Biology subjects

Sesterhenn, F.

Publications and source records attributed to Sesterhenn, F..

3 recordsLinked to original sources

bioRxiv2018

Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen

Throughout the last decades, vaccination has been key to prevent and eradicate infectious diseases. However, many pathogens (e.g. respiratory syncytial virus (RSV), influenza, dengue and others) have resisted vaccine development efforts, largely due to the failure to induce potent antibody responses targeting conserved epitopes. Deep profiling of human B-cells often reveals potent neutralizing antibodies that emerge from natural infection, but these specificities are generally subdominant (i.e., are present in low titers). A major challenge for next-generation vaccines is to overcome established immunodominance hierarchies and focus antibody responses on crucial neutralization epitopes. Here, we show that a computationally designed epitope-focused immunogen presenting a single RSV neutralization epitope elicits superior epitope-specific responses compared to the viral fusion protein. In addition, the epitope-focused immunogen efficiently boosts antibodies targeting the Palivizumab epitope, resulting in enhanced neutralization. Overall, we show that epitope-focused immunogens can boost subdominant neutralizing antibody responses in vivo and reshape established antibody hierarchies.

immunology↗

bioRxiv2018

rstoolbox: management and analysis of computationally designed structural ensembles.

MotivationComputational protein design (CPD) calculations rely on the generation of large amounts of data on the search for the best sequences. As such, CPD workflows generally include the batch generation of designed decoys (sampling) followed by ranking and filtering stages to select those with optimal metrics (scoring). Due to these factors, the proper analysis of the decoy population is a key element for the effective selection of designs for experimental validation.\n\nResultsHere, we present a set of tools for the analysis of protein design ensembles. The tool is oriented towards protein designers with basic coding training aiming to process efficiently their decoy sets as well as for protocol developers interested in benchmarking their new approaches. Although initially devised to process Rosetta design outputs, the library is extendable to other design tools.\n\nAvailability and Implementationrstoolbox is implemented for python2.7 and 3.5+. Code is freely available at https://github.com/lpdi-epfl/rstoolbox under the MIT license. Full documentation and examples can be found at https://lpdi-epfl.github.io/rstoolbox.

bioinformatics↗

bioRxiv2018

Rosetta FunFolDes - a general framework for the computational design of functional proteins

The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function, nevertheless, it remains a challenge for state-of-the-art methodologies. Here, we present a computational design approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins. We performed extensive benchmarks, where the most unexpected finding was that the design of function into proteins may not necessarily reside in the global minimum of the energetic landscape, which could have important implications in the field. We have computationally designed and experimentally characterized a distant structural template and a de novo \"functionless\" fold, two prototypical design challenges, to present important viral epitopes. Overall, we present an accessible strategy to repurpose old protein folds for new functions, which may lead to important improvements on the computational design of functional proteins.

bioinformatics↗