Dataset Information

Multidimensional signatures in antimicrobial peptides.

ABSTRACT: Conventional analyses distinguish between antimicrobial peptides by differences in amino acid sequence. Yet structural paradigms common to broader classes of these molecules have not been established. The current analyses examined the potential conservation of structural themes in antimicrobial peptides from evolutionarily diverse organisms. Using proteomics, an antimicrobial peptide signature was discovered to integrate stereospecific sequence patterns and a hallmark three-dimensional motif. This striking multidimensional signature is conserved among disulfide-containing antimicrobial peptides spanning biological kingdoms, and it transcends motifs previously limited to defined peptide subclasses. Experimental data validating this model enabled the identification of previously unrecognized antimicrobial activity in peptides of known identity. The multidimensional signature model provides a unifying structural theme in broad classes of antimicrobial peptides, will facilitate discovery of antimicrobial peptides as yet unknown, and offers insights into the evolution of molecular determinants in these and related host defense effector molecules.

SUBMITTER: Yount NY

PROVIDER: S-EPMC409924 | biostudies-literature | 2004 May

REPOSITORIES: biostudies-literature

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Publications

Multidimensional signatures in antimicrobial peptides.

Yount Nannette Y NY Yeaman Michael R MR

Proceedings of the National Academy of Sciences of the United States of America 20040426 19

Conventional analyses distinguish between antimicrobial peptides by differences in amino acid sequence. Yet structural paradigms common to broader classes of these molecules have not been established. The current analyses examined the potential conservation of structural themes in antimicrobial peptides from evolutionarily diverse organisms. Using proteomics, an antimicrobial peptide signature was discovered to integrate stereospecific sequence patterns and a hallmark three-dimensional motif. Th ...[more]

PMID: 15118082

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Project description:BackgroundStratified or personalised medicine targets treatments for groups of individuals with a disorder based on individual heterogeneity and shared factors that influence the likelihood of response. Psychiatry has traditionally defined diagnoses by constellations of co-occurring signs and symptoms that are assigned a categorical label (e.g. schizophrenia). Trial methodology in psychiatry has evaluated interventions targeted at these categorical entities, with diagnoses being equated to disorders. Recent insights into both the nosology and neurobiology of psychiatric disorder reveal that traditional categorical diagnoses cannot be equated with disorders. We argue that current quantitative methodology (1) inherits these categorical assumptions, (2) allows only for the discovery of average treatment response, (3) relies on composite outcome measures and (4) sacrifices valuable predictive information for stratified and personalised treatment in psychiatry.Methods and findingsTo achieve a truly 'stratified psychiatry' we propose and then operationalise two necessary steps: first, a formal multi-dimensional representation of disorder definition and clinical state, and second, the similar redefinition of outcomes as multidimensional constructs that can expose within- and between-patient differences in response. We use the categorical diagnosis of schizophrenia-conceptualised as a label for heterogeneous disorders-as a means of introducing operational definitions of stratified psychiatry using principles from multivariate analysis. We demonstrate this framework by application to the Clinical Antipsychotic Trials of Intervention Effectiveness dataset, showing heterogeneity in both patient clinical states and their trajectories after treatment that are lost in the traditional categorical approach with composite outcomes. We then systematically review a decade of registered clinical trials for cognitive deficits in schizophrenia highlighting existing assumptions of categorical diagnoses and aggregate outcomes while identifying a small number of trials that could be reanalysed using our proposal.ConclusionWe describe quantitative methods for the development of a multi-dimensional model of clinical state, disorders and trajectories which practically realises stratified psychiatry. We highlight the potential for recovering existing trial data, the implications for stratified psychiatry in trial design and clinical treatment and finally, describe different kinds of probabilistic reasoning tools necessary to implement stratification.

Dataset Information

Multidimensional signatures in antimicrobial peptides.

Publications

Multidimensional signatures in antimicrobial peptides.

Similar Datasets

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