Molecular Systems Design & Engineering最新文献

Applying local interpretable model-agnostic explanations to identify substructures that are responsible for mutagenicity of chemical compounds 应用局部可解释的模型--不可知论解释，确定导致化合物致突变性的亚结构

IF 3.6 3区工程技术 Q1 Chemistry

Molecular Systems Design & Engineering

Pub Date : 2024-06-05 DOI: 10.1039/d4me00038b

Lucca Caiaffa Santos Rosa, Andre Silva Pimentel

The local interpretable model-agnostic explanations method was applied to identify substructures that represent the mutagenicity of chemical compounds using machine learning models. Random forest and extremely randomized trees were used to build models to be explained using the Hansen and Bursi Ames mutagenicity datasets. The models were analyzed using precision, recall, F1, and accuracy metrics. The aim of this study is to address the challenge of identifying substructures that indicate the mutagenicity of chemical compounds. The goal is to provide stable and consistent explanations for the mutagenicity of chemical compounds, which is crucial for trust and acceptance of the findings, especially in the sensitive field of computational toxicology. This approach is significant as it contributes to the interpretability and explainability of machine learning models, particularly in the context of identifying substructures associated with mutagenicity, thereby advancing the field of computational toxicology. Identifying substructures that represent the mutagenicity of chemical compounds is important because it can help predict the potential toxicity of new chemical compounds. This is particularly relevant in fields such as drug development and environmental toxicology, where the potential risks of exposure to new compounds need to be carefully evaluated. Some examples of chemical compounds that have been identified as mutagenic include epoxides, N-aryl compounds, nitro compounds, aromatic amines, N-oxides, nitro-containing compounds, and polycyclic aromatic hydrocarbons with a bay-region. These examples demonstrate the importance of identifying and studying mutagenic chemical compounds to better understand their potential risks and adverse effects on human health and the environment.

利用机器学习模型，采用局部可解释模型-不可知论解释方法来识别代表化合物致突变性的子结构。使用随机森林和极端随机树建立模型，并利用汉森和布尔西-艾姆斯诱变数据集进行解释。使用精确度、召回率、F1 和准确度指标对模型进行了分析。本研究的目的是应对识别表明化合物致突变性的亚结构这一挑战。其目的是为化合物的致突变性提供稳定一致的解释，这对研究结果的信任度和接受度至关重要，尤其是在敏感的计算毒理学领域。这种方法意义重大，因为它有助于提高机器学习模型的可解释性和可解释性，特别是在识别与致突变性相关的子结构方面，从而推动计算毒理学领域的发展。识别代表化合物致突变性的子结构非常重要，因为这有助于预测新化合物的潜在毒性。这与药物开发和环境毒理学等领域尤其相关，因为这些领域需要仔细评估接触新化合物的潜在风险。已确定为诱变化合物的一些例子包括环氧化物、N-芳基化合物、硝基化合物、芳香胺、N-氧化物、含硝基化合物以及带有畦区的多环芳烃。这些例子说明了识别和研究诱变化合物的重要性，以便更好地了解它们对人类健康和环境的潜在风险和不利影响。

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引用次数: 0

GREEN SYNTHESIS OF THERMO/PHOTOCHROMIC DOPED CELLULOSE POLYMER: A BIOCOMPATIBLE FILM FOR POTENTIAL APPLICATION IN COLD CHAIN VISUAL TRACKING 热/光变色掺杂纤维素聚合物的绿色合成：一种可应用于冷链视觉跟踪的生物相容性薄膜

IF 3.6 3区工程技术 Q1 Chemistry

Molecular Systems Design & Engineering

Pub Date : 2024-05-31 DOI: 10.1039/d4me00055b

Simone D'Agostino, Alessandra Azzali, Maria Di Filippo, Lucia Bertucioli, Silvia Panzavolta, Sophie Lilburn, Fabrizia Grepioni

To mitigate food losses and ensure a robust cold chain in transportation, sensors play a pivotal role in swiftly and visibly monitoring storage conditions. Molecules with thermo- and photochromic properties and N-salicylideneanilines and derivatives, have emerged as promising candidates due to synthesis simplicity and ability to respond to stimuli. In this study we have synthesized a family of anils through mechanochemistry, focusing on H/F substituents on the bromoaniline residue. A suitable photochromic compound was identified and incorporated into a carboxymethyl cellulose (CMC) biopolymer matrix, to produce a photochromic composite film. UV radiation induced a color change in the film from colorless to red: reversibility was evaluated at different temperatures by means of UV-Vis spectroscopy. The composite film maintained a deep red color at -19 °C and 4 °C for seven weeks, while rapidly reversing to white/yellowish at room temperature, making it suitable for cold chain transport and scenarios requiring rapid visual inspection of storage conditions.

为了减少食品损失并确保运输过程中的冷链稳固，传感器在迅速、明显地监测储存条件方面发挥着关键作用。具有热致变色和光致变色特性的分子以及 N-缩水甘油基苯胺及其衍生物，由于合成简单且能对刺激做出反应，已成为很有前途的候选物质。在这项研究中，我们通过机械化学方法合成了一系列苯胺，重点是溴苯胺残基上的 H/F 取代基。我们找到了一种合适的光致变色化合物，并将其加入到羧甲基纤维素（CMC）生物聚合物基质中，制成了一种光致变色复合薄膜。紫外线辐射使薄膜的颜色从无色变为红色：在不同温度下，通过紫外可见光谱法评估了可逆性。这种复合薄膜在-19 °C和4 °C温度下保持深红色长达七周，而在室温下则迅速变为白色/淡黄色，因此适用于冷链运输和需要快速目测储存条件的场合。

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引用次数: 0

A Zn(ii) pillared-layer ultramicroporous metal–organic framework with matching molecular pockets for C2H2/CO2 separation† 具有匹配分子口袋的 Zn(II) 柱状层超微多孔金属有机框架，用于分离 C2H2/CO2

IF 3.2 3区工程技术 Q2 CHEMISTRY, PHYSICAL

Molecular Systems Design & Engineering

Pub Date : 2024-05-31 DOI: 10.1039/D4ME00066H

Rong Yang, Yu Wang, Tao Zhang, Zhen Xu, Jian-Wei Cao and Kai-Jie Chen

Similar sizes and boiling points of acetylene (C₂H₂) and carbon dioxide (CO₂) make CO₂ separation from C₂H₂/CO₂ mixtures challenging. In this work, a pillared-layer ultramicroporous Zn-mipa-datz material featuring a C₂H₂-matching cavity was successfully prepared to achieve high-efficiency C₂H₂/CO₂ separation. The separation performance of Zn-mipa-datz on C₂H₂/CO₂ mixtures was investigated through gas adsorption isotherms and dynamic breakthrough experiments. Zn-mipa-datz possessed high C₂H₂ separation efficiency for C₂H₂/CO₂ mixtures. The molecular simulation demonstrated that the strong C₂H₂–host interaction was achieved by the synergistic effect of C–N electrostatic interactions and C–H⋯N H bonds.

乙炔（C2H2）和二氧化碳（CO2）的尺寸和沸点相似，这使得从 C2H2/CO2 混合物中分离二氧化碳具有挑战性。本研究成功制备了一种具有 C2H2 匹配空腔的层状超微孔 Zn-mipa-datz 材料，以实现 C2H2/CO2 的高效分离。通过气体吸附等温线和动态突破实验研究了 Zn-mipa-datz 对 C2H2/CO2 混合物的分离性能。Zn-mipa-datz 在 C2H2/CO2 混合物中具有很高的 C2H2 分离效率。分子模拟表明，C-N 静电作用和 C-H-N H 键的协同效应实现了强大的 C2H2-宿主相互作用。

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引用次数: 0

Post-SELEX modifications with locked nucleic acids (LNA) of a SEA-specific DNA aptamer assisted by in silico modelling 用锁定核酸 (LNA) 对一种 SEA 特异性 DNA 类似物进行 SELEX 后修饰，并辅以硅学建模

IF 3.6 3区工程技术 Q1 Chemistry

Molecular Systems Design & Engineering

Pub Date : 2024-05-28 DOI: 10.1039/d4me00043a

Ricardo Oliveira, Eva Pinho, Nuno Filipe Azevedo, Carina Almeida

Post-SELEX modifications assisted by in silico modelling are powerful tools to improve the performance of aptamers, by providing a rational approach for the selection of modified-versions of aptamers. In this study, a complete in silico analysis of the three-dimensional structure of a previously selected DNA aptamer (Apt5) against staphylococcal enterotoxin A (SEA) was performed. Locked nucleic acid (LNA) modifications were introduced in key locations and their effect on the aptamer structure and docking were evaluated. Promising LNA aptamers were then synthetized and their dissociation constants (K_D), as well as stability, were evaluated. From the in silico analysis, it was possible to identify three promising LNA variations that did not affect drastically the three-dimensional structure and the molecular docking with the toxin. The K_D of the LNA aptamers were higher than the DNA aptamer (Apt5: K_D = 13 ± 2 nM, LNA13: K_D = 157 ± 39 nM, LNA14: K_D = 74 ± 24 nM, LNA15: K_D = 143 ± 28 nM), but remained in the low nanomolar range. Even so, the K_D of LNA14 was not significantly different (P < 0.05) compared to the value of the original aptamer and the introduction of LNA increased its thermal stability, increasing the range of functionality of the original aptamer. However, the introduced modifications were not enough to increase the biological stability of the aptamer, remaining susceptible to a complete degradation by endonucleases and exonucleases in 5 minutes. Altough partial modifications with LNA may not be able to overcome all the limitations of DNA aptamers, post-SELEX modifications assisted by in silico modelling have shown promising results in predicting functional modified aptamers, avoiding a time-consuming and expensive trial and error approach.

通过硅学建模辅助 SELEX 后修饰是提高适配体性能的有力工具，它为选择修饰后的适配体提供了一种合理的方法。在本研究中，我们对之前筛选出的针对葡萄球菌肠毒素 A（SEA）的 DNA 短肽（Apt5）的三维结构进行了完整的硅学分析。在关键位置引入了锁定核酸（LNA）修饰，并评估了它们对适配体结构和对接的影响。然后合成了有前景的 LNA 合酶，并评估了它们的解离常数（KD）和稳定性。通过硅学分析，确定了三种有前景的 LNA 变体，它们不会对三维结构和与毒素的分子对接产生重大影响。LNA 嵌合体的 KD 值高于 DNA 嵌合体（Apt5：KD = 13 ± 2 nM；LNA13：KD = 157 ± 39 nM；LNA14：KD = 74 ± 24 nM；LNA15：KD = 143 ± 28 nM），但仍保持在较低的纳摩尔范围内。即便如此，LNA14 的 KD 与原始适配体的 KD 值相比并无显著差异（P < 0.05），而且 LNA 的引入增加了其热稳定性，扩大了原始适配体的功能范围。然而，引入的修饰并不足以提高链接酶的生物稳定性，它仍然容易在 5 分钟内被内切酶和外切酶完全降解。虽然用 LNA 进行部分修饰可能无法克服 DNA 类似物的所有局限性，但在硅学建模的辅助下，SELEX 后修饰在预测功能性修饰类似物方面显示出了很好的效果，避免了耗时且昂贵的反复试验方法。

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引用次数: 0

Supramolecular assembly of multifunctional protein gels via an N-glycosylation consensus sequence fusion domain 通过 N-糖基化共识序列融合域超分子组装多功能蛋白质凝胶

IF 3.6 3区工程技术 Q1 Chemistry

Molecular Systems Design & Engineering

Pub Date : 2024-05-24 DOI: 10.1039/d4me00029c

Eric Douglas Hill, Stephen Michel, Natasha R Sequeira, Benjamin Keselowsky, Gregory A Hudalla

Polypeptide fusion tags that can direct the assembly of folded proteins into supramolecular networks are attractive for creating functional biomaterials. A practical challenge is identifying polypeptide sequences that form supramolecular networks in response to specific user-controlled stimuli, which is advantageous for producing polypeptide-protein fusions using cell-based expression hosts. Here, we report an N-glycosylation tag, (GGGSGGGSGGNWTT)₁₀ or “NGT,” that assembles into a supramolecular network at reduced temperatures when fused to a folded protein. For example, NGT fused to superfolder green fluorescent protein (NGTsfGFP) formed materials that emitted green fluorescence in blue light, while NGT fused to NanoLuc luciferase (NGTnL) formed materials that emitted blue light in the presence of the chemical substrate furimazine. Oscillatory rheology established the materials as weak viscoelastic gels that can undergo shear-thinning and self-healing. Gel formation could be disrupted by mutating the asparagines in NGT to glutamines, introducing a chaotropic agent, or modifying the asparagines in NGT with glucose, suggesting a role for hydrogen bonds involving asparagine in supramolecular network formation. A mixture of soluble NGTsfGFP and NGTnL formed a multifunctional gel at reduced temperature that demonstrated bioluminescence resonance energy transfer between the nL and sfGFP domains in the presence of furimazine. Collectively, these data establish NGT as a temperature-responsive polypeptide tag that can be used to create functional biomaterials from soluble fusion proteins synthesized by cell-based hosts.

能引导折叠蛋白质组装成超分子网络的多肽融合标签对创造功能性生物材料很有吸引力。一个实际的挑战是识别能在用户控制的特定刺激下形成超分子网络的多肽序列，这对利用基于细胞的表达宿主生产多肽-蛋白质融合体是有利的。在这里，我们报告了一种 N-糖基化标签 (GGGSGGSGGGNWTT)10 或 "NGT"，当它与折叠蛋白融合时，可在较低温度下组装成超分子网络。例如，与超级折叠绿色荧光蛋白（NGTsfGFP）融合的 NGT 形成的材料在蓝光下发出绿色荧光，而与 NanoLuc 荧光素酶（NGTnL）融合的 NGT 形成的材料在化学底物呋喃嗪的存在下发出蓝光。振荡流变学确定了这些材料是弱粘弹性凝胶，可以发生剪切稀化和自我修复。通过将 NGT 中的天冬酰胺突变为谷氨酸、引入混沌剂或用葡萄糖修饰 NGT 中的天冬酰胺，可以破坏凝胶的形成。可溶性 NGTsfGFP 和 NGTnL 的混合物在低温下形成了多功能凝胶，在呋喃嗪的存在下，nL 和 sfGFP 结构域之间发生了生物发光共振能量转移。总之，这些数据证明 NGT 是一种温度响应性多肽标签，可用于利用细胞宿主合成的可溶性融合蛋白制造功能性生物材料。

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引用次数: 0

Two conjectures on 3D Voronoi Structures: A Toolkit with Biomedical Case Studies 关于 3D Voronoi 结构的两个猜想：生物医学案例研究工具包

IF 3.6 3区工程技术 Q1 Chemistry

Molecular Systems Design & Engineering

Pub Date : 2024-05-24 DOI: 10.1039/d4me00036f

Lucy Todd, Matthew H.W. Chin, Marc-Olivier Coppens

3D Voronoi scaffolds are widely applied in the field of additive manufacturing as they are known for their light weight structural resilience and share many topological similarities to various natural (bone, tumours, lymph node) and synthetic environments (foam, functionally gradient porous materials). Unfortunately, the structural design features that promote these topological similarities (such as the number of vertices) are often unpredictable and require the trial and error of varying design features to achieve the desired 3D Voronoi structure. This article provides a toolkit, consisting of equations, based on over 12,000 3D Voronoi structures. These equations allow design features, such as the number of generating points (G), to be efficiently and accurately predicted based on the desired structural parameters (within ±3 G). Based on these equations we are proposing, to the best of our knowledge, two new mathematical conjectures that relate the number of vertices or edges, and the average edge length to G in Voronoi structures. These equations have been validated for a wide range of parameter values and Voronoi network sizes. A design code is provided allowing any of over 12,000 structures to be selected, easily adjusted based on user requirements, and 3D printed. Biomedical case studies relevant to T-cell culturing, bone scaffolds and kidney tumours are presented to illustrate the design code.

三维 Voronoi 支架广泛应用于增材制造领域，因为它们以轻质结构弹性著称，与各种自然环境（骨骼、肿瘤、淋巴结）和合成环境（泡沫、功能梯度多孔材料）具有许多拓扑相似性。遗憾的是，促进这些拓扑相似性的结构设计特征（如顶点数量）往往是不可预测的，需要反复试验不同的设计特征，才能实现理想的三维 Voronoi 结构。本文以 12,000 多个三维 Voronoi 结构为基础，提供了一个由方程式组成的工具包。通过这些方程，可以根据所需的结构参数（±3 G 以内）高效、准确地预测设计特征，如生成点数量 (G)。据我们所知，基于这些方程，我们提出了两个新的数学猜想，它们将顶点或边的数量以及平均边长与 Voronoi 结构中的 G 相关联。这些等式已在广泛的参数值和 Voronoi 网络大小中得到验证。提供的设计代码允许从超过 12,000 种结构中选择任何一种，并可根据用户要求轻松调整和 3D 打印。为说明设计代码，还介绍了与 T 细胞培养、骨支架和肾肿瘤相关的生物医学案例研究。

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引用次数: 0

Heteroatoms chemical tailoring of aluminum nitrite nanotubes as biosensors for 5-hydroxyindole acetic acid (a biomarker for carcinoid tumors): insights from a computational study 亚硝酸铝纳米管的异质原子化学定制作为 5-羟基吲哚乙酸（类癌的生物标记物）的生物传感器：计算研究的启示

IF 3.6 3区工程技术 Q1 Chemistry

Molecular Systems Design & Engineering

Pub Date : 2024-05-22 DOI: 10.1039/d4me00019f

Chioma B. Ubah, Martilda U. Akem, Innocent Benjamin, Henry O. Edet, Adedapo S. Adeyinka, Hitler Louis

This study aims to elucidate the properties of aluminum nitrite nanotubes (AlNNT) encapsulated with phosphorus (P@AlNNT), sulphur (S@AlNNT), and silicon (Si@AlNNT) heteroatoms for use as biosensors for 5-hydroxyindoleacetic acid (5HIAA). It was considered an indicative biomarker for carcinoid tumors and investigated using the density functional theory (DFT) at the ωB97XD/def2svp level of theory. With adsorption energies of −0.009 eV, 0.055 eV, and 0.044 eV for 5HIAA_P@AINNT, 5HIAA_S@AINNT, and 5HIAA_Si@AINNT, respectively, the 5HIAA_P@AINNT was the only favorable system for adsorption of 5HIAA. According to the topological investigation, the hydrogen bond strength was in the order of 5HIAA_Si@AlNNT > 5HIAA_S@AlNNT > 5HIAA_P@AlNNT. This was also confirmed by NCI-RDG analysis. Regarding sensory parameters, as per the fraction of electron transfer, 5HIAA_S@AlNNT had the highest propensity to react with the sensor followed by 5HIAA_Si@AlNNT. The order of recovery time (τ) was recorded to be 5HIAA_P@AlNNT < 5HIAA_S@AlNNT < 5HIAA_Si@AlNNT. It was recorded that the systems 5HIAA_S@AlNNT and 5HIAA_Si@AlNNT had longer recovery times at 310 K when compared to their recovery times at 298 K. However, the system 5HIAA_P@AlNNT records a minute shorter recovery time at 298 K compared to its recovery time at 310 K. Results from molecular dynamic simulation reveal that 5HIAA_S@AlNNT and 5HIAA_Si@AlNNT are more thermally stable, which is necessary for reliable and accurate detection. System 5HIAA_P@AlNNT records the most favourable adsorption property and considerable sensing characteristics.

本研究旨在阐明包裹有磷（P@AlNNT）、硫（S@AlNNT）和硅（Si@AlNNT）杂原子的亚硝酸铝纳米管（AlNNT）作为 5-羟基吲哚乙酸（5HIAA）生物传感器的特性。它被认为是类癌的指示性生物标记物，并使用密度泛函理论（DFT）在ωB97XD/def2svp理论水平上对其进行了研究。5HIAA_P@AINNT、5HIAA_S@AINNT和5HIAA_Si@AINNT的吸附能分别为-0.009 eV、0.055 eV和0.044 eV，5HIAA_P@AINNT是唯一有利于吸附5HIAA的体系。根据拓扑学研究，氢键强度依次为 5HIAA_Si@AlNNT > 5HIAA_S@AlNNT > 5HIAA_P@AlNNT。NCI-RDG 分析也证实了这一点。在感官参数方面，根据电子转移的比例，5HIAA_S@AlNNT 与传感器发生反应的倾向性最高，其次是 5HIAA_Si@AlNNT。根据记录，恢复时间（τ）的顺序为 5HIAA_P@AlNNT < 5HIAA_S@AlNNT < 5HIAA_Si@AlNNT。分子动力学模拟结果表明，5HIAA_S@AlNNT 和 5HIAA_Si@AlNNT 在 310 K 时的恢复时间比在 298 K 时的恢复时间长。5HIAA_P@AlNNT 系统具有最有利的吸附特性和相当可观的传感特性。

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引用次数: 0

Insights into controlling bacterial cellulose nanofiber film properties through balancing thermodynamic interactions and colloidal dynamics 通过平衡热力学相互作用和胶体动力学控制细菌纤维素纳米纤维膜特性的启示

IF 3.6 3区工程技术 Q1 Chemistry

Molecular Systems Design & Engineering

Pub Date : 2024-05-21 DOI: 10.1039/d4me00058g

Aban Mandal, Kuotian Liao, Hareesh Iyer, Junhao Lin, Xinqi Li, Shuai Zhang, Eleftheria Roumeli

In recent years, nanocellulose has emerged as a sustainable and environmentally friendly alternative to traditional petroleum-derived structural polymers. Sourced either from plants, algae, or bacteria, nanocellulose can be processed into colloid, gel, film and fiber forms. However, the required fundamental understanding of process parameters that govern the morphology and structure-property relationships of nanocellulose systems, from colloidal suspensions to bulk materials, has not been developed and generalized for all forms of cellulose. This further hinders the more widespread adoption of this biopolymer in applications. Our study investigates the dispersion of cellulose nanofibers (CNFs) produced by a bacterial-yeast co-culture, in solvents, highlighting the role of thermodynamic interactions in influencing their colloidal behavior. By adjusting Hansen solubility parameters, we controlled the thermodynamic relationship between CNFs and solvents across various concentrations, studying the dilute to semi-dilute regimes. Rheological measurements revealed that the threshold at which a concentration-based regime transition occurs is distinctly solvent-dependent. Complementing rheological analysis with small angle X-ray scattering and zeta potential measurements, our findings reveal that enhancing CNF-solvent interactions increases excluded volume in the dilute regime, emphasizing the importance of the balance between fiber-fiber and fiber-solvent interactions. Moreover, we investigated the transition from colloidal to solid state by creating films from dispersions with varying interaction parameters in semi-dilute regimes. Through mechanical testing and scanning electron microscopy imaging of the fracture surfaces, we highlight the significance of electrokinetic effects in such transitions, as dispersions with higher electrokinetic stabilization gave rise to stronger and tougher films despite having less favorable thermodynamic interaction parameters. Our work provides insights into the thermodynamic and electrokinetic interplay that governs bacterial CNF dispersion, offering a foundation for future application and a deeper understanding of nanocellulose's colloidal and structure-property relationships.

近年来，纳米纤维素已成为传统石油衍生结构聚合物的可持续环保替代品。纳米纤维素来源于植物、藻类或细菌，可加工成胶体、凝胶、薄膜和纤维等形式。然而，对于纳米纤维素系统（从胶体悬浮液到块状材料）的形态和结构-性能关系所需的工艺参数的基本了解，还没有针对所有形式的纤维素进行开发和普及。这进一步阻碍了这种生物聚合物的广泛应用。我们的研究调查了细菌-酵母共培养产生的纤维素纳米纤维（CNFs）在溶剂中的分散情况，强调了热力学相互作用在影响其胶体行为中的作用。通过调整汉森溶解度参数，我们控制了不同浓度的 CNFs 和溶剂之间的热力学关系，研究了稀释到半稀释体系。流变测量结果表明，发生基于浓度的体系转换的阈值明显取决于溶剂。通过小角 X 射线散射和 zeta 电位测量对流变学分析进行补充，我们的研究结果表明，增强 CNF 与溶剂之间的相互作用会增加稀释体系中的排除体积，从而强调了纤维与纤维之间以及纤维与溶剂之间相互作用平衡的重要性。此外，我们还通过在半稀释状态下利用不同的相互作用参数从分散体中生成薄膜，研究了从胶体状态到固态状态的转变。通过对断裂表面进行机械测试和扫描电子显微镜成像，我们强调了电动力学效应在这种转变中的重要性，因为尽管热力学相互作用参数不太有利，但具有较高电动力学稳定性的分散体却能产生更强、更坚韧的薄膜。我们的工作深入揭示了支配细菌 CNF 分散的热力学和电动相互作用，为未来的应用奠定了基础，并加深了对纳米纤维素胶体和结构-性能关系的理解。

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引用次数: 0

Innovative strategies for the quantitative modeling of blood–brain barrier (BBB) permeability: harnessing the power of machine learning-based q-RASAR approach† 血脑屏障（BBB）渗透性定量建模的创新策略：利用基于机器学习的 q-RASAR 方法的力量

IF 3.2 3区工程技术 Q2 CHEMISTRY, PHYSICAL

Molecular Systems Design & Engineering

Pub Date : 2024-05-20 DOI: 10.1039/D4ME00056K

Vinay Kumar, Arkaprava Banerjee and Kunal Roy

In the current research, we have unveiled an advanced technique termed the quantitative read-across structure–activity relationship (q-RASAR) framework to harness the power of machine learning (ML) for significantly enhancing the precision of predictions related to blood–brain barrier (BBB) permeability. It is important to emphasize that the central objective of this study is not to introduce an additional model for predicting BBB permeability. Instead, our focus is on highlighting the improvement in quantitatively predicting the BBB permeability of organic compounds by utilizing the q-RASAR approach. This innovative methodology strives to enhance the precision of evaluating neuropharmacological implications and streamline the drug development process. In this investigation, we developed a machine learning (ML)-based q-RASAR PLS regression model using a large dataset comprising 1012 diverse classes of heterocyclic and aromatic compounds, obtained from the freely accessible B3DB database (accessible at https://github.com/theochem/B3DB) to predict BBB permeability during the lead discovery phase for central nervous system (CNS) drugs. The model's predictive capability underwent validation using two external sets, encompassing a total of 1 130 315 compounds, including synthetic compounds and natural products (NPs) for data gap filling and other two external sets comprising 116 drug-like/drug compounds from the FDA and ChEMBL databases to assess the model's reliability against the reported BBB permeability values. This study aimed to bridge the data gap by employing a predictive regression model to estimate the BBB permeability for both synthetic compounds and natural products (NPs). To further enhance predictability, we have developed various other ML-based q-RASAR models. The insights from the developed model highlight the pivotal roles played by hydrophobicity, electronic effects, degree of ionization, and steric factors as essential features facilitating the traversal of the blood–brain barrier. This research not only advances our understanding of the molecular determinants influencing the permeability of central nervous system drugs but also establishes a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in the realms of drug development and design.

在目前的研究中，我们推出了一种被称为定量交叉结构-活性关系（q-RASAR）框架的先进技术，以利用机器学习（ML）的力量显著提高与血脑屏障（BBB）通透性有关的预测精度。需要强调的是，本研究的核心目标并不是引入一个额外的模型来预测血脑屏障的通透性。相反，我们的重点是强调利用 q-RASAR 方法在定量预测有机化合物的 BBB 渗透性方面的改进。这种创新方法致力于提高神经药理学影响评估的精确度，并简化药物开发流程。在这项研究中，我们开发了一个基于机器学习（ML）的q-RASAR PLS回归模型，该模型使用了一个大型数据集，其中包括1012种不同类别的杂环化合物和芳香化合物，这些数据集来自可免费访问的B3DB数据库（可在https://github.com/theochem/B3DB），用于预测中枢神经系统（CNS）药物先导发现阶段的BBB渗透性。该模型的预测能力通过两组外部数据进行了验证，其中一组包括合成化合物和天然产物（NPs），共计 1 130 315 个化合物，用于填补数据缺口；另外两组外部数据包括来自 FDA 和 ChEMBL 数据库的 116 个类药物/药物化合物，用于评估该模型与所报告的 BBB 渗透性值之间的可靠性。本研究旨在采用预测回归模型来估算合成化合物和天然产物（NPs）的生物BB渗透性，从而弥补数据缺口。为了进一步提高预测能力，我们还开发了其他各种基于 ML 的 q-RASAR 模型。从所开发的模型中得到的启示强调了疏水性、电子效应、电离程度和立体因素在促进穿越血脑屏障的基本特征中所起的关键作用。这项研究不仅加深了我们对影响中枢神经系统药物渗透性的分子决定因素的理解，还为快速评估各种化合物建立了一个多功能计算平台，有助于在药物开发和设计领域做出明智的决策。

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引用次数: 0

Recent Advances in 3D Bioprinting of Polysaccharide-based Bioinks for Fabrication of Bioengineered Tissues 基于多糖的三维生物打印技术在制造生物工程组织方面的最新进展

IF 3.6 3区工程技术 Q1 Chemistry

Molecular Systems Design & Engineering

Pub Date : 2024-05-16 DOI: 10.1039/d4me00001c

Nagaraja Kasula, Pratic Dhokare, Amitava Bhattacharyya, Insup Noh

Bioink in three-dimensional (3D) bioprinting of biomimetic tissue scaffolds has emerged as a key factor for the success of tissue engineering and regenerative medicine. The bioinks used for extrusion 3D bioprinting have hydrogel matrices with different kinds of polymeric biomaterials such as proteins, peptides, polysaccharides, hydrophilic synthetic polymers, and others. Natural polysaccharides such as alginate, chitosan, and hyaluronic acid, have garnered significant attention as bioink materials due to their excellent biocompatibility, extracellular matrix mimetic properties, biodegradability, injectability, bioprintablilty and structural versatility among their many advantages, even though many research groups focus on the study of protein-based bioinks to utilize their high potential of cell adhesiveness. This review encompasses recent advancements of polysaccharide-based hydrogels and bioinks for the bioengineered tissue regeneration and reconstruction, especially by focusing on fabrication of multilayered complex structures for biomimetic tissue engineering applications.

三维（3D）生物打印仿生组织支架中的生物墨水已成为组织工程和再生医学取得成功的关键因素。用于挤压三维生物打印的生物墨水具有不同种类高分子生物材料的水凝胶基质，如蛋白质、肽、多糖、亲水性合成聚合物等。海藻酸、壳聚糖和透明质酸等天然多糖具有良好的生物相容性、细胞外基质模拟特性、生物降解性、可注射性、生物打印性和结构多样性等诸多优点，因此作为生物墨水材料备受关注。本综述介绍了多糖基水凝胶和生物沉材料在生物工程组织再生和重建方面的最新进展，尤其是多层复杂结构的制造在仿生组织工程中的应用。

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