Ebony Shire, André A. B. Coimbra, Carlos Barba Ostria, Leonardo Rios-Solis, Diego López Barreiro
Structural proteins like silk, squid ring teeth, elastin, collagen, or resilin, among others, are inspiring the development of new sustainable biopolymeric materials for applications including healthcare, food, soft robotics, or textiles. Furthermore, advances in the fields of soft materials and synthetic biology have a joint great potential to guide the design of novel structural proteins, despite both fields progressing mostly in a separate fashion so far. Using recombinant DNA technologies and microbial fermentations, we can design new structural proteins with monomer-level sequence control and a dispersity of ca. 1.0, based on permutations of tandem repeats derived from natural structural proteins. However, the molecular design of recombinant and repetitive structural proteins is a nontrivial task that is generally approached using low-throughput trial-and-error experimentation. Here, we review recent progress in this area, in terms of structure–function relationships and DNA synthesis technologies. We also discuss experimental and computational advances towards the establishment of rapid prototyping pipelines for this family of biopolymers. Finally, we highlight future challenges to make protein-based materials a commercially viable alternative to current fossil-based polymers.
蚕丝、乌贼环齿、弹性蛋白、胶原蛋白或树脂蛋白等结构蛋白正在激发人们开发新型可持续生物聚合物材料，其应用领域包括医疗保健、食品、软机器人或纺织品。此外，软性材料和合成生物学领域的进步在指导新型结构蛋白质的设计方面具有共同的巨大潜力，尽管迄今为止这两个领域的进展大多各自为政。利用 DNA 重组技术和微生物发酵技术，我们可以根据从天然结构蛋白中提取的串联重复序列的排列组合，设计出具有单体级序列控制和约 1.0 分散性的新型结构蛋白。然而，重组和重复结构蛋白的分子设计并非易事，通常需要通过低通量的试错实验来完成。在此，我们从结构-功能关系和 DNA 合成技术的角度回顾了这一领域的最新进展。我们还讨论了在为这一系列生物聚合物建立快速原型管道方面取得的实验和计算进展。最后，我们强调了使基于蛋白质的材料成为目前化石基聚合物的商业可行替代品所面临的未来挑战。
{"title":"Molecular design of protein-based materials – state of the art, opportunities and challenges at the interface between materials engineering and synthetic biology","authors":"Ebony Shire, André A. B. Coimbra, Carlos Barba Ostria, Leonardo Rios-Solis, Diego López Barreiro","doi":"10.1039/d4me00122b","DOIUrl":"https://doi.org/10.1039/d4me00122b","url":null,"abstract":"Structural proteins like silk, squid ring teeth, elastin, collagen, or resilin, among others, are inspiring the development of new sustainable biopolymeric materials for applications including healthcare, food, soft robotics, or textiles. Furthermore, advances in the fields of soft materials and synthetic biology have a joint great potential to guide the design of novel structural proteins, despite both fields progressing mostly in a separate fashion so far. Using recombinant DNA technologies and microbial fermentations, we can design new structural proteins with monomer-level sequence control and a dispersity of <em>ca.</em> 1.0, based on permutations of tandem repeats derived from natural structural proteins. However, the molecular design of recombinant and repetitive structural proteins is a nontrivial task that is generally approached using low-throughput trial-and-error experimentation. Here, we review recent progress in this area, in terms of structure–function relationships and DNA synthesis technologies. We also discuss experimental and computational advances towards the establishment of rapid prototyping pipelines for this family of biopolymers. Finally, we highlight future challenges to make protein-based materials a commercially viable alternative to current fossil-based polymers.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mark S. Bannon, Jeffrey F. Ellena, Aditi S. Gourishankar, Spencer R. Marsh, Dilza Trevisan-Silva, Nicholas E. Sherman, L. Jane Jourdan, Robert G. Gourdie, Rachel A. Letteri
Peptides are naturally potent and selective therapeutics with massive potential; however, low cell membrane permeability limits their clinical implementation, particularly for hydrophilic, anionic peptides with intracellular targets. To overcome this limitation, esterification of anionic carboxylic acids on therapeutic peptides can simultaneously increase hydrophobicity and net charge to facilitate cell internalization, whereafter installed esters can be cleaved hydrolytically to restore activity. To date, however, most esterified therapeutics contain either a single esterification site or multiple esters randomly incorporated on multiple sites. This investigation provides molecular engineering insight into how the number and position of esters installed onto the therapeutic peptide α carboxyl terminus 11 (αCT11, RPRPDDLEI) with 4 esterification sites affect hydrophobicity and the hydrolysis process that reverts the peptide to its original form. After installing methyl esters onto αCT11 using Fischer esterification, we isolated 5 distinct products and used 2D nuclear magnetic resonance spectroscopy, reverse-phase high performance liquid chromatography, and mass spectrometry to determine which residues were esterified in each and the resulting increase in hydrophobicity. We found esterifying the C-terminal isoleucine to impart the largest increase in hydrophobicity. Monitoring ester hydrolysis showed the C-terminal isoleucine ester to be the most hydrolytically stable, followed by the glutamic acid, whereas esters on aspartic acids hydrolyze rapidly. LC-MS revealed the formation of transient intramolecular aspartimides prior to hydrolysis to carboxylic acids. In vitro proof-of-concept experiments showed esterifying αCT11 to increase cell migration into a scratch, highlighting the potential of multi-site esterification as a tunable, reversible strategy to enable the delivery of therapeutic peptides.
{"title":"Multi-site esterification: a tunable, reversible strategy to tailor therapeutic peptides for delivery","authors":"Mark S. Bannon, Jeffrey F. Ellena, Aditi S. Gourishankar, Spencer R. Marsh, Dilza Trevisan-Silva, Nicholas E. Sherman, L. Jane Jourdan, Robert G. Gourdie, Rachel A. Letteri","doi":"10.1039/d4me00072b","DOIUrl":"https://doi.org/10.1039/d4me00072b","url":null,"abstract":"Peptides are naturally potent and selective therapeutics with massive potential; however, low cell membrane permeability limits their clinical implementation, particularly for hydrophilic, anionic peptides with intracellular targets. To overcome this limitation, esterification of anionic carboxylic acids on therapeutic peptides can simultaneously increase hydrophobicity and net charge to facilitate cell internalization, whereafter installed esters can be cleaved hydrolytically to restore activity. To date, however, most esterified therapeutics contain either a single esterification site or multiple esters randomly incorporated on multiple sites. This investigation provides molecular engineering insight into how the number and position of esters installed onto the therapeutic peptide α carboxyl terminus 11 (αCT11, RPRPDDLEI) with 4 esterification sites affect hydrophobicity and the hydrolysis process that reverts the peptide to its original form. After installing methyl esters onto αCT11 using Fischer esterification, we isolated 5 distinct products and used 2D nuclear magnetic resonance spectroscopy, reverse-phase high performance liquid chromatography, and mass spectrometry to determine which residues were esterified in each and the resulting increase in hydrophobicity. We found esterifying the C-terminal isoleucine to impart the largest increase in hydrophobicity. Monitoring ester hydrolysis showed the C-terminal isoleucine ester to be the most hydrolytically stable, followed by the glutamic acid, whereas esters on aspartic acids hydrolyze rapidly. LC-MS revealed the formation of transient intramolecular aspartimides prior to hydrolysis to carboxylic acids. <em>In vitro</em> proof-of-concept experiments showed esterifying αCT11 to increase cell migration into a scratch, highlighting the potential of multi-site esterification as a tunable, reversible strategy to enable the delivery of therapeutic peptides.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metal–organic frameworks (MOFs) are promising platforms for designing photoresponsive materials due to their structural versatility and tunable properties. However, challenges remain in fine-tuning the photoresponsive behavior while maintaining the high stability of MOFs. In this study, we synthesized a MOF containing redox-active pyromellitic diimide (PMDI) groups and unsaturated Zr6 clusters (named Zr-PMDI-DMF) and fine-tuned its photochromic properties by exchanging the coordination solvent molecules on the Zr sites. Unlike traditional Zr6 clusters with bidentate carboxylate coordination, Zr-PMDI-DMF features monodentate carboxylate coordination with the exposed Zr sites occupied by solvent molecules. We post-synthetically exchanged the coordinated N, N-dimethylformamide (DMF) solvent molecules with 2-(dimethylamino)ethanol (DMAE), N-methyltetrahydropyrrole (NMP), and dimethyl sulfoxide (DMSO), and determined the structures of the coordinated solvent molecules using single-crystal X-ray diffraction. Through photochromic and bleaching cycle experiments, electron paramagnetic resonance spectroscopy, and density functional theory calculations, we found that the coordinated solvents act as electron donors. In contrast, the PMDI ligands act as electron acceptors, causing the intra-framework electron transfer and the photochromism. The rate of the photochromic response correlated with the electron-donating ability of the solvents, following the trend of DMAE > NMP > DMSO > DMF.
{"title":"Controlling the Photochromism of Zirconium Pyromellitic Diimide-Based Metal-Organic Frameworks through Coordinating Solvents","authors":"Youcong Li, Jiahao Dong, Yue Zhao, Lei Gao, Yu-Hao Gu, Shuai Yuan","doi":"10.1039/d4me00104d","DOIUrl":"https://doi.org/10.1039/d4me00104d","url":null,"abstract":"Metal–organic frameworks (MOFs) are promising platforms for designing photoresponsive materials due to their structural versatility and tunable properties. However, challenges remain in fine-tuning the photoresponsive behavior while maintaining the high stability of MOFs. In this study, we synthesized a MOF containing redox-active pyromellitic diimide (PMDI) groups and unsaturated Zr6 clusters (named Zr-PMDI-DMF) and fine-tuned its photochromic properties by exchanging the coordination solvent molecules on the Zr sites. Unlike traditional Zr6 clusters with bidentate carboxylate coordination, Zr-PMDI-DMF features monodentate carboxylate coordination with the exposed Zr sites occupied by solvent molecules. We post-synthetically exchanged the coordinated N, N-dimethylformamide (DMF) solvent molecules with 2-(dimethylamino)ethanol (DMAE), N-methyltetrahydropyrrole (NMP), and dimethyl sulfoxide (DMSO), and determined the structures of the coordinated solvent molecules using single-crystal X-ray diffraction. Through photochromic and bleaching cycle experiments, electron paramagnetic resonance spectroscopy, and density functional theory calculations, we found that the coordinated solvents act as electron donors. In contrast, the PMDI ligands act as electron acceptors, causing the intra-framework electron transfer and the photochromism. The rate of the photochromic response correlated with the electron-donating ability of the solvents, following the trend of DMAE > NMP > DMSO > DMF.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lingfeng Gui, Alan Armstrong, Amparo Galindo, Fareed Bhasha Sayyed, Stanley P Kolis, Claire Adjiman
Developing an accurate predictive model of solvent effects on reaction kinetics is a challenging task, yet it can play an important role in process development. While first-principles or machine learning models are often compute- or data-intensive, simple surrogate models, such as multivariate linear or quadratic regression models, are useful when computational resources and data are scarce. The judicious choice of a small set of training data, i.e., a set of solvents in which quantum mechanical (QM) calculations of liquid-phase rate constants are to be performed is critical to obtaining a reliable model. This is, however, made especially challenging by the highly irregular shape of the discrete space of possible experiments (solvent choices). In this work, we demonstrate that when choosing a set of computer experiments to generate training data, the D-optimality criterion value of the chosen set correlates well with the likelihood of achieving good model performance. With the Menschutkin reaction of pyridine and phenacyl bromide as a case study, this finding is further verified via the evaluation of the surrogate models regressed using D-optimal solvent sets generated from four distinct selection spaces. We also find that incorporating quadratic terms in the surrogate model and choosing the D-optimal solvent set from a selection space similar to the test set can significantly improve the accuracy of reaction rate constant predictions while using a small training dataset. Our approach holds promise for the use of statistical optimality criteria for other types of computer experiments, supporting the construction of surrogate models with reduced resource and data requirements.
就溶剂对反应动力学的影响建立精确的预测模型是一项极具挑战性的任务，但却能在工艺开发中发挥重要作用。第一原理或机器学习模型通常是计算或数据密集型的，而简单的代用模型，如多元线性或二次回归模型，在计算资源和数据稀缺的情况下非常有用。要获得可靠的模型，明智地选择一小组训练数据（即一组溶剂，在其中对液相速率常数进行量子力学（QM）计算）至关重要。然而，由于可能的实验（溶剂选择）的离散空间形状极不规则，这尤其具有挑战性。在这项工作中，我们证明了在选择一组计算机实验来生成训练数据时，所选实验组的 D-optimality 标准值与获得良好模型性能的可能性密切相关。以吡啶和苯酰溴的 Menschutkin 反应为例，通过评估使用从四个不同选择空间生成的 D-最优溶剂集回归的代用模型，进一步验证了这一发现。我们还发现，在代用模型中加入二次项，并从与测试集类似的选择空间中选择 D 最佳溶剂集，可以显著提高反应速率常数预测的准确性，同时只需使用少量的训练数据集。我们的方法有望在其他类型的计算机实验中使用统计最优性标准，支持在减少资源和数据需求的情况下构建代用模型。
{"title":"On the design of optimal computer experiments to model solvent effects on reaction kinetics","authors":"Lingfeng Gui, Alan Armstrong, Amparo Galindo, Fareed Bhasha Sayyed, Stanley P Kolis, Claire Adjiman","doi":"10.1039/d4me00074a","DOIUrl":"https://doi.org/10.1039/d4me00074a","url":null,"abstract":"Developing an accurate predictive model of solvent effects on reaction kinetics is a challenging task, yet it can play an important role in process development. While first-principles or machine learning models are often compute- or data-intensive, simple surrogate models, such as multivariate linear or quadratic regression models, are useful when computational resources and data are scarce. The judicious choice of a small set of training data, i.e., a set of solvents in which quantum mechanical (QM) calculations of liquid-phase rate constants are to be performed is critical to obtaining a reliable model. This is, however, made especially challenging by the highly irregular shape of the discrete space of possible experiments (solvent choices). In this work, we demonstrate that when choosing a set of computer experiments to generate training data, the D-optimality criterion value of the chosen set correlates well with the likelihood of achieving good model performance. With the Menschutkin reaction of pyridine and phenacyl bromide as a case study, this finding is further verified via the evaluation of the surrogate models regressed using D-optimal solvent sets generated from four distinct selection spaces. We also find that incorporating quadratic terms in the surrogate model and choosing the D-optimal solvent set from a selection space similar to the test set can significantly improve the accuracy of reaction rate constant predictions while using a small training dataset. Our approach holds promise for the use of statistical optimality criteria for other types of computer experiments, supporting the construction of surrogate models with reduced resource and data requirements.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yongbaek Kim, Hiroto Isobe, Keishi Nishio, Kazuki Murai
Biomineralization has garnered attention not only for its fundamental role in understanding the mechanisms of biomineral formation but also as a method for fabricating next-generation functional materials. In this study, we investigated the nucleation, crystal growth, and particle growth processes of calcium phosphates (CaPs) formed using selective mineralization at the hydrogel interface induced by the fusion of peptide hydrogels. After 1 day of mineralization, band-like white precipitates were observed at the fusion interface of the hydrogels. Notably, the nucleation and crystal growth of the mineralized CaP exhibited different behaviors owing to the differences in the properties of the reaction interface for mineralization. The selective nucleation and crystal growth of the CaPs at the hydrogel interface were attributed to (1) the local concentration of mineral sources near the peptide network, driven by electrostatic interactions between the polar functional groups and mineral source ions, and (2) selective crystal growth of the CaPs induced by the nanostructure of the surface functional groups.
生物矿化不仅在理解生物矿物形成机制方面发挥着基础性作用，而且还是制造下一代功能材料的一种方法，因而备受关注。在本研究中，我们研究了多肽水凝胶融合诱导的水凝胶界面选择性矿化形成的磷酸钙（CaPs）的成核、晶体生长和颗粒生长过程。矿化一天后，在水凝胶的融合界面上观察到了带状白色沉淀。值得注意的是，由于矿化反应界面的性质不同，矿化 CaP 的成核和晶体生长表现出不同的行为。水凝胶界面上 CaPs 的选择性成核和晶体生长归因于：(1) 极性官能团和矿物源离子之间的静电作用驱动了肽网络附近矿物源的局部富集；(2) 表面官能团的纳米结构诱导了 CaPs 的选择性晶体生长。
{"title":"Selective mineralization at hydrogel interface induced by fusion between peptide hydrogels","authors":"Yongbaek Kim, Hiroto Isobe, Keishi Nishio, Kazuki Murai","doi":"10.1039/d4me00112e","DOIUrl":"https://doi.org/10.1039/d4me00112e","url":null,"abstract":"Biomineralization has garnered attention not only for its fundamental role in understanding the mechanisms of biomineral formation but also as a method for fabricating next-generation functional materials. In this study, we investigated the nucleation, crystal growth, and particle growth processes of calcium phosphates (CaPs) formed using selective mineralization at the hydrogel interface induced by the fusion of peptide hydrogels. After 1 day of mineralization, band-like white precipitates were observed at the fusion interface of the hydrogels. Notably, the nucleation and crystal growth of the mineralized CaP exhibited different behaviors owing to the differences in the properties of the reaction interface for mineralization. The selective nucleation and crystal growth of the CaPs at the hydrogel interface were attributed to (1) the local concentration of mineral sources near the peptide network, driven by electrostatic interactions between the polar functional groups and mineral source ions, and (2) selective crystal growth of the CaPs induced by the nanostructure of the surface functional groups.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junlong Yao, Zongqiang Fu, Huan Yang, Lin Gao, Xueliang Jiang, Wei Nie, Zhengguang Sun, Haolan Lu, Meiyun Lin, Jinglou Xu
Multifunctional composites with rapid self-healing performance have been widely applied in various fields. However, different types of fillers result in decreased self-healing efficiency and present agglomeration and poor compatibility especially at high filler contents. Here, based on the different surface modifications of barium titanate (BT) and silicon carbide (SiC) and the amide-bond synergistic effects between these fillers, self-healing supramolecular composites with high filler contents (up to 30%) are reported, and exhibit high strength, dielectric and thermal-conduction properties. Modification significantly improves the dispersion of these fillers, and greatly enhances the coexistence and synergy between these fillers. This three-phase amide-bonded supramolecular composite exhibits a high tensile strength of 3.22 MPa compared to other self-healing materials such as self-healing hydrogels, a high dielectric constant of 23, a high thermal conductivity of 0.36 W m−1 K−1 and a superior self-healing efficiency of above 94%. These performances are ascribed to the formation of amide bonds between the amino groups in 3-aminopropyltriethoxysilane (KH550)-modified silicon carbide (SiC-NH2) and the carboxyl groups in tartaric acid (TA)-modified barium titanate (BT-TA), which can provide efficient supramolecular interactions between different fillers, as well as more reversible hydrogen bonding for the matrix. This three-phase amide-bonded supramolecular composite provides an effective strategy to improve the self-healing properties of multifunctional composites, and will bring pioneering functions to electronic packaging materials, dielectric energy storage materials, environmental energy and other fields, which can open up broad application prospects.
{"title":"Construction of amide-bonded supramolecular multifunctional fillers towards boosted self-healing, thermal conductivity and dielectric properties","authors":"Junlong Yao, Zongqiang Fu, Huan Yang, Lin Gao, Xueliang Jiang, Wei Nie, Zhengguang Sun, Haolan Lu, Meiyun Lin, Jinglou Xu","doi":"10.1039/d4me00114a","DOIUrl":"https://doi.org/10.1039/d4me00114a","url":null,"abstract":"Multifunctional composites with rapid self-healing performance have been widely applied in various fields. However, different types of fillers result in decreased self-healing efficiency and present agglomeration and poor compatibility especially at high filler contents. Here, based on the different surface modifications of barium titanate (BT) and silicon carbide (SiC) and the amide-bond synergistic effects between these fillers, self-healing supramolecular composites with high filler contents (up to 30%) are reported, and exhibit high strength, dielectric and thermal-conduction properties. Modification significantly improves the dispersion of these fillers, and greatly enhances the coexistence and synergy between these fillers. This three-phase amide-bonded supramolecular composite exhibits a high tensile strength of 3.22 MPa compared to other self-healing materials such as self-healing hydrogels, a high dielectric constant of 23, a high thermal conductivity of 0.36 W m<small><sup>−1</sup></small> K<small><sup>−1</sup></small> and a superior self-healing efficiency of above 94%. These performances are ascribed to the formation of amide bonds between the amino groups in 3-aminopropyltriethoxysilane (KH550)-modified silicon carbide (SiC-NH<small><sub>2</sub></small>) and the carboxyl groups in tartaric acid (TA)-modified barium titanate (BT-TA), which can provide efficient supramolecular interactions between different fillers, as well as more reversible hydrogen bonding for the matrix. This three-phase amide-bonded supramolecular composite provides an effective strategy to improve the self-healing properties of multifunctional composites, and will bring pioneering functions to electronic packaging materials, dielectric energy storage materials, environmental energy and other fields, which can open up broad application prospects.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qinghe Gao, Tammo Dukker, Artur M. Schweidtmann, Jana M. Weber
The estimation of polymer properties is of crucial importance in many domains such as energy, healthcare, and packaging. Recently, graph neural networks (GNNs) have shown promising results for the prediction of polymer properties based on supervised learning. However, the training of GNNs in a supervised learning task demands a huge amount of polymer property data that is time-consuming and computationally/experimentally expensive to obtain. Self-supervised learning offers great potential to reduce this data demand through pre-training the GNNs on polymer structure data only. These pre-trained GNNs can then be fine-tuned on the supervised property prediction task using a much smaller labeled dataset. We propose to leverage self-supervised learning techniques in GNNs for the prediction of polymer properties. We employ a recent polymer graph representation that includes essential features of polymers, such as monomer combinations, stochastic chain architecture, and monomer stoichiometry, and process the polymer graphs through a tailored GNN architecture. We investigate three self-supervised learning setups: (i) node- and edge-level pre-training, (ii) graph-level pre-training, and (iii) ensembled node-, edge- & graph-level pre-training. We additionally explore three different transfer strategies of fully connected layers with the GNN architecture. Our results indicate that the ensemble node-, edge- & graph-level self-supervised learning with all layers transferred depicts the best performance across dataset size. In scarce data scenarios, it decreases the root mean square errors by 28.39% and 19.09% for the prediction of electron affinity and ionization potential compared to supervised learning without the pre-training task.
{"title":"Self-supervised graph neural networks for polymer property prediction","authors":"Qinghe Gao, Tammo Dukker, Artur M. Schweidtmann, Jana M. Weber","doi":"10.1039/d4me00088a","DOIUrl":"https://doi.org/10.1039/d4me00088a","url":null,"abstract":"The estimation of polymer properties is of crucial importance in many domains such as energy, healthcare, and packaging. Recently, graph neural networks (GNNs) have shown promising results for the prediction of polymer properties based on supervised learning. However, the training of GNNs in a supervised learning task demands a huge amount of polymer property data that is time-consuming and computationally/experimentally expensive to obtain. Self-supervised learning offers great potential to reduce this data demand through pre-training the GNNs on polymer structure data only. These pre-trained GNNs can then be fine-tuned on the supervised property prediction task using a much smaller labeled dataset. We propose to leverage self-supervised learning techniques in GNNs for the prediction of polymer properties. We employ a recent polymer graph representation that includes essential features of polymers, such as monomer combinations, stochastic chain architecture, and monomer stoichiometry, and process the polymer graphs through a tailored GNN architecture. We investigate three self-supervised learning setups: (i) node- and edge-level pre-training, (ii) graph-level pre-training, and (iii) ensembled node-, edge- & graph-level pre-training. We additionally explore three different transfer strategies of fully connected layers with the GNN architecture. Our results indicate that the ensemble node-, edge- & graph-level self-supervised learning with all layers transferred depicts the best performance across dataset size. In scarce data scenarios, it decreases the root mean square errors by 28.39% and 19.09% for the prediction of electron affinity and ionization potential compared to supervised learning without the pre-training task.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electrowetting display (EWD) technology is among the most promising reflective display technologies due to its full-color capabilities and fast video-speed performance. The colored EWD inks are typically prepared by dissolving soluble organic dyes in non-polar solvents, which significantly influence the color performance, electro-optical behaviour, and longevity of EWD devices. In this study, density functional theory (DFT) at the PBE1PBE/6-31G* level and time-dependent density functional theory (TD-DFT) at the M06-2X/6-31G* level were utilized to calculate a series of benzobisthiadiazole-based donor–acceptor–donor (D–A–D) type near-infrared organic dyes for EWDs, providing structural and spectral data to aid in spectral assignment. The quantum chemical calculations' results align with our experimental synthesis data, showing molecular colors spanning blue, green, and cyan. Detailed investigations into the properties of these dyes, including absorption, electro-optical response, and photo-stability, were conducted. The experimental outcomes indicate that these organic dyes are excellent candidates for EWD applications.
{"title":"Computational-assisted molecular design, synthesis and application of benzobisthiadiazole-based near-infrared dye in electrowetting displays","authors":"Junheng Chen, Haoteng Lin, Xintong Wang, Dinggui He, Baoyi Luo, Yuanyuan Guo, Wangqiao Chen, Guofu Zhou","doi":"10.1039/d4me00115j","DOIUrl":"https://doi.org/10.1039/d4me00115j","url":null,"abstract":"Electrowetting display (EWD) technology is among the most promising reflective display technologies due to its full-color capabilities and fast video-speed performance. The colored EWD inks are typically prepared by dissolving soluble organic dyes in non-polar solvents, which significantly influence the color performance, electro-optical behaviour, and longevity of EWD devices. In this study, density functional theory (DFT) at the PBE1PBE/6-31G* level and time-dependent density functional theory (TD-DFT) at the M06-2X/6-31G* level were utilized to calculate a series of benzobisthiadiazole-based donor–acceptor–donor (D–A–D) type near-infrared organic dyes for EWDs, providing structural and spectral data to aid in spectral assignment. The quantum chemical calculations' results align with our experimental synthesis data, showing molecular colors spanning blue, green, and cyan. Detailed investigations into the properties of these dyes, including absorption, electro-optical response, and photo-stability, were conducted. The experimental outcomes indicate that these organic dyes are excellent candidates for EWD applications.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The development of hole transport materials with desirable properties is important for the fabrication of efficient organic light-emitting diodes (OLEDs). The present work demonstrates an approach for developing a library of phenothiazine-based hole transport materials (HTMs) for OLED application with considerably good triplet energy (theoretical). Furthermore, the single-crystal structure analysis at the molecular level for some of the developed molecules reveals the possibility of poor electronic communications between the corresponding units. Theoretical studies on transition dipole orientation revealed that all the present phenothiazine-based molecules have appreciable transition dipole orientation. Hence, the objective of the current work has been to assess the impact of chemical structures on certain features of a group of phenothiazine-based functional molecular HTMs with donor–acceptor characteristics. Finally, the hole-only devices (HODs) were fabricated with the synthesized materials as HTMs, and these showed an enhancement in current density with the increase in operating voltage from ∼2–8 V. All these theoretical and experimental outcomes suggested that the present set of molecules could be used as possible efficient HTMs for OLED applications.
开发具有理想特性的空穴传输材料对于制造高效有机发光二极管（OLED）非常重要。本研究展示了一种开发基于吩噻嗪的空穴传输材料（HTMs）库的方法，这些材料具有相当好的三重能（理论值），可用于有机发光二极管。此外，对一些已开发分子进行的分子级单晶结构分析表明，相应单元之间的电子通信可能很差。对过渡偶极取向的理论研究表明，目前所有基于吩噻嗪的分子都具有明显的过渡偶极取向。因此，当前工作的目标是评估化学结构对一组具有供体-受体特性的吩噻嗪基功能分子 HTM 某些特征的影响。最后，以合成的材料为 HTM 制作了纯空穴器件 (HOD)，这些器件的电流密度随着工作电压在 2-8 V 之间的增加而增加。
{"title":"Design strategy and molecular level understanding: hole transport materials with suitable transition dipole orientation for OLEDs","authors":"Krishan Kumar, Sunil Kumar, Anirban Karmakar, Dipanshu Sharma, Feng-Rong Chen, Mangey Ram Nagar, Jwo-Huei Jou, Subrata Banik, Subrata Ghosh","doi":"10.1039/d3me00127j","DOIUrl":"https://doi.org/10.1039/d3me00127j","url":null,"abstract":"The development of hole transport materials with desirable properties is important for the fabrication of efficient organic light-emitting diodes (OLEDs). The present work demonstrates an approach for developing a library of phenothiazine-based hole transport materials (HTMs) for OLED application with considerably good triplet energy (theoretical). Furthermore, the single-crystal structure analysis at the molecular level for some of the developed molecules reveals the possibility of poor electronic communications between the corresponding units. Theoretical studies on transition dipole orientation revealed that all the present phenothiazine-based molecules have appreciable transition dipole orientation. Hence, the objective of the current work has been to assess the impact of chemical structures on certain features of a group of phenothiazine-based functional molecular HTMs with donor–acceptor characteristics. Finally, the hole-only devices (HODs) were fabricated with the synthesized materials as HTMs, and these showed an enhancement in current density with the increase in operating voltage from ∼2–8 V. All these theoretical and experimental outcomes suggested that the present set of molecules could be used as possible efficient HTMs for OLED applications.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Porous nanostructures exhibit remarkable nanoplatforms for payload delivery to diseased cells with high loading capacity, favorable release profiles, improved hemocompatibility, biocompatibility, and safe clearance after biodegradation. Metal–organic frameworks (MOFs), periodic mesoporous organosilica (PMO), or biodegradable periodic mesoporous organosilica (BPMO) epitomize a similar category of structured and crystalline porous coordinated compounds or nanocomposites. Additionally, their elevated surface-to-volume ratio, customizable porous configurations, and convenient attachment of favorable ligands to the central metal ions enhance drug loading and release, further demonstrating their potential for drug delivery applications. This review focuses on these materials, including Fe-MOFs, Cu-MOFs, Zr-MOFs, PMO and BPMO, along with multicompartmental mesoporous nanostructures, detailing their specific engineering, chemistry, and optimal drug delivery applications.
{"title":"Crafting porous nanoscaled architecture as a potential frontier for drug delivery","authors":"Koyeli Girigoswami, Pragya Pallavi, Agnishwar Girigoswami","doi":"10.1039/d4me00098f","DOIUrl":"https://doi.org/10.1039/d4me00098f","url":null,"abstract":"Porous nanostructures exhibit remarkable nanoplatforms for payload delivery to diseased cells with high loading capacity, favorable release profiles, improved hemocompatibility, biocompatibility, and safe clearance after biodegradation. Metal–organic frameworks (MOFs), periodic mesoporous organosilica (PMO), or biodegradable periodic mesoporous organosilica (BPMO) epitomize a similar category of structured and crystalline porous coordinated compounds or nanocomposites. Additionally, their elevated surface-to-volume ratio, customizable porous configurations, and convenient attachment of favorable ligands to the central metal ions enhance drug loading and release, further demonstrating their potential for drug delivery applications. This review focuses on these materials, including Fe-MOFs, Cu-MOFs, Zr-MOFs, PMO and BPMO, along with multicompartmental mesoporous nanostructures, detailing their specific engineering, chemistry, and optimal drug delivery applications.","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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