Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation-induced damage.

Oxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS susceptibility essential. The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by γ-irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery, and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein-intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties.

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide-MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (P < 2 × 10-16), including CD8+ T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.

Evolution of herbivore-induced early defense signaling was shaped by genome-wide duplications in Nicotiana.

Herbivore-induced defenses are widespread, rapidly evolving and relevant for plant fitness. Such induced defenses are often mediated by early defense signaling (EDS) rapidly activated by the perception of herbivore associated elicitors (HAE) that includes transient accumulations of jasmonic acid (JA). Analyzing 60 HAE-induced leaf transcriptomes from closely-related Nicotiana species revealed a key gene co-expression network (M4 module) which is co-activated with the HAE-induced JA accumulations but is elicited independently of JA, as revealed in plants silenced in JA signaling. Functional annotations of the M4 module were consistent with roles in EDS and a newly identified hub gene of the M4 module (NaLRRK1) mediates a negative feedback loop with JA signaling. Phylogenomic analysis revealed preferential gene retention after genome-wide duplications shaped the evolution of HAE-induced EDS in Nicotiana. These results highlight the importance of genome-wide duplications in the evolution of adaptive traits in plants.