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Loas, A.

Publications and source records attributed to Loas, A..

3 recordsLinked to original sources

Erythrocyte-targeted immunomodulatory antigens enabled by in vivo selection of D-peptides

Targeting of antigens to erythrocytes can be used to selectively mitigate their immunogenicity, but the methods to equip a variety of cargoes with erythrocyte-targeting properties are limited. Here we identified a D-peptide that targets murine erythrocytes and decreases anti-drug antibody responses when conjugated to the protective antigen from Bacillus anthracis, a protein of therapeutic interest. The D-peptide likewise decreases inflammatory anti-ovalbumin (OVA) CD8+ T cell responses when attached to a peptide antigen derived from OVA. To discover this targeting ligand, we leveraged mass spectrometry to decode a randomized D-peptide library selected in mice, extending the application of synthetic libraries to in vivo affinity selections.

bioengineering

Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we couple supervised and unsupervised deep learning with high-throughput experimentation to drive the design of high-activity, novel sequences reaching 10 kDa that deliver antisense oligonucleotides to the nucleus of cells. The models, in which natural and unnatural residues are represented as topological fingerprints, decipher and visualize sequence-activity predictions. The new variants boost antisense activity by 50-fold, are effective in animals, are nontoxic, and can also deliver proteins into the cytosol. Machine learning can discover functional polymers that enhance cellular uptake of biotherapeutics, with significant implications toward developing therapies for currently untreatable diseases. One sentence summaryDeep learning generates de novo large functional abiotic polymers that deliver antisense oligonucleotides to the nucleus.

bioinformatics

The first-in-class peptide binder to the SARS-CoV-2 spike protein

Coronavirus disease 19 (COVID-19) is an emerging global health crisis. With over 7 million confirmed cases to date, this pandemic continues to expand, spurring research to discover vaccines and therapies. SARS-CoV-2 is the novel coronavirus responsible for this disease. It initiates entry into human cells by binding to angiotensin-converting enzyme 2 (ACE2) via the receptor binding domain (RBD) of its spike protein (S). Disrupting the SARS-CoV-2-RBD binding to ACE2 with designer drugs has the potential to inhibit the virus from entering human cells, presenting a new modality for therapeutic intervention. Peptide-based binders are an attractive solution to inhibit the RBD-ACE2 interaction by adequately covering the extended protein contact interface. Using molecular dynamics simulations based on the recently solved cryo-EM structure of ACE2 in complex with SARS-CoV-2-RBD, we observed that the ACE2 peptidase domain (PD) 1 helix is important for binding SARS-CoV-2-RBD. Using automated fast-flow peptide synthesis, we chemically synthesized a 23-mer peptide fragment of the ACE2 PD 1 helix (SBP1) composed entirely of proteinogenic amino acids. Chemical synthesis of SBP1 was complete in 1.5 hours, and after work up and isolation >20 milligrams of pure material was obtained. Bio-layer interferometry (BLI) revealed that SBP1 associates with micromolar affinity to insect-derived SARS-CoV-2-RBD protein obtained from Sino Biological. Association of SBP1 was not observed to an appreciable extent to HEK cell-expressed SARS-CoV-2-RBD proteins and insect-derived variants acquired from other vendors. Moreover, competitive BLI assays showed SBP1 does not outcompete ACE2 binding to Sino Biological insect-derived SARS-CoV-2-RBD. Further investigations are ongoing to gain insight into the molecular and structural determinants of the variable binding behavior to different SARS-CoV-2-RBD protein variants.

bioengineering