We quantified the statistical energy landscapes for binding, from which we can characterize the distributions of the binding free energy (affinity), the equilibrium constants, the kinetics and the specificity by exploring the different ligands binding with a particular receptor.

Our study provides new insights into the statistical nature of thermodynamics, kinetics and function from different ligands binding with a specific receptor or equivalently specific ligand binding with different receptors.

Therefore by exploring the sequences of ligands (typically 106 ~ 109 sequences, a large number which is perfect for the statistical description) or small molecule databases (the number of small molecules can be on the order of 1060), one can explore the biological specificity and function by mimicking the natural evolution selection process with the fittest surviving from the ensemble of ligands .

The combinatorial library or virtual screening of small molecule database therefore provides a natural laboratory for uncovering the fundamental principles in biomolecular binding and design through the study of the statistical features of the ensembles of the ligands or small molecules with different sequences binding to a specific receptor.

The experimental features on binding indicate that the appropriate physical variable is the free energy of binding of ligands to the receptor, not the energy.

When we discuss about the distribution of the free energies, we mean the sampling of different free binding energies from the different ligands with different sequences binding to the same receptor.

Collecting the free energies from different ligands binding to the same receptor, we can find the distributions of the free energy.

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.

Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data.

Combined with a multi-target model, it can be used to predict binding motifs for targets with no known ligands .

Moreover, the proposed approach can be employed without known ligands for the target protein because it can leverage recent multi-target machine learning predictors [10, 14] where ligands for similar targets can serve as an initial training set.

Split and pool combinatorial peptide synthesis is a simple but efficient way to synthesize a very Wide spectrum of peptide ligands .

It has been used for the discovery of ligands for receptors [30, 31], for proteins [32—35] and for transcription factors [36, 37].

Cell lines were treated (or left untreated, control) with ligands or the ALK inhibitor TAE684 as indicated in Table 1.

For organelle fractionation phosphoproteomics, cell lines were treated with ligands (LAN-6 and TrkA-CFP-expressing SK-N-BE(2): NGF, SMS-KCNzBDNF) for 10 min at 37°C.

Ligands were bound to cells at 4°C for 1 hr, then cells were warmed to 37°C for 10 min or 1 hr.

LYN and FYN also increased in fractions 16—22 in response to both ligands (Fig 7A—7D).

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)

The main difference between these theories rests in how conformational transitions proceed upon binding of ligands .

Where L0 is the allosteric constant between the R and T states in the absence of ligands , xFJ-J-represent effector concentrations binding to specific allosteric sites, KRM and KTJ-J- denote the effectors dissociation constants for each state, éRO and em are the free enzyme fractions in both conformational states as function of the respective rate parameters (kR,kT) and reactant concentrations (x), 111 represents the number of allosteric sites, and finally, r and tare the number of positive and negative effectors binding to the allosteric sites in the R and T states, respectively.

[34] have derived a simple expression to determine the allosteric constant in the absence of ligands assuming symmetric binding for the two states (Equation 24).