Integrating knowledge of protein sequence with protein function for the prediction and validation of new MALT1 substrates

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.