PlantSecKB: the Plant Secretome and Subcellular Proteome KnowledgeBase
Bibliographic record
Abstract
Normal 0 MicrosoftInternetExplorer4 Normal 0 MicrosoftInternetExplorer4 Prediction and curation of protein subcellular locations is essential for protein functional annotation. We developed the Plant Secretome and Subcellular Proteome KnowledgeBase (PlantSecKB) for the plant research community to access and curate plant protein subcellular locations, with a focus on secreted proteins. The database is constructed with all the available plant protein data retrieved from the UniProtKB database and plant protein sequences predicted from EST data assembled by the PlantGDB project. The database contains information collected from three sources: (1) subcellular locations that were curated or computationally predicted in the UniProtKB; (2) subcellular locations and features predicted by eight computational tools; (3) secreted proteins that were curated from recent literature. The categories of subcellular locations include secretome, mitochondria, chloroplast, cytosol, cytoskeleton, endoplasmic reticulum, Golgi apparatus, lysosome, peroxisome, nucleus, vacuole, and plasma membrane. The data can be searched by using UniProt accession number or ID, GenBank GI or RefSeq accession number, gene name, and keywords. Species specific secretome and subcellular proteomes can be searched and downloaded into a FASTA file. BLAST is available to allow users to search the database based on protein sequences. Community curation for subcellular locations of plant proteins is also supported. A primary analysis revealed that monocots and dicots had a similar proportion of secretomes, and monocots had a significantly higher proportion of proteins distributed to mitochondria (both membrane and non-membrane) and chloroplast membrane, while dicots had significantly more proteins distributed to cytosol and nucleus. This database aims to facilitate plant protein research and is available at http://proteomics.ysu.edu/secretomes/plant.php. /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:Times New Roman;} /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:Times New Roman;}
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.037 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".