SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

Mass spectrometry (MS)-based phosphoproteomics enables the quantification of proteome-wide phosphorylation in cells and tissues. A major challenge in MS-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. By leveraging large-scale phosphoproteomics data, machine learning has become an increasingly popular approach for computationally predicting substrates of kinases. However, the small number of high-quality experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together impact the performance of existing approaches. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, including six published datasets and a new muscle differentiation dataset, and both traditional and deep learning models, we first demonstrate that a ‘pseudo-positive’ learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data re-sampling based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model (‘SnapKin’) incorporating the above two learning strategies into a ‘snapshot’ ensemble learning algorithm. We demonstrate that the SnapKin model achieves overall the best performance in kinase-substrate prediction. Together, we propose SnapKin as a promising approach for predicting substrates of kinases from large-scale phosphoproteomics data. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

Structure-based prediction of KDAC6 substrates validated by enzymatic assay reveals determinants of promiscuity and detects new potential substrates

Histone deacetylases play important biological roles well beyond the deacetylation of histone tails, and therefore have recently been renamed to acetyl-lysine deacetylases (KDACs). In particular, KDAC6 is involved in multiple cellular processes such as apoptosis, cytoskeleton reorganization, and protein folding, affecting substrates such as □-tubulin, Hsp90 and cortactin proteins. We have applied a biochemical enzymatic assay to measure the activity of KDAC6 on a set of candidate unlabeled peptides. These served for the calibration of a structure-based substrate prediction protocol, Rosetta FlexPepBind, previously used for the successful substrate prediction of KDAC8 and other enzymes. The calibration process and comparison of the results between KDAC6 and KDAC8 highlighted structural differences that explain the already reported promiscuity of KDAC6. A proteome-wide screen of reported acetylation sites using our calibrated protocol together with the enzymatic assay provide new peptide substrates and avenues to novel potential functional regulatory roles of this promiscuous, multi-faceted enzyme.Graphical abstract

Functional Genotyping of Sulfurospirillum spp. in Mixed Cultures Allowed the Identification of a New Tetrachloroethene Reductive Dehalogenase

ABSTRACTReductive dehalogenases are the key enzymes involved in the anaerobic respiration of organohalides such as the widespread groundwater pollutant tetrachloroethene. The increasing number of available bacterial genomes and metagenomes gives access to hundreds of new putative reductive dehalogenase genes that display a high level of sequence diversity and for which substrate prediction remains very challenging. In this study, we present the development of a functional genotyping method targeting the diverse reductive dehalogenases present inSulfurospirillumspp., which allowed us to unambiguously identify a new reductive dehalogenase from our tetrachloroethene-dechlorinating SL2 bacterial consortia. The new enzyme, named PceATCE, shows 92% sequence identity with the well-characterized PceA enzyme ofSulfurospirillum multivorans, but in contrast to the latter, it is restricted to tetrachloroethene as a substrate. Its apparent higher dechlorinating activity with tetrachloroethene likely allowed its selection and maintenance in the bacterial consortia among other enzymes showing broader substrate ranges. The sequence-substrate relationships within tetrachloroethene reductive dehalogenases are also discussed.