标题： 用于药基因中变异效应预测的矩阵变分自编码器 显示英文标题

标题： A Matrix Variational Auto-Encoder for Variant Effect Prediction in Pharmacogenes

摘要： 变异效应预测器（VEPs）旨在评估蛋白质变异的功能影响，传统上依赖于多序列比对（MSAs）。这种方法假设自然发生的变异是适应的，这一假设受到药物基因组学的挑战，其中一些药物基因经历了较低的进化压力。深度突变扫描（DMS）数据集提供了另一种方法，通过为变异提供定量适应性分数。在这项工作中，我们提出了一种基于变压器的矩阵变分自编码器（matVAE），具有结构化先验，并在33个对应的DMS数据集上评估其性能，这些数据集对应于ProteinGym基准中的26种药物靶标和ADME蛋白。我们的模型在MSAs上训练（matVAE-MSA）在DMS数据集上的零样本预测中优于最先进的DeepSequence模型，尽管使用的参数数量少一个数量级，并且在推理时需要更少的计算。我们还将matVAE-MSA与matENC-DMS进行了比较，这是一个在DMS数据上训练的容量相似的模型，发现后者在监督预测任务中表现更好。此外，将AlphaFold生成的结构整合到我们的变压器模型中进一步提高了性能，结果可与在MSAs上训练并微调的DeepSequence相媲美。这些发现突显了DMS数据集替代MSAs的潜力，而不会显著降低预测性能，激励进一步开发DMS数据集并探索它们之间的关系以增强变异效应预测。 显示英文摘要

摘要： Variant effect predictors (VEPs) aim to assess the functional impact of protein variants, traditionally relying on multiple sequence alignments (MSAs). This approach assumes that naturally occurring variants are fit, an assumption challenged by pharmacogenomics, where some pharmacogenes experience low evolutionary pressure. Deep mutational scanning (DMS) datasets provide an alternative by offering quantitative fitness scores for variants. In this work, we propose a transformer-based matrix variational auto-encoder (matVAE) with a structured prior and evaluate its performance on 33 DMS datasets corresponding to 26 drug target and ADME proteins from the ProteinGym benchmark. Our model trained on MSAs (matVAE-MSA) outperforms the state-of-the-art DeepSequence model in zero-shot prediction on DMS datasets, despite using an order of magnitude fewer parameters and requiring less computation at inference time. We also compare matVAE-MSA to matENC-DMS, a model of similar capacity trained on DMS data, and find that the latter performs better on supervised prediction tasks. Additionally, incorporating AlphaFold-generated structures into our transformer model further improves performance, achieving results comparable to DeepSequence trained on MSAs and finetuned on DMS. These findings highlight the potential of DMS datasets to replace MSAs without significant loss in predictive performance, motivating further development of DMS datasets and exploration of their relationships to enhance variant effect prediction.