Highlights d Phosphoproteomics analysis of SARS-CoV-2-infected cells uncovers signaling rewiring d Infection promotes host p38 MAPK cascade activity and shutdown of mitotic kinases d Infection stimulates CK2-containing filopodial protrusions with budding virus d Kinase activity analysis identifies potent antiviral drugs and compounds

The COVID-19 (Coronavirus disease-2019) pandemic, caused by the SARS-CoV-2 coronavirus, is a significant threat to public health and the global economy. SARS-CoV-2 is closely related to the more lethal but less transmissible coronaviruses SARS-CoV-1 and MERS-CoV. Here, we have carried out comparative viral-human protein-protein interaction and viral protein localization analysis for all three viruses. Subsequent functional genetic screening identified host factors that functionally impinge on coronavirus proliferation, including Tom70, a mitochondrial chaperone protein that interacts with both SARS-CoV-1 and SARS-CoV-2 Orf9b, an interaction we structurally characterized using cryo-EM. Combining genetically-validated host factors with both COVID-19 patient genetic data and medical billing records identified important molecular mechanisms and potential drug treatments that merit further molecular and clinical study.

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Protein turnover is a key aspect of cellular homeostasis and proteome dynamics. However, there is little consensus on which properties of a protein determine its lifetime in the cell. In this work, we exploit two reliable datasets of experimental protein degradation rates to learn models and uncover determinants of protein degradation, with particular focus on properties that can be derived from the sequence. Our work shows that simple sequence features suffice to obtain predictive models of which the output correlates reasonably well with the experimentally measured values. We also show that intrinsic disorder may have a larger effect than previously reported, and that the effect of PEST regions, long thought to act as specific degradation signals, can be better explained by their disorder. We also find that determinants of protein degradation depend on the cell types or experimental conditions studied. This analysis serves as a first step towards the development of more complex, mature computational models of degradation of proteins and eventually of their full life cycle. Proteins 2017; 85:1593-1601. © 2017 Wiley Periodicals, Inc.

Health-care and social service providers affected by climate-related disasters play a pivotal role in response and recovery but yet are at a disproportionate risk for mental health symptoms such as posttraumatic stress disorder (PTSD), secondary traumatic stress, anxiety, and burnout. Factors such as social support and resilience may protect these providers from stress related symptoms. To explore providers' responses to recent disasters, this study examined mental health distress, work-related stress, and protective factors in Texas and Puerto Rico-both of which were struck by hurricanes in 2017. This study was conducted with N ϭ 1,101 health-care and social service providers 10 to 12 months after hurricanes Harvey and Maria. Providers completed measures of PTSD, anxiety, burnout, secondary traumatic stress, compassion satisfaction, social support, and resilience. Frequencies were calculated to determine percentages of those who scored above the clinical cutoff for mental health symptoms. One-way analyses of variance explored differences in mental health symptoms between Texas and Puerto Rico. Bivariate correlations examined the relationships between all measures. Puerto Rican participants scored significantly higher on measures of PTSD, anxiety, and compassion satisfaction. Participants in Texas reported significantly higher burnout and resilience. Measures of PTSD, anxiety, burnout, and secondary traumatic stress were positively correlated. Social support, resilience, and compassion satisfaction were inversely correlated with measures of distress. Findings confirm high rates of mental health distress among providers during the disaster recovery. Given our findings, it is critical for accessible, evidence-informed interventions be available for providers.

Motivation Polyketide synthases are enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by cis-AT modular polyketide synthases (PKSs), which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein-protein interactions. The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Results Here, we introduce PKSpop, which uses a coevolution-based protein-protein interaction prediction algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (93% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for protein-protein interactions than coevolution between ketosynthase and acyl carrier protein domains. Availability The code is available on http://www.bif.wur.nl/ (under ‘Software’) Supplementary information Supplementary data are available at Bioinformatics online.

Protein degradation is a key component of the regulation of gene expression and is at the center of several pathogenic processes. Proteins are regularly degraded, but there is large variation in their lifetimes, and the kinetics of protein degradation are not well understood. Many different factors can influence protein degradation rates, painting a highly complex picture. This has been partially unravelled in recent years thanks to invaluable advances in proteomics techniques. In this Mini-Review, we give a global vision of the determinants of protein degradation rates with the backdrop of the current understanding of proteolytic systems to give a contemporary view of the field.

Motivation Predicting residue–residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance. Results We have designed Ouroboros, a novel algorithm to reduce such noise in intermolecular contact prediction. Our method iterates between weighting proteins according to how likely they are to interact based on the correlated mutations signal, and predicting correlated mutations based on the weighted sequence alignment. We show that this approach accurately discriminates between protein interaction versus non-interaction and simultaneously improves the prediction of intermolecular contact residues compared to a naive application of correlated mutation analysis. This requires no training labels concerning interactions or contacts. Furthermore, the method relaxes the assumption of one-to-one interaction of previous approaches, allowing for the study of many-to-many interactions. Availability and implementation Source code and test data are available at www.bif.wur.nl/. Supplementary information Supplementary data are available at Bioinformatics online.