PWM2Vec：一种从冠状病毒刺突序列中识别病毒宿主的有效嵌入方法
PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences
摘要：
COVID-19大流行仍是未知数，是一个重要的未决问题。有人猜测蝙蝠可能是起源。同样，还有许多密切相关的（冠状）病毒，如SARS，发现通过果子狸传播。研究可能成为人类致命病毒的潜在携带者和传播者的不同宿主对于理解、减轻和预防当前和未来的流行病至关重要。在冠状病毒中，表面（S）蛋白或刺突蛋白是决定宿主特异性的重要部分，因为它是病毒和宿主细胞膜之间的接触点。在这篇论文中，我们根据冠状病毒的刺突蛋白质序列对5000多个冠状病毒的宿主进行了分类，将它们分成了鸟类、蝙蝠、骆驼、猪、人类和黄鼠狼等不同宿主的集群。我们提出了一种基于众所周知的位置权重矩阵（PWM），我们称之为PWM 2 Vec的特征嵌入，并用于从这些冠状病毒的刺突蛋白质序列生成特征向量。虽然我们的嵌入是受到PWM在生物学应用中的成功的启发，例如确定蛋白质功能，或识别转录因子结合位点，但我们是第一个（据我们所知）在病毒序列的宿主分类背景下使用PWM来生成固定长度的特征向量表示。真实世界数据的结果表明，在使用PWM 2 Vec，我们能够执行与基线模型相比，良好的性能。我们还使用信息增益来测量不同氨基酸的重要性，以显示对于预测给定冠状病毒的宿主重要的氨基酸。
英文摘要：
COVID-19 pandemic, is still unknown and is an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona-) viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is an important part of determining host specificity since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among avians, bats, camels, swines, humans and weasels, to name a few. We propose a feature embedding based on the well-known position-weight matrix (PWM), which we call PWM2Vec, and use to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications such as determining protein function, or identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs in the context of host classification from viral sequences to generate a fixed-length feature vector representation. The results on the real world data show that in using PWM2Vec, we are able to perform comparably well as compared to baseline models. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
