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The prediction of the durability of immunity against COVID-19 is relevant, and longitudinal studies are essential for unraveling the details regarding protective SARS‐CoV‐2 antibody responses. It has become challenging to discriminate between COVID-19 vaccine- and infection-induced immune responses since all approved vaccines in Europe and the USA are based on the viral spike (S) protein, which is also the most commonly used antigen in immunoassays measuring immunoglobulins (Igs) against SARS-CoV-2. We have developed a nucleocapsid (N) protein-based sandwich ELISA for detecting pan anti-SARS-CoV-2 Ig with a sensitivity and specificity of 97%. Generalized mixed models were used to determine the degree of long‐term humoral immunity against the N protein and the receptor-binding domain (RBD) of the S protein in a cohort of infected individuals to distinguish between COVID-19 vaccine- and infection-induced immunity. N-specific waning could be observed in individuals who did not experience reinfection, while individuals who experienced reinfection had a new significant increase in N-specific Ig levels. In individuals that seroconverted without a reinfection, 70.1% remained anti-N seropositive after 550 days. The anti-RBD Ig dynamics were unaffected by reinfection but exhibited a clear increase in RBD-specific Ig when vaccination was initiated. In conclusion, a clear difference in the dynamics of the antibody response against N protein and RBD was observed over time. Anti-N protein-specific Igs can be detected up to 18 months after SARS-CoV-2 infection allowing long-term discrimination of infectious and vaccine antibody responses., The prediction of the durability of immunity against COVID-19 is relevant, and longitudinal studies are essential for unraveling the details regarding protective SARS‐CoV‐2 antibody responses. It has become challenging to discriminate between COVID-19 vaccine- and infection-induced immune responses since all approved vaccines in Europe and the USA are based on the viral spike (S) protein, which is also the most commonly used antigen in immunoassays measuring immunoglobulins (Igs) against SARS-CoV-2. We have developed a nucleocapsid (N) protein-based sandwich ELISA for detecting pan anti-SARS-CoV-2 Ig with a sensitivity and specificity of 97%. Generalized mixed models were used to determine the degree of long‐term humoral immunity against the N protein and the receptor-binding domain (RBD) of the S protein in a cohort of infected individuals to distinguish between COVID-19 vaccine- and infection-induced immunity. N-specific waning could be observed in individuals who did not experience reinfection, while individuals who experienced reinfection had a new significant increase in N-specific Ig levels. In individuals that seroconverted without a reinfection, 70.1% remained anti-N seropositive after 550 days. The anti-RBD Ig dynamics were unaffected by reinfection but exhibited a clear increase in RBD-specific Ig when vaccination was initiated. In conclusion, a clear difference in the dynamics of the antibody response against N protein and RBD was observed over time. Anti-N protein-specific Igs can be detected up to 18 months after SARS-CoV-2 infection allowing long-term discrimination of infectious and vaccine antibody responses. IMPORTANCE Longitudinal studies are essential to unravel details regarding the protective antibody responses after COVID-19 infection and vaccination. It has become challenging to distinguish long-term immune responses to SARS-CoV-2 infection and vaccination since most approved vaccines are based on the viral spike (S) protein, which is also

INTRODUCTION: Responses to COVID-19 vaccination in patients with chronic pulmonary diseases are poorly characterised. We aimed to describe humoral responses following two doses of BNT162b2 mRNA COVID-19 vaccine and identify risk factors for impaired responses.METHODS: Prospective cohort study including adults with chronic pulmonary diseases and healthcare personnel as controls (1:1). Blood was sampled at inclusion, 3 weeks, 2 and 6 months after first vaccination. We reported antibody concentrations as geometric means with 95% CI of receptor binding domain (RBD)-IgG and neutralising antibody index of inhibition of ACE-2/RBD interaction (%). A low responder was defined as neutralising index in the lowest quartile (primary outcome) or RBD-IgG <225 AU/mL plus neutralising index <25% (secondary outcome), measured at 2 months. We tested associations using Poisson regression.RESULTS: We included 593 patients and 593 controls, 75% of all had neutralising index ≥97% at 2 months. For the primary outcome, 34.7% of patients (n=157/453) and 12.9% of controls (n=46/359) were low responders (p<0.0001). For the secondary outcome, 8.6% of patients (n=39/453) and 1.4% of controls (n=5/359) were low responders (p<0.001). Risk factors associated with low responder included increasing age (per decade, adjusted risk ratio (aRR) 1.17, 95% CI 1.03 to 1.32), Charlson Comorbidity Index (per point) (aRR 1.15, 95% CI 1.05 to 1.26), use of prednisolone (aRR 2.08, 95% CI 1.55 to 2.77) and other immunosuppressives (aRR 2.21, 95% CI 1.65 to 2.97).DISCUSSION: Patients with chronic pulmonary diseases established functional humoral responses to vaccination, however lower than controls. Age, comorbidities and immunosuppression were associated with poor immunological responses.

GPI-anchors constitute a very important post-translational modification, linking many proteins to the outer face of the plasma membrane in eukaryotic cells. Since experimental validation of GPI-anchoring signals is slow and costly, computational approaches for predicting them from amino acid sequences are needed. However, the most recent GPI predictor is more than a decade old and considerable progress has been made in machine learning since then. We present a new dataset and a novel method, NetGPI, for GPI signal prediction. NetGPI is based on recurrent neural networks, incorporating an attention mechanism that simultaneously detects GPI anchoring signals and points out the location of their w-sites. The performance of NetGPI is superior to existing methods with regards to discrimination between GPI-anchored proteins and other secretory proteins and approximate (+/- 1 position) placement of the w-site. NetGPI is available at: https://services.healthtech.dtu.dk/service.php?NetGPI. The code repository is available at: https://github.com/mhgislason/netgpi-1.1.

A crucial process in the production of industrial enzymes is recombinant gene expression, which aims to induce enzyme overexpression of the genes in a host microbe. Current approaches for securing overexpression rely on molecular tools such as adjusting the recombinant expression vector, adjusting cultivation conditions, or performing codon optimizations. However, such strategies are time-consuming, and an alternative strategy would be to select genes for better compatibility with the recombinant host. Several methods for predicting soluble expression are available; however, they are all optimized for the expression host Escherichia coli and do not consider the possibility of an expressed protein not being soluble. We show that these tools are not suited for predicting expression potential in the industrially important host Bacillus subtilis. Instead, we build a B. subtilis-specific machine learning model for expressibility prediction. Given millions of unlabelled proteins and a small labeled dataset, we can successfully train such a predictive model. The unlabeled proteins provide a performance boost relative to using amino acid frequencies of the labeled proteins as input. On average, we obtain a modest performance of 0.64 area-under-the-curve (AUC) and 0.2 Matthews correlation coefficient (MCC). However, we find that this is sufficient for the prioritization of expression candidates for high-throughput studies. Moreover, the predicted class probabilities are correlated with expression levels. A number of features related to protein expression, including base frequencies and solubility, are captured by the model.

The native subcellular location (also referred to as localization or cellular compartment) of a protein is the one in which it acts most frequently; it is one aspect of protein function. Do ten eukaryotic model organisms differ in their location spectrum, i.e., the fraction of its proteome in each of seven major cellular compartments? As experimental annotations of locations remain biased and incomplete, we need prediction methods to answer this question. After systematic bias corrections, the complete but faulty prediction methods appeared to be more appropriate to compare location spectra between species than the incomplete more accurate experimental data. This work compared the location spectra for ten eukaryotes: Homo sapiens (human), Gorilla gorilla (gorilla), Pan troglodytes (chimpanzee), Mus musculus (mouse), Rattus norvegicus (rat), Drosophila melanogaster (fruit/vinegar fly), Anopheles gambiae (African malaria mosquito), Caenorhabitis elegans (nematode), Saccharomyces cerevisiae (baker's yeast), and Schizosaccharomyces pombe (fission yeast). The two largest classes were predicted to be the nucleus and the cytoplasm together accounting for 47-62% of all proteins, while 7-21% of the proteins were predicted in the plasma membrane and 4-15% to be secreted. Overall, the predicted location spectra were largely similar. However, in detail, the differences sufficed to plot trees (UPGMA) and 2D (PCA) maps relating the ten organisms using a simple Euclidean distance in seven states (location classes). The relations based on the simple predicted location spectra captured aspects of cross-species comparisons usually revealed only by much more detailed evolutionary comparisons. Most interestingly, known phylogenetic relations were reproduced better by paralog-only than by ortholog-only trees.

In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state-of-the-art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria, and chloroplasts or other plastids. By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, that is, the one following the initial methionine, has a strong influence on the classification. We observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position 2, compared with 20% in other plant proteins. We also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide. The importance of this feature for predictions has not been highlighted before., QC 20191126