mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization.

Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.

Discovery of Novel Noncovalent KRAS G12D Inhibitors through Structure-Based Virtual Screening and Molecular Dynamics Simulations.

The development of effective inhibitors targeting the Kirsten rat sarcoma viral proto-oncogene (KRASG12D) mutation, a prevalent oncogenic driver in cancer, represents a significant unmet need in precision medicine. In this study, an integrated computational approach combining structure-based virtual screening and molecular dynamics simulation was employed to identify novel noncovalent inhibitors targeting the KRASG12D variant. Through virtual screening of over 1.7 million diverse compounds, potential lead compounds with high binding affinity and specificity were identified using molecular docking and scoring techniques. Subsequently, 200 ns molecular dynamics simulations provided critical insights into the dynamic behavior, stability, and conformational changes of the inhibitor-KRASG12D complexes, facilitating the selection of lead compounds with robust binding profiles. Additionally, in silico absorption, distribution, metabolism, excretion (ADME) profiling, and toxicity predictions were applied to prioritize the lead compounds for further experimental validation. The discovered noncovalent KRASG12D inhibitors exhibit promises as potential candidates for targeted therapy against KRASG12D-driven cancers. This comprehensive computational framework not only expedites the discovery of novel KRASG12D inhibitors but also provides valuable insights for the development of precision treatments tailored to this oncogenic mutation.

Intermolecular 3D-MoRSE Descriptors for Fast and Accurate Prediction of Electronic Couplings in Organic Semiconductors.

The theoretical rational design of organic semiconductors faces an obstacle in that the performance of organic semiconductors depends very much on their stacking and local morphology (for example, phase domains), which involves numerous molecules. Simulation becomes computationally expensive as intermolecular electronic couplings have to be calculated from density functional theory. Therefore, developing fast and accurate methods for intermolecular electronic coupling estimation is essential. In this work, by developing a series of new intermolecular 3D descriptors, we achieved fast and accurate prediction of electronic couplings in both crystalline and amorphous thin films. Three groups of developed descriptors could perform faster and higher accuracy prediction on electronic couplings than the most advanced state-of-the-art descriptors. This work paves the way for large-scale simulations, high-throughput calculations, and screening of organic semiconductors.

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction.

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.

GAM-MDR: probing miRNA-drug resistance using a graph autoencoder based on random path masking.

MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA-drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA-drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub:https://github.com/ZZCrazy00/GAM-MDR.

MHAM-NPI: Predicting ncRNA-protein interactions based on multi-head attention mechanism.

Non-coding RNA (ncRNA) is a functional RNA molecule that plays a key role in various fundamental biological processes, such as gene regulation. Therefore, studying the connection between ncRNA and proteins holds significant importance in exploring the function of ncRNA. Although many efficient and accurate methods have been developed by modern biological scientists, accurate predictions still pose a major challenge for various issues. In our approach, we utilize a multi-head attention mechanism to merge residual connections, allowing for the automatic learning of ncRNA and protein sequence features. Specifically, the proposed method projects node features into multiple spaces based on multi-head attention mechanism, thereby obtaining different feature interaction patterns in these spaces. By stacking interaction layers, higher-order interaction modes can be derived, while still preserving the initial feature information through the residual connection. This strategy effectively leverages the sequence information of ncRNA and protein, enabling the capture of hidden high-order features. The final experimental results demonstrate the effectiveness of our method, with AUC values of 97.4%, 98.5%, and 94.8% achieved on the NPInter v2.0, RPI807, and RPI488 datasets, respectively. These impressive results solidify our method as a powerful tool for exploring the connection between ncRNAs and proteins. We have uploaded the implementation code on GitHub: https://github.com/ZZCrazy00/MHAM-NPI.

Adsorption and foaming properties of edible egg yolk peptide nanoparticles: Effect of particle aggregation.

The adsorption and foaming properties of an edible colloidal nanoparticle (EYPNs), self-assembled from the food-derived, amphiphilic egg yolk peptides, were investigated, with the aim of evaluating their potential as efficient particulate stabilizers for development of aqueous food foams. The influence of particle aggregation induced by the changes of environmental conditions (mainly the pH) on these properties of EYPN systems was determined. Our results showed that the EYPNs are a highly pH-responsive system, showing the pH-dependent particle aggregation behavior, which is found to strongly affect the interfacial adsorption and macroscopic foaming behaviors of systems. Compared to high pH (6.0-9.0), the EYPNs at low pH (2.0-5.0) showed higher surface activity with a lower equilibrated surface tension as well as a higher packing density of particles and particle aggregates at the interface, probably due to the reduced electrostatic adsorption barrier. Accordingly, the EYPNs at these low pH values exhibited significantly higher foamability and foam stability. The presence of large particle clusters and/or aggregates formed at low pH in the continuous phase may contribute to the foam stability of EYPNs. These results indicate that our edible peptide-based nanoparticle EYPNs can be used as a new class of Pickering-type foam stabilizer for the design of food foams with controlled material properties, which may have sustainable applications in foods, cosmetics, and personal care products.

Self-Assembled Egg Yolk Peptide Micellar Nanoparticles as a Versatile Emulsifier for Food-Grade Oil-in-Water Pickering Nanoemulsions.

Pickering emulsions stabilized by food-grade particles have garnered increasing interest in recent years due to their promising applications in biorelated fields such as foods, cosmetics, and drug delivery. However, it remains a big challenge to formulate nanoscale Pickering emulsions from these edible particles. Herein we show that a new Pickering nanoemulsion that is stable, monodisperse, and controllable can be produced by employing the spherical micellar nanoparticles (EYPNs), self-assembled from the food-derived, amphiphilic egg yolk peptides, as an edible particulate emulsifier. As natural peptide-based nanoparticles, the EYPNs have a small particle size, intermediate wettability, high surface activity, and deformability at the interface, which enable the formation of stable Pickering nanodroplets with a mean dynamic light scattering diameter below 200 nm and a polydispersity index below 0.2. This nanoparticle system is versatile for different oil phases with various polarities and demonstrates the easy control of nanodroplet size through tuning the microfluidization conditions or the ratio of EYPNs to oil phase. These food-grade Pickering nanoemulsions, obtained when the internal phase is an edible vegetable oil, have superior stability during long-term storage and spray-drying based on the irreversible and compact adsorption of intact EYPNs at the nanodroplet surface. This is the first finding of a natural edible nano-Pickering emulsifier that can be used solely to make stable food Pickering nanoemulsions with the qualities of simplicity, versatility, low cost, and the possibility of controllable and mass production, which make them viable for many sustainable applications.

CX-5461-loaded nucleolus-targeting nanoplatform for cancer therapy through induction of pro-death autophagy.

Various drugs have been designed in the past to act on intracellular targets. For the desired effects to be exerted, these drugs should reach and accumulate in specific subcellular organelles. CX-5461 represents a potent small-molecule inhibitor of rRNA synthesis that specifically inhibits the transcription driven by RNA polymerase (Pol) I and induces tumor cell death through triggering a pro-death autophagy. In the current study an innovative kind of CX-5461-loaded mesoporous silica nano-particles enveloped by polyethylene glycol (PEG), polydopamine (PDA) and AS-1411 aptamer (MSNs-CX-5461@PDA-PEG-APt) with the aim of treating cancer cells was constructed, in which the high-surface-area MSNs allowed for high drug loading, PDA acted as gatekeeper to prevent the leakage of CX-5461 from MSNs, PEG grafts on PDA surfaces increased the stable and biocompatible property in physiological condition, and AS-1411 aptamer promoted the nucleolar accumulation of CX-5461. MSNs-CX-5461@PDA-PEG-APt was characterized regarding releasing characteristics, steadiness, encapsulation of drugs, phase boundary potential as well as sizes of particles. Expectedly, In vitro assays showed that aptamer AS-1411 significantly increased the nucleolar accumulation of CX-5461. The aptamer-tagged CX-5461-loaded MSNs demonstrated to be more cytotoxic to cervical cancer cells compared to the control MSNs, due to relatively strong inhibition of rRNA transcription and induction of pro-death autophagy. The in vivo treatment with AS-1411-tagged CX-5461-loaded MSNs showed a stronger distribution in tumor tissues by animal imaging assay and a significantly higher inhibition effect on the growth of HeLa xenografts compared to AS-1411-untagged CX-5461-loaded MSNs. In addition, histology analysis indicated that MSNs-CX-5461@PDA-PEG-APt did not exhibit any significant toxicity on main organs. These results collectively suggested that MSNs-CX-5461@PDA-PEG-APt represents both a safe and potentially nucleolus-targeting anti-cancer drug. STATEMENT OF SIGNIFICANCE: Many drugs function in specific subcellular organelles. CX-5461 is a specific inhibitor of nucleolar rRNA synthesis. Here, we reported a novel aptamer-tagged nucleolus-targeting CX-5461-loaded nanoparticle, which specifically accumulated in nucleoli and significantly inhibited the tumor growth in vitro and in vivo through inhibiting rRNA transcription and triggering a pro-death autophagy.

[Limbal epithelial autograft transplantation in treatment of pterygium].

OBJECTIVE: To observe the therapeutic effects of limbal epithelial autograft transplantation and pterygium excision in the treatment of pterygium. METHODS: A prospective randomized paired-eye trial was studied. There were 208 patients (229 eyes) with initial pterygium, and they were allocated to two groups: excision of pterygium with limbal epithelial autograft transplantation surgery (A group, 106 cases and 124 eyes) and simple pterygium excision (B group, 102 cases and 105 eyes). The criteria for recovery were corneal transparency with stable epithelial healing and no abnormal proliferation of pterygium-like tissue. The post-operative follow-up periods ranged from 18 approximately 28 (22.4 +/- 4.9) months. RESULTS: Some of the patients lost follow-up. In the eyes followed up, 5 of 11 2 eyes (4.5%) in A group and 41 of 96 eyes (42.7%) in B group were recurred, the difference being very significant (P < 0.001). CONCLUSION: To provide a new stem cell source, limbal epithelial autograft transplantation, for an injured limb us is a reasonable therapeutic method for the treatment of pterygium.