BackgroundDrug side-effects, or adverse drug reactions, have become a major public health concern. It is one of the main causes of failure in the process of drug development, and of drug withdrawal once they have reached the market. Therefore, in silico prediction of potential side-effects early in the drug discovery process, before reaching the clinical stages, is of great interest to improve this long and expensive process and to provide new efficient and safe therapies for patients.ResultsIn the present work, we propose a new method to predict potential side-effects of drug candidate molecules based on their chemical structures, applicable on large molecular databanks. A unique feature of the proposed method is its ability to extract correlated sets of chemical substructures (or chemical fragments) and side-effects. This is made possible using sparse canonical correlation analysis (SCCA). In the results, we show the usefulness of the proposed method by predicting 1385 side-effects in the SIDER database from the chemical structures of 888 approved drugs. These predictions are performed with simultaneous extraction of correlated ensembles formed by a set of chemical substructures shared by drugs that are likely to have a set of side-effects. We also conduct a comprehensive side-effect prediction for many uncharacterized drug molecules stored in DrugBank, and were able to confirm interesting predictions using independent source of information.ConclusionsThe proposed method is expected to be useful in various stages of the drug development process.

Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system–wide approaches for linking different scales of drug actions; namely drug-protein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs.Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the co-occurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug–targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles.Supplementary information: Datasets and all results are available at http://web.kuicr.kyoto-u.ac.jp/supp/smizutan/target-effect/.Availability: Software is available at the above supplementary website.Contact: yamanishi@bioreg.kyushu-u.ac.jp, or goto@kuicr.kyoto-u.ac.jp

BackgroundPredicting which molecules can bind to a given binding site of a protein with known 3D structure is important to decipher the protein function, and useful in drug design. A classical assumption in structural biology is that proteins with similar 3D structures have related molecular functions, and therefore may bind similar ligands. However, proteins that do not display any overall sequence or structure similarity may also bind similar ligands if they contain similar binding sites. Quantitatively assessing the similarity between binding sites may therefore be useful to propose new ligands for a given pocket, based on those known for similar pockets.ResultsWe propose a new method to quantify the similarity between binding pockets, and explore its relevance for ligand prediction. We represent each pocket by a cloud of atoms, and assess the similarity between two pockets by aligning their atoms in the 3D space and comparing the resulting configurations with a convolution kernel. Pocket alignment and comparison is possible even when the corresponding proteins share no sequence or overall structure similarities. In order to predict ligands for a given target pocket, we compare it to an ensemble of pockets with known ligands to identify the most similar pockets. We discuss two criteria to evaluate the performance of a binding pocket similarity measure in the context of ligand prediction, namely, area under ROC curve (AUC scores) and classification based scores. We show that the latter is better suited to evaluate the methods with respect to ligand prediction, and demonstrate the relevance of our new binding site similarity compared to existing similarity measures.ConclusionsThis study demonstrates the relevance of the proposed method to identify ligands binding to known binding pockets. We also provide a new benchmark for future work in this field. The new method and the benchmark are available at http://cbio.ensmp.fr/paris/.

The pentose-phosphate pathway provides reductive power and nucleotide precursors to the cell through oxidative and nonoxidative branches, respectively. 6-Phosphogluconolactonase is the second enzyme of the oxidative branch and catalyzes the hydrolysis of 6-phosphogluconolactones, the products of glucose 6-phosphate oxidation by glucose-6-phosphate dehydrogenase. The role of 6-phosphogluconolactonase was still questionable, because 6-phosphogluconolactones were believed to undergo rapid spontaneous hydrolysis. In this work, nuclear magnetic resonance spectroscopy was used to characterize the chemical scheme and kinetic features of the oxidative branch. We show that 6-phosphogluconolactones have in fact a nonnegligible lifetime and are highly electrophilic compounds. The ␦ form (1-5) of the lactone is the only product of glucose 6-phosphate oxidation. Subsequently, it leads to the ␥ form (1-4) by intramolecular rearrangement. However, only the ␦ form undergoes spontaneous hydrolysis, the ␥ form being a "dead end" of this branch. The ␦ form is the only substrate for 6-phosphogluconolactonase. Therefore, 6-phosphogluconolactonase activity accelerates hydrolysis of the ␦ form, thus preventing its conversion into the ␥ form. Furthermore, 6-phosphogluconolactonase guards against the accumulation of ␦-6-phosphogluconolactone, which may be toxic through its reaction with endogenous cellular nucleophiles. Finally, the difference between activity of human, Trypanosoma brucei, and Plasmodium falciparum 6-phosphogluconolactonases is reported and discussed.

Background: The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules in the transmembrane ligand-binding site is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies.

A conformational study of the double‐stranded decanucleotide d(GCCG*G*ATCGC) · d(GCGATCCGGC), with the G* guanines chelating a cis‐Pt(NH3)2 moiety, has been accomplished using 1H and 31P NMR, and molecular mechanics. Correlation of the NMR data with molecular models has disclosed an equilibrium between several kinked conformations and has ruled out an unkinked structure. The deformation is localized at the CG*G*· CCG trinucleotide where the helix is kinked by approximately 60° towards the major groove and unwound by 12–19°. The models revealed an unexpected mobility of the cytosine complementary to the 5′‐G*. This cytosine can stack on either branch of the kinked complementary strand. The energy barrier between the two positions has been calculated to be ≤ 12 kJ/mol. The NMR data are in support of rapid flip‐flopping of this cytosine. An explanation for the strong downfield shift observed in the 31P resonance of the G*pG* phosphate is given.

We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-points pharmacophores present in the 3D structures of molecules, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demanding exact implementation of these kernels, as well as fast approximations related to the classical fingerprint-based approaches. Experimental results suggest that this new approach outperforms state-of-the-art algorithms based on the 2D structure of molecules for the detection of inhibitors of several drug targets.

The identification of rules governing molecular recognition between drug chemical substructures and protein functional sites is a challenging issue at many stages of the drug development process. In this paper we develop a novel method to extract sets of drug chemical substructures and protein domains that govern drug-target interactions on a genome-wide scale. This is made possible using sparse canonical correspondence analysis (SCCA) for analyzing drug substructure profiles and protein domain profiles simultaneously. The method does not depend on the availability of protein 3D structures. From a data set of known drug-target interactions including enzymes, ion channels, G protein-coupled receptors, and nuclear receptors, we extract a set of chemical substructures shared by drugs able to bind to a set of protein domains. These two sets of extracted chemical substructures and protein domains form components that can be further exploited in a drug discovery process. This approach successfully clusters protein domains that may be evolutionary unrelated but that bind a common set of chemical substructures. As shown in several examples, it can also be very helpful for predicting new protein-ligand interactions and addressing the problem of ligand specificity. The proposed method constitutes a contribution to the recent field of chemogenomics that aims to connect the chemical space with the biological space.