Abstract: We show that amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families, demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization, functional sites, and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing, more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation, greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method.

Abstract: With the advent of next-generation DNA sequencing, the pace of inherited orphan disease gene identification has increased dramatically, a situation that will continue for at least the next several years. At present, the numbers of such identified disease genes significantly outstrips the number of laboratories available to investigate a given disorder, an asymmetry that will only increase over time. The hope for any genetic disorder is, where possible and in addition to accurate diagnostic test formulation, the development of therapeutic approaches. To this end, we propose here the development of a strategic toolbox and preclinical research pathway for inherited orphan disease. Taking much of what has been learned from rare genetic disease research over the past two decades, we propose generalizable methods utilizing transcriptomic, system-wide chemical biology datasets combined with chemical informatics and, where possible, repurposing of FDA approved drugs for pre-clinical orphan disease therapies. It is hoped that this approach may be of utility for the broader orphan disease research community and provide funding organizations and patient advocacy groups with suggestions for the optimal path forward. In addition to enabling academic pre-clinical research, strategies such as this may also aid in seeding startup companies, as well as further engaging the pharmaceutical industry in the treatment of rare genetic disease.

Abstract: BACKGROUND: Understanding drug bioactivities is crucial for early-stage drug discovery, toxicology studies and clinical trials. Network pharmacology is a promising approach to better understand the molecular mechanisms of drug bioactivities. With a dramatic increase of rich data sources that document drugs' structural, chemical, and biological activities, it is necessary to develop an automated tool to construct a drug-target network for candidate drugs, thus facilitating the drug discovery process. RESULTS: We designed a computational workflow to construct drug-target networks from different knowledge bases including DrugBank, PharmGKB, and the PINA database. To automatically implement the workflow, we created a web-based tool called DTome (Drug-Target interactome tool), which is comprised of a database schema and a user-friendly web interface. The DTome tool utilizes web-based queries to search candidate drugs and then construct a DTome network by extracting and integrating four types of interactions. The four types are adverse drug interactions, drug-target interactions, drug-gene associations, and target-/gene-protein interactions. Additionally, we provided a detailed network analysis and visualization process to illustrate how to analyze and interpret the DTome network. The DTome tool is publicly available at http://bioinfo.mc.vanderbilt.edu/DTome. CONCLUSIONS: As demonstrated with the antipsychotic drug clozapine, the DTome tool was effective and promising for the investigation of relationships among drugs, adverse interaction drugs, drug primary targets, drug-associated genes, and proteins directly interacting with targets or genes. The resultant DTome network provides researchers with direct insights into their interest drug(s), such as the molecular mechanisms of drug actions. We believe such a tool can facilitate identification of drug targets and drug adverse interactions.

Abstract: Discovering the unintended 'off-targets' that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use a computational strategy to predict the activity of 656 marketed drugs on 73 unintended 'side-effect' targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 muM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug-target-adverse drug reaction network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-1. The clinical relevance of this inhibition was borne out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.

Abstract: We have developed FINDSITE(X), an extension of FINDSITE, a protein threading based algorithm for the inference of protein binding sites, biochemical function and virtual ligand screening, that removes the limitation that holo protein structures (those containing bound ligands) of a sufficiently large set of distant evolutionarily related proteins to the target be solved; rather, predicted protein structures and experimental ligand binding information are employed. To provide the predicted protein structures, a fast and accurate version of our recently developed TASSER(VMT), TASSER(VMT)-lite, for template-based protein structural modeling applicable up to 1000 residues is developed and tested, with comparable performance to the top CASP9 servers. Then, a hybrid approach that combines structure alignments with an evolutionary similarity score for identifying functional relationships between target and proteins with binding data has been developed. By way of illustration, FINDSITE(X) is applied to 998 identified human G-protein coupled receptors (GPCRs). First, TASSER(VMT)-lite provides updates of all human GPCR structures previously modeled in our lab. We then use these structures and the new function similarity detection algorithm to screen all human GPCRs against the ZINC8 nonredundant (TC < 0.7) ligand set combined with ligands from the GLIDA database (a total of 88,949 compounds). Testing (excluding GPCRs whose sequence identity > 30% to the target from the binding data library) on a 168 human GPCR set with known binding data, the average enrichment factor in the top 1% of the compound library (EF(0.01)) is 22.7, whereas EF(0.01) by FINDSITE is 7.1. For virtual screening when just the target and its native ligands are excluded, the average EF(0.01) reaches 41.4. We also analyze off-target interactions for the 168 protein test set. All predicted structures, virtual screening data and off-target interactions for the 998 human GPCRs are available at http://cssb.biology.gatech.edu/skolnick/webservice/gpcr/index.html .

Abstract: A Merck molecular force field classical potential combined with Poisson-Boltzmann electrostatics (MMFF/PB) has been used to estimate the binding free energy of seven guest molecules (six tertiary amines and one primary amine) into a synthetic receptor (acyclic cucurbit[4]uril congener) and two benzimidazoles into cyclic cucurbit[7]uril (CB[7]) and cucurbit[8]uril (CB[8]) hosts. In addition, binding enthalpies for the benzimidazoles were calculated with density functional theory (DFT) using the B3LYP functional and a polarizable continuum model (PCM). Although in most cases the MMFF/PB approach returned reasonable agreements with the experiment (+/-2 kcal/mol), significant, much larger deviations were reported in the case of three host-guest pairs. All four binding enthalpy predictions with the DFT/PCM method suffered 70% or larger deviations from the calorimetry data. Results are discussed in terms of the molecular models used for guest-host complexation and the quality of the intermolecular potentials.

Abstract: The goal of many metabolomic studies is to identify the molecular structure of endogenous molecules that are differentially expressed among sampled or treatment groups. The identified compounds can then be used to gain an understanding of disease mechanisms. Unfortunately, despite recent advances in a variety of analytical techniques, small molecule (<1000 Da) identification remains difficult. Rarely can a chemical structure be determined from experimental "features" such as retention time, exact mass, and collision induced dissociation spectra. Thus, without knowing structure, biological significance remains obscure. In this study, we explore an identification method in which the measured exact mass of an unknown is used to query available chemical databases to compile a list of candidate compounds. Predictions are made for the candidates using models of experimental features that have been measured for the unknown. The predicted values are used to filter the candidate list by eliminating compounds with predicted values substantially different from the unknown. The intent is to reduce the list of candidates to a reasonable number that can be obtained and measured for confirmation. To facilitate this exploration, we measured data and created models for two experimental features; MS Ecom(5)(0) (the energy in electronvolts required to fragment 50% of a selected precursor ion) and HPLC retention index. Using a data set of 52 compounds, Ecom(5)(0) models were developed based on both Molconn and CODESSA structural descriptors. These models gave r(2) values of 0.89 to 0.94 depending on the number of inputs, the modeling algorithm chosen, and whether neutral or protonated structures were used. The retention index model was developed with 400 compounds using a back-propagation artificial neural network and 33 Molconn structure descriptors. External validation gave a v(2) = 0.87 and standard error of 38 retention index units. As a test of the validity of the filtering approach, the Ecom(5)(0) and retention index models, along with exact mass and collision induced dissociation spectra matching, were used to identify 1,3-dicyclohexylurea in human plasma. This compound was not previously known to exist in human biofluids and its elemental formula was identical to 315 other candidate compounds downloaded from PubChem. These results suggest that the use of Ecom(5)(0) and retention index predictive models can improve nontargeted metabolite structure identification using HPLC/MS derived structural features.

Abstract: BACKGROUND: Natural product-likeness of a molecule, i.e. similarity of this molecule to the structure space covered by natural products, is a useful criterion in screening compound libraries and in designing new lead compounds. A closed source implementation of a natural product-likeness score, that finds its application in virtual screening, library design and compound selection, has been previously reported by one of us. In this note, we report an open-source and open-data re-implementation of this scoring system, illustrate its efficiency in ranking small molecules for natural product likeness and discuss its potential applications. RESULTS: The Natural-Product-Likeness scoring system is implemented as Taverna 2.2 workflows, and is available under Creative Commons Attribution-Share Alike 3.0 Unported License at http://www.myexperiment.org/packs/183.html. It is also available for download as executable standalone java package from http://sourceforge.net/projects/np-likeness/under Academic Free License. CONCLUSIONS: Our open-source, open-data Natural-Product-Likeness scoring system can be used as a filter for metabolites in Computer Assisted Structure Elucidation or to select natural-product-like molecules from molecular libraries for the use as leads in drug discovery.

Abstract: Diphenylpyraline hydrochloride (DPP) is an internationally available antihistamine that produces therapeutic antiallergic effects by binding to histamine H(1) receptors. The complete neuropharmacological and behavioral profile of DPP, however, remains uncharacterized. Here we describe studies that suggest DPP may fit the profile of a potential agonist replacement medication for cocaine addiction. Aside from producing the desired histamine reducing effects, many antihistamines can also elicit psychomotor activation and reward, both of which are associated with increased dopamine concentrations in the nucleus accumbens (NAc). The primary aim of this study was to investigate the potential ability of DPP to inhibit the dopamine transporter, thereby leading to elevated dopamine concentrations in the NAc in a manner similar to cocaine and other psychostimulants. The psychomotor activating and rewarding effects of DPP were also investigated. For comparative purposes cocaine, a known dopamine transporter inhibitor, psychostimulant and drug of abuse, was used as a positive control. As predicted, both cocaine (15 mg/kg) and an equimolar dose of DPP (14 mg/kg) significantly inhibited dopamine uptake in the NAc in vivo and produced locomotor activation, although the time-course of pharmacological effects of the two drugs was different. In comparison to cocaine, DPP showed a prolonged effect on dopamine uptake and locomotion. Furthermore, cocaine, but not DPP, produced significant conditioned place preference, a measure of drug reward. The finding that DPP functions as a potent dopamine uptake inhibitor without producing significant rewarding effects suggests that DPP merits further study as a potential candidate as an agonist pharmacotherapy for cocaine addiction.

Abstract: UNLABELLED: BACKGROUND: Experimental screening of chemical compounds for biological activity is a time consuming and expensive practice. In silico predictive models permit inexpensive, rapid "virtual screening" to prioritize selection of compounds for experimental testing. Both experimental and in silico screening can be used to test compounds for desirable or undesirable properties. Prior work on prediction of mutagenicity has primarily involved identification of toxicophores rather than whole-molecule predictive models. In this work, we examined a range of in silico predictive classification models for prediction of mutagenic properties of compounds, including methods such as J48 and SMO which have not previously been widely applied in cheminformatics. RESULTS: The Bursi mutagenicity data set containing 4337 compounds (Set 1) and a Benchmark data set of 6512 compounds (Set 2) were taken as input data set in this work. A third data set (Set 3) was prepared by joining up the previous two sets. Classification algorithms including Naive Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters were used for model generation on these data sets. Models built using the combined performed better than those developed from the Benchmark data set. Significantly, Random Forest outperformed other classifiers for all the data sets, especially for Set 3 with 89.27% accuracy, 89% precision and ROC of 95.3%. To validate the developed models two external data sets, AID1189 and AID1194, with mutagenicity data were tested showing 62% accuracy with 67% precision and 65% ROC area and 91% accuracy, 91% precision with 96.3% ROC area respectively. A Random Forest model was used on approved drugs from DrugBank and metabolites from the Zinc Database with True Positives rate almost 85% showing the robustness of the model. CONCLUSION: We have created a new mutagenicity benchmark data set with around 8,000 compounds. Our work shows that highly accurate predictive mutagenicity models can be built using machine learning methods based on chemical descriptors and trained using this set, and these models provide a complement to toxicophores based methods. Further, our work supports other recent literature in showing that Random Forest models generally outperform other comparable machine learning methods for this kind of application.