Vaccine hesitancy and emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) escaping vaccine-induced immune responses highlight the urgency for new COVID-19 therapeutics. Engineered angiotensin-converting enzyme 2 (ACE2) proteins with augmented binding affinities for SARS-CoV-2 spike (S) protein may prove to be especially efficacious against multiple variants. Using molecular dynamics simulations and functional assays, we show that three amino acid substitutions in an engineered soluble ACE2 protein markedly augmented the affinity for the S protein of the SARS-CoV-2 WA-1/2020 isolate and multiple VOCs: B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma) and B.1.617.2 (Delta). In humanized K18-hACE2 mice infected with the SARS-CoV-2 WA-1/2020 or P.1 variant, prophylactic and therapeutic injections of soluble ACE22.v2.4-IgG1 prevented lung vascular injury and edema formation, essential features of CoV-2-induced SARS, and above all improved survival. These studies demonstrate broad efficacy in vivo of an engineered ACE2 decoy against SARS-CoV-2 variants in mice and point to its therapeutic potential.

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A reoccurring challenge in bioinformatics is predicting the phenotypic consequence of amino acid variation in proteins. Due to recent advances in sequencing techniques, sufficient genomic data is becoming available to train models that predict the evolutionary statistical energies for each sequence, but there is still inadequate experimental data to directly predict functional effects. One approach to overcome this data scarcity is to apply transfer learning and train more models with available datasets. In this study, we propose a set of transfer learning algorithms, we call TLmutation, which implements a supervised transfer learning algorithm that transfers knowledge from survival data to a protein function of interest in the same protein followed by an unsupervised transfer learning algorithm that extends the knowledge to a homologous protein. We explore the application of our algorithms in three cases. First, we test the supervised transfer on dozens of previously published mutagenesis datasets to complete and refine 1 missing datapoints. We further investigate these datasets to identify which variants build better predictors of variant functions. In the second case, we apply the algorithm to predict higher-order mutations solely from single point mutagenesis data. Finally, we perform the unsupervised transfer learning algorithm to predict mutational effects of homologous proteins from experimental datasets. These algorithms are generalized to transfer knowledge between Markov random field models. We show the benefit of our transfer learning algorithms to utilize informative deep mutational data and provide new insights into protein variant functions. As these algorithms are generalized to transfer knowledge between Markov random field models, we expect these algorithms to be applicable to other disciplines.

A reoccurring challenge in bioinformatics is predicting the phenotypic consequence of amino acid variation in proteins. With the recent advancements in sequencing techniques, sufficient genomic data has become available to train models that predict the evolutionary statistical energies, but there is still inadequate experimental data to directly predict functional effects. One approach to overcome this data scarcity is to apply transfer learning and train more models with available datasets. In this study, 1. CC-BY-NC-ND 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is madeThe copyright holder for this preprint (which this version posted April 18, 2020. ;

The initiation of T-cell immune responses requires professional antigen-presenting cells. Emerging data point towards an important role for macrophages (M/) in the priming of naïve T cells. In this study we analyzed the efficiency and the mechanisms by which M/ derived from spleen (Sp-M/) or bone marrow (BM-M/) present Lymphocytic choriomeningitis virus (LCMV) antigens to epitope-specific T cells. We demonstrate that because of phagosomal maturation, Sp-M/ downregulate their ability to cross-present cell-associated, but not soluble, antigens, as they are further differentiated in culture without altering their capacity to directly present virus antigens after infection. We propose that Sp-M/ are extremely efficient at direct and cross-presentation. However, if these cells undergo further M-CSF-dependent maturation, they will adapt to be more scavenger and phagocytic and concurrently reduce their cross-presenting capacity. Accordingly, Sp-M/ can have an important role in regulating T-cell responses through cross-presentation depending on their differentiation state.

The reuptake of the neurotransmitter serotonin from the synaptic cleft by the serotonin transporter, SERT, is essential for proper neurological signaling. Biochemical studies have shown that Thr276 of transmembrane helix 5 is a site of PKG-mediated SERT phosphorylation, which has been proposed to shift the SERT conformational equilibria to promote inward-facing states, thus enhancing 5-HT transport. Recent structural and simulation studies have provided insights into the conformation transitions during substrate transport but have not shed light on SERT regulation via post-translational modifications. Using molecular dynamics simulations and Markov state models, we investigate how Thr276 phosphorylation impacts the SERT mechanism and its role in enhancing transporter stability and function. Our simulations show that Thr276 phosphorylation alters the hydrogen-bonding network involving residues on transmembrane helix 5. This in turn decreases the free energy barriers for SERT to transition to the inward-facing state, thus facilitating 5-HT import. The results provide atomistic insights into in vivo SERT regulation and can be extended to other pharmacologically important transporters in the solute carrier family.