CurlySMILES: a chemical language to customize and annotate encodings of molecular and nanodevice structures

Representation of molecular structures with persistent homology for machine learning applications in chemistry

Machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties.

Epigenetic Modulators as Potential Multi-targeted Drugs Against Hedgehog Pathway for Treatment of Cancer

The Sonic hedgehog signalling is known to play a crucial role in regulating embryonic development, cancer stem cell maintenance and tissue patterning. Dysregulated hedgehog signalling has been reported to affect tumorigenesis and drug response in various human malignancies. Epigenetic therapy relying on DNA methyltransferase and Histone deacetylase inhibitors are being proposed as potential drug candidates considering their efficiency in preventing development of cancer progenitor cells, killing drug resistant cells and also dictating "on/off" switch of tumor suppressor genes and oncogenes. In this docking approach, epigenetic modulators were virtually screened for their efficiency in inhibiting key regulators of SHH pathway viz., sonic hedgehog, Smoothened and Gli using polypharmacological approach. The control drugs and epigenetic modulators were docked with PDB protein structures using AutoDock vina and further checked for their drug-likeness properties. Further molecular dynamics simulation using VMD and NAMD, and MMP/GBSA energy calculation were employed for verifying the stability and entropy of the ligand-receptor complex. EPZ-6438 and GSK 343 (EZH2 inhibitors), CHR 3996 and Mocetinostat (HDAC inhibitors), GSK 126 (HKMT inhibitor) and UNC 1215 (L3MBTL3 antagonist) exhibited multiple-targeted approach in modulating HH signalling. This is the first study to report these epigenetic drugs as potential multi-targeted hedgehog pathway inhibitors. Thus, epigenetic polypharmacology approach can be explored as a better alternative to challenges of acute long term toxicity and drug resistance occurring due to traditional single targeted chemotherapy in the future.

BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules

Having a compact yet robust structurally based identifier or representation system is a key enabling factor for efficient sharing and dissemination of research results within the chemistry community, and such systems lay down the essential foundations for future informatics and data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system. This is because, unlike other disciplines in chemistry, the basic premise that each distinct chemical species corresponds to a well-defined chemical structure does not hold for polymers. Polymers are intrinsically stochastic molecules that are often ensembles with a distribution of chemical structures. This difficulty limits the applicability of all deterministic representations developed for small molecules. In this work, a new representation system that is capable of handling the stochastic nature of polymers is proposed. The new system is based on the popular "simplified molecular-input line-entry system" (SMILES), and it aims to provide representations that can be used as indexing identifiers for entries in polymer databases. As a pilot test, the entries of the standard data set of the glass transition temperature of linear polymers (Bicerano, 2002) were converted into the new BigSMILES language. Furthermore, it is hoped that the proposed system will provide a more effective language for communication within the polymer community and increase cohesion between the researchers within the community.

Modelling of substrate access and substrate binding to cephalosporin acylases

Semisynthetic cephalosporins are widely used antibiotics currently produced by different chemical steps under harsh conditions, which results in a considerable amount of toxic waste. Biocatalytic synthesis by the cephalosporin acylase from Pseudomonas sp. strain N176 is a promising alternative. Despite intensive engineering of the enzyme, the catalytic activity is still too low for a commercially viable process. To identify the bottlenecks which limit the success of protein engineering efforts, a series of MD simulations was performed to study for two acylase variants (WT, M6) the access of the substrate cephalosporin C from the bulk to the active site and the stability of the enzyme-substrate complex. In both variants, cephalosporin C was binding to a non-productive substrate binding site (E86α, S369β, S460β) at the entrance to the binding pocket, preventing substrate access. A second non-productive binding site (G372β, W376β, L457β) was identified within the binding pocket, which competes with the active site for substrate binding. Noteworthy, substrate binding to the protein surface followed a Langmuir model resulting in binding constants K = 7.4 and 9.2 mM for WT and M6, respectively, which were similar to the experimentally determined Michaelis constants KM = 11.0 and 8.1 mM, respectively.

Synthesis, Anti-inflammatory, Antimicrobial Potential and Molecular Docking Studies of 4,5-Disubstituted-1,2,4-Triazole Thioacetate Derivatives

Background:

In the present study synthesis and biological assessment of nine new ethyl [(4,5-disubstituted- 4H-1,2,4-triazol-3-yl)sulfanyl]acetate derivatives 2(a-i) is performed.

Methods:

The title compounds were characterized by their analytical and spectral data. All the synthesized compounds were screened for their in vivo anti-inflammatory activity using carrageenaninduced rat paw oedema method and in vitro antimicrobial activity. All the compounds exhibited good anti-inflammatory activity; especially compound 2h produced the maximum effect i.e., 62.5 % comparable to that of standard, diclofenac. The antimicrobial screening results indicated that some of the newly synthesized compounds showed good antibacterial activity, especially against Escherichia coli.

Results:

All the synthesized thioacetate derivatives of triazoles were also studied for their interactions with the enzymes COX-I and COX-II, two important targets of inflammation pathway, through docking analysis. All the compounds showed good binding affinities with both the enzymes with a maximum value of -8.1 for 2e kcal/mol against COX-I.

Conclusion:

Docking analysis predicted that our compounds reduce inflammation nonselectively by inhibiting both COX-I and COX-II of inflammatory pathway just like other nonselactive NSAIDS.

Screening of caspase-3 inhibitors from natural molecule database using e-pharmacophore and docking studies

Caspase a protease family member, have a vital role in cell death and inflammation process. Caspase-3, an effector caspase controls the regulation of apoptosis and has an anti apoptotic function. The mechanical significance of restoring apoptosis signaling to selectively target malignant cells is utilized to develop strong therapeutic strategies by the caspase family of mortality - induction molecules. Caspase-3 has currently no clear role in treatment for tumor progression and tumor sensitivity. The present study was aimed to screen caspase for potential inhibitors using computer aided docking methodologies. For this, zinc natural molecule database molecules were screened using e-pharmacophore and ADME protocols along with docking studies. Docking analysis selected two molecules, namely ZINC13341044 and ZINC13507846 with G-scores -5.27 and -6.19 respectively. These two potential hits are predicted as caspase inhibitors based on the results and can be further processed for in vitro validation.

Programming languages in chemistry: a review of HTML5/JavaScript

This is one part of a series of reviews concerning the application of programming languages in chemistry, edited by Dr. Rajarshi Guha. This article reviews the JavaScript technology as it applies to the chemistry discipline. A discussion of the history, scope and technical details of the programming language is presented.

A parallelized molecular collision cross section package with optimized accuracy and efficiency

A new parallelized calculation package predicts collision cross sections with high accuracy and efficiency.

Aqueous solution interactions with sex hormone-binding globulin and estradiol: a theoretical investigation

Sex hormone-binding globulin (SHBG) is a binding protein that regulates the availability of steroid hormones in the plasma. Although best known as a steroid carrier, recent studies have associated SHBG in modulating behavioral aspects related to sexual receptivity. Among steroids, estradiol (17β-estradiol, oestradiol or E2), documented as the most active endogenous female hormone, exerts important physiological roles in both reproductive and non-reproductive functions. In this framework, we employed molecular dynamics (MD) and docking techniques for quantifying the interaction energy between a complex aqueous solution, composed by different salts, SHBG and E2. As glucose concentration resembles measured levels in diabetes, special emphasis was devoted to analyzing the interaction energy between this carbohydrate, SHBG and E2 molecules. The calculations revealed remarkable interaction energy between glucose and SHBG surface. Surprisingly, a movement of solute components toward SHBG was observed, yielding clusters surrounding the protein. The high energy and short distance between glucose and SHBG suggests a possible scenario in favor of a detainment state between the sugar and the protein. In this context, we found that glucose clustering does not insert modification on binding site area nor over binding energy SHBG-E2 complex, in spite of protein superficial area increment. The calculations also point to a more pronounced interaction between E2 and glucose, considering the hormone immersed in the solution. In summary, our findings contribute to a better comprehension of both SHBG and E2 interplay with aqueous solution components.

Unraveling the mechanism of l-gulonate-3-dehydrogenase inhibition by ascorbic acid: Insights from molecular modeling

l-Gulonate dehydrogenase (GuDH) is a crucial enzyme in the non-phosphorylated sugar metabolism or glucuronate-xylulose (GX) pathway. Some naturally occurring compounds inhibit GuDH. Ascorbic acid is one of such inhibitors for GuDH. However, the exact mechanism by which ascorbic acid inhibits GuDH is still unknown. In this study, we try to investigate GuDH inhibition using computational approaches by generating a model for buffalo GuDH. We used this model to perform blind dockings of ascorbic acid to GuDH. Some docked conformations of ascorbic acid bind near Asp39 and have steric clashes with crystal structure conformation of NADH. To assess the dynamic stability of the GuDH-ascorbic acid complex, we performed six molecular dynamics simulations for GuDH, three each in its free form and in complex with ascorbic acid for 50 ns, to obtain 300 ns of trajectories in total. During the simulations, ascorbic acid interacted with several residues nearby Asp39. As Asp39 is an important residue for NADH binding and specificity, the interaction of ascorbic acid near Asp39 hinders further NADH binding and ultimately affects the enzymatic functioning of GuDH. In this study, we analyze these interactions between ascorbic acid and GuDH. Our analysis reveals novel details on the mechanism of GuDH inhibition by ascorbic acid.

Mammalian Cells Engineered To Produce New Steroids

Steroids can be difficult to modify through traditional organic synthesis methods, but many enzymes regio- and stereoselectively process a wide variety of steroid substrates. We tested whether steroid-modifying enzymes could make novel steroids from non-native substrates. Numerous genes encoding steroid-modifying enzymes, including some bacterial enzymes, were expressed in mammalian cells by transient transfection and found to be active. We made three unusual steroids by stable expression, in HEK293 cells, of the 7α-hydroxylase CYP7B1, which was selected because of its high native product yield. These cells made 7α,17α-dihydroxypregnenolone and 7β,17α-dihydroxypregnenolone from 17α-hydroxypregnenolone and produced 11α,16α-dihydroxyprogesterone from 16α-hydroxyprogesterone. The last two products were the result of CYP7B1-catalyzed hydroxylation at previously unobserved sites. A Rosetta docking model of CYP7B1 suggested that these substrates' D-ring hydroxy groups might prevent them from binding in the same way as the native substrates, bringing different carbon atoms close to the active ferryl oxygen atom. This new approach could potentially use other enzymes and substrates to produce many novel steroids for drug candidate testing.

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges

Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affinity prediction and ranking in the last two D3R Grand Challenges in computer-aided drug design and discovery. D3R Grand Challenge 2 focused on the pose prediction, binding affinity ranking and free energy prediction for Farnesoid X receptor ligands. Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2. The latest competition, D3R Grand Challenge 3 (GC3), is considered as the most difficult challenge so far. It has five subchallenges involving Cathepsin S and five other kinase targets, namely VEGFR2, JAK2, p38-α, TIE2, and ABL1. There is a total of 26 official competitive tasks for GC3. Our predictions were ranked 1st in 10 out of these 26 tasks.

SPICES: a particle-based molecular structure line notation and support library for mesoscopic simulation

Simplified Particle Input ConnEction Specification (SPICES) is a particle-based molecular structure representation derived from straightforward simplifications of the atom-based SMILES line notation. It aims at supporting tedious and error-prone molecular structure definitions for particle-based mesoscopic simulation techniques like Dissipative Particle Dynamics by allowing for an interplay of different molecular encoding levels that range from topological line notations and corresponding particle-graph visualizations to 3D structures with support of their spatial mapping into a simulation box. An open Java library for SPICES structure handling and mesoscopic simulation support in combination with an open Java Graphical User Interface viewer application for visual topological inspection of SPICES definitions are provided.

Using SMILES strings for the description of chemical connectivity in the Crystallography Open Database

Computer descriptions of chemical molecular connectivity are necessary for searching chemical databases and for predicting chemical properties from molecular structure. In this article, the ongoing work to describe the chemical connectivity of entries contained in the Crystallography Open Database (COD) in SMILES format is reported. This collection of SMILES is publicly available for chemical (substructure) search or for any other purpose on an open-access basis, as is the COD itself. The conventions that have been followed for the representation of compounds that do not fit into the valence bond theory are outlined for the most frequently found cases. The procedure for getting the SMILES out of the CIF files starts with checking whether the atoms in the asymmetric unit are a chemically acceptable image of the compound. When they are not (molecule in a symmetry element, disorder, polymeric species,etc.), the previously published cif_molecule program is used to get such image in many cases. The program package Open Babel is then applied to get SMILES strings from the CIF files (either those directly taken from the COD or those produced by cif_molecule when applicable). The results are then checked and/or fixed by a human editor, in a computer-aided task that at present still consumes a great deal of human time. Even if the procedure still needs to be improved to make it more automatic (and hence faster), it has already yielded more than 160,000 curated chemical structures and the purpose of this article is to announce the existence of this work to the chemical community as well as to spread the use of its results.

pyEFP: Automatic decomposition of the complex molecular systems into rigid polarizable fragments

We present an open source tool able to describe intermolecular electrostatic interactions within the framework of the effective fragment potential (EFP) method. Complex molecular structure is subdivided into compact rigid fragments and parameters of their interactions are obtained from ab initio calculations. Automatic procedure allows for searching of these parameters into the existing database and merge new fragments into it. A set of standard fragments useful for the studies of organic semiconductors is also provided. Input files both for purely EFP and hybrid QM/MM calculations can be generated. The program is written in python and freely available on GitHub: https://github.com/ale-odinokov/pyEFP © 2017 Wiley Periodicals, Inc.

Catalytic enantioselective acyl transfer: the case for 4-PPY with a C-3 carboxamide peptide auxiliary based on synthesis and modelling studies

A series of 4-pyrrolidinopyridine (4-PPY) C-3 carboxamides containing peptide-based side chains have been synthesised and evaluated in the kinetic resolution of a small library of chiral benzylic secondary alcohols. A key design element was the incorporation of a tryptophan residue in the peptide side chain for promoting π-stacking between peptide side chain and the pyridinium ring of the N-acyl intermediate, in which modelling was used as a structure-based guiding tool. Together, a catalyst containing a LeuTrp-N-Boc side chain (catalyst 8) was identified that achieved s-values up to and in slight excess of 10. A transition-state model based on the modelling is proposed to explain the origin of enantioselectivity. This study establishes the usefulness of modelling as a structure-based guiding tool for enantioselectivity optimization as well as the potential for developing scalable peptide-based DMAP-type catalysts for large-scale resolution work.

Crystal structure prediction is changing from basic science to applied technology

Prediction of true polymorphs as dynamic ensembles in contrast to hypothetical static crystal structures.

Impact of Molecular Descriptors on Computational Models

Molecular descriptors encode a wide variety of molecular information and have become the support of many contemporary chemoinformatic and bioinformatic applications. They grasp specific molecular features (e.g., geometry, shape, pharmacophores, or atomic properties) and directly affect computational models, in terms of outcome, performance, and applicability. This chapter aims to illustrate the impact of different molecular descriptors on the structural information captured and on the perceived chemical similarity among molecules. After introducing the fundamental concepts of molecular descriptor theory and application, a step-by-step retrospective virtual screening procedure guides users through the fundamental processing steps and discusses the impact of different types of molecular descriptors.

Identification of chlamydial T3SS inhibitors through virtual screening against T3SS ATPase

Chlamydia trachomatis is a widespread sexually transmitted pathogen that resides within a special vacuole inside host cells. Although acute infection can be treated with antibiotics, chlamydia can enter persistent state, leading to chronic infection that is difficult to cure. Thus, novel anti-chlamydial compounds active against persistent chlamydia are required. Chlamydiae rely upon type III secretion system (T3SS) to inject effector proteins into host cell cytoplasm, and T3SS inhibitors are viewed as promising compounds for treatment of chlamydial infections. C. trachomatis ATPase SctN is an important T3SS component and has not been targeted before. We thus used virtual screening against homology modeled SctN structure to search for SctN inhibitors. Selected compounds were tested for their ability to inhibit chlamydial survival and development within eukaryotic cells, and for the ability to suppress normal T3SS functioning. We identified two compounds that were able to block normal protein translocation through T3SS and inhibit chlamydial survival within eukaryotic cells in 50-100 μm concentrations. These two novel T3SS inhibitors also possessed relatively low toxicity toward eukaryotic cells. A small series of derivatives was further synthesized for the most active of two inhibitors to probe SAR properties.

Differential α-amylase/α-glucosidase inhibitory activities of plant-derived phenolic compounds: a virtual screening perspective for the treatment of obesity and diabetes

Recently, due to their biological properties, polyphenol-rich functional foods have been proposed to be unique supplementary and nutraceutical treatments for diabetes mellitus. Inhibition of α-amylase and α-glucosidase enzymes using natural products (especially polyphenols) is a novel oral policy to regulate carbohydrate metabolism and hyperglycemia. The present study aims to evaluate the α-amylase and α-glucosidase inhibitory activity of 26 polyphenols using molecular docking and virtual screening studies. The results speculate that among selected compounds caffeic acid, curcumin, cyanidin, daidzein, epicatechin, eridyctiol, ferulic acid, hesperetin, narenginin, pinoresinol, quercetin, resveratrol and syringic acid can significantly inhibit the α-glucosidase enzyme. In addition, catechin, hesperetin, kaempferol, silibinin and pelargonidin are potent α-amylase inhibitors. Therefore the primary structure of polyphenols can change the inhibitory effect versus the α-amylase and α-glucosidase enzymes. Finally, we speculate that consumption of polyphenol-rich functional foods (by considering the best dose of each compound and assessing their possible side effects) in diabetic patients may be useful for regulating carbohydrate metabolism and related disorders. The findings of the current study may also shed light on a way of generating a new class of amylase/glucosidase inhibitors that will discriminately inhibit the on-target enzymes with negligible undesired off-target side effects.

Adaptive neuro-fuzzy inference system-applied QSAR with bond dissociation energy for antioxidant activities of phenolic compounds

The aim of this study was to develop quantitative structure-activity relationship (QSAR) models for predicting antioxidant activities of phenolic compounds. The bond dissociation energy of O-H bond (BDE) was calculated by semi-empirical quantum chemical methods. As a new parameter for QSAR models, sum of reciprocals of BDE of enol and phenol groups (X _BDE ) was calculated. Significant correlations were observed between X _BDE and antioxidant activities, and X _BDE was introduced as a parameter for developing QSAR models. Linear regression-applied QSAR models and adaptive neuro-fuzzy inference system (ANFIS)-applied QSAR models were developed. QSAR models by both of linear regression and ANFIS achieved high prediction accuracies. Among the developed models, ANFIS-applied models achieved better prediction accuracies than linear regression-applied models. From these results, the proposed parameter of X _BDE was confirmed as an appropriate variable for predicting and analysing antioxidant activities of phenolic compounds. Also, the ANFIS could be applied on QSAR models to improve prediction accuracy.

Non a Priori Automatic Discovery of 3D Chemical Patterns: Application to Mutagenicity

This article introduces a new type of structural fragment called a geometrical pattern. Such geometrical patterns are defined as molecular graphs that include a labelling of atoms together with constraints on interatomic distances. The discovery of geometrical patterns in a chemical dataset relies on the induction of multiple decision trees combined in random forests. Each computational step corresponds to a refinement of a preceding set of constraints, extending a previous geometrical pattern. This paper focuses on the mutagenicity of chemicals via the definition of structural alerts in relation with these geometrical patterns. It follows an experimental assessment of the main geometrical patterns to show how they can efficiently originate the definition of a chemical feature related to a chemical function or a chemical property. Geometrical patterns have provided a valuable and innovative approach to bring new pieces of information for discovering and assessing structural characteristics in relation to a particular biological phenotype.

3D-QSAR studies of some reversible Acetyl cholinesterase inhibitors based on CoMFA and ligand protein interaction fingerprints using PC-LS-SVM and PLS-LS-SVM

A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q²_LOO-CV=1, R²_ext=0.97). External validation criteria revealed that both methods can be used in generating reasonable QSAR models. It was concluded that due to ability of interaction descriptors in prediction of binding mode, using this approach can be implemented in future 3D-QSAR softwares.

Unique identifiers for small molecules enable rigorous labeling of their atoms

Rigorous characterization of small organic molecules in terms of their structural and biological properties is vital to biomedical research. The three-dimensional structure of a molecule, its 'photo ID', is inefficient for searching and matching tasks. Instead, identifiers play a key role in accessing compound data. Unique and reproducible molecule and atom identifiers are required to ensure the correct cross-referencing of properties associated with compounds archived in databases. The best approach to this requirement is the International Chemical Identifier (InChI). However, the current implementation of InChI fails to provide a complete standard for atom nomenclature, and incorrect use of the InChI standard has resulted in the proliferation of non-unique identifiers. We propose a methodology and associated software tools, named ALATIS, that overcomes these shortcomings. ALATIS is an adaptation of InChI, which operates fully within the InChI convention to provide unique and reproducible molecule and all atom identifiers. ALATIS includes an InChI extension for unique atom labeling of symmetric molecules. ALATIS forms the basis for improving reproducibility and unifying cross-referencing across databases.

High conductance values in π-folded molecular junctions

Folding processes play a crucial role in the development of function in biomacromolecules. Recreating this feature on synthetic systems would not only allow understanding and reproducing biological functions but also developing new functions. This has inspired the development of conformationally ordered synthetic oligomers known as foldamers. Herein, a new family of foldamers, consisting of an increasing number of anthracene units that adopt a folded sigmoidal conformation by a combination of intramolecular hydrogen bonds and aromatic interactions, is reported. Such folding process opens up an efficient through-space charge transport channel across the interacting anthracene moieties. In fact, single-molecule conductance measurements carried out on this series of foldamers, using the scanning tunnelling microscopy-based break-junction technique, reveal exceptionally high conductance values in the order of 10-1 G0 and a low length decay constant of 0.02 Å-1 that exceed the values observed in molecular junctions that make use of through-space charge transport pathways.

Insights into a key sulfite scavenger enzyme sulfite oxidase (SOX) gene in plants

Sulfite oxidase (SOX) is a crucial molybdenum cofactor-containing enzyme in plants that re-oxidizes the sulfite back to sulfate in sulfite assimilation pathway. However, studies of this crucial enzyme are quite limited hence this work was attempted to understand the SOXs in four plant species namely, Arabidopsis thaliana, Solanum lycopersicum, Populus trichocarpa and Brachypodium distachyon. Herein studied SOX enzyme was characterized with both oxidoreductase molybdopterin binding and Mo-co oxidoreductase dimerization domains. The alignment and motif analyses revealed the highly conserved primary structure of SOXs. The phylogeny constructed with additional species demonstrated a clear divergence of monocots, dicots and lower plants. In addition, to further understand the phylogenetic relationship and make a functional inference, a structure-based phylogeny was constructed using normalized RMSD values in five superposed models from four modelled plant SOXs herein and one previously characterized chicken SOX structure. The plant and animal SOXs showed a clear divergence and also implicated their functional divergences. Based on tree topology, monocot B. distachyon appeared to be diverged from other dicots, pointing out a possible monocot-dicot split. The expression patterns of sulfite scavengers including SOX were differentially modulated under cold, heat, salt and high light stresses. Particularly, they tend to be up-regulated under high light and heat while being down-regulated under cold and salt stresses. The presence of cis-regulatory motifs associated with different stresses in upstream regions of SOX genes was thus justified. The protein-protein interaction network of AtSOX and network enrichment with gene ontology (GO) terms showed that most predicted proteins, including sulfite reductase, ATP sulfurylases and APS reductases were among prime enzymes involved in sulfite pathway. Finally, SOX-sulfite docked structures indicated that arginine residues particularly Arg374 is crucial for SOX-sulfite binding and additional two other residues such as Arg51 and Arg103 may be important for SOX-sulfite bindings in plants.

Limited options for low-global-warming-potential refrigerants

Hydrofluorocarbons, currently used as refrigerants in air-conditioning systems, are potent greenhouse gases, and their contribution to climate change is projected to increase. Future use of the hydrofluorocarbons will be phased down and, thus replacement fluids must be found. Here we show that only a few pure fluids possess the combination of chemical, environmental, thermodynamic, and safety properties necessary for a refrigerant and that these fluids are at least slightly flammable. We search for replacements by applying screening criteria to a comprehensive chemical database. For the fluids passing the thermodynamic and environmental screens (critical temperature and global warming potential), we simulate performance in small air-conditioning systems, including optimization of the heat exchangers. We show that the efficiency-versus-capacity trade-off that exists in an ideal analysis disappears when a more realistic system is considered. The maximum efficiency occurs at a relatively high volumetric refrigeration capacity, but there are few fluids in this range.

Collision cross sections and ion structures: development of a general calculation method via high-quality ion mobility measurements and theoretical modeling

Theoretical collision cross section calculations revisited for reliable ion structural studies.

Modelling temperature-dependent properties of polymorphic organic molecular crystals

We present a large-scale study of the temperature-dependence of structures, free energy differences and properties of polymorphic molecular organic crystals. Lattice-vibrational Gibbs free energy differences between 475 pairs of polymorphs of organic molecular crystals have been calculated at 0 K and at their respective melting points, using a highly accurate anisotropic multipole-based force field and including thermal expansion through the use of a (negative) thermal pressure. Re-ranking of the relative thermodynamic stability of the polymorphs in each pair indicates the possibility of an enantiotropic phase transition between the crystal structures, which occurs in 21% of the studied systems. While vibrational contributions to free energies can have a significant effect on thermodynamic stability, the impact of thermal expansion on polymorph free energy differences is generally very small. We also calculate thermal expansion coefficients for the 864 crystal structures and investigate the temperature-dependence of mechanical properties, and pairwise differences in these properties between polymorphs.

Immersive virtual reality in computational chemistry: Applications to the analysis of QM and MM data

The role of Virtual Reality (VR) tools in molecular sciences is analyzed in this contribution through the presentation of the Caffeine software to the quantum chemistry community. Caffeine, developed at Scuola Normale Superiore, is specifically tailored for molecular representation and data visualization with VR systems, such as VR theaters and helmets. Usefulness and advantages that can be gained by exploiting VR are here reported, considering few examples specifically selected to illustrate different level of theory and molecular representation.

Recognizing chemicals in patents: a comparative analysis

Recently, methods for Chemical Named Entity Recognition (NER) have gained substantial interest, driven by the need for automatically analyzing todays ever growing collections of biomedical text. Chemical NER for patents is particularly essential due to the high economic importance of pharmaceutical findings. However, NER on patents has essentially been neglected by the research community for long, mostly because of the lack of enough annotated corpora. A recent international competition specifically targeted this task, but evaluated tools only on gold standard patent abstracts instead of full patents; furthermore, results from such competitions are often difficult to extrapolate to real-life settings due to the relatively high homogeneity of training and test data. Here, we evaluate the two state-of-the-art chemical NER tools, tmChem and ChemSpot, on four different annotated patent corpora, two of which consist of full texts. We study the overall performance of the tools, compare their results at the instance level, report on high-recall and high-precision ensembles, and perform cross-corpus and intra-corpus evaluations. Our findings indicate that full patents are considerably harder to analyze than patent abstracts and clearly confirm the common wisdom that using the same text genre (patent vs. scientific) and text type (abstract vs. full text) for training and testing is a pre-requisite for achieving high quality text mining results.

Protein Data Bank Japan (PDBj): updated user interfaces, resource description framework, analysis tools for large structures

The Protein Data Bank Japan (PDBj, http://pdbj.org), a member of the worldwide Protein Data Bank (wwPDB), accepts and processes the deposited data of experimentally determined macromolecular structures. While maintaining the archive in collaboration with other wwPDB partners, PDBj also provides a wide range of services and tools for analyzing structures and functions of proteins. We herein outline the updated web user interfaces together with RESTful web services and the backend relational database that support the former. To enhance the interoperability of the PDB data, we have previously developed PDB/RDF, PDB data in the Resource Description Framework (RDF) format, which is now a wwPDB standard called wwPDB/RDF. We have enhanced the connectivity of the wwPDB/RDF data by incorporating various external data resources. Services for searching, comparing and analyzing the ever-increasing large structures determined by hybrid methods are also described.

Assessment of the interaction procedure between Pt(IV) prodrug [Pt(5,5'-dmbpy)Cl4 and human serum albumin: Combination of spectroscopic and molecular modeling technique

In this study, a cytotoxic Pt(IV) complex [Pt(5,5'-dmbpy)Cl₄ (5,5'-dmbpy is 5,5'-dimethyl-2,2'-bipyridine) was selected to investigate its affinity to human serum albumin (HSA) by spectroscopy and molecular docking methods. This complex has a bidentate nitrogen donor ligand with four chloride anions attached to a Pt(IV) metal in a distorted octahedral environment. The fluorescence data showed this complex quench the intrinsic fluorescence of HSA through a static quenching mechanism. The binding constant (K_b) and the number of binding sites (n) were obtained based on the results of fluorescence measurements. UV-vis, circular dichroism spectroscopy, and three-dimensional fluorescence spectroscopy proved that the Pt(IV) complex could slightly change the secondary structure of protein. Thermodynamic parameters show that the Pt(IV) complex binds to HSA through electrostatic and Vander Waals interactions with one binding site. The molecular docking results confirmed the spectroscopic results and showed that Pt(IV) complex is embedded into subdomain IIA of protein. The aim of this study is to describe the performance of effective anti-cancer drugs when faced with proteins such as HSA.

Jmol SMILES and Jmol SMARTS: specifications and applications

Background

SMILES and SMARTS are two well-defined structure matching languages that have gained wide use in cheminformatics. Jmol is a widely used open-source molecular visualization and analysis tool written in Java and implemented in both Java and JavaScript. Over the past 10 years, from 2007 to 2016, work on Jmol has included the development of dialects of SMILES and SMARTS that incorporate novel aspects that allow new and powerful applications.

Results

The specifications of “Jmol SMILES” and “Jmol SMARTS” are described. The dialects most closely resemble OpenSMILES and OpenSMARTS. Jmol SMILES is a superset of OpenSMILES, allowing a freer format, including whitespace and comments, the addition of “processing directives” that modify the meaning of certain aspects of SMILES processing such as aromaticity and stereochemistry, a more extensive treatment of stereochemistry, and several minor additions. Jmol SMARTS similarly adds these same modifications to OpenSMARTS, but also adds a number of additional “primitives” and elements of syntax tuned to matching 3D molecular structures and selecting their atoms. The result is an expansion of the capabilities of SMILES and SMARTS primarily for use in 3D molecular analysis, allowing a broader range of matching involving any combination of 3D molecular structures, SMILES strings, and SMARTS patterns. While developed specifically for Jmol, these dialects of SMILES and SMARTS are independent of the Jmol application itself.

Conclusions

Jmol SMILES and Jmol SMARTS add value to standard SMILES and SMARTS. Together they have proven exceptionally capable in extracting valuable information from 3D structural models, as demonstrated in Jmol. Capabilities in Jmol enabled by Jmol SMILES and Jmol SMARTS include efficient MMFF94 atom typing, conformational identification, SMILES comparisons without canonicalization, identification of stereochemical relationships, quantitative comparison of 3D structures from different sources (including differences in Kekulization), conformational flexible fitting, and atom mapping used to synchronize interactive displays of 2D structures, 3D structures, and spectral correlations, where data are being drawn from multiple sources.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-016-0160-4) contains supplementary material, which is available to authorized users.

An in silico algorithm for identifying stabilizing pockets in proteins: test case, the Y220C mutant of the p53 tumor suppressor protein

The p53 tumor suppressor protein performs a critical role in stimulating apoptosis and cell cycle arrest in response to oncogenic stress. The function of p53 can be compromised by mutation, leading to increased risk of cancer; approximately 50% of cancers are associated with mutations in the p53 gene, the majority of which are in the core DNA-binding domain. The Y220C mutation of p53, for example, destabilizes the core domain by 4 kcal/mol, leading to rapid denaturation and aggregation. The associated loss of tumor suppressor functionality is associated with approximately 75 000 new cancer cases every year. Destabilized p53 mutants can be 'rescued' and their function restored; binding of a small molecule into a pocket on the surface of mutant p53 can stabilize its wild-type structure and restore its function. Here, we describe an in silico algorithm for identifying potential rescue pockets, including the algorithm's integration with the Dynameomics molecular dynamics data warehouse and the DIVE visual analytics engine. We discuss the results of the application of the method to the Y220C p53 mutant, entailing finding a putative rescue pocket through MD simulations followed by an in silico search for stabilizing ligands that dock into the putative rescue pocket. The top three compounds from this search were tested experimentally and one of them bound in the pocket, as shown by nuclear magnetic resonance, and weakly stabilized the mutant.

Open source molecular modeling

The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. An updated online version of this catalog can be found at https://opensourcemolecularmodeling.github.io.

In silico design and screening of hypothetical MOF-74 analogs and their experimental synthesis

We present the in silico design of MOFs exhibiting 1-dimensional rod topologies by enumerating MOF-74-type analogs based on the PubChem Compounds database. We simulate the adsorption behavior of CO₂ in the generated analogs and experimentally validate a novel MOF-74 analog, Mg₂(olsalazine).

In this work we present the in silico design of metal-organic frameworks (MOFs) exhibiting 1-dimensional rod topologies. We introduce an algorithm for construction of this family of MOF topologies, and illustrate its application for enumerating MOF-74-type analogs. Furthermore, we perform a broad search for new linkers that satisfy the topological requirements of MOF-74 and consider the largest database of known chemical space for organic compounds, the PubChem database. Our in silico crystal assembly, when combined with dispersion-corrected density functional theory (DFT) calculations, is demonstrated to generate a hypothetical library of open-metal site containing MOF-74 analogs in the 1-D rod topology from which we can simulate the adsorption behavior of CO₂. We finally conclude that these hypothetical structures have synthesizable potential through computational identification and experimental validation of a novel MOF-74 analog, Mg₂(olsalazine).

Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis

BACKGROUND

Increasing resistance to anti-tuberculosis drugs has driven the need for developing new drugs. Resources such as the tropical disease research (TDR) target database and AssessDrugTarget can help to prioritize putative drug targets. Hower, these resources do not necessarily map to metabolic pathways and the targets are not involved in dormancy. In this study, we specifically identify drug resistance pathways to allow known drug resistant mutations in one target to be offset by inhibiting another enzyme of the same metabolic pathway. One of the putative targets, Rv1712, was analysed by modelling its three dimensional structure and docking potential inhibitors.

RESULTS

We mapped 18 TB drug resistance gene products to 15 metabolic pathways critical for mycobacterial growth and latent TB by screening publicly available microarray data. Nine putative targets, Rv1712, Rv2984, Rv2194, Rv1311, Rv1305, Rv2195, Rv1622c, Rv1456c and Rv2421c, were found to be essential, to lack a close human homolog, and to share >67 % sequence identity and >87 % query coverage with mycobacterial orthologs. A structural model was generated for Rv1712, subjected to molecular dynamic simulation, and identified 10 compounds with affinities better than that for the ligand cytidine-5'-monophosphate (C5P). Each compound formed more interactions with the protein than C5P.

CONCLUSIONS

We focused on metabolic pathways associated with bacterial drug resistance and proteins unique to pathogenic bacteria to identify novel putative drug targets. The ten compounds identified in this study should be considered for experimental studies to validate their potential as inhibitors of Rv1712.

Exploring structural requirements for the inhibition of Plasmodium falciparum calcium-dependent protein kinase-4 (PfCDPK-4) using multiple in silico approaches

Plasmodial protein kinases represent one of the most important thrust areas for antimalarial drug discovery.

Synthesis of 2-arylated thiadiazolopyrimidones by Suzuki–Miyaura cross-coupling: a new class of nucleotide pyrophosphatase (NPPs) inhibitors

Over expression of nucleotide pyrophosphatase (NPPs) activity is associated with chondrocalcinosis, osteoarthritis, type 2 diabetes, neurodegenerative diseases, allergies and cancer metastasis.

Phytochemica: a platform to explore phytochemicals of medicinal plants

Plant-derived molecules (PDMs) are known to be a rich source of diverse scaffolds that could serve as the basis for rational drug design. Structured compilation of phytochemicals from traditional medicinal plants can facilitate prospection for novel PDMs and their analogs as therapeutic agents. Atropa belladonna, Catharanthus roseus, Heliotropium indicum, Picrorhiza kurroa and Podophyllum hexandrum are important Himalayan medicinal plants, reported to have immense therapeutic properties against various diseases. We present Phytochemica, a structured compilation of 963 PDMs from these plants, inclusive of their plant part source, chemical classification, IUPAC names, SMILES notations, physicochemical properties and 3-dimensional structures with associated references. Phytochemica is an exhaustive resource of natural molecules facilitating prospection for therapeutic molecules from medicinally important plants. It also offers refined search option to explore the neighbourhood of chemical space against ZINC database to identify analogs of natural molecules at user-defined cut-off. Availability of phytochemical structured dataset may enable their direct use in in silico drug discovery which will hasten the process of lead identification from natural products under proposed hypothesis, and may overcome urgent need for phytomedicines. Compilation and accessibility of indigenous phytochemicals and their derivatives can be a source of considerable advantage to research institutes as well as industries.

Database URL: home.iitj.ac.in/∼bagler/webservers/Phytochemica

SerpentinaDB: a database of plant-derived molecules of Rauvolfia serpentina

BACKGROUND

Plant-derived molecules (PDMs) are known to be a rich source of diverse scaffolds that could serve as a basis for rational drug design. Structured compilation of phytochemicals from traditional medicinal plants can facilitate prospection for novel PDMs and their analogs as therapeutic agents. Rauvolfia serpentina is an important medicinal plant, endemic to Himalayan mountain ranges of Indian subcontinent, reported to be of immense therapeutic value against various diseases.

DESCRIPTION

We present SerpentinaDB, a structured compilation of 147 R. serpentina PDMs, inclusive of their plant part source, chemical classification, IUPAC, SMILES, physicochemical properties, and 3D chemical structures with associated references. It also provides refined search option for identification of analogs of natural molecules against ZINC database at user-defined cut-off.

CONCLUSION

SerpentinaDB is an exhaustive resource of R. serpentina molecules facilitating prospection for therapeutic molecules from a medicinally important source of natural products. It also provides refined search option to explore the neighborhood of chemical space against ZINC database to identify analogs of natural molecules obtained as leads. In a previous study, we have demonstrated the utility of this resource by identifying novel aldose reductase inhibitors towards intervention of complications of diabetes.

Armochaetoglobins A-J: Cytochalasan Alkaloids from Chaetomium globosum TW1-1, a Fungus Derived from the Terrestrial Arthropod Armadillidium vulgare

Ten new cytochalasan alkaloids, termed armochaetoglobins A-J (1-10), and four known chaetoglobosins (11-14) were isolated from a methanol extract of Chaetomium globosum TW1-1, a fungus isolated from the medicinal terrestrial arthropod Armadillidium vulgare. Their structures were elucidated by a combination of spectroscopy, single-crystal X-ray crystallography, and ECD calculations. Armochaetoglobins A-E (1-5) represented the first examples of seco-chaetoglobosins arising from an oxidative cleavage of C-19 and C-20. Among these compounds, armochaetoglobin A (1) features an unusual pyrrole ring. The cytotoxic activities of 2-10 were evaluated, and armochaetoglobin H (8) showed moderate inhibitory activities against five human cancer cell lines, with IC50 values ranging from 3.31 to 9.83 μM.

Identification of small-molecule inhibitors of calcineurin-NFATc signaling that mimic the PxIxIT motif of calcineurin binding partners

Calcineurin (CN), a serine and threonine protein phosphatase that depends on Ca(2+) and calmodulin for its activity, is the target of the immunosuppressant drugs cyclosporin A (CsA) and tacrolimus (FK506). CN dephosphorylates and activates members of the NFATc (nuclear factor of activated T cells) family of transcription factors in T cells by binding to their conserved PxIxIT motif. Upon dephosphorylation, NFATc proteins translocate to the nucleus, where they stimulate the expression of genes encoding cytokines and chemokines that are required for T cell proliferation and the immune response. We performed a pharmacophore-based virtual screening of ~5.5 million commercially available, "drug-like" compounds to identify nonpeptidic compounds that inhibited the CN-dependent activation of NFATc signaling and that could serve as potential drug candidates for immunosuppressive therapy. Of 32 compounds that mimicked the PxIxIT motif, 7 competed with NFATc for binding to CN in vitro without interfering with the phosphatase activity of CN. Furthermore, in activated human CD4(+) T cells, four of the seven compounds inhibited the expression of NFATc-dependent genes, cytokine production, and cell proliferation, suggesting that these may have therapeutic potential as immunosuppressive agents.