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Based on stakeholder theory, our study tested whether responsible leadership would influence employees' work engagement through two different mechanisms-general distributive justice climate and perceived supervisor support-and whether work engagement would lead to employees' creativity. We collected sampling data over eight weeks from 116 employees (Level 1, n = 519) who work in different companies in China. Growth modeling analysis was employed to test the hypotheses. Results indicated that work engagement was predicted by responsible leadership. The responsible leadership-work engagement relationship was also found to be mediated by general distributive justice climate and perceived supervisor support. Moreover, we found that work engagement was positively related to employees' creativity in dynamic nature. We discussed the theoretical and practical implications and suggested some directions for future research., Author(s): Huan Li [sup.1] , Chungwai So [sup.1] , Hanxiao Chen [sup.2] , Yating Wang [sup.3] , Saisai Sun [sup.1] Author Affiliations: (1) grid.412515.6, 0000 0001 1702 5894, School of [...]

C22H20B2F4N4, orthorhombic, Pbca (no. 61), a = 9.2506(2) Å, b = 36.1677(13) Å, c = 12.9475(3) Å, V = 4331.9 Å3, Z = 8, Rgt(F) = 0.0729, wRref(F2) = 0.1992, T = 294.4 K.

Motivation The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA–small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA–small molecule binding affinity prediction has been proposed. The prediction of RNA–small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA–small molecule binding affinity accurately. Results In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA–small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA–small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening. Availability and implementation https://github.com/SaisaiSun/RLaffinity [ABSTRACT FROM AUTHOR]

C14H15B2F4I2N4, triclinic, P1̄ (no. 2), a = 7.5458(7) Å, b = 8.5659(6) Å, c = 8.6089(5) Å, α = 108.189(6)°, β = 110.492(7)°, γ = 98.678(7)°, V = 473.6 Å3, Z = 1, Rgt(F) = 0.0334, wRref(F2) = 0.0831, T = 296.5 K.

C14H15B2BrF4N4, orthorhombic, Pbca (no. 61), a = 11.970(1) Å, b = 10.049(1) Å, c = 27.847(1) Å, V = 3349.6 Å3, Z = 8, Rgt(F) = 0.0697, wRref(F2) = 0.1585, T = 296 K. CCDC no.: 2080820 [ABSTRACT FROM AUTHOR]

C22H20B2F4N4, orthorhombic, Pbca (no. 61), a = 9.2506(2) Å, b = 36.1677(13) Å, c = 12.9475(3) Å, V = 4331.9 Å3, Z = 8, Rgt(F) = 0.0729, wRref(F2) = 0.1992, T = 294.4 K. [ABSTRACT FROM AUTHOR]

C22H20B2F4N4, orthorhombic, Pbca (no. 61), a = 9.2506(2) Å, b = 36.1677(13) Å, c = 12.9475(3) Å, V = 4331.9 Å3, Z = 8, Rgt(F) = 0.0729, wRref(F2) = 0.1992, T = 294.4 K. [ABSTRACT FROM AUTHOR]

Motivation Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness. Results We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L /10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness. Availability and implementation The webserver and a standalone package are available at: http://yanglab.nankai.edu.cn/RNAcontact/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to address this challenge. To the best of our knowledge, there is only one method (RNAsnap) available for RNA solvent accessibility prediction. However, its performance is unsatisfactory for protein-free RNAs. Results We developed RNAsol, a new algorithm to predict RNA solvent accessibility. RNAsol was built based on improved sequence profiles from the covariance models and trained with the long short-term memory (LSTM) neural networks. Independent tests on the same datasets from RNAsnap show that RNAsol achieves the mean Pearson's correlation coefficient (PCC) of 0.43/0.26 for the protein-bound/protein-free RNA molecules, which is 26.5%/136.4% higher than that of RNAsnap. When the training set is enlarged to include both types of RNAs, the PCCs increase to 0.49 and 0.46 for protein-bound and protein-free RNAs, respectively. The success of RNAsol is attributed to two aspects, including the improved sequence profiles constructed by the sequence-profile alignment and the enhanced training by the LSTM neural networks. Availability and implementation http://yanglab.nankai.edu.cn/RNAsol/ Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation The interactions between protein and nucleic acids play a key role in various biological processes. Accurate recognition of the residues that bind nucleic acids can facilitate the study of uncharacterized protein–nucleic acids interactions. The accuracy of existing nucleic acids-binding residues prediction methods is relatively low. Results In this work, we introduce NucBind, a novel method for the prediction of nucleic acids-binding residues. NucBind combines the predictions from a support vector machine-based ab-initio method SVMnuc and a template-based method COACH-D. SVMnuc was trained with features from three complementary sequence profiles. COACH-D predicts the binding residues based on homologous templates identified from a nucleic acids-binding library. The proposed methods were assessed and compared with other peering methods on three benchmark datasets. Experimental results show that NucBind consistently outperforms other state-of-the-art methods. Though with higher accuracy, similar to many other ab-initio methods, cross prediction between DNA and RNA-binding residues was also observed in SVMnuc and NucBind. We attribute the success of NucBind to two folds. The first is the utilization of improved features extracted from three complementary sequence profiles in SVMnuc. The second is the combination of two complementary methods: the ab-initio method SVMnuc and the template-based method COACH-D. Availability and implementation http://yanglab.nankai.edu.cn/NucBind Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

C14H15B2F4I2N4, triclinic, P1̄ (no. 2), a = 7.5458(7) Å, b = 8.5659(6) Å, c = 8.6089(5) Å, α = 108.189(6)°, β = 110.492(7)°, γ = 98.678(7)°, V = 473.6 Å3, Z = 1, Rgt(F) = 0.0334, wRref(F2) = 0.0831, T = 296.5 K. [ABSTRACT FROM AUTHOR]

C14H15B2BrF4N4, orthorhombic, Pbca (no. 61), a = 11.970(1) Å, b = 10.049(1) Å, c = 27.847(1) Å, V = 3349.6 Å3, Z = 8, Rgt(F) = 0.0697, wRref(F2) = 0.1585, T = 296 K. CCDC no.: 2080820 [ABSTRACT FROM AUTHOR]

C14H15B2F4I2N4, triclinic, P1̄ (no. 2), a = 7.5458(7) Å, b = 8.5659(6) Å, c = 8.6089(5) Å, α = 108.189(6)°, β = 110.492(7)°, γ = 98.678(7)°, V = 473.6 Å3, Z = 1, Rgt(F) = 0.0334, wRref(F2) = 0.0831, T = 296.5 K. [ABSTRACT FROM AUTHOR]