Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation
Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation
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基于主动学习指导的粗粒度分子模拟发现自组装π结合肽
DOI：
10.1021/acs.jpcb.0c00708
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发表时间：
2020-05-14
影响因子：
3.3
通讯作者：
Ferguson, Andrew L.
中科院分区：
文献类型：
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作者：
Shmilovich, Kirill;Mansbach, Rachael A.;Ferguson, Andrew L.
电子活性有机分子作为能量收集和传输的新型软材料显示出巨大的前景。自组装纳米聚集体由偶联寡肽组成，由寡肽翅膀两侧的芳香核心组成，在水溶性和生物相容性的底物中提供了新兴的光电特性。纳米聚集体的性质可以通过调整核心化学和肽的组成来控制，但序列-结构-功能关系的表征仍然很差。在这项工作中，我们在主动学习协议中采用粗粒度分子动力学模拟，采用深度表征学习和贝叶斯优化来有效识别能够组装具有良好电子活性pi核堆叠的伪1d纳米聚集体的分子。我们考虑DXXX-OPV3-XXXD寡肽家族，其中D是一个Asp残基，OPV3是一个低聚苯乙烯低聚物（1,4-二苯基苯），以确定所有20(3)= 8000个可能序列中表现最好的XXX三肽。通过直接模拟该空间的2.3%，我们确定了相对于先前工作中报道的分子预测表现出更好的组装。光谱聚类的顶级候选人揭示了新的设计规则控制组装。这项工作建立了对DXXX-OPV3-XXXD组装的新理解，确定了有前途的实验测试新候选物，并提出了一个计算设计平台，可以普遍扩展到其他基于肽和肽样系统。
Electronically active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from pi-conjugated oligopeptides composed of an aromatic core flanked by oligopeptide wings offer emergent optoelectronic properties within a water-soluble and biocompatible substrate. Nanoaggregate properties can be controlled by tuning core chemistry and peptide composition, but the sequence-structure-function relations remain poorly characterized. In this work, we employ coarse-grained molecular dynamics simulations within an active learning protocol employing deep representational learning and Bayesian optimization to efficiently identify molecules capable of assembling pseudo-1D nanoaggregates with good stacking of the electronically active pi-cores. We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an Asp residue and OPV3 is an oligophenylenevinylene oligomer (1,4-distyrylbenzene), to identify the top performing XXX tripeptides within all 20(3) = 8000 possible sequences. By direct simulation of only 2.3% of this space, we identify molecules predicted to exhibit superior assembly relative to those reported in prior work. Spectral clustering of the top candidates reveals new design rules governing assembly. This work establishes new understanding of DXXX-OPV3-XXXD assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems.