ArticleJournal of computational biology : a journal of computational molecular cell biology2014
A Bayesian sampler for optimization of protein domain hierarchies.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Article
- Structural basis for pseudokinase-mediated regulation of GCN2 in the integrated stress response.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Structural and evolutionary insights into understudied bacterial serine-threonine pseudokinase families.Biochemical Society transactions · 2025Review
- Deep learning decodes species-specific codon usage signatures in Brassica from coding sequences.Scientific reports · 2025Article
- Identification and classification of ion-channels across the tree of life provide functional insights into understudied CALHM channels.bioRxiv : the preprint server for biology · 2025Article
- An atlas of bacterial serine-threonine kinases reveals functional diversity and key distinctions from eukaryotic kinases.Science signaling · 2025Article
- Evolutionary and Functional Analysis of Caspase-8 and ASC Interactions to Drive Lytic Cell Death, PANoptosis.Molecular biology and evolution · 2025Article
- Informatic challenges and advances in illuminating the druggable proteome.Drug discovery today · 2024Review
- Mechanistic and evolutionary insights into isoform-specific 'supercharging' in DCLK family kinases.eLife · 2023Article
- Structural and biochemical insight into a modular β-1,4-galactan synthase in plants.Nature plants · 2023Article
- Identifying Function Determining Residues in Neuroimmune Semaphorin 4A.International journal of molecular sciences · 2022Article
- SPARC: Structural properties associated with residue constraints.Computational and structural biotechnology journal · 2022Article
- Computational tools and resources for pseudokinase research.Methods in enzymology · 2022Review
- Evolution of Functional Diversity in the Holozoan Tyrosine Kinome.Molecular biology and evolution · 2021Article
- Cataloguing the dead: breathing new life into pseudokinase research.The FEBS journal · 2020Review
- Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases.eLife · 2020Article
- A survey of TIR domain sequence and structure divergence.Immunogenetics · 2020Article
- Deep Analysis of Residue Constraints (DARC): identifying determinants of protein functional specificity.Scientific reports · 2020Article
- Lipid-targeting pleckstrin homology domain turns its autoinhibitory face toward the TEC kinases.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- The crystal structure of the protein kinase HIPK2 reveals a unique architecture of its CMGC-insert region.The Journal of biological chemistry · 2019Article
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Abstract
The process of identifying and modeling functionally divergent subgroups for a specific protein domain class and arranging these subgroups hierarchically has, thus far, largely been done via manual curation. How to accomplish this automatically and optimally is an unsolved statistical and algorithmic problem that is addressed here via Markov chain Monte Carlo sampling. Taking as input a (typically very large) multiple-sequence alignment, the sampler creates and optimizes a hierarchy by adding and deleting leaf nodes, by moving nodes and subtrees up and down the hierarchy, by inserting or deleting internal nodes, and by redefining the sequences and conserved patterns associated with each node. All such operations are based on a probability distribution that models the conserved and divergent patterns defining each subgroup. When we view these patterns as sequence determinants of protein function, each node or subtree in such a hierarchy corresponds to a subgroup of sequences with similar biological properties. The sampler can be applied either de novo or to an existing hierarchy. When applied to 60 protein domains from multiple starting points in this way, it converged on similar solutions with nearly identical log-likelihood ratio scores, suggesting that it typically finds the optimal peak in the posterior probability distribution. Similarities and differences between independently generated, nearly optimal hierarchies for a given domain help distinguish robust from statistically uncertain features. Thus, a future application of the sampler is to provide confidence measures for various features of a domain hierarchy.
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