Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry.

A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrometer analyzes the proteolytic peptides of a protein rather than the proteins themselves. We have previously designed a Bayesian hierarchical probabilistic model, Triqler, for combining peptide identification and quantification errors into probabilities of proteins being differentially abundant. However, the model was developed for data from data-dependent acquisition. Here, we show that Triqler is also compatible with data-independent acquisition data after applying minor alterations for the missing value distribution. Furthermore, we find that it has better performance than a set of compared state-of-the-art protein summarization tools when evaluated on data-independent acquisition data.

Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities.

Machine learning has been an integral part of interpreting data from mass spectrometry (MS)-based proteomics for a long time. Relatively recently, a machine-learning structure appeared successful in other areas of bioinformatics, Transformers. Furthermore, the implementation of Transformers within bioinformatics has become relatively convenient due to transfer learning, i.e., adapting a network trained for other tasks to new functionality. Transfer learning makes these relatively large networks more accessible as it generally requires less data, and the training time improves substantially. We implemented a Transformer based on the pretrained model TAPE to predict MS2 intensities. TAPE is a general model trained to predict missing residues from protein sequences. Despite being trained for a different task, we could modify its behavior by adding a prediction head at the end of the TAPE model and fine-tune it using the spectrum intensity from the training set to the well-known predictor Prosit. We demonstrate that the predictor, which we call Prosit Transformer, outperforms the recurrent neural-network-based predictor Prosit, increasing the median angular similarity on its hold-out set from 0.908 to 0.929. We believe that Transformers will significantly increase prediction accuracy for other types of predictions within MS-based proteomics.

Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration.

Motor deficits are observed in Alzheimer's disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans.

NADPH oxidase promotes pancreatic cancer cell survival via inhibiting JAK2 dephosphorylation by tyrosine phosphatases.

BACKGROUND & AIMS: Growth factors, such as insulin-like growth factor-1 (IGF-I), protect pancreatic cancer (PaCa) cells from death. We recently showed that reactive oxygen species (ROS) produced by nicotinamide adenine dinucleotide phosphate (NADPH) oxidase Nox4 mediate the antiapoptotic effect of growth factors. Here, we examine the mechanisms of the antiapoptotic role of NADPH oxidase. We hypothesized that ROSs produced by NADPH oxidase inhibit key protein tyrosine phosphatases (PTPs) and thus sustain the activation of kinases mediating antiapoptotic pathways in PaCa cells. METHODS: Transfections and pharmacologic inhibition were used to assess the effects of NADPH oxidase on Janus kinase 2 (JAK2) kinase, the low molecular weight-protein tyrosine phosphatase (LMW-PTP), and apoptosis. RESULTS: We found that 1 target of ROSs is JAK2, an important antiapoptotic kinase in PaCa cells. Both serum-induced and IGF-I biphasic JAK2 phosphorylation, with a rapid (minutes) and transient first phase, and a slow and sustained (24-72 hours) second phase. Nox4 mediated the sustained phase of JAK2 phosphorylation, which was required for the antiapoptotic effects of IGF-I and serum. Transfection experiments identified the LMW-PTP as a negative regulator of sustained JAK2 phosphorylation. Growth factors inhibited LMW-PTP through its oxidation by NADPH oxidase. LMW-PTP colocalizes with Nox4 both in PaCa cells and in human pancreatic adenocarcinoma. CONCLUSIONS: The results suggest a novel signaling pathway, in which NADPH oxidase activation results in inhibition of PTPs, such as LMW-PTP, leading, in turn, to enhanced and sustained phosphorylation of kinases, such as JAK2, and suppression of apoptosis. This pathway mediates the prosurvival effect of ROSs and suggests new targets for pancreatic cancer treatment.