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Lopez, C. A.

Publications and source records attributed to Lopez, C. A..

2 recordsLinked to original sources

Oligomeric state of the ZIKV E protein defines protective immune responses.

The current leading Zika vaccine candidates in clinical testing are based on live or killed virus platforms, which have safety issues, especially in pregnant women. Zika subunit vaccines, however, have shown poor performance in preclinical studies. We hypothesized that Zika Envelope (E) protein subunit vaccines have performed poorly because the antigens tested have been recombinant E monomers that do not display critical quaternary structure epitopes present on Zika E protein homodimers that cover the surface of the virus. To test this hypothesis, we engineered and produced stable recombinant E protein homodimers. Unlike the E monomer, the dimer was recognized by strongly neutralizing monoclonal antibodies isolated from Zika-immune individuals. In a mouse model of vaccination, the dimeric antigen stimulated strongly neutralizing antibodies that targeted epitopes that were similar to epitopes recognized by human antibodies following natural Zika virus infection. In contrast, the monomer antigen stimulated lower levels of neutralizing antibodies directed to simple epitopes on domain III of E protein. In a mouse model of ZIKV challenge, only E dimer antigen stimulated protective antibodies, not the monomer. These results highlight the importance of mimicking the highly structured flavivirus surface when designing subunit vaccines. The flavivirus field has a long history of using E monomers as vaccine antigens with limited success. These results are applicable to developing second generation subunit vaccines against Zika as well as other medically important flaviviruses such as dengue and yellow fever viruses.

microbiology

Unsupervised Machine Learning for Analysis of Coexisting Lipid Phases and Domain Growth in Biological Membranes

Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that are capable to decompose and categorize the evolution of thousands if not millions of lipids involved in the phenomenon. The interpretation and analysis of Molecular Dynamics (MD) simulations representing temporal and spatial changes in such systems is still a challenging task. Here, we present a new unsupervised machine learning approach based on Nonnegative Matrix Factorization, called NMFk, that successfully extracts physically meaningful features from neighborhood profiles derived from coarse-grained MD simulations of ternary lipid mixture. Our results demonstrate that leveraging NMFk can (a) determine the role of different lipid molecules in phase separation, (b) characterize the formation of nano-domains of lipids, (c) determine the timescales of interest and (d) extract physically meaningful features that uniquely describe the phase separation with broad implications.

bioinformatics