Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning

Rishikesh Magar; Prakarsh Yadav; Amir Barati Farimani

The out of class results demonstrate that our model is capable of generalizing the prediction to a completely novel virus epitope. Since COVID-19 is completely a new virus, we can conclude that our model prediction performance will be accurate. The fact that our model prediction is 100% for SARS out of class test demonstrate its capability of effectively predicting the antibodies for COVID-19 which is from SARS family. In order to be more comprehensive, we created co-mutations out of 5 stable point mutations (C3, C7, C14, C17, C18, see Table S1 in Supporting Information for the list of all 18 candidates). This resulted . CC-BY-NC-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.14.992156 doi: bioRxiv preprint in 5 new structures (Co1, Co2, Co3, Co4, Co5 in Table S2 ) that were screened using XGBoost for neutralization. Among all 5 co-mutations, Co5 did not neutralize. To check the stability of these 4 neutralizing co-mutations, MD simulations were performed and Co1, Co2 and Co4 were found to be stable (Figure 4b) . The list of the final 8 stable mutations and co-mutations are tabulated in Table 1 and the PDB structures are available as Supporting Information.