Designing novel protein sequences for a desired 3D topological fold is a
fundamental yet non-trivial task in protein engineering. Challenges exist due
to the complex sequence--fold relationship, as well as the difficulties to
capture the diversity of the sequences (therefore structures and functions)
within a fold. To overcome these challenges, we propose Fold2Seq, a novel
transformer-based generative framework for designing protein sequences
conditioned on a specific target fold. To model the complex sequence--structure
relationship, Fold2Seq jointly learns a sequence embedding using a transformer
and a fold embedding from the density of secondary structural elements in 3D
voxels. On test sets with single, high-resolution and complete structure inputs
for individual folds, our experiments demonstrate improved or comparable
performance of Fold2Seq in terms of speed, coverage, and reliability for
sequence design, when compared to existing state-of-the-art methods that
include data-driven deep generative models and physics-based RosettaDesign. The
unique advantages of fold-based Fold2Seq, in comparison to a structure-based
deep model and RosettaDesign, become more evident on three additional
real-world challenges originating from low-quality, incomplete, or ambiguous
input structures. Source code and data are available at
this https URL

本研究提出了一种名为 Fold2Seq 的基于转换器的生成框架，以设计新的蛋白质序列并获得所需的三维拓扑折叠形态，结果表明 Fold2Seq 相比现有的最先进方法在速度、覆盖率及可靠性方面具有改进或类似的表现，进一步的应用表明 Fold2Seq 具有优于人工设计和物理学方法的独特优势。

Fold2Seq: 一种基于序列（1D）- 折叠（3D）嵌入的蛋白设计生成模型

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model  for Protein Design

We present a new method for design problems wherein the goal is to maximize
or specify the value of one or more properties of interest. For example, in
protein design, one may wish to find the protein sequence that maximizes
fluorescence. We assume access to one or more, potentially black box,
stochastic "oracle" predictive functions, each of which maps from input (e.g.,
protein sequences) design space to a distribution over a property of interest
(e.g. protein fluorescence). At first glance, this problem can be framed as one
of optimizing the oracle(s) with respect to the input. However, many
state-of-the-art predictive models, such as neural networks, are known to
suffer from pathologies, especially for data far from the training
distribution. Thus we need to modulate the optimization of the oracle inputs
with prior knowledge about what makes `realistic' inputs (e.g., proteins that
stably fold). Herein, we propose a new method to solve this problem,
Conditioning by Adaptive Sampling, which yields state-of-the-art results on a
protein fluorescence problem, as compared to other recently published
approaches. Formally, our method achieves its success by using model-based
adaptive sampling to estimate the conditional distribution of the input
sequences given the desired properties.

提出一种基于自适应采样的设计问题方法，解决优化预测模型输入时的路径问题和先验知识问题，用于蛋白质设计领域并获得了最先进的结果。

自适应取样的条件用于鲁棒设计

Conditioning by adaptive sampling for robust design

The design of novel proteins has many applications but remains an attritional
process with success in isolated cases. Meanwhile, deep learning technologies
have exploded in popularity in recent years and are increasingly applicable to
biology due to the rise in available data. We attempt to link protein design
and deep learning by using variational autoencoders to generate protein
sequences conditioned on desired properties. Potential copper and calcium
binding sites are added to non-metal binding proteins without human
intervention and compared to a hidden Markov model. In another use case, a
grammar of protein structures is developed and used to produce sequences for a
novel protein topology. One candidate structure is found to be stable by
molecular dynamics simulation. The ability of our model to confine the vast
search space of protein sequences and to scale easily has the potential to
assist in a variety of protein design tasks.

利用变分自编码器生成所需属性的蛋白质序列，不需要人为干预，可添加潜在的铜和钙结合位点并与隐马尔可夫模型进行比较，同时开发了蛋白质结构语法用来产生一种新型蛋白质拓扑结构。该模型能够限制蛋白质序列的搜索空间，易于扩展，有助于各种蛋白质设计任务。