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Anatomy of protein structures: visualizing how a one-dimensional protein chain folds into a three-dimensional shape.

ABSTRACT: Here, we depict the anatomy of protein structures in terms of the protein folding process. Via an iterative, top-down dissecting procedure, tertiary structures are spliced down to reveal their anatomy: first, to produce domains (defined by visual three-dimensional inspection criteria); then, hydrophobic folding units (HFU); and, at the end of a multilevel process, a set of building blocks. The resulting anatomy tree organization not only clearly depicts the organization of a one-dimensional polypeptide chain in three-dimensional space but also straightforwardly describes the most likely folding pathway(s). Comparison of the tree with the formation of the hydrophobic folding units through combinatorial assembly of the building blocks illustrates how the chain folds in a sequential or a complex folding pathway. Further, the tree points to the kinetics of the folding, whether the chain is a fast or a slow folder, and the probability of misfolding. Our ability to successfully dissect the protein into an anatomy tree illustrates that protein folding is a hierarchical process and further validates a building blocks protein folding model.

SUBMITTER: Tsai CJ

PROVIDER: S-EPMC17290 | biostudies-literature | 2000 Oct

REPOSITORIES: biostudies-literature

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Anatomy of protein structures: visualizing how a one-dimensional protein chain folds into a three-dimensional shape.

Tsai C J CJ Maizel J V JV Nussinov R R

Proceedings of the National Academy of Sciences of the United States of America 20001001 22

Here, we depict the anatomy of protein structures in terms of the protein folding process. Via an iterative, top-down dissecting procedure, tertiary structures are spliced down to reveal their anatomy: first, to produce domains (defined by visual three-dimensional inspection criteria); then, hydrophobic folding units (HFU); and, at the end of a multilevel process, a set of building blocks. The resulting anatomy tree organization not only clearly depicts the organization of a one-dimensional poly ...[more]

PMID: 11050234

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Project description:BACKGROUND:The words 'infection' and 'inflammation' lack specific definitions. Here, such words are not defined. Instead, the ability to visualize host-microbial interactions was explored. METHODS:Leukocyte differential counts and four bacterial species (Staphylococcus aureus, Streptococcus dysgalactiae, Staphylococcus chromogenes, and Escherichia coli) were determined or isolated in a cross-sectional and randomized study conducted with 611 bovine milk samples. Two paradigms were evaluated: (i) the classic one, which measures non-structured (count or percent) data; and (ii) a method that, using complex data structures, detects and differentiates three-dimensional (3D) interactions among lymphocytes (L), macrophages (M), and neutrophils (N). RESULTS:Classic analyses failed to differentiate bacterial-positive (B+) from -negative (B-) observations: B- and B+ data overlapped, even when statistical significance was achieved. In contrast, the alternative approach showed distinct patterns, such as perpendicular data inflections, which discriminated microbial-negative/mononuclear cell-predominating (MCP) from microbial-positive/phagocyte-predominating (PP) subsets. Two PP subcategories were distinguished, as well as PP/culture-negative (false-negative) and MCP/culture-positive (false-positive) observations. In 3D space, MCP and PP subsets were perpendicular to one another, displaying ? 91% specificity or sensitivity. Findings supported five inferences: (i) disease is not always ruled out by negative bacterial tests; (ii) low total cell counts can coexist with high phagocyte percents; (iii) neither positive bacterial isolation nor high cell counts always coincide with PP profiles; (iv) statistical significance is not synonymous with discrimination; and (v) hidden relationships cannot be detected when simple (non-structured) data formats are used and statistical analyses are performed before data subsets are identified, but can be uncovered when complexity is investigated. CONCLUSIONS:Pattern recognition-based assessments can detect host-microbial interactions usually unobserved. Such cutoff-free, confidence interval-free, gold standard-free approaches provide interpretable information on complex entities, such as 'infection' and 'inflammation', even without definitions. To investigate disease dynamics, combinations of observational and experimental longitudinal studies, on human and non-human infections, are recommended.

Dataset Information

Anatomy of protein structures: visualizing how a one-dimensional protein chain folds into a three-dimensional shape.

Publications

Anatomy of protein structures: visualizing how a one-dimensional protein chain folds into a three-dimensional shape.

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