A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy

Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.

GRP78 promotes bone metastasis of prostate cancer by regulating bone microenvironment through Sonic hedgehog signaling

Bone metastasis is the leading cause of tumor-related deaths in patients with prostate cancer (PCa). The interactions between PCa and the bone microenvironment form a vicious cycle. However, the complex molecular mechanism by which PCa regulates the bone microenvironment remains unclear. To determine the role of glucose-regulated protein (GRP78) in bone metastasis and growth, we established intracardiac injection and tibial injection models, and performed their histological staining. To assess the effect of GRP78 on the differentiation of osteoblasts and osteoclasts, we performed cell co-culture, enzyme-linked immunosorbent assay, alizarin red staining, and tartrate-resistant acid phosphatase staining. We found that GRP78 is upregulated in PCa tissues and that its upregulation is associated with PCa progression in patients. Functional experiments showed that GRP78 overexpression in PCa cells considerably promotes bone metastasis and induces bone microstructure changes. Silencing GRP78 substantially inhibits the migration and invasion of PCa cells in vitro and bone metastasis and tumor growth in vivo. Mechanistically, GRP78 promotes the migration and invasion of PCa cells via the Sonic hedgehog (Shh) signaling pathway. Cell co-culture showed that GRP78 promotes the differentiation of osteoblasts and osteoclasts through Shh signaling. Our findings suggest that tumor-bone matrix interactions owing to GRP78-activated paracrine Shh signaling by PCa cells regulate the differentiation of osteoblasts and osteoclasts. This process promotes bone metastasis and the proliferation of PCa cells in the bone microenvironment. Targeting the GRP78/Shh axis can serve as a therapeutic strategy to prevent bone metastasis and improve the quality of life of patients with PCa.

[Robotic-assisted laparoscopic modified ventral onlay lingual mucosal graft in complex ureteral stricture construction: experience of eight cases]

Objective: To examine the efficacy of robot-assisted laparoscopic modified ventral onlay lingual mucosal graft for complex ureteral stricture. Methods: The clinical data of 8 patients with ureteral stricture admitted to the Department of Urology, General Hospital of Southern Theater Command from May to October 2022 were retrospectively analyzed. There were 6 males and 2 females, aged (45.1±10.2) years (range: 34 to 64 years), body mass index (24.6±2.0) kg/m2 (range: 20.7 to 26.6 kg/m2). Five cases on the left side, 3 cases on the right side, the length of the ureteral structure was (3.1±0.7) cm (range: 2.2 to 4.5 cm). The value of preoperative serum creatinine was (113.8±22.3) μmol/L (range: 96 to 15 μmol/L). Before excising the structure segment, the titched anastomosed part of the dorsal wall of the ureter, and then the posteriorly augmented anastomotic, the remaining ventral side was augmented with a onlay lingual mucosa graft, then the omentum flap was used to wrap the reconstructed ureteral segment. The lingual mucosa graft with a length of 2.5 to 5.0 cm and a width of 1.0 to 1.5 cm was cut according to the actual structure. The surgery information of the patient, complications, and recent follow-up were recorded. Results: The operation under robot-assisted laparoscopy was performed successfully in the 8 patients without conversion to open surgery. The duration of the operation was (226.9±22.8) minutes (range: 210 to 255 minutes), estimated blood loss was (93.8±25.9) ml (range: 75 to 150 ml), the retention time of the postoperative drainage tube was (4.8±1.3) days (range: 3 to 7 days), and the duration of postoperative hospitalization was (11.1±3.6) days (range: 9 to 14 days). One week after the operation, the patient could pronounce correctly, enunciate clearly, and eat normally. Double J tubes were removed 4 to 8 weeks after the operation. The follow-up time in this group was 3 to 9 months, the follow-up patients underwent imaging and other examinations, which showed a significant improvement in hydronephrosis on the affected side, and the value of renal pelvic separation on the affected side was (1.4±0.8) cm (range: 0 to 2.3 cm). The serum creatinine value was (100.1±24.9) μmol/L (range: 76 to 155 μmol/L). Three months after the operation, the ureteroscopy showed that the ureter was smooth and the mucosa was normal. Conclusions: Robot-assisted laparoscopic ureteroplasty with a lingual mucosal graft is a safe and feasible operation for complex ureteral stricture without serious complications, which provides a surgical option for repairing ureteral stricture.

StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture

One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs. Tumor-homing peptides (THPs) have emerged as a promising solution to this issue, due to their capability to specifically bind to and accumulate in tumor tissues while minimally impacting healthy tissues. THPs are short oligopeptides that offer a superior biological safety profile, with minimal antigenicity, and faster incorporation rates into target cells/tissues. However, identifying THPs experimentally, using methods such as phage display or in vivo screening, is a complex, time-consuming task, hence the need for computational methods. In this study, we proposed StackTHPred, a novel machine learning-based framework that predicts THPs using optimal features and a stacking architecture. With an effective feature selection algorithm and three tree-based machine learning algorithms, StackTHPred has demonstrated advanced performance, surpassing existing THP prediction methods. It achieved an accuracy of 0.915 and a 0.831 Matthews Correlation Coefficient (MCC) score on the main dataset, and an accuracy of 0.883 and a 0.767 MCC score on the small dataset. StackTHPred also offers favorable interpretability, enabling researchers to better understand the intrinsic characteristics of THPs. Overall, StackTHPred is beneficial for both the exploration and identification of THPs and facilitates the development of innovative cancer therapies.

Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences

N6-methyladinosine (m6A) modification is the most abundant co-transcriptional modification in eukaryotic RNA and plays important roles in cellular regulation. Traditional high-throughput sequencing experiments used to explore functional mechanisms are time-consuming and labor-intensive, and most of the proposed methods focused on limited species types. To further understand the relevant biological mechanisms among different species with the same RNA modification, it is necessary to develop a computational scheme that can be applied to different species. To achieve this, we proposed an attention-based deep learning method, adaptive-m6A, which consists of convolutional neural network, bi-directional long short-term memory and an attention mechanism, to identify m6A sites in multiple species. In addition, three conventional machine learning (ML) methods, including support vector machine, random forest and logistic regression classifiers, were considered in this work. In addition to the performance of ML methods for multi-species prediction, the optimal performance of adaptive-m6A yielded an accuracy of 0.9832 and the area under the receiver operating characteristic curve of 0.98. Moreover, the motif analysis and cross-validation among different species were conducted to test the robustness of one model towards multiple species, which helped improve our understanding about the sequence characteristics and biological functions of RNA modifications in different species.

Holistic similarity-based prediction of phosphorylation sites for understudied kinases

Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time-consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions. Nevertheless, the number of experimentally verified phosphorylation sites for most kinases is relatively small, and the targeting phosphorylation sites are still unidentified for some kinases. In fact, there is little research related to these understudied kinases in the literature. Thus, this study aims to create predictive models for these understudied kinases. A kinase-kinase similarity network was generated by merging the sequence-, functional-, protein-domain- and 'STRING'-related similarities. Thus, besides sequence data, protein-protein interactions and functional pathways were also considered to aid predictive modelling. This similarity network was then integrated with a classification of kinase groups to yield highly similar kinases to a specific understudied type of kinase. Their experimentally verified phosphorylation sites were leveraged as positive sites to train predictive models. The experimentally verified phosphorylation sites of the understudied kinase were used for validation. Results demonstrate that 82 out of 116 understudied kinases were predicted with adequate performance via the proposed modelling strategy, achieving a balanced accuracy of 0.81, 0.78, 0.84, 0.84, 0.85, 0.82, 0.90, 0.82 and 0.85, for the 'TK', 'Other', 'STE', 'CAMK', 'TKL', 'CMGC', 'AGC', 'CK1' and 'Atypical' groups, respectively. Therefore, this study demonstrates that web-like predictive networks can reliably capture the underlying patterns in such understudied kinases by harnessing relevant sources of similarities to predict their specific phosphorylation sites.

Wadsley-Roth Crystallographic Shear Structure Niobium-Based Oxides: Promising Anode Materials for High-Safety Lithium-Ion Batteries

Wadsley-Roth crystallographic shear structure niobium-based oxides are of great interest in fast Li+ storage due to their unique 3D open tunnel structures that offer facile Li+ diffusion paths. Their moderate lithiation potential and reversible redox couples hold the great promise in the development of next-generation lithium-ion batteries (LIBs) that are characterized by high power density, long lifespan, and high safety. Despite these outstanding merits, there is still extensive advancement space for further enhancing their electrochemical kinetics. And the industrial feasibility of Wadsley-Roth crystallographic shear structure niobium-based oxides as anode materials for LIBs requires more systematic research. In this review, recent progress in this field is summarized with the aim of realizing the practical applications of Wadsley-Roth phase anode materials in commercial LIBs. The review focuses on research toward the crystalline structure analyses, electrochemical reaction mechanisms, modification strategies, and full cell performance. In addition to highlighting the current research advances, the outlook and perspective on Wadsley-Roth anode materials is also concisely provided.