7A) thus highlighting that Tie-2 and VEGFR-l axes, as well as their cognate angiogenic TEM ligands Ang-2 and PIGF represent attractive therapeutic targets in breast cancer.

The identification of the ligands and the pathways controlling the highly pro-angiogenic activity of tumor TEM is of paramount significance because it represents the rationale for a treatment directing TEM away from being cells supporting tumor growth.

Moreover, the in silico modeling and predictions helped us to focus on the most clinically relevant monocytic ligands and to spare precious patient specimen.

1C): 1) experimental measurement of the responses of TEM differentiated in vitro to a set of ligands , 2) construction of a dynamic regulatory network based on these experimental data, 3) in silico prediction of the treatments altering TEM behavior, 4) experimental validation of computationally predicted treatments using ivdTEM and 5) validation the best predicted treatments in patient TEM (Fig.

The limited amounts of patient TEM and the combinatorial nature of the ligands precluded experimental testing of all the ligand combinations and was the rationale for building an integrative and predictive model of TEM behavior.

2 and S3 Table) were combined to infer relevant relationships (or links) between ligands and receptors.

In a traditional approach, it would have been unfeasible to experimentally test the complete set of up to three simultaneous perturbations using 12 distinct ligands , which would have led to 596 ligand combinations.

Identification of critical ligands impacting the phenotype and pro-angiogenic activity of TEM differentiated in vitro—Antagonistic effect of TG F-B and synergistic effects of TN For on TEM pro-angiogenic phenotype and function

Our strategy was to expose TEM to several treatments to identify the ligands and pathways critically controlling their pro-angiogenic activity.

TEM differentiated in vitro were exposed to an-giogenic factors (VEGF, PlGF and ANG-1, ANG-2 which are the ligands of VEGFR-l and TIE-2 respectively) in combination with either TGF-[3 or TNF-0c and the changes in their phenotype, angiogenic activity and paracrine secretion profile were examined.

Having identified the critical ligands and pathways controlling TEM plasticity, we next examined in TEM differentiated in vitro Whether differential gene expression might also contribute to the molecular basis of TEM plastic behavior.

The diversity set contained 206 ligands from 6 target-specific drug classes with known target annotations (including 46 angiotensin-converting enzyme (ACE), 47 cyclin-de-pendent kinase 2 (CDK2), 23 heat-shock protein 90 (HSP90), 34 HIV reverse-transcriptase (HIVRT), 25 HMG-CoA reductase (HMGA) and 31 Poly [ADP-ribose] polymerase (PARP) inhibitors) (S1 Table).

Based on the chemical similarity network generated by the latter chemical search criteria, we then assessed the prediction accuracy (percentage of correctly predicted ligands ) for each drug class by considering the top five consensus targets ranked by S-scores; meanwhile, we applied a set of S-score cutoffs for hit enrichment to reduce the target pool (Fig.

While we cannot exclude smaller peaks as false positives, as they may represent an experimentally verified interaction of the reference compounds in the ChEMBL database, the higher peaks nevertheless represent the most common targets and off-targets among the analyzed ligands .

To retrieve structurally similar ligands from the bioactivity database, two chemical similarity search functions were used: a threshold similarity search based on a Tanimoto coefficient (Tc) score and a Z-score (81 Text) [39,40].

The target annotations of the selected ChEMBL compounds (baits) most similar to input ligands were subsequently retrieved from the ChEMBL and PubChem databases (Fig.

Based on the output of ligand similarity comparisons, a chemical similarity network was constructed by connecting pairs of ligands with similarity above a Tc threshold according to a weighted adjacency matrix (Fig.

For example, target inference is based on finding a single most similar annotated compound for a given query ligand, which may not provide consistent target prediction for a group of structurally similar ligands .

When multiple ligands were analyzed by the CSNAP approach, diverse compound structures were clustered into distinct chemical similarity sub-networks corresponding to a specific “chemotype” (i.e.

Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands .

In the ligand network, a k-core would be a set of ligands such every two ligands within the set are similar to each other (i.e., they have an edge in the network).

Those structures actually correspond to the same target sequence (Q10714 UniProt id) being solved with no ligands .

Those ligands are predicted to bind the same predicted binding site of the human COX-1 thanks to its similarity to the crystal structure of optimal cutoff (max value)