  The linear sequence of amino acids determines protein structure and function.
Protein design, known as the inverse of protein structure prediction, aims to
obtain a novel protein sequence that will fold into the defined structure.
Recent works on computational protein design have studied designing sequences
for the desired backbone structure with local positional information and
achieved competitive performance. However, similar local environments in
different backbone structures may result in different amino acids, indicating
that protein structure's global context matters. Thus, we propose the
Global-Context Aware generative de novo protein design method (GCA), consisting
of local and global modules. While local modules focus on relationships between
neighbor amino acids, global modules explicitly capture non-local contexts.
Experimental results demonstrate that the proposed GCA method outperforms
state-of-the-arts on de novo protein design. Our code and pretrained model will
be released.
