Effects of Exogenous Gamma-Aminobutyric Acid on Absorption and Regulation of Ion in Wheat Under Salinity Stress

International audienceGamma-aminobutyric acid (GABA), a four-carbon non-protein amino acid, is a significant component of the free amino acid pool, there are numerous reports that rapid and large increases in GABA levels occur in plants, in response to a variety of biotic and abiotic stress. Based on its metabolism and putative roles in plants, GABA is considered a natural chemical to increase wheat salt-tolerance. So this study investigated the exogenous GABA on wheat seedling (Triticum aestivum L. cv. Changwu134 and zhouyuan9369) growth and absorption of salt ions under normal or salt-stressed conditions. The results demonstrated that salt stress inhibited growth of wheat seedlings, decreased dry weight and water content, altered ion balance within the stressed seedlings. Pretreatment with 50 mg/L GABA increased seedling biomass and K+ content in leaves, decreased Na+ content in leaves and roots under salt-stressed conditions by improving Na+ exclusion, K+ retention. These results indicated that exogenous 50 mg/L GABA improved seedling growth and alleviated the inhibition due to salt stress of wheat by altered ion balance. Exogenous GABA has the capability of restraining transportation of salt ions to leaves and sustaining normal function of leaves. And the effect of exogenous GABA is obvious in common variety (zhouyuan9369) than in salt-tolerance variety (changwu134)

Development of Portable Dynamic Ion Flux Detecting Equipment

International audienceNon-destructive testing of plant organs, tissues, and cells has important implications in studying the immediate physiological status of plants. The portable dynamic ion flux test equipment (PDIFTE) was developed based on Fick’s first law of diffusion and the Nernst equation to achieve the ion flux measurement in pmol cm−2s−1. This equipment integrates micro-imaging, micro-signal processing, automation and control, and biosensor technologies with the original signal acquisition and conditioning module, the motion control module, the macro 3D automatically control platform, micro digital imaging system, electrostatic shielding coating, ion-selective microelectrode, and other components. PDIFTE can detect H+, K+, Na+, Mg2+, Ca2+, Cd2+, Cl−, $${\text{NO}}_{3}^{ - }$$, and $${\text{NH}}_{4}^{ + }$$. This device can be used in the physiological mechanism research of salt-resistant, drought-resistant, cold-tolerant, heavy metal-resistant, and disease-resistant plants. It can also be used in the research on plant nutrition, ion channel-related gene function, and crop resistant breeding screening

Molecular Principle of Topotecan Resistance by Topoisomerase I Mutations through Molecular Modeling Approaches

Originally isolated from natural products, camptothecin (CPT) has provided extensive playing fields for the development of antitumor drugs. Two of the most successful analogs of CPT, topotecan and irinotecan, have been approved by the FDA for the treatment of colon cancer and ovarian cancer, as well as other cancers. However, the emergence of drug resistance mutations in topoisomerase I is a big challenge for the effective therapy of these drugs. Therefore, in this study, a series of computational approaches from molecular dynamics (MD) simulations to steered molecular dynamics (SMD) simulations and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) binding free energy calculations were employed to uncover the molecular principle of the topotecan resistance induced by three mutations in DNA topoisomerase I, including E418K, G503S, and D533G. Our results demonstrate a remarkable correlation between the binding free energies predicted by MM/GBSA and the rupture forces computed by SMD, and moreover, the theoretical results given by MM/GBSA and SMD are in excellent agreement with the experimental data for ranking the inhibitory activities: WT > E418K > G503S > D533G. In order to explore the drug resistance mechanism that underlies the loss of the binding affinity of topotecan, the binding modes of topotecan bound to the WT and mutated receptors were presented, and comparisons of the binding geometries and energy contributions on a per residue basis of topotecan between the WT complex and each mutant were also discussed. The results illustrate that the mutations of E418K, G503S, and D533G have great influence on the binding of topotecan to topoisomerase I bound with DNA, and the variations of the polar interactions play critical roles in the development of drug resistance. The information obtained from this study provides useful clues for designing improved topoisomerase I inhibitors for combating drug resistance

Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop

Protein loop modeling is the most challenging yet highly non-trivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab initio, hybrid and deep learning (DL) methods fall significantly short of either atomic accuracy or computational efficiency. Moreover, an overarching focus on backbone atoms has resulted in a dearth of attention given to side-chain conformation, a critical aspect in a host of downstream applications including ligand docking, molecular dynamics simulation and drug design. To overcome these limitations, we present KarmaLoop, a novel paradigm that distinguishes itself as the first DL method centered on full-atom (encompassing both backbone and side-chain heavy atoms) protein loop modeling. Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency, with the average RMSD improved by over two-fold compared to the second-best baseline method across different tasks, and manifests at least two orders of magnitude speedup in general. Consequently, our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling, with the potential to hasten the advancement of protein engineering, antibody-antigen recognition, and drug design.Comment: 20 pages, 6 figures, journal articles and keywords:Protein loop modeling, Loop prediction, Antibody H3 loop, Deep Learnin

In Silico Exploration for Novel Type-I Inhibitors of Tie-2/TEK: The Performance of Different Selection Strategy in Selecting Virtual Screening Candidates

Orientador: Prof. Dr. Rodrigo Vasconcelos MachadoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Humanas, Programa de Pós-Graduação em Letras. Defesa : Curitiba, 14/02/2020Inclui referências: p. 120-130Área de concentração: Estudos literáriosResumo: Os chamados paratextos editoriais teorizados por Gérard Genette surgem como uma espécie de vestíbulo para o texto e representam escolhas do autor ou do editor da obra, carregando sempre uma intenção que justifica sua existência. Nessa perspectiva, este estudo buscou apresentar como prefácios - autorais e alógrafos, presentes no conjunto de obras da escritora mineira Conceição Evaristo podem indicar caminhos editoriais seguidos pela autora, bem como a construção de sua imagem ao longo de diferentes edições, inclusive as de uma mesma obra publicadas em períodos distintos. Ao mesmo tempo, esta pesquisa buscou verificar como esses mesmos paratextos são utilizados para colocar em evidência junto ao leitor questões centrais da obra da escritora - o sujeito-mulher-negra, a violência contra corpos negros e o expediente da memória e ancestralidade. Fazem parte dessa análise os textos introdutórios de Ponciá Vicêncio, Becos da Memória, Insubmissas Lágrimas de Mulheres, Olhos d'água e Histórias de Leves Enganos e Parecenças. Os resultados apontam para uma nova utilidade para o prefácio, considerando Evaristo como uma voz social da coletividade e detentora de um fazer literário ancorado no que ela mesma definiu como escrevivência. Palavras-chave: Conceição Evaristo. Literatura Negra. História do Livro. Paratextos editoriais. Prefácios.Abstract: The paratexts theorized by Gérard Genette appear as a sort of vestibule for the text and represent the author's or publisher's choices, always carrying an intention that justifies its existence. From this perspective, this study aims to present as prefaces - own and allographs, in the works of writer Conceição Evaristo can indicate editorial paths followed by the writer, as well as the construction of her image along different editions, including the same one published in different years. At the same time, this research aims to verify how these same paratexts highlight the central issues of the writer's work - the black woman, the violence against blacks and the expedient of memory and ancestry. It is part of this analysis the prefaces of Poncia Vicêncio, Becos da Memória, Insubmissas Lágrimas de Mulheres, Olhos d'água, e Histórias de Leves Enganos e Parecenças. The results point to a different use for the preface, considering Evaristo as a social voice of the collectivity and holder of a self-anchored writing, as she defined as escrevivência. Palavras-chave: Conceição Evaristo. Black Literature. Book History. Paratexts. Prefaces

Development and Evaluation of an Integrated Virtual Screening Strategy by Combining Molecular Docking and Pharmacophore Searching Based on Multiple Protein Structures

In this study, we developed and evaluated a novel parallel virtual screening strategy by integrating molecular docking and complex-based pharmacophore searching based on multiple protein structures. First, the capacity of molecular docking or pharmacophore searching based on any single structure from nine crystallographic structures of Rho kinase 1 (ROCK1) to distinguish the known ROCK1 inhibitors from noninhibitors was evaluated systematically. Then, the naı̈ve Bayesian classification or recursive partitioning technique was employed to integrate the predictions from molecular docking and complex-based pharmacophore searching based on multiple crystallographic structures of ROCK1, and the integrated protocol yields much better performance than molecular docking or complex-based pharmacophore searching based on any single ROCK1 structure. Finally, the well-validated integrated virtual screening protocol was applied to identify potential inhibitors of ROCK1 from traditional chinese medicine (TCM). The obtained potential active compounds from TCM are structurally novel and diverse compared with the known inhibitors of ROCK1, and they may afford valuable clues for the development of potent ROCK1 inhibitors

Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking

Abstract Graph neural networks (GNNs) have been widely used in molecular property prediction, but explaining their black-box predictions is still a challenge. Most existing explanation methods for GNNs in chemistry focus on attributing model predictions to individual nodes, edges or fragments that are not necessarily derived from a chemically meaningful segmentation of molecules. To address this challenge, we propose a method named substructure mask explanation (SME). SME is based on well-established molecular segmentation methods and provides an interpretation that aligns with the understanding of chemists. We apply SME to elucidate how GNNs learn to predict aqueous solubility, genotoxicity, cardiotoxicity and blood–brain barrier permeation for small molecules. SME provides interpretation that is consistent with the understanding of chemists, alerts them to unreliable performance, and guides them in structural optimization for target properties. Hence, we believe that SME empowers chemists to confidently mine structure-activity relationship (SAR) from reliable GNNs through a transparent inspection on how GNNs pick up useful signals when learning from data

Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network

Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website