论文标题
快速区分DNA和蛋白质序列优化分子设计
Fast differentiable DNA and protein sequence optimization for molecular design
论文作者
论文摘要
设计具有提高功能的DNA和蛋白质序列具有极大加速合成生物学的潜力。准确预测序列生物适应性的机器学习模型已成为分子设计的强大工具。激活最大化为可区分的模型提供了简单的设计策略：首先通过连续表示近似地编码序列，然后通过梯度上升对预测器甲骨文进行迭代优化。虽然优雅，但这种方法遭受了消失的梯度，可能会导致预测病理，从而导致收敛性不佳。在这里，我们建立在先前提出的直通近似方法上，以通过离散序列样本进行优化。通过使跨位置的核苷酸逻辑标准化并引入自适应熵变量，我们删除了由过大或偏斜的采样参数产生的瓶颈。与以前的激活最大化版本相比，我们称之为快速的SEQPROP的算法最大收敛速度高达100倍，并为许多应用程序找到了改进的适应性最佳功能。我们通过设计DNA和蛋白质序列的六个深度学习预测因子（包括蛋白质结构预测因子）来证明快速SEQPROP。
Designing DNA and protein sequences with improved function has the potential to greatly accelerate synthetic biology. Machine learning models that accurately predict biological fitness from sequence are becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, this method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. Here, we build on a previously proposed straight-through approximation method to optimize through discrete sequence samples. By normalizing nucleotide logits across positions and introducing an adaptive entropy variable, we remove bottlenecks arising from overly large or skewed sampling parameters. The resulting algorithm, which we call Fast SeqProp, achieves up to 100-fold faster convergence compared to previous versions of activation maximization and finds improved fitness optima for many applications. We demonstrate Fast SeqProp by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor.
