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Analysis of multiple compound-protein interactions reveals novel bioactive molecules.

ABSTRACT: The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound-protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.

SUBMITTER: Yabuuchi H

PROVIDER: S-EPMC3094066 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Analysis of multiple compound-protein interactions reveals novel bioactive molecules.

Yabuuchi Hiroaki H Niijima Satoshi S Takematsu Hiromu H Ida Tomomi T Hirokawa Takatsugu T Hara Takafumi T Ogawa Teppei T Minowa Yohsuke Y Tsujimoto Gozoh G Okuno Yasushi Y

Molecular systems biology 20110301

The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound-protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. ...[more]

PMID: 21364574

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Project description:Marine environments are a fruitful source of bioactive compounds some of which are the newest leading drugs in medicinal therapeutics. Of particular importance are organisms like sponges and macroalgae and their associated microbiome. Planctomycetes, abundant in macroalgae biofilms, are promising producers of bioactive compounds since they share characteristics, like large genomes and complex life cycles, with the most bioactive bacteria, the Actinobacteria. Furthermore, genome mining revealed the presence of secondary metabolite pathway genes or clusters in 13 analyzed Planctomycetes genomes. In order to assess the antimicrobial production of a large and diverse collection of Planctomycetes isolated from macroalgae from the Portuguese coast, molecular, and bioactivity assays were performed in 40 bacteria from several taxa. Two genes commonly associated with the production of bioactive compounds, nonribosomal peptide synthetases (NRPS), and polyketide synthases (PKS) genes were screened. Molecular analysis revealed that 95% of the planctomycetes potentially have one or both secondary bioactive genes; 85% amplified with PKS-I primers and 55% with NRPS primers. Some of the amplified genes were confirmed to be involved in secondary metabolite pathways. Using bioinformatic tools their biosynthetic pathways were predicted. The secondary metabolite genomic potential of strains LF1, UC8, and FC18 was assessed using in silico analysis of their genomes. Aqueous and organic extracts of the Planctomycetes were evaluated for their antimicrobial activity against an environmental Escherichia coli, E. coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, Staphylococcus aureus ATCC 25923, Bacillus subtilis ATCC 6633, and a clinical isolate of Candida albicans. The screening assays showed a high number of planctomycetes with bioactive extracts revealing antifungal (43%) and antibacterial (54%) activity against C. albicans and B. subtilis, respectively. Bioactivity was observed in strains from Rhodopirellula lusitana, R. rubra, R. baltica, Roseimaritima ulvae, and Planctomyces brasiliensis. This study confirms the bioactive capacity of Planctomycetes to produce antimicrobial compounds and encourages further studies envisaging molecule isolation and characterization for the possible discovery of new drugs.

Project description:BackgroundConformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields.ResultsOur analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD) of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%). On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost.ConclusionsBy incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational ensemble with high accuracy but approximately the same computational cost in comparison with FFBM method. Our analysis also reveals that the performance of conformational generation is irrelevant to the types of force field adopted in characterization of conformational accessibility. Moreover, post energy minimization is not necessary and may even undermine the diversity of conformational ensemble. All the results guide us to explore more empirical criteria like geometric restraints during the conformational process, which may improve the performance of conformational generation in combination with energetic accessibility, regardless of force field types adopted.

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Analysis of multiple compound-protein interactions reveals novel bioactive molecules.

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Analysis of multiple compound-protein interactions reveals novel bioactive molecules.

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