Pectin-Coated Iron-Based Metal-Organic Framework Nanoparticles for Enhanced Foliar Adhesion and Targeted Delivery of Fungicides.

Conventional agrochemicals are underutilized due to their large particle sizes, poor foliar retention rates, and difficult translocation in plants, and the development of functional nanodelivery carriers with high adhesion to the plant body surface and efficient uptake and translocation in plants remains challenging. In this study, a nanodelivery system based on a pectin-encapsulated iron-based MOF (TF@Fe-MOF-PT NPs) was constructed to enhance the utilization of thifluzamide (TF) in rice plants by taking advantage of the pectin affinity for plant cell walls. The prepared TF@Fe-MOF-PT NPs exhibited an average particle size of 126.55 nm, a loading capacity of 27.41%, and excellent dual-stimulus responses to reactive oxygen species and pectinase. Foliar washing experiments showed that the TF@Fe-MOF-PT NPs were efficiently adhered to the surfaces of rice leaves and stems. Confocal laser scanning microscopy showed that fluorescently labeled TF@Fe-MOF-PT NPs were bidirectionally delivered through vascular bundles in rice plants. The in vitro bactericidal activity of the TF@Fe-MOF-PT NPs showed better inhibitory activity than that of a TF suspension (TF SC), with an EC50 of 0.021 mg/L. A greenhouse test showed that the TF@Fe-MOF-PT NPs were more effective than TF SC at 7 and 14 d, with control effects of 85.88 and 78.59%, respectively. It also reduced the inhibition of seed stem length and root length by TF SC and promoted seedling growth. These results demonstrated that TF@Fe-MOF-PT NPs can be used as a pesticide nanodelivery system for efficient delivery and intelligent release in plants and applied for sustainable control of pests and diseases.

Assessment of Amino Acid Electrostatic Parametrizations of the Polarizable Gaussian Multipole Model.

Accurate parametrization of amino acids is pivotal for the development of reliable force fields for molecular modeling of biomolecules such as proteins. This study aims to assess amino acid electrostatic parametrizations with the polarizable Gaussian Multipole (pGM) model by evaluating the performance of the pGM-perm (with atomic permanent dipoles) and pGM-ind (without atomic permanent dipoles) variants compared to the traditional RESP model. The 100-conf-combterm fitting strategy on tetrapeptides was adopted, in which (1) all peptide bond atoms (-CO-NH-) share identical set of parameters and (2) the total charges of the two terminal N-acetyl (ACE) and N-methylamide (NME) groups were set to neutral. The accuracy and transferability of electrostatic parameters across peptides with varying lengths and real-world examples were examined. The results demonstrate the enhanced performance of the pGM-perm model in accurately representing the electrostatic properties of amino acids. This insight underscores the potential of the pGM-perm model and the 100-conf-combterm strategy for the future development of the pGM force field.

Long-term toxic effects of iron-based metal-organic framework nanopesticides on earthworm-soil microorganism interactions in the soil environment.

With the widespread use of controlled-release nanopesticides in field conditions, the interactions between these nanopesticides and biological systems are complex and highly uncertain. The toxicity of iron-based metal organic frameworks (CF@MIL-101-SL) loaded with chlorfenapyr (CF) to terrestrial invertebrate earthworms in filter paper and soil environments and the potential mechanisms of interactions in the nanopesticide-earthworm-cornfield soil microorganism system were investigated for the first time. The results showed that CF@MIL-101-SL was more poisonous to earthworms in the contact filter paper test than suspension concentrate of CF (CF-SC), and conversely, CF@MIL-101-SL was less poisonous to earthworms in the soil test. In the soil environment, the CF@MIL-101-SL treatment reduced oxidative stress and the inhibition of detoxifying enzymes, and reduced tissue and cellular substructural damage in earthworms compared to the CF-SC treatment. Long-term treatment with CF@MIL-101-SL altered the composition and abundance of microbial communities with degradative functions in the earthworm intestine and soil and affected the soil nitrogen cycle by modulating the composition and abundance of nitrifying and denitrifying bacterial communities in the earthworm intestine and soil, confirming that soil microorganisms play an important role in reducing the toxicity of CF@MIL-101-SL to earthworms. In conclusion, this study provides new insights into the ecological risks of nanopesticides to soil organisms.

AmberTools.

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Streamlining and Optimizing Strategies of Electrostatic Parameterization.

Accurate characterization of electrostatic interactions is crucial in molecular simulation. Various methods and programs have been developed to obtain electrostatic parameters for additive or polarizable models to replicate electrostatic properties obtained from experimental measurements or theoretical calculations. Electrostatic potentials (ESPs), a set of physically well-defined observables from quantum mechanical (QM) calculations, are well suited for optimization efforts due to the ease of collecting a large amount of conformation-dependent data. However, a reliable set of QM ESP computed at an appropriate level of theory and atomic basis set is necessary. In addition, despite the recent development of the PyRESP program for electrostatic parameterizations of induced dipole-polarizable models, the time-consuming and error-prone input file preparation process has limited the widespread use of these protocols. This work aims to comprehensively evaluate the quality of QM ESPs derived by eight methods, including wave function methods such as Hartree-Fock (HF), second-order Møller-Plesset (MP2), and coupled cluster-singles and doubles (CCSD), as well as five hybrid density functional theory (DFT) methods, used in conjunction with 13 different basis sets. The highest theory levels CCSD/aug-cc-pV5Z (a5z) and MP2/aug-cc-pV5Z (a5z) were selected as benchmark data over two homemade data sets. The results show that the hybrid DFT method, ωB97X-D, combined with the aug-cc-pVTZ (a3z) basis set, performs well in reproducing ESPs while taking both accuracy and efficiency into consideration. Moreover, a flexible and user-friendly program called PyRESP_GEN was developed to streamline input file preparation. The restraining strengths, along with strategies for polarizable Gaussian multipole (pGM) model parameterizations, were also optimized. These findings and the program presented in this work facilitate the development and application of induced dipole-polarizable models, such as pGM models, for molecular simulations of both chemical and biological significance.

Target deconvolution with matrix-augmented pooling strategy reveals cell-specific drug-protein interactions.

Target deconvolution is a crucial but costly and time-consuming task that hinders large-scale profiling for drug discovery. We present a matrix-augmented pooling strategy (MAPS) which mixes multiple drugs into samples with optimized permutation and delineates targets of each drug simultaneously with mathematical processing. We validated this strategy with thermal proteome profiling (TPP) testing of 15 drugs concurrently, increasing experimental throughput by 60x while maintaining high sensitivity and specificity. Benefiting from the lower cost and higher throughput of MAPS, we performed target deconvolution of the 15 drugs across 5 cell lines. Our profiling revealed that drug-target interactions can differ vastly in targets and binding affinity across cell lines. We further validated BRAF and CSNK2A2 as potential off-targets of bafetinib and abemaciclib, respectively. This work represents the largest thermal profiling of structurally diverse drugs across multiple cell lines to date.

Optimal Scheme to Achieve Energy Conservation in Induced Dipole Models.

Induced dipole models have proven to be effective tools for simulating electronic polarization effects in biochemical processes, yet their potential has been constrained by energy conservation issue, particularly when historical data is utilized for dipole prediction. This study identifies error outliers as the primary factor causing this failure of energy conservation and proposes a comprehensive scheme to overcome this limitation. Leveraging maximum relative errors as a convergence metric, our data demonstrates that energy conservation can be upheld even when using historical information for dipole predictions. Our study introduces the multi-order extrapolation method to quicken induction iteration and optimize the use of historical data, while also developing the preconditioned conjugate gradient with local iterations to refine the iteration process and effectively remove error outliers. This scheme further incorporates a "peek" step via Jacobi under-relaxation for optimal performance. Simulation evidence suggests that our proposed scheme can achieve energy convergence akin to that of point-charge models within a limited number of iterations, thus promising significant improvements in efficiency and accuracy.

Research on Driving Obstacle Detection Technology in Foggy Weather Based on GCANet and Feature Fusion Training.

The issues of the degradation of the visual sensor's image quality in foggy weather and the loss of information after defogging have brought great challenges to obstacle detection during autonomous driving. Therefore, this paper proposes a method for detecting driving obstacles in foggy weather. The driving obstacle detection in foggy weather was realized by combining the GCANet defogging algorithm with the detection algorithm-based edge and convolution feature fusion training, with a full consideration of the reasonable matching between the defogging algorithm and the detection algorithm on the basis of the characteristics of obvious target edge features after GCANet defogging. Based on the YOLOv5 network, the obstacle detection model is trained using clear day images and corresponding edge feature images to realize the fusion of edge features and convolution features, and to detect driving obstacles in a foggy traffic environment. Compared with the conventional training method, the method improves the mAP by 12% and recall by 9%. In contrast to conventional detection methods, this method can better identify the image edge information after defogging, which significantly enhances detection accuracy while ensuring time efficiency. This is of great practical significance for improving the safe perception of driving obstacles under adverse weather conditions, ensuring the safety of autonomous driving.

Transferability of the Electrostatic Parameters of the Polarizable Gaussian Multipole Model.

Accuracy and transferability are the two highly desirable properties of molecular mechanical force fields. Compared with the extensively used point-charge additive force fields that apply fixed atom-centered point partial charges to model electrostatic interactions, polarizable force fields are thought to have the advantage of modeling the atomic polarization effects. Previous works have demonstrated the accuracy of the recently developed polarizable Gaussian multipole (pGM) models. In this work, we assessed the transferability of the electrostatic parameters of the pGM models with (pGM-perm) and without (pGM-ind) atomic permanent dipoles in terms of reproducing the electrostatic potentials surrounding molecules/oligomers absent from electrostatic parameterizations. Encouragingly, both the pGM-perm and pGM-ind models show significantly improved transferability than the additive model in the tests (1) from water monomer to water oligomer clusters; (2) across different conformations of amino acid dipeptides and tetrapeptides; (3) from amino acid tetrapeptides to longer polypeptides; and (4) from nucleobase monomers to Watson-Crick base pair dimers and tetramers. Furthermore, we demonstrated that the double-conformation fittings using amino acid tetrapeptides in the αR and ß conformations can result in good transferability not only across different tetrapeptide conformations but also from tetrapeptides to polypeptides with lengths ranging from 1 to 20 repetitive residues for both the pGM-ind and pGM-perm models. In addition, the observation that the pGM-ind model has significantly better accuracy and transferability than the point-charge additive model, even though they have an identical number of parameters, strongly suggest the importance of intramolecular polarization effects. In summary, this and previous works together show that the pGM models possess both accuracy and transferability, which are expected to serve as foundations for the development of next-generation polarizable force fields for modeling various polarization-sensitive biological systems and processes.

Accurate Reproduction of Quantum Mechanical Many-Body Interactions in Peptide Main-Chain Hydrogen-Bonding Oligomers by the Polarizable Gaussian Multipole Model.

A key advantage of polarizable force fields is their ability to model the atomic polarization effects that play key roles in the atomic many-body interactions. In this work, we assessed the accuracy of the recently developed polarizable Gaussian Multipole (pGM) models in reproducing quantum mechanical (QM) interaction energies, many-body interaction energies, as well as the nonadditive and additive contributions to the many-body interactions for peptide main-chain hydrogen-bonding conformers, using glycine dipeptide oligomers as the model systems. Two types of pGM models were considered, including that with (pGM-perm) and without (pGM-ind) permanent atomic dipoles. The performances of the pGM models were compared with several widely used force fields, including two polarizable (Amoeba13 and ff12pol) and three additive (ff19SB, ff15ipq, and ff03) force fields. Encouragingly, the pGM models outperform all other force fields in terms of reproducing QM interaction energies, many-body interaction energies, as well as the nonadditive and additive contributions to the many-body interactions, as measured by the root-mean-square errors (RMSEs) and mean absolute errors (MAEs). Furthermore, we tested the robustness of the pGM models against polarizability parameterization errors by employing alternative polarizabilities that are either scaled or obtained from other force fields. The results show that the pGM models with alternative polarizabilities exhibit improved accuracy in reproducing QM many-body interaction energies as well as the nonadditive and additive contributions compared with other polarizable force fields, suggesting that the pGM models are robust against the errors in polarizability parameterizations. This work shows that the pGM models are capable of accurately modeling polarization effects and have the potential to serve as templates for developing next-generation polarizable force fields for modeling various biological systems.

PyRESP: A Program for Electrostatic Parameterizations of Additive and Induced Dipole Polarizable Force Fields.

Molecular modeling at the atomic level has been applied in a wide range of biological systems. The widely adopted additive force fields typically use fixed atom-centered partial charges to model electrostatic interactions. However, the additive force fields cannot accurately model polarization effects, leading to unrealistic simulations in polarization-sensitive processes. Numerous efforts have been invested in developing induced dipole-based polarizable force fields. Whether additive atomic charge models or polarizable induced dipole models are used, proper parameterization of the electrostatic term plays a key role in the force field developments. In this work, we present a Python program called PyRESP for performing atomic multipole parameterizations by reproducing ab initio electrostatic potential (ESP) around molecules. PyRESP provides parameterization schemes for several electrostatic models, including the RESP model with atomic charges for the additive force fields and the RESP-ind and RESP-perm models with additional induced and permanent dipole moments for the polarizable force fields. PyRESP is a flexible and user-friendly program that can accommodate various needs during force field parameterizations for molecular modeling of any organic molecules.

Development of a Pantetheine Force Field Library for Molecular Modeling.

Pantetheine is ubiquitous in nature in various forms of pantetheine-containing ligands (PCLs), including coenzyme A and phosphopantetheine. Lack of scalable force field libraries for PCLs has hampered the computational studies of biological macromolecules containing PCLs. We describe here the development of the first generation Pantetheine Force Field (PFF) library that is compatible with Amber force fields; parameterized using Gasteiger, AM1-BCC, or RESP charging methods combined with gaff2 and ff14SB parameter sets. In addition, a "plug-and-play" strategy was employed to enable the systematic charging of computationally expensive molecules sharing common substructural motifs. The validation studies performed on the PFF library showed promising performance where molecular dynamics (MD) simulations results were compared with experimental data of three representative systems. The PFF library represents the first force field library capable of modeling systems containing PCLs in silico and will aid in various applications including protein engineering and drug discovery.

Molecular Basis for Polyketide Ketoreductase-Substrate Interactions.

Polyketides are a large class of structurally and functionally diverse natural products with important bioactivities. Many polyketides are synthesized by reducing type II polyketide synthases (PKSs), containing transiently interacting standalone enzymes. During synthesis, ketoreductase (KR) catalyzes regiospecific carbonyl to hydroxyl reduction, determining the product outcome, yet little is known about what drives specific KR-substrate interactions. In this study, computational approaches were used to explore KR-substrate interactions based on previously solved apo and mimic cocrystal structures. We found five key factors guiding KR-substrate binding. First, two major substrate binding motifs were identified. Second, substrate length is the key determinant of substrate binding position. Third, two key residues in chain length specificity were confirmed. Fourth, phosphorylation of substrates is critical for binding. Finally, packing/hydrophobic effects primarily determine the binding stability. The molecular bases revealed here will help further engineering of type II PKSs and directed biosynthesis of new polyketides.

Elucidation of WW domain ligand binding specificities in the Hippo pathway reveals STXBP4 as YAP inhibitor.

The Hippo pathway, which plays a critical role in organ size control and cancer, features numerous WW domain-based protein-protein interactions. However, ~100 WW domains and 2,000 PY motif-containing peptide ligands are found in the human proteome, raising a "WW-PY" binding specificity issue in the Hippo pathway. In this study, we have established the WW domain binding specificity for Hippo pathway components and uncovered a unique amino acid sequence required for it. By using this criterion, we have identified a WW domain-containing protein, STXBP4, as a negative regulator of YAP. Mechanistically, STXBP4 assembles a protein complex comprising α-catenin and a group of Hippo PY motif-containing components/regulators to inhibit YAP, a process that is regulated by actin cytoskeleton tension. Interestingly, STXBP4 is a potential tumor suppressor for human kidney cancer, whose downregulation is correlated with YAP activation in clear cell renal cell carcinoma. Taken together, our study not only elucidates the WW domain binding specificity for the Hippo pathway, but also reveals STXBP4 as a player in actin cytoskeleton tension-mediated Hippo pathway regulation.

Computational structural enzymology methodologies for the study and engineering of fatty acid synthases, polyketide synthases and nonribosomal peptide synthetases.

Various computational methodologies can be applied to enzymological studies on enzymes in the fatty acid, polyketide, and non-ribosomal peptide biosynthetic pathways. These multi-domain complexes are called fatty acid synthases, polyketide synthases, and non-ribosomal peptide synthetases. These mega-synthases biosynthesize chemically diverse and complex bioactive molecules, with the intermediates being chauffeured between catalytic partners via a carrier protein. Recent efforts have been made to engineer these systems to expand their product diversity. A major stumbling block is our poor understanding of the transient protein-protein and protein-substrate interactions between the carrier protein and its many catalytic partner domains and product intermediates. The innate reactivity of pathway intermediates in two major classes of polyketide synthases has frustrated our mechanistic understanding of these interactions during the biosynthesis of these natural products, ultimately impeding the engineering of these systems for the generation of engineered natural products. Computational techniques described in this chapter can aid data interpretation or used to generate testable models of these experimentally intractable transient interactions, thereby providing insight into key interactions that are difficult to capture otherwise, with the potential to expand the diversity in these systems.

Improved expression of recombinant fusion defensin gene plasmids packed with chitosan-derived nanoparticles and effect on antibacteria and mouse immunity.

In order to develop a secure and competent technique to express the human immune gene for fighting infections, we cloned and expressed the BD2/3 using VR1020 (a eukaryotic expression plasmid). BD2/3 contains human ß-defensin 2 (BD2) and human BD3. To explore safe and effective DNA delivery molecules in vitro and in vivo, the fusion genes of BD2/3 were used as an immune-labelled gene to verify transfection effectivness of modified chitosan (CS). Plasmid of VR1020-BD2/3 was packed with biomaterials: CS, average molecular weight: 25000D; polyethylene glycol-O-chitosan-polyethylenimine (PEG-O-CS-PEI); liposomes (LP); polyamine cationic liposomes (PCL); polyamine cationic liposomes of protamine (PCL-protamine) by ionotropic gelation. We observed that BD2/3 fusion gene showed high bioactivity in vitro and in vivo. The BD2/3 fusion protein inhibited the proliferation of bacteria (S. aureus, S. pneumoniae, P. aeruginosa and E. coli). The Kunming mice were immune to these nanoparticles and we analyzed their delivery efficiency and gene expression effect. BD2/3 results in multiple changes of innate and required immune system of mice. BD2/3 increases expression of IgG, IgG1, IgG2a, IL-2, IL-6, IFN-γ, as well as of lymphocytes and monocytes. Following challenge with virulent E. coli, CD4+ and CD8+ positive T-cell counts were highly elevated in the BD2/3 immunized mice, resulting in higher survival rates of mice. These results indicate that nanoparticles containing modified CS and BD2/3 are potentially safe and effective drugs in vivo to improve the immunity against bacterial infection and enhance innate immunity and adaptive immunity against infectious diseases.

Co-expression of mFat-1 and pig IGF-1 genes by recombinant plasmids in modified chitosan nanoparticles and its synergistic effect on mouse immunity.

To develop a cost-effective molecular regulator to improve growth metabolism and immunity of animals, a recombinant plasmid co-expressing fatty acid desaturase (mFat-1) and pig insulin growth like factor 1 (IGF-1) genes was constructed by the 2 A self-cleavage technique. After entrapment within modified chitosan nanoparticles (chitosan modified with polyethyleneglycol-polyethylenimine, CPP), the recombinant plasmid was injected intramuscularly into mice. Compared with controls, co-expression of mFat-1 and IGF-1 significantly raised the level of serum IGF-1, and increased the liver and muscle docosa hexaenoic acid (DHA) content. Th and Tc cell levels were also elevated, as were expression levels of serum IL-4 and IL-6 genes. These results demonstrate that the immunity and metabolism of an animal can be effectively improved by co-expression of mFat-1 and IGF-1 genes in vivo, which may contribute to further development of novel immunomodulators with beneficial effects on growth metabolism and immunity.