Computational design of multi-state proteins using molecular simulations and machine learning

使用分子模拟和机器学习进行多态蛋白质的计算设计

• 批准号: 2664327
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2664327
• 关键词: Computational; design; multi; state; proteins

The ability to computationally design de novo proteins is a key technology that underpins future progress in synthetic biology and biomaterials discovery. However current de novo design methods tend to produce rigid, hyperstable folds that lack functionality. To progress towards truly functional de novo protein design it is crucial to design proteins that adopt multiple conformational states. This would enable the design of protein as catalysts, or biosensors or bioswitches that respond to environmental changes such as a variation in pH or temperature.1Such an endeavour is challenging for the energy functions in current protein-design software because it requires designing folds that adopt structurally distinct yet energetically similar states. Rigorous molecular dynamics simulation methods that describe protein dynamics and solvation effects at the atomistic level have shown potential for such 'dynamic design' problems.2 However current molecular dynamics methods are too slow and complex for routine application to protein design.This PhD project will validate an interdisciplinary approach that leverages new classes of machine learning algorithms to substantially accelerate the speed of molecular dynamics simulations for de novo protein design problems. Throughout the project we will tackle a range of problems of fundamental importance in protein design, ranging from optimising secondary structure preferences of cyclic peptides,3 to tuning the preferred oligomerisation states of de novo-helical bundles.4 Such systems are well suited to a combined data-driven/physics-driven molecular modelling approach owing to a large amount of existing experimental data, and validated energy functions for atomistic modelling. The accurate modelling of such multi-state systems will open new routes for the design of versatile building blocks for synthetic biology applications, and for the discovery of new biomaterials. Experimental validation of the design method will be performed by designing switchable -helical bundles. Designs will be produced in the lab using either molecular biology or solid phase peptide synthesis and will be characterised in vitro using a range of biophysical techniques.

从头计算设计蛋白质的能力是支撑合成生物学和生物材料发现未来进展的关键技术。然而，当前的从头设计方法倾向于产生缺乏功能性的刚性、超稳定的折叠。为了实现真正功能性的从头蛋白质设计，设计具有多种构象状态的蛋白质至关重要。这将使蛋白质的设计作为催化剂，或生物传感器或生物开关，响应环境变化，如pH值或温度的变化。1这样的努力是具有挑战性的能量功能，在目前的蛋白质设计软件，因为它需要设计折叠，采用结构不同，但能量相似的状态。在原子水平上描述蛋白质动力学和溶剂化效应的严格分子动力学模拟方法已经显示出这种“动态设计”问题的潜力。2然而，目前的分子动力学方法对于蛋白质设计的常规应用来说太慢和复杂。这个博士项目将验证一种跨学科的方法，该方法利用新的机器学习算法来大大加快分子动力学的速度模拟从头蛋白质设计问题。在整个项目中，我们将解决蛋白质设计中一系列具有根本重要性的问题，从优化环肽的二级结构偏好，3到调整新螺旋结构的优选寡聚化状态。4由于大量现有的实验数据，这些系统非常适合数据驱动/物理驱动相结合的分子建模方法。和原子模型的有效能量函数。这种多状态系统的精确建模将为合成生物学应用的多功能构建模块的设计和新生物材料的发现开辟新的途径。设计方法的实验验证将通过设计可切换螺旋束来进行。设计将在实验室中使用分子生物学或固相肽合成，并将在体外使用一系列生物物理技术进行表征。