MiRBooking simulates the stoichiometric mode of action of microRNAs.

In eucaryotes, gene expression is regulated by microRNAs (miRNAs) which bind to messenger RNAs (mRNAs) and interfere with their translation into proteins, either by promoting their degradation or inducing their repression. We study the effect of miRNA interference on each gene using experimental methods, such as microarrays and RNA-seq at the mRNA level, or luciferase reporter assays and variations of SILAC at the protein level. Alternatively, computational predictions would provide clear benefits. However, no algorithm toward this task has ever been proposed. Here, we introduce a new algorithm to predict genome-wide expression data from initial transcriptome abundance. The algorithm simulates the miRNA and mRNA hybridization competition that occurs in given cellular conditions, and derives the whole set of miRNA::mRNA interactions at equilibrium (microtargetome). Interestingly, solving the competition improves the accuracy of miRNA target predictions. Furthermore, this model implements a previously reported and fundamental property of the microtargetome: the binding between a miRNA and a mRNA depends on their sequence complementarity, but also on the abundance of all RNAs expressed in the cell, i.e. the stoichiometry of all the miRNA sites and all the miRNAs given their respective abundance. This model generalizes the miRNA-induced synchronistic silencing previously observed, and described as sponges and competitive endogenous RNAs.

Docking ligands into flexible and solvated macromolecules. 7. Impact of protein flexibility and water molecules on docking-based virtual screening accuracy.

The use of predictive computational methods in the drug discovery process is in a state of continual growth. Over the last two decades, an increasingly large number of docking tools have been developed to identify hits or optimize lead molecules through in-silico screening of chemical libraries to proteins. In recent years, the focus has been on implementing protein flexibility and water molecules. Our efforts led to the development of Fitted first reported in 2007 and further developed since then. In this study, we wished to evaluate the impact of protein flexibility and occurrence of water molecules on the accuracy of the Fitted docking program to discriminate active compounds from inactive compounds in virtual screening (VS) campaigns. For this purpose, a total of 171 proteins cocrystallized with small molecules representing 40 unique enzymes and receptors as well as sets of known ligands and decoys were selected from the Protein Data Bank (PDB) and the Directory of Useful Decoys (DUD), respectively. This study revealed that implementing displaceable crystallographic or computationally placed particle water molecules and protein flexibility can improve the enrichment in active compounds. In addition, an informed decision based on library diversity or research objectives (hit discovery vs lead optimization) on which implementation to use may lead to significant improvements.

Methods for docking small molecules to macromolecules: a user's perspective. 2. Applications.

A large number of research articles describe novel methodologies of docking and/or scoring methods. An even larger number of publications report the successful use of these methods in the identification of novel hit molecules. What is less documented is the application of docking methods in other areas. We review herein the application of docking methods to not only hit identification but also to de novo design, fragment-based drug discovery, lead optimization, metabolism prediction, off-target binding, selectivity, protein structure prediction and drug-drug interaction.

Methods for docking small molecules to macromolecules: a user's perspective. 1. The theory.

Over the last two decades, computationally docking potential protein ligands (e.g., enzyme inhibitors) has become one of the most widely used strategies in computer aided drug design. While these docking methods were developed, some effort focused on their user-friendliness up to a point where they can be used by non-experts with nearly no training, somewhat hiding the underlying theory. However, basic knowledge is still required to avoid pitfalls and misinterpretations of docking experiments. Over the years, we have collected the common mistakes and necessary information for the proper use of docking programs. In this review, we compiled this data for non-experts in the field. In a first section, we present the theory of docking and scoring approaches as well as their limitations, followed by the most recent progress towards the consideration of protein flexibility, water molecules, metal ions, and covalent drugs. In a second section, we describe what we believe are the necessary steps to ensure optimal docking. More specifically, we present the selection of a docking program, available databases of small molecules, macromolecules and biological data, the necessary steps for the preparation of proteins and small molecules, and finally post docking analysis techniques. In the following sections, we compile the sources of biases and describe docking to nucleic acids.

Platinum(II) phenanthroimidazoles for targeting telomeric G-quadruplexes.

A rationally designed progression of phenanthroimidazole platinum(II) complexes were examined for their ability to target telomere-derived intramolecular G-quadruplex DNA. Through the use of circular dichroism, fluorescence displacement assays, and molecular modeling we show that these complexes template and stabilize G-quadruplexes from sequences based on the human telomeric repeat (TTAGGG)(n). The greatest stabilization was observed for the p-chlorophenyl derivative 6((G4)DC(50) =0.31 µM). We also show that the G-quadruplex binding complexes are able to inhibit telomerase activity through a modified telomerase repeat amplification protocol (TRAP-LIG assay). Preliminary cell studies show that complex 6 is preferentially cytotoxic toward cancer over normal cell lines, indicating its potential use in cancer therapy.

Integrating medicinal chemistry, organic/combinatorial chemistry, and computational chemistry for the discovery of selective estrogen receptor modulators with Forecaster, a novel platform for drug discovery.

As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.

Toward a computational tool predicting the stereochemical outcome of asymmetric reactions: development of the molecular mechanics-based program ACE and application to asymmetric epoxidation reactions.

The development and application of ACE, a program that predicts the stereochemical outcome of asymmetric reactions is presented. As major implementations, ACE includes a genetic algorithm to carry out an efficient global conformational search combined with a conjugate gradient minimization routine for local optimization and a corner flap algorithm to search ring conformations. Further improvements have been made that enable ACE to generate Boltzmann populations of conformations, to investigate highly asynchronous reactions, to compute fluctuating partial atomic charges and solvation energy and to automatically construct reactants and products from libraries of catalysts and substrates. Validation on previously investigated reactions (asymmetric Diels Alder cycloadditions and organocatalyzed aldol reactions) followed by application to a number of alkene epoxidation reactions and a comparative study of DFT-derived and ACE-derived predictions demonstrate the accuracy and usefulness of ACE in the context of asymmetric catalyst design.

Chemogenomic approaches for the exploration of GPCR space.

The potential areas of applications of chemogenomic approaches are very large. Thanks to the large amount of knowledge accumulated during years of research, it is now possible to consider the binding of a ligand to a protein in a much larger context. This knowledge combined with the augmentation of computing capabilities allows global approaches to investigate biological and pharmaceutical problems. Classification of proteins, focused libraries, selectivity profiles and elaboration of new ligands for orphan receptors can all be investigated using chemogenomic. G protein-coupled receptors (GPCRs) constitute a large protein family of significant interest in pharmaceutical research. Despite this interest, and excluding the more than 360 nonolfatory proteins, the endogenous ligands of about 100 GPCRs have still not been identified. The main limitation of GPCRs investigation is the lack of 3D structures. The goal of this review is to present different chemogenomic approaches that can be applied to GPCRs. Three types of such approaches are presented: ligand centered, protein centered and protein-ligand centered approaches. For each of them, current limitations and biases are mentioned.

Alignment-free ultra-high-throughput comparison of druggable protein-ligand binding sites.

Inferring the biological function of a protein from its three-dimensional structure as well as explaining why a drug may bind to various targets is of crucial importance to modern drug discovery. Here we present a generic 4833-integer vector describing druggable protein-ligand binding sites that can be applied to any protein and any binding cavity. The fingerprint registers counts of pharmacophoric triplets from the Calpha atomic coordinates of binding-site-lining residues. Starting from a customized data set of diverse protein-ligand binding site pairs, the most appropriate metric and a similarity threshold could be defined for similar binding sites. The method (FuzCav) has been used in various scenarios: (i) screening a collection of 6000 binding sites for similarity to different queries; (ii) classifying protein families (serine endopeptidases, protein kinases) by binding site diversity; (iii) discriminating adenine-binding cavities from decoys. The fingerprint generation and comparison supports ultra-high throughput (ca. 1000 measures/s), does not require prior alignment of protein binding sites, and is able to detect local similarity among subpockets. It is thus particularly well suited to the functional annotation of novel genomic structures with low sequence identity to known X-ray templates.

Development and validation of a novel protein-ligand fingerprint to mine chemogenomic space: application to G protein-coupled receptors and their ligands.

The present study introduces a novel low-dimensionality fingerprint encoding both ligand and target properties which is suitable to mine protein-ligand chemogenomic space. Whereas ligand properties have been represented by standard descriptors, protein cavities are encoded by a fixed length bit string describing pharmacophoric properties of a definite number of binding site residues. In order to simplify the cavity fingerprint, the concept was applied here to a unique family of targets (G protein-coupled receptors) with a homogeneous cavity description. Particular attention was given to set up data sets of really diverse protein-ligand pairs covering as exhaustively as possible both ligand and target spaces. Several machine learning classification algorithms were trained on two sets of roughly 200000 receptor-ligand fingerprints with a different definition of inactive decoys. Cross-validated models show excellent precision (>0.9) in distinguishing true from false pairs with a particular preference for support vector machine classifiers. When applied to two external test sets of GPCR ligands, the most predictive models were not those performing the best in the previous cross-validation. The ability to recover true GPCR ligands (ligand prediction mode) or true GPCRs (receptor prediction mode) depends on multiple parameters: the molecular complexity of the ligands, the chemical space from which ligand decoys are selected to generate false protein-ligand pairs, and the target space under consideration. In most cases, predicting ligands is easier than predicting receptors. Although receptor profiling is possible, it probably requires a more detailed description of the ligand-binding site. Noteworthy, protein-ligand fingerprints outperform the corresponding ligand fingerprints in mining the GPCR-ligand space. Since they can be applied to a much larger number of receptors than ligand-based fingerprints, protein-ligand fingerprints represent a novel and promising way to directly screen protein-ligand pairs in chemogenomic applications.