Combined in vitro and in silico approaches to the assessment of stimulant properties of novel psychoactive substances – The case of the benzofuran 5-MAPB

Novel psychoactive substances (NPS) are increasingly prevalent world-wide although their pharmacological characteristics are largely unknown; those with stimulant properties, due to interactions with the dopamine transporter (DAT), have addictive potential which their users may not realise. We evaluated the binding of 1-(1-benzofuran-5-yl)-N-methylpropan-2-amine (5-MAPB) to rat striatal DAT by means of quantitative autoradiography with [125I]RTI-121, and the effects of 5-MAPB on electrically-evoked dopamine efflux by fast-cyclic voltammetry in rat brain slices. 5-MAPB displaced [125I]RTI-121 in a concentration-dependent manner, with significant effects at 10 and 30 μM. The voltammetry data suggest that 5-MAPB reduces the rate of dopamine reuptake; while the peak dopamine efflux was not increased, the area under the curve was augmented. 5-MAPB can also cause reverse dopamine transport consistent with stimulant properties, more similar to amphetamine than cocaine. Molecular modelling and docking studies compared the binding site of DAT in complex with 5-MAPB to dopamine, amphetamine, 5-APB, MDMA, cocaine and RTI-121. This structural comparison reveals a binding mode for 5-MAPB found in the primary binding (S1) site, central to transmembrane domains 1, 3, 6 and 8, which overlaps with the binding modes of dopamine, cocaine and its analogues. Atomistic molecular dynamics simulations further show that, when in complex with 5-MAPB, DAT can exhibit conformational transitions that spontaneously isomerize the transporter into inward-facing state, similarly to that observed in dopamine-bound DAT. These novel insights, offered by the combination of computational methods of biophysics with neurobiological procedures, provide structural context for NPS at DAT and relate them with their functional properties at DAT as the molecular target of stimulants

Inferring PDZ Domain Multi-Mutant Binding Preferences from Single-Mutant Data

Many important cellular protein interactions are mediated by peptide recognition domains. The ability to predict a domain's binding specificity directly from its primary sequence is essential to understanding the complexity of protein-protein interaction networks. One such recognition domain is the PDZ domain, functioning in scaffold proteins that facilitate formation of signaling networks. Predicting the PDZ domain's binding specificity was a part of the DREAM4 Peptide Recognition Domain challenge, the goal of which was to describe, as position weight matrices, the specificity profiles of five multi-mutant ERBB2IP-1 domains. We developed a method that derives multi-mutant binding preferences by generalizing the effects of single point mutations on the wild type domain's binding specificities. Our approach, trained on publicly available ERBB2IP-1 single-mutant phage display data, combined linear regression-based prediction for ligand positions whose specificity is determined by few PDZ positions, and single-mutant position weight matrix averaging for all other ligand columns. The success of our method as the winning entry of the DREAM4 competition, as well as its superior performance over a general PDZ-ligand binding model, demonstrates the advantages of training a model on a well-selected domain-specific data set

A short update on the structure of drug binding sites on neurotransmitter transporters

<p>Abstract</p> <p>Background</p> <p>The dopamine (DAT), noradrenalin (NET) and serotonin (SERT) transporters are molecular targets for different classes of psychotropic drugs. Cocaine and the SSRI (<it>S</it>)-citalopram block neurotransmitter reuptake competitively, but while cocaine is a non-selective reuptake inhibitor, (<it>S</it>)-citalopram is a selective SERT inhibitor.</p> <p>Findings</p> <p>Here we present comparisons of the binding sites and the electrostatic potential surfaces (EPS) of DAT, NET and SERT homology models based on two different LeuT_Aatemplates; with a substrate (leucine) in an occluded conformation (PDB id <ext-link ext-link-id="2a65" ext-link-type="pdb">2a65</ext-link>), and with an inhibitor (tryptophan) in an open-to-out conformation (PDB id <ext-link ext-link-id="3f3a" ext-link-type="pdb">3f3a</ext-link>). In the occluded homology models, two conserved aromatic amino acids (tyrosine and phenylalanine) formed a gate between the putative binding pockets, and this contact was interrupted in the open to out conformation. The EPS of DAT and NET were generally negative in the vestibular area, whereas the EPS of the vestibular area of SERT was more neutral.</p> <p>Conclusions</p> <p>The findings presented here contribute as an update on the structure of the binding sites of DAT, NET and SERT. The updated models, which have larger ligand binding site areas than models based on other templates, may serve as improved tools for virtual ligand screening.</p

Measuring the physical cohesiveness of proteins using physical interaction enrichment

Motivation: Protein–protein interaction (PPI) networks are a valuable resource for the interpretation of genomics data. However, such networks have interaction enrichment biases for proteins that are often studied. These biases skew quantitative results from comparing PPI networks with genomics data. Here, we introduce an approach named physical interaction enrichment (PIE) to eliminate these biases

Development of a Highly Selective Plasmodium falciparum Proteasome Inhibitor with Anti-malaria Activity in Humanized Mice.

Plasmodium falciparum proteasome (Pf20S) inhibitors are active against Plasmodium at multiple stages-erythrocytic, gametocyte, liver, and gamete activation stages-indicating that selective Pf20S inhibitors possess the potential to be therapeutic, prophylactic, and transmission-blocking antimalarials. Starting from a reported compound, we developed a noncovalent, macrocyclic peptide inhibitor of the malarial proteasome with high species selectivity and improved pharmacokinetic properties. The compound demonstrates specific, time-dependent inhibition of the β5 subunit of the Pf20S, kills artemisinin-sensitive and artemisinin-resistant P. falciparum isolates in vitro and reduces parasitemia in humanized, P. falciparum-infected mice

Combining multiple positive training sets to generate confidence scores for protein–protein interactions

Motivation: High-throughput experimental and computational methods are generating a wealth of protein–protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space

SIDEKICK: Genomic data driven analysis and decision-making framework

<p>Abstract</p> <p>Background</p> <p>Scientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.</p> <p>Results</p> <p>Sidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick.</p> <p>Conclusions</p> <p>Sidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to approach genomic analysis that traditional single gene lists do not, particularly in areas such as interaction discovery.</p

DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions

<p>Abstract</p> <p>Background</p> <p>Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, <it>Drosophila melanogaster</it>.</p> <p>Description</p> <p>DroID, the <it>Drosophila </it>Interactions Database, is a comprehensive interactions database designed specifically for <it>Drosophila</it>. DroID houses published physical protein interactions, genetic interactions, and computationally predicted interactions, including interologs based on data for other model organisms and humans. All interactions are annotated with original experimental data and source information. DroID can be searched and filtered based on interaction information or a comprehensive set of gene attributes from Flybase. DroID also contains gene expression and expression correlation data that can be searched and used to filter datasets, for example, to focus a study on sub-networks of co-expressed genes. To address the inherent noise in interaction data, DroID employs an updatable confidence scoring system that assigns a score to each physical interaction based on the likelihood that it represents a biologically significant link.</p> <p>Conclusion</p> <p>DroID is the most comprehensive interactions database available for <it>Drosophila</it>. To facilitate downstream analyses, interactions are annotated with original experimental information, gene expression data, and confidence scores. All data in DroID are freely available and can be searched, explored, and downloaded through three different interfaces, including a text based web site, a Java applet with dynamic graphing capabilities (IM Browser), and a Cytoscape plug-in. DroID is available at <url>http://www.droidb.org</url>.</p

Substrate binding and translocation of the serotonin transporter studied by docking and molecular dynamics simulations

The serotonin (5-HT) transporter (SERT) plays an important role in the termination of 5-HT-mediated neurotransmission by transporting 5-HT away from the synaptic cleft and into the presynaptic neuron. In addition, SERT is the main target for antidepressant drugs, including the selective serotonin reuptake inhibitors (SSRIs). The three-dimensional (3D) structure of SERT has not yet been determined, and little is known about the molecular mechanisms of substrate binding and transport, though such information is very important for the development of new antidepressant drugs. In this study, a homology model of SERT was constructed based on the 3D structure of a prokaryotic homologous leucine transporter (LeuT) (PDB id: 2A65). Eleven tryptamine derivates (including 5-HT) and the SSRI (S)-citalopram were docked into the putative substrate binding site, and two possible binding modes of the ligands were found. To study the conformational effect that ligand binding may have on SERT, two SERT–5-HT and two SERT–(S)-citalopram complexes, as well as the SERT apo structure, were embedded in POPC lipid bilayers and comparative molecular dynamics (MD) simulations were performed. Our results show that 5-HT in the SERT–5-HTB complex induced larger conformational changes in the cytoplasmic parts of the transmembrane helices of SERT than any of the other ligands. Based on these results, we suggest that the formation and breakage of ionic interactions with amino acids in transmembrane helices 6 and 8 and intracellular loop 1 may be of importance for substrate translocation