Evaluation of DNA-protein complex structures using the deep learning method.

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA-protein complexes. Despite technological advancements, accurately identifying DNA-protein complexes through computational methods still poses a challenge. Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA-protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA-protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA-protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA-protein complexes.

RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study.

RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA's conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I-III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base-base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.

Evaluating native-like structures of RNA-protein complexes through the deep learning method.

RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed. To address this challenge, we thus develop DRPScore, a deep-learning-based approach for identifying native-like RNA-protein structures. DRPScore is tested on representative sets of RNA-protein complexes with various degrees of binding-induced conformation change ranging from fully rigid docking (bound-bound) to fully flexible docking (unbound-unbound). Out of the top 20 predictions, DRPScore selects native-like structures with a success rate of 91.67% on the testing set of bound RNA-protein complexes and 56.14% on the unbound complexes. DRPScore consistently outperforms existing methods with a roughly 10.53-15.79% improvement, even for the most difficult unbound cases. Furthermore, DRPScore significantly improves the accuracy of the native interface interaction predictions. DRPScore should be broadly useful for modeling and designing RNA-protein complexes.

Perceived stress and mobile phone addiction among college students: The roles of self-control and security.

Objective: According to the General Strain Theory, stress can lead to a range of problem behaviors. In the current study, we focused on the association between perceived stress and mobile phone addiction. We hypothesized that this association is mediated by low self-control and that the first path of the mediation is moderated by security. Methods: College students (N = 397; ages 16-21; 51.89% females) from a university in Hunan Province, China, were surveyed by cluster sampling method. The students completed the Smartphone Addiction Scale-Short Version (SAS-SV), the Depression Anxiety Stress Scale (DASS-21), the Self-Control Scale (SCS), and the Security Questionnaire (SQ) during regular class time. SPSS26.0 statistical software was used for descriptive statistics and Pearson correlation analyses, the SPSS macro PROCESS was used to test the mediating effects of self-control and the moderating role of security. Results: Mediation analysis showed that as expected, perceived stress was associated with lower self-control, which in turn was associated with a higher risk for mobile phone addiction. Also as expected, moderated mediation analysis indicated that the association between perceived stress and self-control was moderated by security. Specifically, the relationship between perceived stress and self-control was stronger for low security. Conclusion: This study provides useful insight into the understanding of how perceived stress increases the risk of mobile phone addiction. The results are consistent with the General Strain Theory and further indicate that concrete approaches are required for the prevention and intervention to reduce mobile phone addiction among college students.

Depression and Loneliness as Mediators Between Social Support and Mobile Phone Addiction.

BACKGROUND: Mobile phone addiction among adolescents has attracted a lot of attention in recent years. Previous researches revealed a significant relation between low social support and addiction. This study aim to investigated the association between social support and mobile phone addiction, and the mediating effects of depression and loneliness. SUBJECTS AND METHODS: A total of 1,400 Chinese adolescents aged from 12 to 23 years old was recruited from two middle schools and a college in Hunan Province, China. Participates were selected using the cluster random sampling method. They completed the Mobile Phone Addiction Index, the Self-Rating Depression Scale, the UCLA Loneliness Scale, and the Adolescent Social Support Scale. The study analyzed the correlations between the study variables and the mediating role of depression and loneliness in the relationship between social support and mobile phone addiction. RESULTS: There were significant negative correlation between social support and depression, loneliness, and mobile phone addiction (p<0.001). Both depression and loneliness demonstrated significant positive correlation with mobile phone addiction (p<0.001). Structural equation modeling revealed that both depression and loneliness mediated the association between social support and mobile phone addiction (p<0.001). Depression and loneliness sequentially mediated the association between social support and mobile phone addiction (p<0.001). However, the relation between social support and mobile phone addiction was not significant (p>0.05). CONCLUSIONS: Social support can lower levels of mobile phone addiction among adolescents by reducing depression and loneliness. This study sheds light on the underlying mechanisms between social support and mobile phone addiction, which has profound implications for the prevention and interventions of adolescent problematic mobile phone use.

DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model.

Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structures or simple full-length protein sequences as the input features. Thus, protein-ligand binding affinity prediction remains a fundamental challenge in drug discovery. In this study, we proposed a novel deep learning-based approach, DLSSAffinity, to accurately predict the protein-ligand binding affinity. Unlike the existing methods, DLSSAffinity uses the pocket-ligand structural pairs as the local information to predict short-range direct interactions. Besides, DLSSAffinity also uses the full-length protein sequence and ligand SMILES as the global information to predict long-range indirect interactions. We tested DLSSAffinity on the PDBbind benchmark. The results showed that DLSSAffinity achieves Pearson's R = 0.79, RMSE = 1.40, and SD = 1.35 on the test set. Comparing DLSSAffinity with the existing state-of-the-art deep learning-based binding affinity prediction methods, the DLSSAffinity model outperforms other models. These results demonstrate that combining global sequence and local structure information as the input features of a deep learning model can improve the accuracy of protein-ligand binding affinity prediction.

The TAR binding dynamics and its implication in Tat degradation mechanism.

Human immunodeficiency virus (HIV) is a retrovirus that progressively attacks the human immune system. It is known that the HIV viral protein Tat recruits the host elongation factor, positive transcription elongation factor b (P-TEFb), onto the nascent HIV viral transactivation response element (TAR) RNA to overcome the elongation pause for active transcription of the entire viral genome. Interestingly, there exists an amplifying feedback loop between Tat and TAR-a reduction in Tat increases the elongation pause, resulting in more TAR RNA fragments instead of the entire viral genome transcript, and the TAR fragments as a scaffold for PRC2 complex in turn promote Tat ubiquitination and degradation. In this study, the structural ensembles and binding dynamics of various interfaces in the Tat/TAR/P-TEFb complex are probed by all-atom accelerated sampling molecular dynamics simulations. The results show that a protein-binding inhibitor F07#13 targeting the Tat/P-TEFb interface initiates the above feedback loop and shuts down the active transcription. Another RNA binding inhibitor, JB181, targeting the Tat/TAR interface, can prevent TAR from pulling down the Tat from P-TEFb protein and further reducing Tat degradation. The detailed mechanism of the complex dynamics helps elucidate how Tat and TAR coordinate the regulation between HIV genome transcription versus possible HIV latency.