MARPPI: boosting prediction of protein-protein interactions with multi-scale architecture residual network.

Protein-protein interactions (PPIs) are a major component of the cellular biochemical reaction network. Rich sequence information and machine learning techniques reduce the dependence of exploring PPIs on wet experiments, which are costly and time-consuming. This paper proposes a PPI prediction model, multi-scale architecture residual network for PPIs (MARPPI), based on dual-channel and multi-feature. Multi-feature leverages Res2vec to obtain the association information between residues, and utilizes pseudo amino acid composition, autocorrelation descriptors and multivariate mutual information to achieve the amino acid composition and order information, physicochemical properties and information entropy, respectively. Dual channel utilizes multi-scale architecture improved ResNet network which extracts protein sequence features to reduce protein feature loss. Compared with other advanced methods, MARPPI achieves 96.03%, 99.01% and 91.80% accuracy in the intraspecific datasets of Saccharomyces cerevisiae, Human and Helicobacter pylori, respectively. The accuracy on the two interspecific datasets of Human-Bacillus anthracis and Human-Yersinia pestis is 97.29%, and 95.30%, respectively. In addition, results on specific datasets of disease (neurodegenerative and metabolic disorders) demonstrate the ability to detect hidden interactions. To better illustrate the performance of MARPPI, evaluations on independent datasets and PPIs network suggest that MARPPI can be used to predict cross-species interactions. The above shows that MARPPI can be regarded as a concise, efficient and accurate tool for PPI datasets.

Integrated Circuit Bonding Distance Inspection via Hierarchical Measurement Structure.

Bonding distance is defined by the projected distance on a substrate plane between two solder points of a bonding wire, which can directly affect the morphology of the bonding wire and the performance between internal components of the chip. For the inspection of the bonding distance, it is necessary to accurately recognize gold wires and solder points within the complex imagery of the chip. However, bonding wires at arbitrary angles and small-sized solder points are densely distributed across the complex background of bonding images. These characteristics pose challenges for conventional image detection and deep learning methods to effectively recognize and measure the bonding distances. In this paper, we present a novel method to measure bonding distance using a hierarchical measurement structure. First, we employ an image acquisition device to capture surface images of integrated circuits and use multi-layer convolution to coarsely locate the bonding region and remove redundant background. Second, we apply a multi-branch wire bonding inspection network for detecting bonding spots and segmenting gold wire. This network includes a fine location branch that utilizes low-level features to enhance detection accuracy for small bonding spots and a gold wire segmentation branch that incorporates an edge branch to effectively extract edge information. Finally, we use the bonding distance measurement module to develop four types of gold wire distribution models for bonding spot matching. Together, these modules create a fully automated method for measuring bonding distances in integrated circuits. The effectiveness of the proposed modules and overall framework has been validated through comprehensive experiments.

Structure of the transmembrane protein 2 (TMEM2) ectodomain and its apparent lack of hyaluronidase activity.

Background: Hyaluronic acid (HA) is a major polysaccharide component of the extracellular matrix. HA has essential functions in tissue architecture and the regulation of cell behaviour. HA turnover needs to be finely balanced. Increased HA degradation is associated with cancer, inflammation, and other pathological situations. Transmembrane protein 2 (TMEM2) is a cell surface protein that has been reported to degrade HA into ~5 kDa fragments and play an essential role in systemic HA turnover. Methods: We produced the soluble TMEM2 ectodomain (residues 106-1383; sTMEM2) in human embryonic kidney cells (HEK293) and determined its structure using X-ray crystallography. We tested sTMEM2 hyaluronidase activity using fluorescently labelled HA and size fractionation of reaction products. We tested HA binding in solution and using a glycan microarray. Results: Our crystal structure of sTMEM2 confirms a remarkably accurate prediction by AlphaFold. sTMEM2 contains a parallel ß-helix typical of other polysaccharide-degrading enzymes, but an active site cannot be assigned with confidence. A lectin-like domain is inserted into the ß-helix and predicted to be functional in carbohydrate binding. A second lectin-like domain at the C-terminus is unlikely to bind carbohydrates. We did not observe HA binding in two assay formats, suggesting a modest affinity at best. Unexpectedly, we were unable to observe any HA degradation by sTMEM2. Our negative results set an upper limit for k cat of approximately 10 -5 min -1. Conclusions: Although sTMEM2 contains domain types consistent with its suggested role in TMEM2 degradation, its hyaluronidase activity was undetectable. HA degradation by TMEM2 may require additional proteins and/or localisation at the cell surface.

HSSG: Identification of Cancer Subtypes Based on Heterogeneity Score of A Single Gene.

Cancer is a highly heterogeneous disease, which leads to the fact that even the same cancer can be further classified into different subtypes according to its pathology. With the multi-omics data widely used in cancer subtypes identification, effective feature selection is essential for accurately identifying cancer subtypes. However, the feature selection in the existing cancer subtypes identification methods has the problem that the most helpful features cannot be selected from a biomolecular perspective, and the relationship between the selected features cannot be reflected. To solve this problem, we propose a method for feature selection to identify cancer subtypes based on the heterogeneity score of a single gene: HSSG. In the proposed method, the sample-similarity network of a single gene is constructed, and pseudo-F statistics calculates the heterogeneity score for cancer subtypes identification of each gene. Finally, we construct gene-gene networks using genes with higher heterogeneity scores and mine essential genes from the networks. From the seven TCGA data sets for three experiments, including cancer subtypes identification in single-omics data, the performance in feature selection of multi-omics data, and the effectiveness and stability of the selected features, HSSG achieves good performance in all. This indicates that HSSG can effectively select features for subtypes identification.