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Progressive fuzzy cation-? assembly of biological catecholamines.

ABSTRACT: Biological functions depend on biomolecular assembly processes. Assemblies of lipid bilayers, actins, microtubules, or chromosomes are indispensable for cellular functions. These hierarchical assembly processes are reasonably predictable by understanding chemical structures of the defined building blocks and their interactions. However, biopigment assembly is rather fuzzy and unpredictable because a series of covalently coupled intermediates from catecholamine oxidation pathways progressively form a higher-level hierarchy. This study reports a different yet unexplored type of assembly process named "cation-? progressive assembly." We demonstrated for the first time that the cation-? is the primary mechanism for intermolecular assembly in dopamine-melanin biopigment. We also found that the self-assembled products physically grow and chemically gain new functions "progressively" over time in which cation-? plays important roles. The progressive assembly explains how biological systems produce wide spectra of pigment colors and broad wavelength absorption through energy-efficient processes. Furthermore, we also demonstrate surface-independent wettability control using cation-? progressive assembly.

SUBMITTER: Hong S

PROVIDER: S-EPMC6128673 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Progressive fuzzy cation-π assembly of biological catecholamines.

Hong Seonki S Wang Younseon Y Park Sung Young SY Lee Haeshin H

Science advances 20180907 9

Biological functions depend on biomolecular assembly processes. Assemblies of lipid bilayers, actins, microtubules, or chromosomes are indispensable for cellular functions. These hierarchical assembly processes are reasonably predictable by understanding chemical structures of the defined building blocks and their interactions. However, biopigment assembly is rather fuzzy and unpredictable because a series of covalently coupled intermediates from catecholamine oxidation pathways progressively fo ...[more]

PMID: 30202784

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Project description:BACKGROUND: Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type. RESULTS: Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2. CONCLUSIONS: In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.

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Dataset Information

Progressive fuzzy cation-? assembly of biological catecholamines.

Publications

Progressive fuzzy cation-π assembly of biological catecholamines.

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