DMREF: Collaborative Research: High throughput Exploration of Sequence Space of Peptide Polymers that Exhibit Aqueous Demixing Phase Behavior

非技术描述：蛋白质是令人难以置信的适应性分子。例如，当暴露于刺激，如温度、pH值或光的环境变化时，一些蛋白质会发生构象（形状）变化，从而导致有用的材料特性，如相变，从固体变为液体，或从液体变为固体。理解和预测这些变化是如何在分子水平上发生的，可能会导致创造出一整类能够对环境线索做出反应的软材料。这项研究将开发和应用计算机算法来快速浏览大量蛋白质序列数据，以预测尚未发现的温度敏感肽序列。然后，这些序列可以通过计算机建模来预测由相变引起的构象变化。最后，将进行实验来预测和确定通过结合刺激响应肽序列可以创建的2D和3D材料结构。如果成功，这些方法可以为高效设计和制造具有定制设计特性的各种材料创建一个工具包。技术描述：刺激反应是自然界中蛋白质的一个显著特征，即对化学刺激（如配体结合、磷酸化和甲基化）和物理刺激（如温度、pH值、光和盐浓度的变化）的反应导致剧烈的构象或相变。与蛋白质不同，蛋白质通过丰富的氨基酸序列空间来编码对众多刺激的不同反应，当前的生物启发设计重复多肽集中在巨大的可想象的序列空间的一小部分。因此，拟议研究的主要目标是通过结合实验，快速准确的基于物理的计算机模拟和数据科学，开发通用的材料设计规则，通过系统地探索序列空间，加速发现和开发潜在的大量热响应多肽材料。这项研究将“首次”对低临界溶液温度（LCST）和高临界溶液温度（UCST）相行为的决定因素提供一个完整的原子性理解，使LCST和UCST肽聚合物的分子设计成为可能，并确定如何将它们组合在一起以创建层次有序的纳米结构多肽材料的规则，这些材料表现出独特的形态，可以作为刺激反应的函数进行调整。这些材料可以用作纳米结构的支架和模板，并使各种生物催化、生物电子或分析设备成为可能。PIs还计划发布PIMMS建模包，这是一套用于执行基于晶格的聚合物模拟的工具。PIMMS将为机器学习算法提供支持，使设计响应性蛋白质聚合物成为可能。PIMMS代码库将作为开源发布。通过确保感兴趣的研究人员能够在代码库中贡献模块或实现特定于应用程序的算法，将围绕该语言建立一个用户社区。预计这将使该项目得到更广泛的发展。高级网络基础设施办公室的软件集群对这方面特别感兴趣，该办公室为该奖项提供了共同资金。

Non-technical Description: Proteins are incredibly adaptive molecules. For example, when exposed to a stimulus, such as an environmental change in temperature, pH or light, some proteins undergo conformational (shape) changes that can lead to useful material properties, such as a phase transition, turning from a solid to liquid, or a liquid to solid. Understanding and predicting how these changes occur on the molecular level could lead to the creation of an entire class of soft materials that can respond to environmental cues. This research will develop and apply computer algorithms to quickly go through large amounts of protein sequence data to predict as yet undiscovered temperature-sensitive peptide sequences. These sequences can then be subjected to computer-based modeling to predict conformational changes that ensue from a phase transition. Finally, experiments will be conducted to predict and determine the 2D and 3D materials architectures that can be created by combining stimulus-responsive peptide sequences. If successful, these methods could create a toolkit for the efficient design and fabrication of a large variety of materials with custom-designed properties.Technical Description: Stimulus responsiveness is a striking feature of proteins in Nature, whereby responses to chemical stimuli such as ligand binding, phosphorylation, and methylation, and physical stimuli such as changes in temperature, pH, light, and salt concentration lead to sharp conformational or phase transitions. Unlike proteins, which encode diverse responses to numerous stimuli by richly sampling amino acid sequence space, current bioinspired designs of repetitive polypeptides have focused on a tiny fraction of the vast conceivable expanse of sequence space. The primary goal of the proposed research is thus to develop generalized materials design rules, by combining experiments, fast and accurate physics-based computer simulations, and data science, to accelerate the discovery and development of a potentially huge class of thermally-responsive polypeptide materials by a systematic exploration of sequence space. This research will "for the first time" provide a complete atomistic understanding of the determinants of the lower critical solution temperature (LCST) and upper critical solution temperature (UCST) phase behavior, enable de novo molecular design of LCST and UCST peptide polymers and identify rules on how to combine them to create hierarchically-ordered, nanostructured polypeptide materials that exhibit unique morphologies that can be tuned as a function of their stimulus responsiveness. These materials could serve as nanostructured scaffolds and templates and enable a broad range of biocatalytic, bioelectronic, or assay devices. The PIs also plan to release the PIMMS modeling package, a set of tools for performing lattice-based simulations of polymers. PIMMS will provide support for the machine learning algorithms that enable the design of responsive protein-based polymers. The PIMMS codebase will be released as open source. A user community will be coalesced around the language by ensuring that interested researchers are able to contribute modules to or implement application-specific algorithms within the codebase. This is expected to allow a wider growth of the project. This aspect is of special interest to the software cluster in the Office of Advanced Cyberinfrastructure, which has provided co-funding for this award.