Global Analysis of Small Molecule Binding to Related Protein Targets

Krüger, Felix; Overington, John P.

doi:10.1371/journal.pcbi.1002333

Cited by 45 publications

(47 citation statements)

References 43 publications

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“…For some molecules, the difference between several measured potency values is substantial (Kruger and Overington, 2012). To avoid this, we removed those molecules with such unreliable values for each target.…”

Section: Retrieving Measured Potencies Of Dud-e Actives From Chembldamentioning

confidence: 99%

The impact of compound library size on the performance of scoring functions for structure-based virtual screening

Fresnais

Ballester

2020

Preprint

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Motivation: Larger training datasets have been shown to improve the accuracy of Machine Learning (ML)-based Scoring functions (SFs) for Structure-Based Virtual Screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, have been demonstrated to enable the fast discovery of selective drug leads with at least nanomolar potency. This proof-of-concept was carried out on two targets using a single docking tool along with its SF. It is thus unclear whether this high level of performance would generalise to other targets, docking tools and SFs. Results:We found that screening a larger compound library results in more potent actives being identified in all six additional targets using a different docking tool along with its classical SF. Furthermore, we established that a way to improve the potency of the retrieved molecules further is to rank them with more accurate ML-based SFs (we found this true in four of the six targets). A three-fold increase in average hit rate across targets was also achieved by the ML-based SFs. Lastly, we observed that classical and ML-based SFs often find different actives, which supports using both types of SFs on those targets.

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Section: Retrieving Measured Potencies Of Dud-e Actives From Chembldamentioning

confidence: 99%

The impact of compound library size on the performance of scoring functions for structure-based virtual screening

Fresnais

Ballester

2020

Preprint

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“…In B4, 24.5% of the compoundprotein interactions do not correspond to human proteins. These were mapped to their human orthologs using MetaPhOrs 55 (http://orthology.phylomedb.org, May 2017), following the observation that binding activities can be safely transferred between orthologous proteins 56 , especially if they belong to closely related species, as it is the case for B4 data 57 . Of all the non-human proteins mapped to the human orthologs, 94.4% were mammal proteins.…”

Section: C: Networkmentioning

confidence: 99%

Extending the small molecule similarity principle to all levels of biology

Duran‐Frigola

Pauls

Guitart-Pla

et al. 2019

Preprint

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We present the Chemical Checker (CC), a resource that provides processed, harmonized and integrated bioactivity data on 800,000 small molecules. The CC divides data into five levels of increasing complexity, ranging from the chemical properties of compounds to their clinical outcomes. In between, it considers targets, off-targets, perturbed biological networks and several cell-based assays such as gene expression, growth inhibition and morphological profilings. In the CC, bioactivity data are expressed in a vector format, which naturally extends the notion of chemical similarity between compounds to similarities between bioactivity signatures of different kinds. We show how CC signatures can boost the performance of drug discovery tasks that typically capitalize on chemical descriptors, including target identification and library characterization. Moreover, we demonstrate and experimentally validate that CC signatures can be used to reverse and mimic biological signatures of disease models and genetic perturbations, options that are otherwise impossible using chemical information alone. an organic molecule (A: Chemistry) that interacts with one or several protein receptors (B: Targets), triggering perturbations of biological pathways (C: Networks) and eliciting phenotypic outcomes that can be measured in e.g. cell-based assays (D: Cells) before delivery to patients (E: Clinics). Using these five categories, we classified the information stored in major compound databases, including chemogenomics resources, cell-based screens and, when available, clinical reports of drug effects (Methods).We then divided each level (A-E) into five sublevels (1-5) corresponding to distinct types or scopes of the data. In total, the CC contains 25 well-defined categories meant to illustrate the most relevant aspects of small molecule characterization. In particular, we stored the 2D (A1) and 3D (A2) structures of compounds, together with their scaffolds (A3), functional groups (A4) and physicochemistry (A5). We also retrieved therapeutic targets (B1) and drug metabolizing enzymes (B2), and molecules co-crystallized with protein chains (B3). We fetched literature binding data (B4) from major chemogenomics databases, and high-throughput target screening results (B5). Moving to a higher order of biology, we looked for ontological classifications of compounds (C1) and focused on human metabolites in a genomescale metabolic network (C2). In addition, we kept the pathways (C3), biological processes (C4) and protein-protein interactions (C5) of the previously collected binding data. To capture cell-level information, we gathered differential gene expression profiles (D1) and compound growth-inhibition potencies across cancer cell lines (D2). Similarly, we gathered sensitivity profiles over an array of yeast mutants (chemical genetics) (D3), as well as cell morphology changes (high-content screening) (D4). Additional cell sensitivity data available from the literature were also collected (D5). To organize clinical data, we used the traditio...

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“…It might be plausible, but such compilation need to be justified and are not systematic. Recently Kruger et al showed that in general small molecules binding is conserved for pairs of human to rat orthologs, which is advantageous as rats are frequently used as model systems [30].…”

Section: Bioactivity Curation and Standardizationmentioning

confidence: 99%