  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an
ongoing pandemic infecting 219 million people as of 10/19/21, with a 3.6%
mortality rate. Natural selection can generate favorable mutations with
improved fitness advantages; however, the identified coronaviruses may be the
tip of the iceberg, and potentially more fatal variants of concern (VOCs) may
emerge over time. Understanding the patterns of emerging VOCs and forecasting
mutations that may lead to gain of function or immune escape is urgently
required. Here we developed PhyloTransformer, a Transformer-based
discriminative model that engages a multi-head self-attention mechanism to
model genetic mutations that may lead to viral reproductive advantage. In order
to identify complex dependencies between the elements of each input sequence,
PhyloTransformer utilizes advanced modeling techniques, including a novel Fast
Attention Via positive Orthogonal Random features approach (FAVOR+) from
Performer, and the Masked Language Model (MLM) from Bidirectional Encoder
Representations from Transformers (BERT). PhyloTransformer was trained with
1,765,297 genetic sequences retrieved from the Global Initiative for Sharing
All Influenza Data (GISAID) database. Firstly, we compared the prediction
accuracy of novel mutations and novel combinations using extensive baseline
models; we found that PhyloTransformer outperformed every baseline method with
statistical significance. Secondly, we examined predictions of mutations in
each nucleotide of the receptor binding motif (RBM), and we found our
predictions were precise and accurate. Thirdly, we predicted modifications of
N-glycosylation sites to identify mutations associated with altered
glycosylation that may be favored during viral evolution. We anticipate that
PhyloTransformer may guide proactive vaccine design for effective targeting of
future SARS-CoV-2 variants.
