  How can we design protein sequences folding into the desired structures
effectively and efficiently? AI methods for structure-based protein design have
attracted increasing attention in recent years; however, few methods can
simultaneously improve the accuracy and efficiency due to the lack of
expressive features and autoregressive sequence decoder. To address these
issues, we propose PiFold, which contains a novel residue featurizer and PiGNN
layers to generate protein sequences in a one-shot way with improved recovery.
Experiments show that PiFold could achieve 51.66\% recovery on CATH 4.2, while
the inference speed is 70 times faster than the autoregressive competitors. In
addition, PiFold achieves 58.72\% and 60.42\% recovery scores on TS50 and
TS500, respectively. We conduct comprehensive ablation studies to reveal the
role of different types of protein features and model designs, inspiring
further simplification and improvement. The PyTorch code is available at
\href{https://github.com/A4Bio/PiFold}{GitHub}.
