Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction

TL;DR

This paper introduces EvoGen, a meta generative model that enhances AlphaFold2's ability to predict protein structures accurately with limited sequence data, enabling better performance on orphan sequences and exploring conformational diversity.

Contribution

EvoGen improves AlphaFold2's performance on low-data protein sequences by using virtual homologue sequences, enabling few-shot learning and probabilistic structure generation.

Findings

EvoGen significantly improves folding accuracy for low-homology targets.

The combined method enables exploration of alternative protein conformations.

EvoGen enhances AlphaFold2's applicability to orphan sequences.

Abstract

Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining accurate folding landscape using co-evolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit co-evolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologs. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences, but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method which could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design.