Integrating knowledge of protein sequence with protein function for the prediction and validation of new MALT1 substrates
Bibliographic record
Abstract
We developed a bioinformatics-led substrate discovery workflow to expand the known substrate repertoire of MALT1. Our approach, termed GO-2-Substrates, integrates protein function information, including GO terms from known substrates, with protein sequences to rank substrate candidates by similarity. We applied GO-2-Substrates to MALT1, a paracaspase and master regulator of NF-κB signalling in adaptive immune responses. With only 12 known substrates, the evolutionarily conserved paracaspase functions and phenotypes of Malt1 –/– mice strongly implicate the existence of undiscovered substrates. We tested the ranked predictions from GO-2-Substrates of new MALT1 human substrates by co-expression of candidates transfected with the oncogenic constitutively active cIAP2-MALT1 fusion protein or CARD11/BCL10/MALT1 active signalosome. We identified seven new MALT1 substrates by the co-transfection screen: TANK, TAB3, CASP10, ZC3H12D, ZC3H12B, CILK1 and ILDR2. Using catalytically inactive cIAP2-MALT1 (Cys464Ala), a MALT1 inhibitor, MLT-748, and noncleavable P1-Arg to Ala mutant versions of each substrate in dual transfections, we validated the seven new substrates in vitro. We confirmed the cleavage of endogenous TANK and the RNase ZC3H12D in B cells by Western blotting and mining TAILS N-terminomics datasets, where we also uncovered evidence for these and 12 other candidate substrates by endogenous MALT1. Thus, protein function information improves substrate predictions. The new substrates and other high-ranked MALT1 candidate substrates should open new biological frontiers for further validation and exploration of the function of MALT1 within and beyond NF-κB regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".