Dixson Jamie D, Azad Rajeev K
Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX 76203, USA.
Department of Mathematics, University of North Texas, Denton, TX 76203, USA.
Evol Med Public Health. 2021 Oct 13;9(1):360-373. doi: 10.1093/emph/eoab032. eCollection 2021.
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in ~4.8 million deaths worldwide as of this writing. Almost all conceivable aspects of SARS-CoV-2 have been explored since the virus began spreading in the human population. Despite numerous proposals, it is still unclear how and when the virus gained the ability to efficiently bind to and infect human cells. In an effort to understand the evolution of receptor binding domain (RBD) of the spike protein of SARS-CoV-2, and specifically, how the ability of RBD to bind to angiotensin-converting enzyme 2 receptor (ACE2) of humans evolved in coronaviruses, we have applied an alignment-free technique to infer functional relatedness among betacoronaviruses. This technique, concurrently being optimized for identifying novel prions, was adapted to gain new insights into coronavirus evolution, specifically in the context of the ongoing COVID-19 pandemic. Novel methods for predicting the capacity for coronaviruses, in general, to infect human cells are urgently needed.
proposed method utilizes physicochemical properties of amino acids to develop fully dynamic waveform representations of proteins that encode both the amino acid content and the context of amino acids. These waveforms are then subjected to dynamic time warping (DTW) and distance evaluation to develop a distance metric that is relatively less sensitive to variation in sequence length and primary amino acid composition.
Using our proposed method, we show that in contrast to alignment-based maximum likelihood (ML) and neighbor-joining (NJ) phylogenetic analyses, all bat betacoronavirus spike protein RBDs known to bind to the ACE2 receptor are found within a single physicochemical cluster. Further, other RBDs within that cluster are from pangolin coronaviruses, two of which have already been shown to bind to ACE2 while the others are suspected, yet unverified ACE2 binding domains. This finding is important because both severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2 use the host ACE2 receptor for cell entry. Surveillance for coronaviruses belonging to this cluster could potentially guide efforts to stifle or curtail potential and/or early zoonotic outbreaks with their associated deaths and financial devastation.
Robust methods for predicting human ACE2 receptor binding by the spike protein of coronaviruses are needed for the early detection of zoonotic coronaviruses and biosurveillance to prevent future outbreaks. Here we present a new waveform-based approach that utilizes the physicochemical properties of amino acids to determine the propensity of betacoronaviruses to infect humans. Comparison with the established phylogenetic methods demonstrates the usefulness of this new approach in the biosurveillance of coronaviruses.
截至撰写本文时，由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起的2019冠状病毒病（COVID-19）已在全球导致约480万人死亡。自该病毒开始在人群中传播以来，SARS-CoV-2几乎所有可以想象到的方面都已得到探索。尽管有众多提议，但病毒如何以及何时获得有效结合并感染人类细胞的能力仍不清楚。为了了解SARS-CoV-2刺突蛋白受体结合域（RBD）的进化，特别是RBD与人类血管紧张素转换酶2受体（ACE2）结合的能力在冠状病毒中是如何进化的，我们应用了一种无序列比对技术来推断β冠状病毒之间的功能相关性。这种同时针对识别新型朊病毒进行优化的技术，被用于获得对冠状病毒进化的新见解，特别是在当前COVID-19大流行的背景下。迫切需要预测冠状病毒一般感染人类细胞能力的新方法。
所提出的方法利用氨基酸的物理化学性质来开发蛋白质的完全动态波形表示，该表示既编码氨基酸含量又编码氨基酸的上下文信息。然后对这些波形进行动态时间规整（DTW）和距离评估，以开发一种对序列长度和一级氨基酸组成变化相对不敏感的距离度量。
使用我们提出的方法，我们表明，与基于序列比对的最大似然（ML）和邻接法（NJ）系统发育分析不同，所有已知与ACE2受体结合的蝙蝠β冠状病毒刺突蛋白RBD都位于单个物理化学簇内。此外，该簇内的其他RBD来自穿山甲冠状病毒，其中两个已被证明可与ACE2结合，而其他的则是疑似但未经证实的ACE2结合域。这一发现很重要，因为严重急性呼吸综合征冠状病毒（SARS-CoV）和SARS-CoV-2都利用宿主ACE2受体进入细胞。对属于该簇的冠状病毒进行监测可能会指导努力遏制或减少潜在的和/或早期人畜共患病疫情及其相关的死亡和经济破坏。
需要强大的方法来预测冠状病毒刺突蛋白与人类ACE2受体的结合，以便早期检测人畜共患冠状病毒并进行生物监测以预防未来的疫情爆发。在这里，我们提出了一种基于波形的新方法，该方法利用氨基酸的物理化学性质来确定β冠状病毒感染人类的倾向。与既定的系统发育方法进行比较证明了这种新方法在冠状病毒生物监测中的有用性。
