Computational analyses of small molecules activity from phenotypic screens

Drug discovery is no longer relying on the one gene-one disease paradigm nor on target-based screening alone to discover new drugs. Phenotypic-based screening is regaining momentum to discover new compounds since those assays provide an environment closer to the physiological state of the disease and allow to better anticipate off-target effects and other factors that can limit the efficacy of the drugs. However, uncovering the mechanism of action of the compounds active in those assays relies on in vitro techniques that are expensive and time- consuming. In silico approaches are therefore beneficial to prioritise mechanism of action hypotheses to be tested in such systems. In this thesis, the use of machine learning algorithms for in silico ligand-target prediction for target deconvolution in phenotypic screening datasets was investigated. A computational workflow is presented in Chapter 2, that allows to improve the coverage of mechanism of action hypotheses obtained by combining two conceptually different target prediction algorithms. These models rely on the principle that two structurally similar compounds are likely to have the same target. In Chapter 3 of this thesis, it was shown that structural similarity and the similarity in phenotypic activity are correlated, and the fraction of phenotypically similar compounds that can be expected for an increase in structural similarity was subsequently quantified. Morgan fingerprints were also found to be less sensitive to the dataset employed in these analyses than two other commonly used molecular descriptors. In Chapter 4, the mechanism of action hypotheses obtained through target prediction was compared to those obtained by extracting experimental bioactivity data of compounds active in phenotypic assays. It was then showed that the mechanism of action hypotheses generated from these two types of approach agreed where a large number of compounds were active in the phenotypic assay. When there were fewer compounds active in the phenotypic assay, target prediction complemented the use of experimental bioactivity data and allowed to uncover alternative mechanisms of action for compounds active in these assays. Finally, the in silico target prediction workflow described in Chapter 2 was applied in Chapter 5 to deconvolute the activity of compounds in a kidney cyst growth reduction assay, aimed at discovering novel therapeutic opportunities for polycystic kidney disease. A metric was developed to rank predicted targets according to the activity of the compounds driving their prediction. Gene expression data and occurrences in the literature were combined with the target predictions to further narrow down the most probable mechanisms of action of cyst growth reducing compounds in the screen. Two target predictions were proposed as a potential mechanism for the reduction of kidney cyst growth, one of which agreed with docking studies.European Research Counci

Computer-aided design of multi-target ligands at A1R, A2AR and PDE10A, key proteins in neurodegenerative diseases.

Compounds designed to display polypharmacology may have utility in treating complex diseases, where activity at multiple targets is required to produce a clinical effect. In particular, suitable compounds may be useful in treating neurodegenerative diseases by promoting neuronal survival in a synergistic manner via their multi-target activity at the adenosine A1 and A2A receptors (A1R and A2AR) and phosphodiesterase 10A (PDE10A), which modulate intracellular cAMP levels. Hence, in this work we describe a computational method for the design of synthetically feasible ligands that bind to A1 and A2A receptors and inhibit phosphodiesterase 10A (PDE10A), involving a retrosynthetic approach employing in silico target prediction and docking, which may be generally applicable to multi-target compound design at several target classes. This approach has identified 2-aminopyridine-3-carbonitriles as the first multi-target ligands at A1R, A2AR and PDE10A, by showing agreement between the ligand and structure based predictions at these targets. The series were synthesized via an efficient one-pot scheme and validated pharmacologically as A1R/A2AR–PDE10A ligands, with IC50 values of 2.4–10.0 μM at PDE10A and Ki values of 34–294 nM at A1R and/or A2AR. Furthermore, selectivity profiling of the synthesized 2-amino-pyridin-3-carbonitriles against other subtypes of both protein families showed that the multi-target ligand 8 exhibited a minimum of twofold selectivity over all tested off-targets. In addition, both compounds 8 and 16 exhibited the desired multi-target profile, which could be considered for further functional efficacy assessment, analog modification for the improvement of selectivity towards A1R, A2AR and PDE10A collectively, and evaluation of their potential synergy in modulating cAMP levels

Epigenetic regulation of the human genome: coherence between promoter activity and large-scale chromatin environment

International audienceIncreasing knowledge of chromatin structure in various cell types raises the challenge of deciphering the contribution of epigenetic modifications to the regulation of nuclear functions in mammals. In a recent study, we have analysed the genome-wide distributions of thirteen epigenetic marks in the human cell line K562 at 100 kb resolution of Mean Replication Timing (MRT) data. Using classical clustering techniques, we have shown that the combinatorial complexity of these epigenetic data can be reduced to four predominant chromatin states that replicate at different periods of the S-phase. C1 is an early replicating transcriptionally active euchromatin state, C2 a mid-S repressive type of chromatin associated with Polycomb complexes, C3 a silent chromatin with lack of chromatin marks that replicates later than C2 but before C4, a HP1-associated heterochromatin state that replicates at the end of S-phase. These four chromatin states display remarkable similarities with those recently reported in fly, worm and plants at higher ∼ 1 kb resolution of gene expression data. Here, we extend our integrative analysis of epigenetic data in the K562 human cell line to this smaller scale by focusing on gene promoters (±3 kb around transcription start sites). We show that these promoters can similarly be classified into four main chromatin states: P1 regroups all the marks of transcriptionally active chromatin and corresponds to CpG rich promoters of highly expressed genes; P2 is notably associated with the histone modification H3K27me3 that is the mark of a polycomb repressed chromatin state; P3 corresponds to promoters that are not enriched for any available marks as the signature of a ‘null’ or ‘black’ silent heterochromatin state and P4 characterizes the few gene promoters that contain only the constitutive heterochromatin histone modification H3K9me3. When investigating the coherence between promoter activity (P1, P2, P3 or P4) and the large-scale chromatin environment (C1, C2, C3 or C4), we find that the higher the gene density in a considered 100 kb-window, the higher (resp. the lower) the probability of a P1 active promoter (resp. silent P2, P3 and P4 promoters) to be surrounded by an open euchromatin C1 (resp. facultative C2, black C3 or HP1-associated C4 heterochromatin) environment. From large to small scales, it is mainly C4 and to a lesser extent C3 heterochromatin environments both corresponding to gene poor regions, that strongly conditions promoters to belong to the inactive P3 and P4 classes. If C1 (resp. C2) environment surrounds a majority of corresponding active P1 (resp. P2) promoters, it also contains a non-negligible proportion of inactive P2 and P3 (resp. active P1 and inactive P3) promoters. When further investigating the large-scale organization of human genes with respect to ‘master’ replication origins that were shown to border megabase-sized U-shaped MRT domains, we reveal some significant enrichment of highly expressed P1 genes in a closed neighbourhood of these early initiation zones consistently with the gradient of chromatin states observed from C1 at U-domain borders followed by C2, C3 and C4 at U-domain centers. On the contrary to P2 promoters that are mainly found in the C2 environment at finite distance (∼200–300 kb) from U-domain borders, the inactive P3 and P4 promoters are distributed rather homogeneously inside U-domains. The generalization of our study to different cell types including ES, somatic and cancer cells is likely to provide new insight on the global reorganization of replication domains during differentiation (or disease) in relation to coordinated changes in chromatin environment and gene expression

Principal Component Analysis (PCA).

<p>Two-dimensional (2D) projections of the data on (A) the plane defined by the first (PC1) and second (PC2) principal components, and (B) the plane defined by the second (PC2) and the third (PC3) principal components. The densities were computed by a kernel density estimation. The density values are indicated by a color (white: high density, yellow: moderate density, green: low density) and a contour plot.</p

Sequence composition in the four chromatin states.

<p>(A) Boxplots of GC content computed in 100 kb non-overlapping windows per chromatin state. (B) Boxplots of CpG o/e computed in 100 kb non-overlapping windows per chromatin states. Same color coding as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003233#pcbi-1003233-g003" target="_blank">Fig. 3A</a>.</p

Gene expression in the four chromatin states.

<p>(A) c.d.f. of gene expression (measured in , see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003233#s3" target="_blank">Materials and Methods</a>) in the four chromatin states. (B) Density of promoters in the 4 chromatin states as a function of gene expression (genes were grouped into bins of width 0.05 in unit). Same color coding as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003233#pcbi-1003233-g003" target="_blank">Fig. 3A</a>.</p

Spearman correlation matrix between epigenetics marks and mean replication timing (MRT).

<p>For each pair of variables we computed the Spearman correlation over all 100-overlapping windows with a valid score. Spearman correlation value is color coded using the color map shown on the right. A white line separates the MRT from epigenetics marks. Correlations with MRT (from late to early) are placed at the top and the right of the matrix. Lines for the thirteen epigenetic marks were reorganized by a hierarchical clustering using Spearman correlation distances (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003233#pcbi.1003233.e037" target="_blank">Equation 1</a>) as illustrated by the dendrogram on the left of the graph. This ordering implies that highly correlated epigenetic marks are close to each other.</p

Repartition of transcription factors in the four chromatin states.

<p>Boxplots of the decimal logarithm of transcription factor ChiP-seq read density in 100 kb non-overlapping windows per chromatin state. Same color coding as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003233#pcbi-1003233-g003" target="_blank">Fig. 3A</a>.</p

Distribution of the four chromatin states inside replication timing U-domains.

<p>(A) The 876 K562 U-domains were centered and ordered vertically from the smallest (top) to the largest (bottom). All transcriptionally active chromatin state C1 100-kb-windows were represented by an horizontal segment of the corresponding length. (B) Same as (A) for the Pc repressed by chromatin state C2. (C) Same as (A) for the silent unmarked chromatin state C3. (D) Same as (A) for the HP1 heterochromatin state C4. (E) Mean coverage of chromatin state with respect to the distance to the closest U-domain border for U-domains smaller than 0.8 Mb. Error bars represent the standard deviation of the mean. (F) Same as (E) for U-domains of size between 0.8 Mb and 1.2 Mb. (G) Same as (E) for U-domains of size between 1.2 Mb and 1.8 Mb. (H) Same as (E) for U-domains of size between 1.8 Mb and 3.0 Mb. Same color coding as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003233#pcbi-1003233-g003" target="_blank">Fig. 3A</a>.</p

MRT in the four chromatin states.

<p>(A) Boxplots of MRT computed in 100 kb non-overlapping windows per chromatin state. (B) Empirical cumulative distribution function (c.d.f.) of MRT in the four chromatin states. Same color coding as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003233#pcbi-1003233-g003" target="_blank">Fig. 3A</a>.</p