Expanding the watch list for potential Ebola virus antibody escape mutations

Abstract

The 2014 outbreak of Ebola virus disease (EVD) in Western Africa is the largest recorded filovirus disease outbreak and led to the death of over 11,000 people. The recent EVD outbreaks (since May 2018) in the Democratic Republic of the Congo has already claimed the lives of over 250 people. Tackling Ebola virus (EBOV) outbreaks remains a challenge. Over the years, significant efforts have been put into developing vaccines or antibody therapies which rely on an envelope glycoprotein (GP) of Zaire ebolavirus (strain Mayinga-76). Therefore, one key approach for combating EVD epidemics is to predict mutations that may diminish the effectiveness of the treatment. In a previous study we generated a watch list of potential antibody escape mutations of EBOV GP against the monoclonal antibody KZ52. Molecular modeling methods were applied to the three-dimensional experimental structure of EBOV GP bound to KZ52 to predict the effect of every possible single mutation in EBOV GP. The final watch list contained 34 mutations that were predicted to destabilize binding of KZ52 to EBOV GP but did not affect EBOV GP folding and its ability to form trimers. In this study, we expand our watch list by including three more monoclonal antibodies with distinct epitopes on GP, namely Antibody 100 (Ab100), Antibody 114 (Ab114) and 13F6-1-2. Our updated watch list contains 127 mutations, three of which have been seen in humans or are experimentally associated with reduced efficacy of antibody treatment. We believe mutations on this watch list require attention since they provide information about circumstances in which interventions could lose the effectiveness.

High-resolution profiling of pathways of escape for SARS-CoV-2 spike-binding antibodies

“…Mapping COVID-19 patient serum epitopes by alanine scanning has helped identify sites of antibody binding by removing important side-chain interactions (Shrock et al, 2020), but studies with other viruses have shown that escape can be mediated by mutations at sites not directly in contact with the antibody via introduction of nearby charged or bulky aa (Dingens et al, 2019;Doud et al, 2017;Patel et al, 2019). This concept was evident in this study, for example within the FP epitope for patient 8, where addition of negatively charged aa led to escape at site S816, but addition of an alanine did not.…”

High resolution profiling of pathways of escape for SARS-CoV-2 spike-binding antibodies

“…Our results also demonstrate the power of interrogating the role of every possible amino acid at every site on the S protein. Mapping COVID-19 patient serum epitopes by alanine scanning has helped identify sites of antibody binding by removing important side chains interactions (Shrock et al, 2020), but studies with other viruses have shown that escape can be mediated by mutations at sites not directly in contact with the antibody via introduction of nearby charged or bulky amino acids (Dingens et al, 2019; Doud et al, 2017; Patel et al, 2019). This concept was evident in this study, for example within the FP epitope for patient 8, where addition of negatively charged amino acids led to escape at site S816 but addition of an alanine did not.…”

Predicting the Viability of Beta-Lactamase: How Folding and Binding Free Energies Correlate with Beta-Lactamase Fitness

“…In the following, we term those simulations in which molecular dynamics relaxation was performed before FoldX free energies were computed MD+FoldX simulations. Past studies performed by our team have shown that relaxing the wild type structure before introducing mutations can significantly improve the predictive capacity of FoldX on proteins, such as TEM-1, on which FoldX was not explicitly trained [57,58]. In our MD+FoldX simulations, the final clean structure file was used to carry out atomistic molecular dynamics simulations using the protocol reported in our previous studies [57,58].…”

“…Past studies performed by our team have shown that relaxing the wild type structure before introducing mutations can significantly improve the predictive capacity of FoldX on proteins, such as TEM-1, on which FoldX was not explicitly trained [57,58]. In our MD+FoldX simulations, the final clean structure file was used to carry out atomistic molecular dynamics simulations using the protocol reported in our previous studies [57,58]. Briefly, the GROMACS 2018.4 software package was used to perform the MD simulations with the AMBER99SB*-ILDN force field [59].…”