The "canonical" proteasomal degradation signal is a substrateanchored polyubiquitin chain. However, a handful of proteins were shown to be targeted following monoubiquitination. In this study, we established-in both human and yeast cells-a systematic approach for the identification of monoubiquitination-dependent proteasomal substrates. The cellular wild-type polymerizable ubiquitin was replaced with ubiquitin that cannot form chains. Using proteomic analysis, we screened for substrates that are nevertheless degraded under these conditions compared with those that are stabilized, and therefore require polyubiquitination for their degradation. For randomly sampled representative substrates, we confirmed that their cellular stability is in agreement with our screening prediction. Importantly, the two groups display unique features: monoubiquitinated substrates are smaller than the polyubiquitinated ones, are enriched in specific pathways, and, in humans, are structurally less disordered. We suggest that monoubiquitination-dependent degradation is more widespread than assumed previously, and plays key roles in various cellular processes. monoubiquitination | 26S proteasome | protein degradation | ubiquitin replacement

Background: Current protocols yield crystals for <30% of known proteins, indicating that automatically identifying crystallizable proteins may improve high-throughput structural genomics efforts. We introduce CRYSTALP2, a kernel-based method that predicts the propensity of a given protein sequence to produce diffraction-quality crystals. This method utilizes the composition and collocation of amino acids, isoelectric point, and hydrophobicity, as estimated from the primary sequence, to generate predictions. CRYSTALP2 extends its predecessor, CRYSTALP, by enabling predictions for sequences of unrestricted size and provides improved prediction quality.

Obtaining diffraction quality crystals remains one of the major bottlenecks in structural biology. The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely usedXtalPredalgorithm [Slabinskiet al.(2007),Protein Sci.16, 2472–2482] was developed.XtalPredclassifies proteins into five `crystallization classes' based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The newXtalPred-RF(random forest) achieves significant improvement of the prediction of crystallization success over the originalXtalPred. To illustrate this,XtalPred-RFwas tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e.twofold) for the top class of predicted targets.

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ObjectiveTo test the safety, tolerability, and urate‐elevating capability of the urate precursor inosine taken orally or by feeding tube in people with amyotrophic lateral sclerosis (ALS).MethodsThis was a pilot, open‐label trial in 25 participants with ALS. Treatment duration was 12 weeks. The dose of inosine was titrated at pre‐specified time points to elevate serum urate levels to 7–8 mg/dL. Primary outcomes were safety (as assessed by the occurrence of adverse events [AEs]) and tolerability (defined as the ability to complete the 12‐week study on study drug). Secondary outcomes included biomarkers of oxidative stress and damage. As an exploratory analysis, observed outcomes were compared with a virtual control arm built using prediction algorithms to estimate ALSFRS‐R scores.ResultsTwenty‐four out of 25 participants (96%) completed 12 weeks of study drug treatment. One participant was unable to comply with study visits and was lost to follow‐up. Serum urate rose to target levels in 6 weeks. No serious AEs attributed to study drug and no AEs of special concern, such as urolithiasis and gout, occurred. Selected biomarkers of oxidative stress and damage had significant changes during the study period. Observed changes in ALSFRS‐R did not differ from baseline predictions.InterpretationInosine appeared safe, well tolerated, and effective in raising serum urate levels in people with ALS. These findings, together with epidemiological observations and preclinical data supporting a neuroprotective role of urate in ALS models, provide the rationale for larger clinical trials testing inosine as a potential disease‐modifying therapy for ALS.

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The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses.