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Detection pipeline enables discovery of protein structural mimicry

Claim that the newly developed computational detection pipeline allows discovery of parasite protein structural mimicry cases.

Confidence
80%
active

Evidence Quote

“The detection pipeline enables systematic discovery of parasite protein structural mimicry.”

Relationship

Structural mimicry detection pipeline enables structural mimicry

Arguments

Structural mimicry detection pipelinesubject
True structural mimicryobject

Connections (3)

Reasoning connecting viral mimicry, immune modulation, and detection pipeline utilityInferenceChain
Petabase-scale sequence alignment increases viral discoveryAssociation
Reasoning for detection and benchmarking of viral protein structural mimicryInferenceChain

Evidence

“Evidence that the developed structural mimicry detection pipeline successfully recovers well-studied viral protein mimics and is ready for wider deployment.”

Prachee Avasthi et al. (2025). A method for computational discovery of viral structural mimics doi:10.57844/arcadia-1eu9-gcsx ↗

“Summarizes the workflow, functions, tool choices, and evaluation of the peptigate pipeline for predicting and annotating bioactive peptides from transcriptomes and protein sequences.”

Software: Implementing useful and innovative computing.

“Reference describing Viro3D, a comprehensive database of virus protein structure predictions.”

Litvin U et al. (2024). Viro3D: a comprehensive database of virus protein structure predictions doi:10.1101/2024.12.19.629443 ↗

“Reference describing the AlphaFold method for highly accurate protein structure prediction.”

Jumper J et al. (2021). Highly accurate protein structure prediction with AlphaFold doi:10.1038/s41586-021-03819-2 ↗

“Reference describing Foldseek, a fast and accurate protein structure search tool.”

van Kempen M et al. (2023). Fast and accurate protein structure search with Foldseek doi:10.1038/s41587-023-01773-0 ↗

“Reference describing ColabFold, a tool to facilitate protein folding predictions.”

Mirdita M et al. (2022). ColabFold: making protein folding accessible to all doi:10.1038/s41592-022-01488-1 ↗

“Evidence line providing support from the reference on using Chlamydomonas flagellum as a model for human ciliary disease.”

The Chlamydomonas Flagellum as a Model for Human Ciliary Disease

“Evidence line showing restoration of motility in Chlamydomonas mutants modeling spermatogenic failure.”

Rescuing Chlamydomonas motility in mutants modeling spermatogenic failure

“Evidence supporting characterization of yeast mutants defective in glycerol catabolism.”

Isolation and characterization of Saccharomyces cerevisiae mutants defective in glycerol catabolism

“Evidence describing the phenotypes of a Chlamydomonas insertional mutant disrupting flagellar central pair microtubule-associated structures.”

Characterization of a Chlamydomonas Insertional Mutant that Disrupts Flagellar Central Pair Microtubule-associated Structures

“Evidence summarizing selection with recurrent backcrossing to develop congenic lines used for quantitative trait locus analysis.”

Selection with Recurrent Backcrossing to Develop Congenic Lines for Quantitative Trait Loci Analysis

“Evidence line summarizing findings that actin cytoskeleton functions are conserved in absence of canonical actin-binding proteins.”

Paredez AR et al. (2011) An actin cytoskeleton with evolutionarily conserved functions in the absence of canonical actin-binding proteins

“Evidence summarizing deep learning-based mapping of crawling-cell morphodynamics in feature space.”

Imoto et al. (2021) on deep learning-based mapping of crawling-cell morphodynamics

“Evidence from Miyata et al. (2020) on the evolutionary history of motility systems across living organisms.”

Miyata et al. (2020) on the tree of motility

“Evidence line supporting characterization of Saccharomyces cerevisiae mutants defective in glycerol catabolism from the cited reference.”

Isolation and characterization of Saccharomyces cerevisiae mutants defective in glycerol catabolism

“Evidence line describing selection with recurrent backcrossing to develop congenic lines for quantitative trait locus analysis, supported by the cited reference.”

Selection with Recurrent Backcrossing to Develop Congenic Lines for Quantitative Trait Loci Analysis