DMREF: Collaborative Research on 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，当暴露于刺激时，例如温度、pH或光的环境变化，一些蛋白质经历构象（形状）变化，这可以导致有用的材料性质，例如相变，从固体变成液体，或从液体变成固体。理解和预测这些变化如何在分子水平上发生，可能会导致创造出一整类可以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，快速通过大量的蛋白质序列数据来预测尚未发现的温度敏感肽序列。然后，这些序列可以进行基于计算机的建模，以预测相变引起的构象变化。最后，将进行实验来预测和确定可以通过组合刺激响应肽序列来创建的2D和3D材料架构。如果成功的话，这些方法可以创建一个工具包，用于高效设计和制造具有定制设计特性的各种材料。技术描述：刺激响应性是自然界中蛋白质的显著特征，由此对化学刺激（如配体结合、磷酸化和甲基化）和物理刺激（如温度、pH、光的变化）的响应，和盐浓度导致急剧的构象或相变。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，目前重复多肽的生物灵感设计集中在序列空间的广阔范围中的一小部分。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来开发通用材料设计规则，通过系统探索序列空间来加速发现和开发潜在的巨大类别的热响应多肽材料。这项研究将“首次”提供对下临界溶解温度（LCST）和上临界溶解温度（UCST）相行为的决定因素的完整原子理解，使LCST和UCST肽聚合物的从头分子设计成为可能，并确定如何将它们联合收割机组合以创建分层有序的规则，纳米结构的多肽材料，其表现出独特的形态，可以作为其刺激响应性的函数进行调节。这些材料可以作为纳米结构的支架和模板，并使广泛的生物催化，生物电子，或测定装置。PI还计划发布PIMMS建模包，这是一套用于执行基于网格的聚合物模拟的工具。PIMMS将为机器学习算法提供支持，从而能够设计响应性蛋白质聚合物。PIMMS代码库将作为开源发布。一个用户社区将围绕该语言合并，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。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此特别感兴趣，该办公室为该奖项提供了共同资助。

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.