Explore large scale fragment-based chemoproteomics screening data
if an app is inactive, click Restart this Space button
Explore the interactions between ligands and proteins. This is the main navigator to the chemoproteomics data, containing profiles for 407 fragments. The app also showcases competition assays for a few selected fragments
Input a set of proteins and see how many fragments interact with them according to our chemoproteomics data. We categorize proteins by their promiscuity/specificity levels to easily detect reported effect such as labeling bias
Explore fragment profiles from an enrichment perspective. We capture protein annotations of multiple scopes, from domains and families to molecular functions and cellular localization. We offer global and detailed views for each fragment
Predict whether your fully-functionalized fragment of interest is likely to be promiscuous or associated with a specific interactome signature. We have predefined 10 interactome signatures capturing high-level biological processes that emerged from our chemoproteomics data
Build a machine learning model on the fly to predict potential interactions between your fully-functionalized fragments and proteins of interest. Sets of proteins are accepted and organized in coherent subsets to maximize the chance of obtaining a good model
NO. To change selected protein, there is no need to select whole existing term or delete or type new. Just place the mouse cursor in the search box and just start to type new protein! Old text is automatically cleared
Names like caspase or ddb1 appear many times in description of proteins, therefore search engine is not able to pick the correct one. Type specific input such as DNA damage-binding protein 1 or DDB1 DNA dama.. to find DDB1. Search with UniProt Accession (Q16531 for DDB1) is better !
Gen1 Fragment selection menu is dynamically updated based on the selected Protein and threshold filters applied. If you are interested in a specific Gen1 Fragment, then clear all filters, i.e., select 'no filter' option for P values, adjusted P values and filterSet (fS) and/or select a promiscuous Protein such as TOMM22
That is correct. Plots are static. They are not dynamically updated based on threshold values. Changing the threshold for P values, adjusted P values and filterSet (fS) only updates the Table. Pre- set (i.e. default) filterSet (fS or fS2) were used to create plots
Order of Fragments displayed in search menu matches the original order in the Table. Re-adjusting the table, for example: sort the table based on Foldchange (Fc) values or number of Protein/Fragment hits will not change the order of the Fragments in the search menu
Changing the threhold for P values, adjusted P values and filterSet (fS) updates the original Table, consequently results in a change in the order of Gen1 Fragment displayed in search menu
10% hit/regulated ratio for Proteins and 5% for Fragments
| Global | number of Proteins | AUROC |
|---|---|---|
| Prom-0 | 100+ | 0.818 |
| Prom-1 | 200+ | 0.800 |
| Prom-2 | 300+ | 0.713 |
| Specific | number of Proteins | number of Fragments | AUROC |
|---|---|---|---|
| Prom-0-0 | 5+ | < 4 | 0.818 |
| Prom-0-1 | 10+ | 4 - 40 | 0.809 |
| Prom-0-2 | 50+ | > 40 | 0.857 |
| Prom-1-0 | 10+ | < 4 | 0.771 |
| Prom-1-1 | 15+ | 4 - 40 | 0.855 |
| Prom-1-2 | 100+ | > 40 | 0.858 |
| Prom-2-0 | 50+ | < 4 | 0.771 |
| Prom-2-1 | 100+ | 4 - 40 | 0.743 |
| Prom-2-2 | 200+ | > 40 | 0.814 |
| Signature Models | Ontology | AUROC |
|---|---|---|
| Sign-0 | Endoplasmic reticulum | 0.872 |
| Sign-1 | RNA binding | 0.727 |
| Sign-2 | Lysosome | 0.938 |
| Sign-3 | Proteasome complex | 0.616 |
| Sign-4 | Mitochondria | 0.519 |
| Sign-5 | Nucleous | 0.702 |
| Sign-6 | Transmembrane transporter | 0.756 |
| Sign-7 | Microtubule | 0.557 |
| Sign-8 | Organic acid binding | 0.706 |
| Sign-9 | Envelope | 0.806 |