Biology subjects

Pankow, A.

Publications and source records attributed to Pankow, A..

3 recordsLinked to original sources

bioRxiv2021

Multivariate mining of an alpaca immune repertoire identifies potent cross-neutralising SARS-CoV-2 nanobodies

Conventional approaches to isolate and characterize nanobodies are laborious and cumbersome. Here we combine phage display, multivariate enrichment, and novel sequence analysis techniques to annotate an entire nanobody repertoire from an immunized alpaca. We combine this approach with a streamlined screening strategy to identify numerous anti-SARS-CoV-2 nanobodies, and use neutralization assays and Hydrogen/Deuterium exchange coupled to mass spectrometry (HDX-MS) epitope mapping to characterize their potency and specificity. Epitope mapping revealed that the binding site is a key determinant of neutralization potency, rather than affinity alone. The most potent nanobodies bind to the receptor binding motif of the RBD, directly preventing interaction with the host cell receptor ACE2, and we identify two exceptionally potent members of this category (with monomeric IC50s around 13 and 16 ng/ml). Other nanobodies bind to a more conserved epitope on the side of the RBD, and are able to potently neutralize the SARS-CoV-2 founder virus (42 ng/ml), the beta variant (B.1.351/501Y.V2) (35 ng/ml), and also cross-neutralize the more distantly related SARS-CoV-1 (0.46 g/ml). The approach presented here is well suited for the screening of phage libraries to identify functional nanobodies for various biomedical and biochemical applications.

immunology↗

bioRxiv2021

A bispecific monomeric nanobody induces SARS-COV-2 spike trimer dimers

Antibodies binding to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike have therapeutic promise, but emerging variants show the potential for virus escape. This emphasizes the need for therapeutic molecules with distinct and novel neutralization mechanisms. Here we isolated a nanobody that interacts simultaneously with two RBDs from different spike trimers of SARS-CoV-2, rapidly inducing the formation of spike trimer-dimers leading to the loss of their ability to attach to the host cell receptor, ACE2. We show that this nanobody potently neutralizes SARS-CoV-2, including the B.1.351 variant, and cross-neutralizes SARS-CoV. Furthermore, we demonstrate the therapeutic potential of the nanobody against SARS-CoV-2 and the B.1.351 variant in a human ACE2 transgenic mouse model. This naturally elicited bispecific monomeric nanobody establishes a novel strategy for potent inactivation of viral antigens and represents a promising antiviral against emerging SARS-CoV-2 variants.

immunology↗

bioRxiv2020

Estimating the timing of HIV infection from unmutated sequences

For HIV, the time since infection can be estimated from sequence data for acutely infected samples. One popular approach relies on the star-like nature of phylogenies generated under exponential population growth, and the resulting Poisson distribution of mutations away from the founding variant. However, real-world complications, such as APOBEC hypermutation and multiple-founder transmission, present a challenge to this approach, requiring data curation to remove these signals before reasonable timing estimates may be obtained. Here we suggest a simple alternative approach that derives the timing estimate not from the entire mutational spectrum but from the proportion of sequences that have no mutations. This can be approximated quickly and is robust to phenomena such as multiple founder transmission and APOBEC hypermutation. Our approach is Bayesian, and we adopt a conjugate prior to obtain closed form posterior distributions at negligible computational expense. Using real data and simulations, we show that this approach provides accurate timing estimates and credible intervals without the inconvenience of data curation and is robust to complicating phenomena that can mislead existing approaches or cause them to fail entirely. For immediate use we provide an implementation via Google Sheets, which offers bulk analysis of multiple datasets, as well as more detailed individual-donor analyses. For inclusion in data processing pipelines we provide implementations in three languages: Julia, R, and Python.

bioinformatics↗