Biology subjects

Fili, M.

Publications and source records attributed to Fili, M..

3 recordsLinked to original sources

bioRxiv2022

A New Classification Method Based on Dynamic Ensemble Selection and its Application to Predict Variance Patterns in HIV-1 Env

Therapeutics that target the envelope glycoproteins (Envs) of human immunodeficiency virus type 1 (HIV-1) effectively reduce virus levels in patients. However, due to mutations, new Env variants are frequently generated, which may be resistant to the treatments. The appearance of such sequence variance at any Env position is seemingly random. A better understanding of the spatiotemporal patterns of variance across Env may lead to the development of new therapeutic strategies. We hypothesized that, at any time point in a patient, positions with sequence variance are clustered on the three-dimensional structure of Env. To test this hypothesis, we examined whether variance at any Env position can be predicted by the variance measured at adjacent positions. Sequences from 300 HIV-infected patients were applied to a new algorithm we developed. The k-best classifiers (KBC) method is a dynamic ensemble selection technique that identifies the best classifier(s) within the neighborhood of a new observation. It applies bootstrap resampling to generate out-of-bag samples that are used with the resampled set to evaluate each classifier. For many positions of Env, primarily in the CD4-binding site, KBC accurately predicted variance based on the variance at their adjacent positions. KBC improved performance compared to the initial learners, static ensemble, and other baseline models. KBC also outperformed other algorithms for predicting variance at multi-position footprints of therapeutics on Env. These understandings can be applied to refine models that predict future changes in HIV-1 Env. More generally, we propose KBC as a new high-performance dynamic ensemble selection technique.

bioinformatics↗

bioRxiv2022

Patterns of Volatility Across the Spike Protein Accurately Predict the Emergence of Mutations within SARS-CoV-2 Lineages

Mutations in the spike glycoprotein of SARS-CoV-2 allow the virus to probe the sequence space in search of higher-fitness states. New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with such mutations. Interestingly, the sites of mutation in these sublineages vary between the VOCs. Whether such differences reflect the random nature of mutation appearance or distinct evolutionary spaces of spike in the VOCs is unclear. Here we show that each position of spike has a lineage-specific likelihood for mutations to appear and dominate descendent sublineages. This likelihood can be accurately estimated from the lineage-specific mutational profile of spike at a protein-wide level. The mutability environment of each position, including adjacent sites on the protein structure and neighboring sites on the network of comutability, accurately forecast changes in descendent sublineages. Mapping of imminent changes within the VOCs can contribute to the design of immunogens and therapeutics that address future forms of SARS-CoV-2.

bioinformatics↗

bioRxiv2021

Limited variation between SARS-CoV-2-infected individuals in domain specificity and relative potency of the antibody response against the spike glycoprotein

The spike protein of SARS-CoV-2 is arranged as a trimer on the virus surface, composed of three S1 and three S2 subunits. Infected and vaccinated individuals generate antibodies against spike, which can neutralize the virus. Most antibodies target the receptor-binding domain (RBD) and N-terminal domain (NTD) of S1; however, antibodies against other regions of spike have also been isolated. The variation between infected individuals in domain specificity of the antibodies and in their relative neutralization efficacy is still poorly characterized. To this end, we tested serum and plasma samples from 85 COVID-19 convalescent subjects using 7 immunoassays that employ different domains, subunits and oligomeric forms of spike to capture the antibodies. Samples were also tested for their neutralization of pseudovirus containing SARS-CoV-2 spike and of replication-competent SARS-CoV-2. We observed strong correlations between the levels of NTD- and RBD-specific antibodies, with a fixed ratio of each type to all anti-spike antibodies. The relative potency of the response (defined as the measured neutralization efficacy relative to the total level of spike-targeting antibodies) also exhibited limited variation between subjects, and was not associated with the overall amount of anti-spike antibodies produced. Accordingly, the ability of immunoassays that use RBD, NTD and different forms of S1 or S1/S2 as capture antigens to estimate the neutralizing efficacy of convalescent samples was largely similar. These studies suggest that host-to-host variation in the polyclonal response elicited against SARS-CoV-2 spike is primarily limited to the quantity of antibodies generated rather than their domain specificity or relative neutralization potency. IMPORTANCEInfection by SARS-CoV-2 elicits antibodies against various domains of the spike protein, including the RBD, NTD and S2. Different infected individuals generate vastly different amounts of anti-spike antibodies. By contrast, as we show here, there is a remarkable similarity in the properties of the antibodies produced. Different individuals generate the same proportions of antibodies against each domain of the spike protein. Furthermore, the relationship between the amount of anti-spike antibodies produced and their neutralization efficacy of SARS-CoV-2 is highly conserved. Therefore, the observed variation in the neutralizing activity of the antibody response in COVID-19 convalescent subjects is caused by differences in the amounts of antibodies rather than their recognition properties or relative antiviral activity. These findings suggest that COVID-19 vaccine strategies that focus on enhancing the overall level of the antibodies will likely elicit a more uniformly efficacious protective response.

microbiology↗