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Compound library development guided by protein structure similarity clustering and natural product structure.

ABSTRACT: To identify biologically relevant and drug-like protein ligands for medicinal chemistry and chemical biology research the grouping of proteins according to evolutionary relationships and conservation of molecular recognition is an established method. We propose to employ structure similarity clustering of the ligand-sensing cores of protein domains (PSSC) in conjunction with natural product guided compound library development as a synergistic approach for the identification of biologically prevalidated ligands with high fidelity. This is supported by the concepts that (i) in nature spatial structure is more conserved than amino acid sequence, (ii) the number of fold types characteristic for all protein domains is limited, and (iii) the underlying frameworks of natural product classes with multiple biological activities provide evolutionarily selected starting points in structural space. On the basis of domain core similarity considerations and irrespective of sequence similarity, Cdc25A phosphatase, acetylcholinesterase, and 11beta-hydroxysteroid dehydrogenases type 1 and type 2 were grouped into a similarity cluster. A 147-member compound collection derived from the naturally occurring Cdc25A inhibitor dysidiolide yielded potent and selective inhibitors of the other members of the similarity cluster with a hit rate of 2-3%. Protein structure similarity clustering may provide an experimental opportunity to identify supersites in proteins.

SUBMITTER: Koch MA

PROVIDER: S-EPMC534721 | biostudies-literature | 2004 Nov

REPOSITORIES: biostudies-literature

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Compound library development guided by protein structure similarity clustering and natural product structure.

Koch Marcus A MA Wittenberg Lars-Oliver LO Basu Sudipta S Jeyaraj Duraiswamy A DA Gourzoulidou Eleni E Reinecke Kerstin K Odermatt Alex A Waldmann Herbert H

Proceedings of the National Academy of Sciences of the United States of America 20041117 48

To identify biologically relevant and drug-like protein ligands for medicinal chemistry and chemical biology research the grouping of proteins according to evolutionary relationships and conservation of molecular recognition is an established method. We propose to employ structure similarity clustering of the ligand-sensing cores of protein domains (PSSC) in conjunction with natural product guided compound library development as a synergistic approach for the identification of biologically preva ...[more]

PMID: 15548605

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Project description:Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug candidates are usually analogous to or derived from the molecular structures of NC. In order to express the relationship physically realistically using a computer, it is essential to have a molecular descriptor set that can adequately represent the characteristics of the molecular structures belonging to the NC's chemical space. Although several topological descriptors have been developed to describe the physical, chemical, and biological properties of organic molecules, especially synthetic compounds, and have been widely used for drug discovery researches, these descriptors have limitations in expressing NC-specific molecular structures. To overcome this, we developed a novel molecular fingerprint, called Natural Compound Molecular Fingerprints (NC-MFP), for explaining NC structures related to biological activities and for applying the same for the natural product (NP)-based drug development. NC-MFP was developed to reflect the structural characteristics of NCs and the commonly used NP classification system. NC-MFP is a scaffold-based molecular fingerprint method comprising scaffolds, scaffold-fragment connection points (SFCP), and fragments. The scaffolds of the NC-MFP have a hierarchical structure. In this study, we introduce 16 structural classes of NPs in the Dictionary of Natural Product database (DNP), and the hierarchical scaffolds of each class were calculated using the Bemis and Murko (BM) method. The scaffold library in NC-MFP comprises 676 scaffolds. To compare how well the NC-MFP represents the structural features of NCs compared to the molecular fingerprints that have been widely used for organic molecular representation, two kinds of binary classification tasks were performed. Task I is a binary classification of the NCs in commercially available library DB into a NC or synthetic compound. Task II is classifying whether NCs with inhibitory activity in seven biological target proteins are active or inactive. Two tasks were developed with some molecular fingerprints, including NC-MFP, using the 1-nearest neighbor (1-NN) method. The performance of task I showed that NC-MFP is a practical molecular fingerprint to classify NC structures from the data set compared with other molecular fingerprints. Performance of task II with NC-MFP outperformed compared with other molecular fingerprints, suggesting that the NC-MFP is useful to explain NC structures related to biological activities. In conclusion, NC-MFP is a robust molecular fingerprint in classifying NC structures and explaining the biological activities of NC structures. Therefore, we suggest NC-MFP as a potent molecular descriptor of the virtual screening of NC for natural product-based drug development.

Project description:The Ebola virus and its close relative, the Marburg virus, both belong to the family Filoviridae and are highly hazardous and contagious viruses. With a mortality rate ranging from 23% to 90%, depending on the specific outbreak, the development of effective antiviral interventions is crucial for reducing fatalities and mitigating the impact of Marburg virus outbreaks. In this investigation, a virtual screening approach was employed to evaluate 2042 natural compounds for their potential interactions with the VP35 protein of the Marburg virus. Average and worst binding energies were calculated for all 20 poses, and compounds that exhibited binding energies <-6 kcal/mol in both criteria were selected for further analysis. Based on binding energies, only six compounds (Estradiol benzoate, INVEGA (paliperidone), Isosilybin, Protopanaxadiol, Permethrin, and Bufalin) were selected for subsequent investigations, focusing on interaction analysis. Among these selected compounds, Estradiol benzoate, INVEGA (paliperidone), and Isosilybin showed strong hydrogen bonds, while the others did not. In this study, the compounds Myricetin, Isosilybin, and Estradiol benzoate were subjected to a molecular dynamics (MD) simulation and free binding energy calculation using MM/GBSA analysis. The reference component Myricetin served as a control. Estradiol benzoate exhibited the most stable and consistent root-mean-square deviation (RMSD) values, whereas Isosilybin showed significant fluctuations in RMSD. The compound Estradiol benzoate exhibited the lowest ΔG binding free energy (-22.89 kcal/mol), surpassing the control compound's binding energy (-9.29 kcal/mol). Overall, this investigation suggested that Estradiol benzoate possesses favorable binding free energies, indicating a potential inhibitory mechanism against the VP35 protein of the Marburg virus. The study proposes that these natural compounds could serve as a therapeutic option for preventing Marburg virus infection. However, experimental validation is required to further corroborate these findings.

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Compound library development guided by protein structure similarity clustering and natural product structure.

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Compound library development guided by protein structure similarity clustering and natural product structure.

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