论文标题
Terahertz信号刺激的蛋白质相互作用的选择性
Selectivity of Protein Interactions Stimulated by Terahertz Signals
论文作者
论文摘要
已经确定Terahertz(THZ)带信号可以通过谐振模式与生物分子相互作用。具体而言,此处感兴趣的是蛋白质激活。我们的研究目标是显示使用THZ信号在蛋白质分子内部指导机械信号如何控制其结构的变化并激活相关的生化和生物力学事件。为了确定这一点,我们制定了一个选择性度量,该指标量化了系统性能,并捕获了纳米反滕纳(Nanoantenna)诱导所需蛋白质分子/种群构象变化的能力。该度量提供了-1和1之间的分数,该分数表明我们对系统具有实现靶向蛋白质相互作用的控制程度。为了制定选择性度量,我们首先使用由外力驱动的langevin随机方程来对蛋白质行为进行建模。然后,我们通过计算驱动蛋白质的稳态能量,然后将模型推广以说明蛋白质群体来确定蛋白质折叠的概率。我们的数值分析结果表明,仅当目标人群由于撞击THZ信号而出现折叠行为时,才能达到最大选择性得分。从实现的选择性值中,我们得出结论,系统响应不仅取决于共振频率,而且还取决于控制参数的系统,即纳米反滕纳力,阻尼常数和每个蛋白质种群的丰度。提出的工作阐明了与基于电磁网络的控制相关的潜力,这可能导致大量在医疗领域的应用,从生物感应到靶向治疗。
It has been established that Terahertz (THz) band signals can interact with biomolecules through resonant modes. Specifically, of interest here, protein activation. Our research goal is to show how directing the mechanical signaling inside protein molecules using THz signals can control changes in their structure and activate associated biochemical and biomechanical events. To establish that, we formulate a selectivity metric that quantifies the system performance and captures the capability of the nanoantenna to induce a conformational change in the desired protein molecule/population. The metric provides a score between -1 and 1 that indicates the degree of control we have over the system to achieve targeted protein interactions. To develop the selectivity measure, we first use the Langevin stochastic equation driven by an external force to model the protein behavior. We then determine the probability of protein folding by computing the steady-state energy of the driven protein and then generalize our model to account for protein populations. Our numerical analysis results indicate that a maximum selectivity score is attained when only the targeted population experiences a folding behavior due to the impinging THz signal. From the achieved selectivity values, we conclude that the system response not only depends on the resonant frequency but also on the system controlling parameters namely, the nanoantenna force, the damping constant, and the abundance of each protein population. The presented work sheds light on the potential associated with the electromagnetic-based control of protein networks, which could lead to a plethora of applications in the medical field ranging from bio-sensing to targeted therapy.