Chemical Approaches for Studying the Biology and Pharmacology of Membrane Transporters: The Histidine/Large Amino Acid Transporter SLC7A5 as a Benchmark

The localization of membrane transporters at the forefront of natural barriers makes these proteins very interesting due to their involvement in the absorption and distribution of nutrients and xenobiotics, including drugs. Over the years, structure/function relationship studies have been performed employing several strategies, including chemical modification of exposed amino acid residues. These approaches are very meaningful when applied to membrane transporters, given that these proteins are characterized by both hydrophobic and hydrophilic domains with a different degree of accessibility to employed chemicals. Besides basic features, the chemical targeting approaches can disclose information useful for pharmacological applications as well. An eminent example of this picture is the histidine/large amino acid transporter SLC7A5, known as LAT1 (Large Amino Acid Transporter 1). This protein is crucial in cell life because it is responsible for mediating the absorption and distribution of essential amino acids in peculiar body districts, such as the blood brain barrier and placenta. Furthermore, LAT1 can recognize a large variety of molecules of pharmacological interest and is also considered a hot target for drugs due to its over-expression in virtually all human cancers. Therefore, it is not surprising that the chemical targeting approach, coupled with bioinformatics, site-directed mutagenesis and transport assays, proved fundamental in describing features of LAT1 such as the substrate binding site, regulatory domains and interactions with drugs that will be discussed in this review. The results on LAT1 can be considered to have general applicability to other transporters linked with human diseases.     

Receptor-based computational screening of compound databases: the main docking-scoring engines

The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance with many new and exciting techniques. The number of protein structures and/or chemical ligands is constantly growing, through the use of parallel chemistry, X-ray crystallography, NMR or homology modeling methods and so is the theoretical understanding of protein-ligand interactions. As such, structure-based approaches to drug-design and in silico screening are becoming routine part of most modern lead discovery programs. Prioritization of compound libraries is an extremely important task that aims at the rapid identification of tight-binding ligands and ultimately new therapeutic compounds. These in silico approaches combined with other experimental methods facilitate the design of new medicines to treat cardiovascular, degenerative, infectious, and neoplastic diseases, among others. Here, we review key concepts and specific features of several selected ligand-receptor docking/scoring methods while several other topics pertaining to the field of in silico screening are reviewed in the following articles of this special issue of Current Protein and Peptide Science.     

A metabolomic study of Asian and American Ginseng based on RRLC-QTOF/MS methods

In order to understand the significant differences in activities and effects between Asian ginseng (ASG) and American ginseng (AMG), it is important to study the correlation between chemical structures and biological activities in the two types of herbs. However, more attention has been paid to the analysis of ginsenosides in previous reports distinguishing between ASG and AMG. There are some other bioactive compounds besides ginsenosides, however, few studies have focused on a systematic comparison of these types of compounds. Two metabolomic methods were developed in this study by qualitative data acquisition using normal phase liquid chromatography mass spectrometry (NPLC-MS) and reverse phase liquid chromatography mass spectrometry (RPLC-MS) respectively, in combination with principal component analysis (PCA). Results show that both NPLC-MS and RPLC-MS-based metabolomic methods are feasible in composition profiling, biomarker screening as well as in discrimination of ASG and AMG. 17 ginsenosides were identified as analytical markers in RPLC-MS-based metabolomic method. In comparison, using NPLC-MS-based method, 5 ginsonosides, two amino acids as well as 1 oligosaccharide were identified as analytical markers. Therefore, RPLC-MS-based metabolomic method exhibits better profiling in ginsenosides, while NPLC-MS-based metabolomic method offers the advantage that multiple active ingredients can be determined simultaneously. The two methods are both helpful in identification of biomarker as well as in discrimination of American ginsengs from Asian ginsengs.     

In silico predictions of hERG channel blockers in drug discovery: from ligand-based and target-based approaches to systems chemical biology

The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several drugs with different therapeutic indications and recognized as hERG blockers were recently withdrawn due to the risk of QT prolongation, arrhythmia and Torsade de Pointes. In silico techniques can provide a priori knowledge of hERG blockers, thus reducing the costs associated with screening assays. Significant progress has been made in structure-based and ligand-based drug design and a number of models have been developed to predict hERG blockage. Although approaches such as homology modeling in combination with docking and molecular dynamics bring us closer to understand the drug-channel interactions whereas QSAR and classification models provide a faster assessment and detection of hERG-related drug toxicity, limitation on the applicability domain of the current models and integration of data from diverse in vitro approaches are still issues to challenge. Therefore, this review will emphasize on current methods to predict hERG blockers and the need of consistent data to improve the quality and performance of the in silico models. Finally, integration of network-based analysis on drugs inducing potentially long-QT syndrome and arrhythmia will be discussed as a new perspective for a better understanding of the drug responses in systems chemical biology.     

Natural product DNA major groove binders

Covering: 1980 to 2011. Major groove recognition of DNA by proteins utilizes the variation in hydrogen bond donor/acceptor content that makes DNA base-pairs distinguishable from one another. Specific ligand-DNA interactions in the major groove are necessary to develop approaches for inhibition of DNA-protein interactions. As opposed to minor groove binders, little research has been achieved in recognition of the DNA major groove. This review summarizes the progress in identification of natural products that bind to the major groove of DNA. We first review the natural products, pluramycins, aflatoxins, azinomycins, leinamycin, neocarzinostatin, and ditercalinium, that are known to possess major groove interacting elements. These compounds, however, interact primarily with DNA by intercalation between base-pair steps. Some of these compounds utilize non-covalent interactions in order to position themselves to alkylate DNA at the nucleophilic N7 positions on nearby purine bases. Finally, recent reports of non-covalent major groove binding with carbohydrates, aminoglycosides in particular, have revealed them as promising leads for DNA major groove binding probes or drugs.     

Classification of data with missing elements and outliers

Missing elements and outliers can often occur in experimental data. The presence of outliers makes the evaluation of any least squares model parameters difficult, while the missing values influence the adequate identification of outliers. Therefore, approaches that can handle incomplete data containing outliers are highly valued. In this paper, we present the expectation-maximization robust soft independent modeling of class analogy approach (EM-S-SIMCA) based on the recently introduced spherical SIMCA method. Several important issues like the possibility of choosing the complexity of the model with the leverage correction procedure, the selection of training and test sets using methods of uniform design for incomplete data and prediction of new samples containing missing elements are discussed. The results of a comparison study showed that EM-S-SIMCA outperforms the classic expectation-maximization SIMCA method. The performance of the method was illustrated on simulated and real data sets and led to satisfactory results.     

Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways

Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.     

Discovery of novel targets with high throughput RNA interference screening

High throughput technologies have the potential to affect all aspects of drug discovery. Considerable attention is paid to high throughput screening (HTS) for small molecule lead compounds. The identification of the targets that enter those HTS campaigns had been driven by basic research until the advent of genomics level data acquisition such as sequencing and gene expression microarrays. Large-scale profiling approaches (e.g., microarrays, protein analysis by mass spectrometry, and metabolite profiling) can yield vast quantities of data and important information. However, these approaches usually require painstaking in silico analysis and low-throughput basic wet-lab research to identify the function of a gene and validate the gene product as a potential therapeutic drug target. Functional genomic screening offers the promise of direct identification of genes involved in phenotypes of interest. In this review, RNA interference (RNAi) mediated loss-of-function screens will be discussed and as well as their utility in target identification. Some of the genes identified in these screens should produce similar phenotypes if their gene products are antagonized with drugs. With a carefully chosen phenotype, an understanding of the biology of RNAi and appreciation of the limitations of RNAi screening, there is great potential for the discovery of new drug targets.     

Biomarker discovery in psychiatric disorders

Schizophrenia is a multifaceted neuropsychiatric disorder. Its onset is the result of complex interactions between genetic, developmental and environmental factors. It almost certainly presents a heterogeneous group of aetiologies which may not be reflected in the symptomatic/clinical presentation of patients. Therefore, a better molecular understanding of the disease onset and progression is urgently needed. The high complexity of the disorder and the heterogeneity of patient populations account for the slow progress of biomarker discovery approaches. Multi-omics profiling approaches can be employed to investigate large numbers of patient and control samples in a single experiment. These large scale experiments are required to identify disease intrinsic molecular signatures as well as patient subgroups with potentially distinct biochemical pathways underpinning their symptoms. In this overview, we describe some of the most important challenges for biomarker discovery for psychiatric disorders and emphasize how these problems contribute to the requirement of large sample numbers. Results of MS-based protein profiling studies in schizophrenia research are reviewed and technical advantages and difficulties of the methodologies described. We outline recent technological advances that generated impressive results in other areas of research and point to their applicability for biomarker discovery in psychiatric disorders.     

Development of a DNA‐Templated Peptide Probe for Photoaffinity Labeling and Enrichment of the Histone Modification Reader Proteins

Histone post‐translational modifications (HPTMs) provide signal platforms to recruit proteins or protein complexes to regulate gene expression. Therefore, the identification of these recruited partners (readers) is essential to understand the underlying regulatory mechanisms. However, it is still a major challenge to profile these partners because their interactions with HPTMs are rather weak and highly dynamic. Herein we report the development of a HPTM dual probe based on DNA‐templated technology and a photo‐crosslinking method for the identification of HPTM readers. By using the trimethylation of histone H3 lysine 4, we demonstrated that this HPTM dual probe can be successfully utilized for labeling and enrichment of HPTM readers, as well as for the discovery of potential HPTM partners. This study describes the development of a new chemical proteomics tool for profiling HPTM readers and can be adapted for broad biomedical applications.     

Rooteomics: the challenge of discovering plant defense-related proteins in roots

In recent years, a strong emphasis has been given in deciphering the function of genes unraveled by the completion of several genome sequencing projects. In plants, functional genomics has been massively used in order to search for gene products of agronomic relevance. As far as root-pathogen interactions are concerned, several genes are recognized to provide tolerance/resistance against potential invaders. However, very few proteins have been identified by using current proteomic approaches. One of the major drawbacks for the successful analysis of root proteomes is the inherent characteristics of this tissue, which include low volume content and high concentration of interfering substances such as pigments and phenolic compounds. The proteome analysis of plant-pathogen interactions provides important information about the global proteins expressed in roots in response to biotic stresses. Moreover, several pathogenic proteins superimpose the plant proteome and can be identified and used as targets for the control of viruses, bacteria, fungi and nematode pathogens. The present review focuses on advances in different proteomic strategies dedicated to the challenging analysis of plant defense proteins expressed during bacteria-, fungi- and nematode-root interactions. Recent developments, limitations of the current techniques, and technological perspectives for root proteomics aiming at the identification of resistance-related proteins are discussed.     

Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.     

Whole-molecule calculation of log p based on molar volume, hydrogen bonds, and simulated 13C NMR spectra

The prediction of Log P is usually accomplished using either substructure or whole-molecule approaches. However, these methods are complicated, and previous whole-molecule approaches have not been successful for the prediction of Log P in very complex molecules. The observed chemical shifts in nuclear magnetic resonance (NMR) spectroscopy are related to the electrostatics at the nucleus, which are influenced by solute-solvent interactions. The different solvation effects on a molecule by either water or methanol have a strong effect on the NMR chemical shift value. Therefore, the chemical shift values observed in an aqueous and organic solvent should correlate to Log P. This paper develops a rapid, objective model of Log P based on molar volume, hydrogen bonds, and differences in calculated 13C NMR chemical shifts for a diverse set of compounds. A partial least squares (PLS) model of Log P built on the sum of carbon chemical shift differences in water and methanol, molar volume, number of hydrogen bond donors and acceptors in 162 diverse compounds gave an r2 value of 0.88. The average r2 for 10 training models of Log P made from 90% of the data was 0.87+/-0.01. The average q2 for 10 leave-10%-out cross-validation test sets was 0.87+/-0.05.     

Target identification strategies in chemical genetics

Chemical inhibitors have had a profound impact on many diverse fields of biology. The goal of chemical genetics is to use small molecules to perturb biological systems in a manner conceptually similar to traditional genetics. Key to the advancement of the chemical genetic paradigm is the further development of tools and approaches for the identification of the protein targets of active compounds identified in chemical genetic screens. This review will address historic examples in which forward chemical genetics yielded new insight into a biological problem through successful identification of the target of an active molecule. The approaches covered have been grouped into two broad classes: target identification by affinity-based methods and target identification by deduction. Strengths and shortcomings of each approach as it pertains to their application to modern chemical genetics will be discussed. Finally, a series of new genomic and proteomic-based techniques for target identification will be described. Although a truly general approach to target identification has yet to be developed, these examples illustrate that there are many effective strategies for successfully elucidating the biological targets of active small molecules.     

Systematic approach to optimize a pretreatment method for ultrasensitive liquid chromatography with tandem mass spectrometry analysis of multiple target compounds in biological samples

In current approaches for new drug development, highly sensitive and robust analytical methods for the determination of test compounds in biological samples are essential. These analytical methods should be optimized for every target compound. However, for biological samples that contain multiple compounds as new drug candidates obtained by cassette dosing tests, it would be preferable to develop a single method that allows the determination of all compounds at once. This study aims to establish a systematic approach that enables a selection of the most appropriate pretreatment method for multiple target compounds without the use of their chemical information. We investigated the retention times of 27 known compounds under different mobile phase conditions and determined the required pretreatment of human plasma samples using several solid‐phase and liquid–liquid extractions. From the relationship between retention time and recovery in a principal component analysis, appropriate pretreatments were categorized into several types. Based on the category, we have optimized a pretreatment method for the identification of three calcium channel blockers in human plasma. Plasma concentrations of these drugs in a cassette‐dose clinical study at microdose level were successfully determined with a lower limit of quantitation of 0.2 pg/mL for diltiazem, 1 pg/mL for nicardipine, and 2 pg/mL for nifedipine.     

Principal component analysis of carbon-13 substituent-induced chemical shifts of some unsaturated compounds

Principal component analysis (PCA) is applied to 32 disubstituted unsaturated compounds (Y–CH₂–X): cyanides, oximes and propenes; bearing 12 -substituents: F, Cl, Br, I, OMe, OEt, SMe, SEt, NMe₂, NEt₂, Me, and Et. The experimental ¹³C chemical shifts for the -carbon and functional carbon atoms are correlated with theoretically derived molecular properties, i.e. partial charges, electronegativity, hardness, dipole moments and the nuclear repulsion energies. In the first PCA, the clustering of these three classes of organic compounds occurred mostly because of the chemical shifts and partial charges, and also of the dipole moments, hardness and electronegativity parameters as confirmed by loading graph. A strong grouping is observed in the second PCA, showing the chemical shift dependence on the type of heteroatom substituents. Therefore, sulfur, nitrogen, oxygen and neutral groups exhibit four types of C-13 SCS influences, indicating that the heteroatom (Y) properties play a significant role on the effects on chemical shifts. The -halogenated compounds represent a very heterogeneous group due to possible orbital interactions between the functional group and the substituent. The third PCA shows the grouping of F, Cl, Br and I derivatives, confirming the second PCA results that same halogen presents the same or very similar effects on the chemical shifts.     

Novel virtual screening protocol based on the combined use of molecular modeling and electron-ion interaction potential techniques to design HIV-1 integrase inhibitors

HIV-1 integrase (IN) is an essential enzyme for viral replication and represents an intriguing target for the development of new drugs. Although a large number of compounds have been reported to inhibit IN in biochemical assays, no drug active against this enzyme has been approved by the FDA so far. In this study, we report, for the first time, the use of the electron-ion interaction potential (EIIP) technique in combination with molecular modeling approaches for the identification of new IN inhibitors. An innovative virtual screening approach, based on the determination of both short- and long-range interactions between interacting molecules, was employed with the aim of identifying molecules able to inhibit the binding of IN to viral DNA. Moreover, results from a database screening on the commercial Asinex Gold Collection led to the selection of several compounds. One of them showed a significant inhibitory potency toward IN in the overall integration assay. Biological investigations also showed, in agreement with modeling studies, that these compounds prevent recognition of DNA by IN in a fluorescence fluctuation assay, probably by interacting with the DNA binding domain of IN.