Panel docking of small-molecule libraries - Prospects to improve efficiency of lead compound discovery

Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation. Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept. By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases. This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites

A practical Java tool for small-molecule compound appraisal

The increased use of small-molecule compound screening by new users from a variety of different academic backgrounds calls for adequate software to administer, appraise, analyse and exchange information obtained from screening experiments. While software and spreadsheet solutions exist, there is a need for software that can be easily deployed and is convenient to use.The Java application cApp addresses this need and aids in the handling and storage of information on small-molecule compounds. The software is intended for the appraisal of compounds with respect to their physico-chemical properties, analysis in relation to adherence to likeness rules as well as recognition of pan-assay interference components and cross-linking with identical entries in the PubChem Compound Database. Results are displayed in a tabular form in a graphical interface, but can also be written in an HTML or PDF format. The output of data in ASCII format allows for further processing of data using other suitable programs. Other features include similarity searches against user-provided compound libraries and the PubChem Compound Database, as well as compound clustering based on a MaxMin algorithm.cApp is a personal database solution for small-molecule compounds which can handle all major chemical formats. Being a standalone software, it has no other dependency than the Java virtual machine and is thus conveniently deployed. It streamlines the analysis of molecules with respect to physico-chemical properties and drug discovery criteria; cApp is distributed under the GNU Affero General Public License version 3 and available from http://www.structuralchemistry.org/pcsb/. To download cApp, users will be asked for their name, institution and email address. A detailed manual can also be downloaded from this site, and online tutorials are available at http://www.structuralchemistry.org/pcsb/capp.php

Renata Gambino, Vedute e visioni. Teorie estetiche e dimensione onirica nelle opere "italiane" di Karl Philipp Moritz

Natural products are universally recognized to contribute valuable chemical diversity to the design of molecular screening libraries. The analysis undertaken in this work, provides a foundation for the generation of fragment screening libraries that capture the diverse range of molecular recognition building blocks embedded within natural products. Physicochemical properties were used to select fragment-sized natural products from a database of known natural products (Dictionary of Natural Products). PCA analysis was used to illustrate the positioning of the fragment subset within the property space of the non-fragment sized natural products in the dataset. Structural diversity was analysed by three distinct methods: atom function analysis, using pharmacophore fingerprints, atom type analysis, using radial fingerprints, and scaffold analysis. Small pharmacophore triplets, representing the range of chemical features present in natural products that are capable of engaging in molecular interactions with small, contiguous areas of protein binding surfaces, were analysed. We demonstrate that fragment-sized natural products capture more than half of the small pharmacophore triplet diversity observed in non fragment-sized natural product datasets. Atom type analysis using radial fingerprints was represented by a self-organizing map. We examined the structural diversity of non-flat fragment-sized natural product scaffolds, rich in sp3 configured centres. From these results we demonstrate that 2-ring fragment-sized natural products effectively balance the opposing characteristics of minimal complexity and broad structural diversity when compared to the larger, more complex fragment-like natural products. These naturally-derived fragments could be used as the starting point for the generation of a highly diverse library with the scope for further medicinal chemistry elaboration due to their minimal structural complexity. This study highlights the possibility to capture a high proportion of the individual molecular interaction motifs embedded within natural products using a fragment screening library spanning 422 structural clusters and comprised of approximately 2800 natural products

A Linear Mixed Model Spline Framework for Analysing Time Course ‘Omics’ Data

<div><p>Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.</p></div

Simulation results.

<p>Averaged sensitivity for LMMSDE and LIMMA after 100 simulations. Differential expression between groups and/or time was tested with increasing noise and fold change (FC) levels.</p

Overview of the analysis framework.

<p>The proposed framework consists of three stages: quality control and filtering; serial modelling of profiles; and analysis with clustering to identify similarities between profiles or with hypothesis testing to identify differences over time, between groups, and/or in group and time interactions.</p

Clustering of filter ratios on proteomic datasets.

<p>Scatterplots of filter ratios <i>R</i>_<i>T</i> on the x-axis against <i>R</i>_<i>I</i> on the y-axis for <b>A</b>) iTraq breast cancer dataset and <b>B</b>) and <b>C</b>) the iTraq kidney rejection dataset for group Allograft Rejection (AR) and Non-Rejection (NR) respectively. Colors indicate clusters from a 2-cluster model-based clustering, with red squares indicating molecules that cluster as ‘informative’ and will remain in the analysis and blue circles indicating ‘non-informative’ molecules that will be removed prior to analysis.</p

iTraq kidney rejection dataset: Gene Ontology (GO) term enrichment analysis.

<p>GO term enrichement analysis based on the proteins identified by LMMSDE as differentially expressed between Allograft Rejection (AR) and Non-Rejection (NR) patients after filtering using a 2-cluster model-based clustering based on <i>R</i>_<i>T</i> and <i>R</i>_<i>I</i>. The top GO biological processes are listed along with their FDR adjusted p-value and log odds ratio (OR).</p

Types of models used to summarize profiles.

<p>The number (proportion) of profiles modelled with each model selected by our proposed LMMS approach. Models are abbreviated as linear (LIN), spline (SPL), subject-specific intercept (SSI), and subject-specific intercept and slope (SSIS). Models were applied to cell line breast cancer data (Cell), <i>Saccharomyces paradoxus</i> evolution data (Yeast), <i>Mus musculus</i> chemotherapy data (Mouse), and <i>Homo Sapiens</i> kidney rejection Non-Rejection (NR) data (Human). The row ‘Removed’ indicates the percentage of filtered profiles using the 2-cluster model-based clustering on <i>R</i>_<i>T</i> and <i>R</i>_<i>I</i>.</p

Filtering ratios of the <i>Mus musculus</i> data.

<p>The filter ratios <i>R</i>_<i>T</i> and <i>R</i>_<i>I</i> were calculated for every molecule. Colors in <b>A</b>) indicate the -log10(p-values) for differential expression over time and in <b>B</b>) the proportion of missing values. <b>C</b>) is after discarding profiles with > 50% of missing values, with colors as in <b>A</b>).</p