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The evolutionary variability of a protein’s residues is highly dependent on protein region and function. Solvent-exposed residues, excluding those at interaction interfaces, are more variable than buried residues whereas active site residues are considered to be conserved. The abovementioned rules apply also to α/β-hydrolase fold proteins - one of the oldest and the biggest superfamily of enzymes with buried active sites equipped with tunnels linking the reaction site with the exterior. We selected soluble epoxide hydrolases as representative of this family to conduct the first systematic study on the evolution of tunnels. We hypothesised that tunnels are lined by mostly conserved residues, and are equipped with a number of specific variable residues that are able to respond to evolutionary pressure. The hypothesis was confirmed, and we suggested a general and detailed way of the tunnels’ evolution analysis based on entropy values calculated for tunnels’ residues. We also found three different cases of entropy distribution among tunnel-lining residues. These observations can be applied for protein reengineering mimicking the natural evolution process. We propose a ‘perforation’ mechanism for new tunnels design via the merging of internal cavities or protein surface perforation. Based on the literature data, such a strategy of new tunnel design could significantly improve the enzyme’s performance and can be applied widely for enzymes with buried active sites.Author SummarySo far very little is known about proteins tunnels evolution. The goal of this study is to evaluate the evolution of tunnels in the family of soluble epoxide hydrolases - representatives of numerous α/β-hydrolase fold enzymes. As a result two types of tunnels evolution analysis were proposed (a general and a detailed approach), as well as a ‘perforation’ mechanism which can mimic native evolution in proteins and can be used as an additional strategy for enzymes redesign.

Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure-activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster-Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an

Determination of the no observed adverse effect level (NOAEL) of a substance is an important step in safety and regulatory assessments. Application of conventional in silico strategies, for example, quantitative structure-activity relationship (QSAR) models, to predict NOAEL values is inherently problematic. Whereas QSAR models for well-defined toxicity endpoints such as Ames mutagenicity or skin sensitization can be developed from mechanistic knowledge of molecular initiating events and adverse outcome pathways, QSAR is not appropriate for predicting a NOAEL value, a concentration at which "no effect" is observed. This paper presents a chemoinformatics approach and explores how it can be further refined through the incorporation of toxicity endpoint-specific information to estimate confidence bounds for the NOAEL of a target substance, given experimentally determined NOAEL values for one or more suitable analogues. With a sufficiently large NOAEL database, we analyze how a difference in NOAEL values for pairs of structures depends on their pairwise similarity, where similarity takes both structural features and physicochemical properties into account. The width of the estimate NOAEL confidence interval is proportional to the uncertainty. Using the new threshold of toxicological concern (TTC) database enriched with antimicrobials, examples are presented to illustrate how uncertainty decreases with increasing analogue quality and also how NOAEL bounds estimation can be significantly improved by filtering the full database to include only substances that are in structure categories relevant to the target and analogue.

Motivation Tunnels, pores, channels, pockets and cavities contribute to proteins architecture and performance. However, analysis and characteristics of transportation pathways and internal binding cavities are performed separately. We aimed to provide universal tool for analysis of proteins integral interior with access to detailed information on the ligands transportation phenomena and binding preferences. Results AQUA-DUCT version 1.0 is a comprehensive method for macromolecules analysis from the intramolecular voids perspective using small ligands as molecular probes. This version gives insight into several properties of macromolecules and facilitates protein engineering and drug design by the combination of the tracking and local mapping approach to small ligands. Availability and implementation http://www.aquaduct.pl. Supplementary information Supplementary data are available at Bioinformatics online.

Several different approaches are used to describe the role of protein compartments and residues in catalysis and to identify key residues suitable for the modification of the activity or selectivity of the desired enzyme. In our research, we applied a combination of molecular dynamics simulations and a water tracking approach to describe the water accessible volume of Solanum tuberosum epoxide hydrolase. Using water as a molecular probe, we were able to identify small cavities linked with the active site: (i) one made up of conserved amino acids and indispensable for the proper positioning of catalytic water and (ii) two others in which modification can potentially contribute to enzyme selectivity and activity. Additionally, we identified regions suitable for de novo tunnel design that could also modify the catalytic properties of the enzyme. The identified hot-spots extend the list of the previously targeted residues used for modification of the regioselectivity of the enzyme. Finally, we have provided an example of a simple and elegant process for the detailed description of the network of cavities and tunnels, which can be used in the planning of enzyme modifications and can be easily adapted to the study of any other protein.

We report here, the synthesis of 1-(1-ethyl-1H-indol-3-yl)-3-pyridin-4-yl-prop-2-en-1-one (2) which was used as a base to the synthesis of new 3-(pyrimidin-4-yl)-1H-indole derivatives; their thioglycoside and N-glycoside derivatives 3–10a, b; pyrane derivatives and pyranopyrimidine derivative 11–13; and tricyclic pyranotriazolo[1, 5-a]pyrimidine 14. Moreover, reaction of N-ethyl-3-acetylindole 2 with phenylhydrazine and hydroxylamine hydrochloride gave pyrazolyl-indole and oxazolyl-indole derivatives 15 and 16, respectively. The structures of the products obtained were confirmed by elemental analysis, IR, 1HNMR and 13C NMR. The newly synthesized compounds were investigated for their antimicrobial activity, and some of them showed high growth inhibition activities. Anti-bacterial activity of the synthesized compounds was further analyzed by the molecular docking approach, a method of simulation of fitting ligands into binding site(s) of macromolecular targets. AutoDock Vina results showed that compounds 5a and 6a, b were located in a pocket in the active site.

Early prediction of safety issues in drug development is at the same time highly desirable and highly challenging. Recent advances emphasize the importance of understanding the whole chain of causal events leading to observable toxic outcomes. Here we describe an integrative modeling strategy based on these ideas that guided the design of eTOXsys, the prediction system used by the eTOX project. Essentially, eTOXsys consists of a central server that marshals requests to a collection of independent prediction models and offers a single user interface to the whole system. Every of such model lives in a self-contained virtual machine easy to maintain and install. All models produce toxicity-relevant predictions on their own but the results of some can be further integrated and upgrade its scale, yielding in vivo toxicity predictions. Technical aspects related with model implementation, maintenance and documentation are also discussed here. Finally, the kind of models currently implemented in eTOXsys is illustrated presenting three example models making use of diverse methodology (3D-QSAR and decision trees, Molecular Dynamics simulations and Linear Interaction Energy theory, and fingerprint-based QSAR).

Motivation The identification and tracking of molecules which enter active site cavity requires screening the positions of thousands of single molecules along several thousand molecular dynamic steps. To fill the existing gap between tools searching for tunnels and pathways and advanced tools employed for accelerated water flux investigations, we have developed AQUA-DUCT. Results AQUA-DUCT is an easy-to-use tool that facilitates analysis of the behaviour of molecules that penetrate any selected region in a protein. It can be used for any type of molecules, e.g. water, oxygen, carbon dioxide, organic solvents, ions. Availability and Implementation Linux, Windows, macOS, OpenBSD, http://www.aquaduct.pl. Supplementary information Supplementary data are available at Bioinformatics online.

The relationship between the structure and a property of a chemical compound is an essential concept in chemistry guiding, for example, drug design. Actually, however, we need economic considerations to fully understand the fate of drugs on the market. We are performing here for the first time the exploration of quantitative structure-economy relationships (QSER) for a large dataset of a commercial building block library of over 2.2 million chemicals. This investigation provided molecular statistics that shows that on average what we are paying for is the quantity of matter. On the other side, the influence of synthetic availability scores is also revealed. Finally, we are buying substances by looking at the molecular graphs or molecular formulas. Thus, those molecules that have a higher number of atoms look more attractive and are, on average, also more expensive. Our study shows how data binning could be used as an informative method when analyzing big data in chemistry.

A number of the structurally diverse chemical compounds with functional diketo acid (DKA) subunit(s) have been revealed by combined online and MoStBiodat 3D pharmacophore-guided ZINC and PubChem database screening. We used the structural data available from such screening to analyse the similarities of the compounds containing the DKA fragment. Generally, the analysis by principal component analysis and self-organizing neural network approaches reveals four families of compounds complying with the chemical constitution (aromatic, aliphatic) of the compounds. From a practical point of view, similar studies may reveal potential bioisosteres of known drugs, e.g. raltegravir/elvitegravir. In this context, it seems that mono-halogenated aryl substructures with para group show the closest similarity to these compounds, in contrast to structures where the aromatic ring is halogenated in both ortho- and para-locations.

A detailed knowledge of hydrogen bond geometry and its directional preferences is vital for in silico investigations of the ligand-receptor short-range non-covalent interactions. The spatial arrangement of the carbonyl and hydroxyl groups seems to determine the capability of β-ketoenol derivatives to recognize the surrounding environment by forming inter- and intra-molecular hydrogen bonds (IHB). In the current study we examined the application of the MoStBioDat platform for a massive database screening of the IHB motifs in β-ketoenol subunits (O=C – C=C – OH). Then, the virtual 3D structural data derived from ZINC and PubChem repository were compared to the experimentally determined CSD data. Differences specific for each database were discovered, which indicated inaccuracies in the simulated data.

Computer-assisted simulations are important for present-day chemical investigations, producing large amount of structural data. In molecular design, we calculate molecular descriptors for factual or virtual structures in chemical space attempting to predict their chemical properties and evaluate potential biological effects. In the current study, we investigated the application of the MoStBiodat software platform for the extensive screening of spatial arrangement and conformational analysis locating intramolecular hydrogen-bonded motifs in catechols. We compared the experimentally determined structural data to those that are simulated using virtual structural data. The relevant topological incoherence among structural and molecular data coming from different sources is thus revealed. Drug Dev Res 72: 209–218, 2011. © 2010 Wiley-Liss, Inc.

For the first time, a set of 56 compounds representing structural derivatives of naturally occurring alpha-asarone as an antifeedants against stored product pests Sitophilus granarius L., Trogoderma granarium Ev., and Tribolium confusum Duv., were subjected to the 3D QSAR studies. Three-dimensional quantitative structure-activity relationships (3D-QSAR) for 56 compounds, including 15 newly synthesized, were performed using comparative molecular field analysis s-CoMFA and SOM-CoMSA techniques. QSAR was conducted based on a combination of biological activity (against Coleoptera larvae and beetles) and various geometrical, topological, quantum-mechanical, electronic, and chromatographic descriptors. The CoMSA formalism coupled with IVE (CoMSA-IVE) allowed us to obtain highly predictive models for Trogoderma granarium Ev. larvae. We have found that this novel method indicates a clear molecular basis for activity and lipophilicity. This investigation will facilitate optimization of the design of new potential antifeedants.

A comparative structure-affinity study of anthraquinone dyes adsorption on cellulose fibre is presented in this paper. We used receptor-dependent 4D-QSAR methods based on grid and neural (SOM) methodology coupled with IVEPLS procedure. The applied RD 4D-QSAR approach focuses mainly on the ability of mapping dye properties to verify the concept of tinctophore in dye chemistry. Moreover, the stochastic SMV procedure to investigate the predictive ability of the method for a large population of 4D-QSAR models was employed. The obtained findings were compared with the previously published RI 3D/4D-QSAR models for the corresponding anthraquinone trainings sets. The neutral (protonated) and anionic (deprotonated) forms of anthraquinone scaffold were examined in order to deal with the uncertainty of the dye ionization state. The results are comparable to both the neutral and anionic dye sets regardless of the occupancy and charge descriptors applied, respectively. It is worth noting that the SOM-4D-QSAR behaves comparably to the cubic counterpart which is observed in each training/test subset specification (4D-QSAR-J o vs SOM- 4D-QSAR o and 4D-QSAR-J q vs SOM-4D-QSAR q ). Additionally, an attempt was made to specify a common set of variables contributing significantly to dye-fiber binding affinity; it was simultaneously performed for some arbitrary chosen SMV models. The presented RD 4D-QSAR methodology together with IVE-PLS procedure provides a robust and predictive modeling technique, which facilitates detailed specification of the molecular motifs significantly contributing to the fiber-dye affinity.

Fragmental topology-activity landscapes (FRAGTAL), a new concept for encoding molecular descriptors for fragonomics into the framework of the molecular database records is presented in this paper. Thus, a structural repository containing biological activity data was searched in a substructure mode by a series of molecular fragments constructed in an incremental or decremental manner. The resulted series of database hits annotated with their activities construct FRAGTAL descriptors encoding a frequency of the certain fragments among active compounds and/or their activities. Actually, this method might be interpreted as a simplified adaptation of the frequent subgraph mining (FSM) method. The FRAGTAL method reconstructs the way in which medicinal chemists are used to designing a prospective drug structure intuitively. A representative example of the practical application of FRAGTAL within the ChemDB Anti-HIV/OI/TB database for disclosing new fragments for HIV-1 integrase inhibition is discussed. In particular, FRAGTAL method identifies ethyl malonate amide (EMA) as the diketo acid (DKA) related arrangement. Since new molecular constructs based on the EMA fragment are still a matter of future investigations we referred to this as anthe DKA offspring.

A detailed knowledge of hydrogen bond geometry and its directional preferences is vital for in silico investigations of the ligand-receptor short-range non-covalent interactions. The spatial arrangement of the carbonyl and hydroxyl groups seems to determine the capability of β-ketoenol derivatives to recognize the surrounding environment by forming inter- and intra-molecular hydrogen bonds (IHB). In the current study we examined the application of the MoStBioDat platform for a massive database screening of the IHB motifs in β-ketoenol subunits (O=C-C=C-OH). Then, the virtual 3D structural data derived from ZINC and PubChem repository were compared to the experimentally determined CSD data. Differences specific for each database were discovered, which indicated inaccuracies in the simulated data.

International audience; Isothiocyanates (ITC) are well-known chemopreventive agents extracted from vegetables. This activity results from the activation of human oxidoreductase. In this letter, the uncompetitive activatory mechanism of ITC was investigated using docking and molecular dynamics simulations. This indicates that NAD(P)H:quinone oxidoreductase can efficiently improve enzyme-substrate recognition within the catalytic site if the ITC activator supports the interaction in the uncompetitive binding site.

Comparative molecular surface analysis (CoMSA) with robust IVE-PLS variable elimination if tested for the benchmark CBG steroid series provides highly predictive RI 3D QSAR models, but failed however to model the activity of sulforaphane (SP) activators of quinone reductase. The application of the SP poses obtained from multipose molecular docking to model the RD IVE-PLS CoMSA resulted in a predictive form. This model indicated lipophilic potential as the activity determinant. The individual molecular surface areas of the highest contribution to the SP activity was identified and visualized by CoMSA contour plots.

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

Three-dimensional quantitative structure–activity relationship (3D QSAR) modeled for α-asarone derivatives using the comparative molecular surface analysis (CoMSA) allowed us to reveal a correlation between the activity of these compounds and the electrostatic potential at the molecular surface. The grid formalism (s-CoMSA) allowed us to indicate a pharmacophore that is of key importance for compound activity. The CoMSA formalism coupled with the iterative variable elimination method gives a highly predictive model.

Shape analysis is a powerful tool in chemistry and drug design, and molecular surface defines shape in the molecular scale. In the current publication we presented a novel formalism for the comparative molecular surface analysis (s-CoMSA). The method enables both quantitative modeling of 3D-QSAR and finding possible pharmacophoric sites. The method provides very predictive models for the CBG activity of the benchmark steroid series, tinctorial properties of the heterocyclic azo dyes and anti-HIV activity of the HEPT series.

We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model.

Highlights • The historical development of the public COSMOS database is provided. • COSMOS NG is a knowledge hub to share toxicity data and in silico tools. • COSMOS NG has broad chemical coverage, with a focus on cosmetics. • Chemical and toxicological data are quality assured through inclusion criteria. • In silico TTC, profiling and read-across workflows are illustrated., The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance – data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.