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Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence.

ABSTRACT: Traditional protein annotation methods describe known domains with probabilistic models representing consensus among homologous domain sequences. However, when relevant signals become too weak to be identified by a global consensus, attempts for annotation fail. Here we address the fundamental question of domain identification for highly divergent proteins. By using high performance computing, we demonstrate that the limits of state-of-the-art annotation methods can be bypassed. We design a new strategy based on the observation that many structural and functional protein constraints are not globally conserved through all species but might be locally conserved in separate clades. We propose a novel exploitation of the large amount of data available: 1. for each known protein domain, several probabilistic clade-centered models are constructed from a large and differentiated panel of homologous sequences, 2. a decision-making protocol combines outcomes obtained from multiple models, 3. a multi-criteria optimization algorithm finds the most likely protein architecture. The method is evaluated for domain and architecture prediction over several datasets and statistical testing hypotheses. Its performance is compared against HMMScan and HHblits, two widely used search methods based on sequence-profile and profile-profile comparison. Due to their closeness to actual protein sequences, clade-centered models are shown to be more specific and functionally predictive than the broadly used consensus models. Based on them, we improved annotation of Plasmodium falciparum protein sequences on a scale not previously possible. We successfully predict at least one domain for 72% of P. falciparum proteins against 63% achieved previously, corresponding to 30% of improvement over the total number of Pfam domain predictions on the whole genome. The method is applicable to any genome and opens new avenues to tackle evolutionary questions such as the reconstruction of ancient domain duplications, the reconstruction of the history of protein architectures, and the estimation of protein domain age. Website and software: http://www.lcqb.upmc.fr/CLADE.

SUBMITTER: Bernardes J

PROVIDER: S-EPMC4966962 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence.

Bernardes Juliana J Zaverucha Gerson G Vaquero Catherine C Carbone Alessandra A

PLoS computational biology 20160729 7

Traditional protein annotation methods describe known domains with probabilistic models representing consensus among homologous domain sequences. However, when relevant signals become too weak to be identified by a global consensus, attempts for annotation fail. Here we address the fundamental question of domain identification for highly divergent proteins. By using high performance computing, we demonstrate that the limits of state-of-the-art annotation methods can be bypassed. We design a new ...[more]

PMID: 27472895

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Project description:BACKGROUND: The tissue origin of low back pain (LBP) or referred lower extremity symptoms (LES) may be identified in about 70% of cases using advanced imaging, discography and facet or sacroiliac joint blocks. These techniques are invasive and availability varies. A clinical examination is non-invasive and widely available but its validity is questioned. Diagnostic studies usually examine single tests in relation to single reference standards, yet in clinical practice, clinicians use multiple tests and select from a range of possible diagnoses. There is a need for studies that evaluate the diagnostic performance of clinical diagnoses against available reference standards. METHODS: We compared blinded clinical diagnoses with diagnoses based on available reference standards for known causes of LBP or LES such as discography, facet, sacroiliac or hip joint blocks, epidurals injections, advanced imaging studies or any combination of these tests. A prospective, blinded validity design was employed. Physiotherapists examined consecutive patients with chronic lumbopelvic pain and/or referred LES scheduled to receive the reference standard examinations. When diagnoses were in complete agreement regardless of complexity, "exact" agreement was recorded. When the clinical diagnosis was included within the reference standard diagnoses, "clinical agreement" was recorded. The proportional chance criterion (PCC) statistic was used to estimate agreement on multiple diagnostic possibilities because it accounts for the prevalence of individual categories in the sample. The kappa statistic was used to estimate agreement on six pathoanatomic diagnoses. RESULTS: In a sample of chronic LBP patients (n = 216) with high levels of disability and distress, 67% received a patho-anatomic diagnosis based on available reference standards, and 10% had more than one tissue origin of pain identified. For 27 diagnostic categories and combinations, chance clinical agreement (PCC) was estimated at 13%. "Exact" agreement between clinical and reference standard diagnoses was 32% and "clinical agreement" 51%. For six pathoanatomic categories (disc, facet joint, sacroiliac joint, hip joint, nerve root and spinal stenosis), PCC was 33% with actual agreement 56%. There was no overlap of 95% confidence intervals on any comparison. Diagnostic agreement on the six most common patho-anatomic categories produced a kappa of 0.31. CONCLUSION: Clinical diagnoses agree with reference standards diagnoses more often than chance. Using available reference standards, most patients can have a tissue source of pain identified.

Dataset Information

Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence.

Publications

Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence.

Similar Datasets

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