before synthesis and this can be achieved using predictive biological activity methods. In this sense, computer aided rational drug design strategies like Quantitative Structure Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of traditional QSAR applications in the development of new agonist molecules with affinity toward adenosine receptors is scarce. This review attempts to summarize the current level of knowledge concerning computational affinity predictions for adenosine receptors using QSAR models based on knowledge of the agonist ligands. Several computational protocols and different 2D and 3D descriptors have been described in the literature for these targets, but more effort is still required in this area.