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The coronavirus disease COVID-19 constitutes the most severe pandemic of the last decades having caused more than 1 million deaths worldwide. The SARS-CoV-2 virus recognizes the angiotensin converting enzyme 2 (ACE2) on the surface of human cells through its spike protein. It has been reported that the coronavirus can mildly infect cats, and ferrets, and perhaps dogs while not pigs, mice, chicken and ducks. Differences in viral infectivity among different species or individuals could be due to amino acid differences at key positions of the host proteins that interact with the virus, the immune response, expression levels of host proteins and translation efficiency of the viral proteins among other factors. Here, first we have addressed the importance that sequence variants of different animal species, human individuals and virus isolates have on the interaction between the RBD domain of the SARS-CoV-2 spike S protein and human angiotensin converting enzyme 2 (ACE2). Second, we have looked at viral translation efficiency by using the tRNA adaptation index. We find that integration of both interaction energy with ACE2 and translational efficiency explains animal infectivity. Humans are the top species in which SARS-CoV-2 is both efficiently translated as well as optimally interacting with ACE2. We have found some viral mutations that increase affinity for hACE and some hACE2 variants affecting ACE2 stability and virus binding. These variants suggest that different sensitivities to coronavirus infection in humans could arise in some cases from allelic variability affecting ACE2 stability and virus binding. Author summary: In these early stages of the COVID-19 pandemic it is urgent to understand all features determining the new virus expansion. Two significant factors conditioning infection are ACE2-mediated SARS-CoV-2 cellular entry and viral proteome translation efficiency. Genomic variability across species, including humans, results in ACE2 variants that destabilize its fold, modify ACE2/SARS-CoV-2 recognition, or both. We also point out the importance of considering waters at the interface of protein-protein interactions when performing in silico mutagenesis. [ABSTRACT FROM AUTHOR]

RNA–protein interactions are crucial for such key biological processes as regulation of transcription, splicing, translation, and gene silencing, among many others. Knowing where an RNA molecule interacts with a target protein and/or engineering an RNA molecule to specifically bind to a protein could allow for rational interference with these cellular processes and the design of novel therapies. Here we present a robust RNA–protein fragment pairbased method, termed RnaX, to predict RNA-binding sites. This methodology, which is integrated into the ModelX tool suite (http://modelx.crg.es), takes advantage of the structural information present in all released RNA–protein complexes. This information is used to create an exhaustive database for docking and a statistical forcefield for fast discrimination of true backbone-compatible interactions. RnaX, together with the protein design forcefield FoldX, enables us to predict RNA–protein interfaces and, when sufficient crystallographic information is available, to reengineer the interface at the sequence-specificity level by mimicking those conformational changes that occur on protein and RNA mutagenesis. These results, obtained at just a fraction of the computational cost of methods that simulate conformational dynamics, open up perspectives for the engineering of RNA–protein interfaces. [ABSTRACT FROM AUTHOR]

Although p53 protein aggregates have been observed in cancer cell lines and tumour tissue, their impact in cancer remains largely unknown. Here, we extensively screened for p53 aggregation phenotypes in tumour biopsies, and identified nuclear inclusion bodies ( nIBs) of transcriptionally inactive mutant or wild-type p53 as the most frequent aggregation-like phenotype across six different cancer types. p53-positive nIBs co-stained with nuclear aggregation markers, and shared molecular hallmarks of nIBs commonly found in neurodegenerative disorders. In cell culture, tumour-associated stress was a strong inducer of p53 aggregation and nIB formation. This was most prominent for mutant p53, but could also be observed in wild-type p53 cell lines, for which nIB formation correlated with the loss of p53's transcriptional activity. Importantly, protein aggregation also fuelled the dysregulation of the proteostasis network in the tumour cell by inducing a hyperactivated, oncogenic heat-shock response, to which tumours are commonly addicted, and by overloading the proteasomal degradation system, an observation that was most pronounced for structurally destabilized mutant p53. Patients showing tumours with p53-positive nIBs suffered from a poor clinical outcome, similar to those with loss of p53 expression, and tumour biopsies showed a differential proteostatic expression profile associated with p53-positive nIBs. p53-positive nIBs therefore highlight a malignant state of the tumour that results from the interplay between (1) the functional inactivation of p53 through mutation and/or aggregation, and (2) microenvironmental stress, a combination that catalyses proteostatic dysregulation. This study highlights several unexpected clinical, biological and therapeutically unexplored parallels between cancer and neurodegeneration. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We previously showed the existence of selective pressure against protein aggregation by the enrichment of aggregation-opposing 'gatekeeper' residues at strategic places along the sequence of proteins. Here we analyzed the relationship between protein lifetime and protein aggregation by combining experimentally determined turnover rates, expression data, structural data and chaperone interaction data on a set of more than 500 proteins. We find that selective pressure on protein sequences against aggregation is not homogeneous but that short-living proteins on average have a higher aggregation propensity and fewer chaperone interactions than long-living proteins. We also find that short-living proteins are more often associated to deposition diseases. These findings suggest that the efficient degradation of high-turnover proteins is sufficient to preclude aggregation, but also that factors that inhibit proteasomal activity, such as physiological ageing, will primarily affect the aggregation of short-living proteins.