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Applications of Systematic Molecular Scaffold Enumeration to Enrich Structure-Activity Relationship Information.

ABSTRACT: Establishing structure-activity relationships (SARs) in hit identification during early stage drug discovery is important in accelerating hit confirmation and expansion. We describe the development of EnCore, a systematic molecular scaffold enumeration protocol using single atom mutations, to enhance the application of objective scaffold definitions and to enrich SAR information from analysis of high-throughput screening output. A list of 43 literature medicinal chemistry compound series, each containing a minimum of 100 compounds, published in the Journal of Medicinal Chemistry was collated to validate the protocol. Analysis using the top representative Level 1 scaffolds this list of literature compound series demonstrated that EnCore could mimic the scaffold exploration conducted when establishing SAR. When EnCore was applied to analyze an HTS library containing over 200?000 compounds, we observed that over 70% of the molecular scaffolds matched extant scaffolds within the library after enumeration. In particular, over 60% of the singleton scaffolds with only one representative compound were found to have structurally related compounds after enumeration. These results illustrate the potential of EnCore to enrich SAR information. A case study using literature cyclooxygenase-2 inhibitors further demonstrates the advantage of EnCore application in establishing SAR from structurally related scaffolds. EnCore complements literature enumeration methods in enabling changes to the physicochemical properties of molecular scaffolds and structural modifications to aliphatic rings and linkers. The enumerated scaffold clusters generated would constitute a comprehensive collection of scaffolds for scaffold morphing and hopping.

SUBMITTER: Mok NY

PROVIDER: S-EPMC6152611 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Applications of Systematic Molecular Scaffold Enumeration to Enrich Structure-Activity Relationship Information.

Mok N Yi NY Brown Nathan N

Journal of chemical information and modeling 20161219 1

Establishing structure-activity relationships (SARs) in hit identification during early stage drug discovery is important in accelerating hit confirmation and expansion. We describe the development of EnCore, a systematic molecular scaffold enumeration protocol using single atom mutations, to enhance the application of objective scaffold definitions and to enrich SAR information from analysis of high-throughput screening output. A list of 43 literature medicinal chemistry compound series, each c ...[more]

PMID: 27990817

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Project description:BackgroundMMP-2 enzyme is a kind of matrix metalloproteinases that digests the denatured collagens and gelatins. It is highly involved in the process of tumor invasion and has been considered as a promising target for cancer therapy. The structural requirements of an MMP-2 inhibitor are: (1) a functional group that binds the zinc ion, and (2) a functional group which interacts with the enzyme backbone and the side chains which undergo effective interactions with the enzyme subsites.MethodsIn the present study, a QSAR model was generated to screen new inhibitors of MMP-2 based on L-hydroxy tyrosine scaffold. Descriptors generation were done by Hyperchem 8, DRAGON and Gaussian98W programs. SPSS and MATLAB programs have been used for multiple linear regression (MLR) and genetic algorithm partial least squares (GA-PLS) analyses and for theoretical validation. Applicability domain of the model was performed to screen new compounds. The binding site potential of all inhibitors was verified by structure-based docking according to their binding energy and then the best inhibitors were selected.ResultsThe best QSAR models in MLR and GA-PLS were reported, with the square correlation coefficient for leave-one-out cross-validation (Q(2) LOO) larger than 0.921 and 0.900 respectively. The created MLR and GA-PLS models indicated the importance of molecular size, degree of branching, flexibility, shape, three-dimensional coordination of different atoms in a molecule in inhibitory activities against MMP-2. The docking study indicated that lipophilic and hydrogen bonding interactions among the inhibitors and the receptor are involved in a ligand-receptor interaction. The oxygen of carbonyl and sulfonyl groups is important for hydrogen bonds of ligand with Leu82 and Ala83. R2 and R3 substituents play a main role in hydrogen bonding interactions. R1 is sited in the hydrophobic pocket. Methylene group can help a ligand to be fitted in the lipophilic pocket, so two methylene groups are better than one. The Phenyl group can create a π-π interaction with Phe86.ConclusionsThe QSAR and docking analyses demonstrated to be helpful tools in the prediction of anti-cancer activities and a guide to the synthesis of new metalloproteinase inhibitors based on L-tyrosine scaffold.

Project description:Under-expression or overexpression of protein kinases has been shown to be associated with unregulated cell signal transduction in cancer cells. Therefore, there is major interest in designing protein kinase inhibitors as anticancer agents. We have previously reported [WR]5, a peptide containing alternative arginine (R) and tryptophan (W) residues as a non-competitive c-Src tyrosine kinase inhibitor. A number of larger cyclic peptides containing alternative hydrophobic and positively charged residues [WR]x (x = 6-9) and hybrid cyclic-linear peptides, [R6K]W6 and [R5K]W7, containing R and W residues were evaluated for their protein kinase inhibitory potency. Among all the peptides, cyclic peptide [WR]9 was found to be the most potent tyrosine kinase inhibitor. [WR]9 showed higher inhibitory activity (IC50 = 0.21 μM) than [WR]5, [WR]6, [WR]7, and [WR]8 with IC50 values of 0.81, 0.57, 0.35, and 0.33 μM, respectively, against c-Src kinase as determined by a radioactive assay using [γ-33P]ATP. Consistent with the result above, [WR]9 inhibited other protein kinases such as Abl kinase activity with an IC50 value of 0.35 μM, showing 2.2-fold higher inhibition than [WR]5 (IC50 = 0.79 μM). [WR]9 also inhibited PKCa kinase activity with an IC50 value of 2.86 μM, approximately threefold higher inhibition than [WR]5 (IC50 = 8.52 μM). A similar pattern was observed against Braf, c-Src, Cdk2/cyclin A1, and Lck. [WR]9 exhibited IC50 values of <0.25 μM against Akt1, Alk, and Btk. These data suggest that [WR]9 is consistently more potent than other cyclic peptides with a smaller ring size and hybrid cyclic-linear peptides [R6K]W6 and [R5K]W7 against selected protein kinases. Thus, the presence of R and W residues in the ring, ring size, and the number of amino acids in the structure of the cyclic peptide were found to be critical in protein kinase inhibitory potency. We identified three putative binding pockets through automated blind docking of cyclic peptides [WR](5-9). The most populated pocket is located between the SH2, SH3, and N-lobe domains on the opposite side of the ATP binding site. The second putative pocket is formed by the same domains and located on the ATP binding site side of the protein. Finally, a third pocket was identified between the SH2 and SH3 domains. These results are consistent with the non-competitive nature of the inhibition displayed by these molecules. Molecular dynamics simulations of the protein-peptide complexes indicate that the presence of either [WR]5 or [WR]9 affects the plasticity of the protein and in particular the volume of the ATP binding site pocket in different ways. These results suggest that the second pocket is most likely the site where these peptides bind and offer a plausible rationale for the increased affinity of [WR]9.

Project description:BackgroundThe maximum clique enumeration (MCE) problem asks that we identify all maximum cliques in a finite, simple graph. MCE is closely related to two other well-known and widely-studied problems: the maximum clique optimization problem, which asks us to determine the size of a largest clique, and the maximal clique enumeration problem, which asks that we compile a listing of all maximal cliques. Naturally, these three problems are NP-hard, given that they subsume the classic version of the NP-complete clique decision problem. MCE can be solved in principle with standard enumeration methods due to Bron, Kerbosch, Kose and others. Unfortunately, these techniques are ill-suited to graphs encountered in our applications. We must solve MCE on instances deeply seeded in data mining and computational biology, where high-throughput data capture often creates graphs of extreme size and density. MCE can also be solved in principle using more modern algorithms based in part on vertex cover and the theory of fixed-parameter tractability (FPT). While FPT is an improvement, these algorithms too can fail to scale sufficiently well as the sizes and densities of our datasets grow.ResultsAn extensive testbed of benchmark graphs are created using publicly available transcriptomic datasets from the Gene Expression Omnibus (GEO). Empirical testing reveals crucial but latent features of such high-throughput biological data. In turn, it is shown that these features distinguish real data from random data intended to reproduce salient topological features. In particular, with real data there tends to be an unusually high degree of maximum clique overlap. Armed with this knowledge, novel decomposition strategies are tuned to the data and coupled with the best FPT MCE implementations.ConclusionsSeveral algorithmic improvements to MCE are made which progressively decrease the run time on graphs in the testbed. Frequently the final runtime improvement is several orders of magnitude. As a result, instances which were once prohibitively time-consuming to solve are brought into the domain of realistic feasibility.

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Applications of Systematic Molecular Scaffold Enumeration to Enrich Structure-Activity Relationship Information.

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Applications of Systematic Molecular Scaffold Enumeration to Enrich Structure-Activity Relationship Information.

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