PIFOLD: TOWARD EFFECTIVE AND EFFICIENT PROTEIN INVERSE FOLDING

Abstract

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 Pi-Fold 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 GitHub.

1. INTRODUCTION

Proteins are linear chains of amino acids that fold into 3D structures to control cellular processes, such as transcription, translation, signaling, and cell cycle control. Creating novel proteins for human purposes could deepen our understanding of living systems and facilitate the fight against the † Equal Contribution, * Corresponding Author.



Figure1: Performance comparison with other graph-based protein design methods. The recovery scores, the inference time costs, and the perplexities are shown in the Y-axis direction, the X-axis direction, and the circle size, respectively. Note that the recovery and perplexity results in the CATH dataset are reported without using any other training data. The inference time is averaged over 100 long protein sequences of average length 1632 on an NVIDIA V100.

