Fundus Fluorescein Angiography (FFA) has been extensively used for the identification, management, and diagnosis of various retinal and choroidal diseases, such as age-related macular degeneration, diabetic retinopathy, retinopathy of prematurity, among others. This exam enables clinicians to evaluate retinal morphology and the pathophysiology of retinal vasculature. However, adverse events, including from mild to severe reactions to sodium fluorescein, have been reported. Titanium dioxide nanoparticles (NPTiO2) have shown significant potential in numerous biological applications. Coating or conjugating these nanoparticles with small molecules can enhance their stability, photochemical properties, and biocompatibility, as well as increase the hydrophilicity of the nanoparticles, making them more suitable for biomedical applications. This work demonstrates the potential use of ultrasmall titanium dioxide nanoparticles conjugated with sodium fluorescein to improve the quality of angiography exams. The strategy of conjugating fluorescein with NPTiO2 successfully enhanced the fluorescence photostability of the contrast agent and increased its retention time in the retina. Preliminary in vivo and in vitro safety tests suggest that these nanoparticles are safe for the intended application demonstrating low tendency to hemolysis, and no significant changes in the retina thickness or in the electroretinography a-wave and b-wave amplitudes. Overall, the conjugation of fluorescein to NPTiO2 has produced a nanomaterial with favorable properties for use as an innovative contrast agent in FFA examinations. By providing a clear description of our methodology of analysis, we also aim to offer better perspectives and reproducible conditions for future research.

RNA interference (RNAi) has been widely utilized to investigate gene functions and has significant potential for control of pest insects. However, recent studies have revealed that the target insect species, dsRNA molecule length, target genes, and other experimental factors can affect the efficiency of RNAi mediated control, restricting the further development and application of this technology. Therefore, the aim of this study was to establish a deep learning model using bioinformatics to help researchers identify dsRNA fragments with the highest RNAi efficiency. In this study, we optimized an existing model, namely, dsRNAPredictor, by designing sub-models based on different sequence lengths. Accordingly, the data were divided into two groups: 130–399 bp and 400–616 bp long sequences. Then, one-hot encoding was employed to extract sequence information. The convolutional neural network framework comprising three convolutional layers, three average pooling layers, a flattened layer, and three dense layers was employed as the classifier. By adjusting the parameters, we established two sub-models for different sequence distributions. Using multiple independent test datasets and conducting hypothesis testing, we demonstrated that our model exhibits superior performance and strong robustness to dsRNAPredictor, respectively. Therefore, our model may help design dsRNAs with pre-screening potential and facilitate further research and applications.

Histone Deacetylases (HDACs) are enzymes that regulate gene expression by removing acetyl groups from histones. They are involved in various diseases, including neurodegenerative, cardiovascular, inflammatory, and metabolic disorders, as well as fibrosis in the liver, lungs, and kidneys. Successfully identifying potent HDAC inhibitors may offer a promising approach to treating these diseases. In addition to experimental techniques, researchers have introduced several in silico methods for identifying HDAC inhibitors. However, these existing computer-aided methods have shortcomings in their modeling stages, which limit their applications. In our study, we present a Streamlined Masked Transformer-based Pretrained (SMTP) encoder, which can be used to generate features for downstream tasks. The training process of the SMTP encoder was directed by masked attention-based learning, enhancing the model's generalizability in encoding molecules. The SMTP features were used to develop 11 classification models identifying 11 HDAC isoforms. We trained SMTP, a lightweight encoder, with only 1.9 million molecules, a smaller number than other known molecular encoders, yet its discriminant ability remains competitive. The results revealed that machine learning models developed using the SMTP feature set outperformed those developed using other feature sets in 8 out of 11 classification tasks. Additionally, chemical diversity analysis confirmed the encoder's effectiveness in distinguishing between two classes of molecules.

Single-molecule photobleaching analysis is a useful approach for quantifying reactive membrane protein oligomerization in membranes. It provides a binary readout of a fluorophore attached to a protein subunit at dilute conditions. However, quantification of protein stoichiometry from this data requires information about the subunit labeling yields and whether there is non-specific background labeling. Any increases in subunit-specific labeling improves the ability to determine oligomeric states with confidence. A common strategy for site-specific labeling is by conjugation of a fluorophore bearing a thiol-reactive maleimide group to a substituted cysteine. Yet, cysteine reactivity can be difficult to predict as it depends on many factors such as solvent accessibility and electrostatics from the surrounding protein structure. Here we report a general methodology for screening potential cysteine labeling sites on purified membrane proteins. We present the results of two example systems for which the dimerization reactions in membranes has been characterized: (1) the CLC-ec1 Cl-/H+ antiporter, an Escherichia coli homologue of voltage-gated chloride ion channels in humans and (2) a mutant form of a member of the family of fluoride channels Fluc from Bordetella pertussis (Fluc-Bpe-N43S). To demonstrate how we identify such sites, we first discuss considerations of residue positions hypothesized to be suitable and describe the specific steps to rigorously assess site-specific labeling while maintaining the functional activity and robust single-molecule fluorescence signals. We find that our initial, well rationalized choices are not strong predictors of success, as rigorous testing of the labeling sites shows that only ≈30 % of sites end up being useful for single-molecule photobleaching studies

The identification of positive selection has been framed as a classification task, with Convolutional Neural Networks (CNNs) already outperforming summary statistics and likelihood-based approaches in accuracy. Despite the prevalence of CNN-based methods that manipulate the pixels of images representing raw genomic data as a preprocessing step to improve classification accuracy, the efficacy of these pixel-rearrangement techniques remains inadequately examined, particularly in the presence of confounding factors like population bottlenecks, migration and recombination hotspots. We introduce a set of pixel rearrangement algorithms aimed at enhancing CNN classification accuracy in detecting selective sweeps. These algorithms are employed to assess the performance of four CNN models for selective sweep detection. Our findings illustrate that the judicious application of rearrangement algorithms notably enhances the overall classification accuracy of a CNN across various datasets simulating confounding factors. We observed that sorting the columns of the genomic matrices has higher on CNN performance than rearranging the sequences. To some extent, these rearrangement algorithms are more robust to misspecified demographic models compared with the utilization of the default preprocessing algorithm as suggested by the respective authors of each CNN architecture. We provide the data rearrangement algorithms as a distinct package available for download at: https://github.com/Zhaohq96/Genetic-data-rearrangement.

Predicting drug-target interaction (DTI) is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising approach. However, there are few solutions to the cold-start problem in DTI prediction scenarios, as these methods rely on existing interaction information to support their modeling. Consequently, they are unable to effectively predict DTIs for new drugs or targets with limited interaction data in the existing work. To this end, we propose a graph transformer method based on meta-learning named MGDTI (short for Meta-learning-based Graph Transformer for Drug-Target Interaction prediction) to fill this gap. Technically, we employ drug-drug similarity and target-target similarity as additional information to mitigate the scarcity of interactions. Besides, we trained MGDTI via meta-learning to be adaptive to cold-start tasks. Moreover, we employed graph transformer to prevent over-smoothing by capturing long-range dependencies. Extensive results on the benchmark dataset demonstrate that MGDTI is effective on DTI prediction under cold-start scenarios.

Therapeutic antibodies have emerged as a promising treatment option for a wide range of diseases. However, the light chain of antibodies can potentially induce amyloidosis, a condition characterized by protein misfolding and aggregation, posing a significant safety concern. Therefore, it is crucial to assess the amyloidogenic risk of therapeutic antibodies during the early stages of drug development. In this study, we introduce AB-Amy 2.0, a new computational model with enhanced performance for assessing the light chain amyloidogenic risk of therapeutic antibodies. By employing pretrained protein language models (PLMs) embeddings, AB-Amy 2.0 achieves higher accuracy in amyloidogenic risk prediction compared with traditional features offering a crucial tool for early-stage identification of antibodies with low aggregation propensity. The AB-Amy 2.0 was trained on antiBERTy embeddings and utilizes the SVM algorithm, resulting in superior performance metrics. On an independent test dataset, the model achieved high sensitivity, specificity, ACC, MCC and AUC of 93.47%, 89.23%, 91.92%, 0.8261 and 0.9739, respectively. These results highlight the effectiveness and robustness of AB-Amy 2.0 in predicting light chain amyloidogenic risk accurately. To facilitate user-friendly access, we have developed an online web server (http://i.uestc.edu.cn/AB-Amy2) and a command line tool (https://github.com/zzyywww/ABAmy2). These resources enable the broader application of this advanced model and promise to enhance the development of safer therapeutic antibodies.