The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.

Since the outbreak of SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, the viral genome has acquired numerous mutations with the potential to alter the viral infectivity and antigenicity. Part of mutations in SARS-CoV-2 spike protein has conferred virus the ability to spread more quickly and escape from the immune response caused by the monoclonal neutralizing antibody or vaccination. Herein, we summarize the spatiotemporal distribution of mutations in spike protein, and present recent efforts and progress in investigating the impacts of those mutations on viral infectivity and antigenicity. As mutations continue to emerge in SARS-CoV-2, we strive to provide systematic evaluation of mutations in spike protein, which is vitally important for the subsequent improvement of vaccine and therapeutic neutralizing antibody strategies.

BACKGROUND: S-glutathionylation is the formation of disulfide bonds between the tripeptide glutathione and cysteine residues of the protein, protecting them from irreversible oxidation and in some cases causing change in their functions. Regulatory glutathionylation of proteins is a controllable and reversible process associated with cell response to the changing redox status. Prediction of cysteine residues that undergo glutathionylation allows us to find new target proteins, which function can be altered in pathologies associated with impaired redox status. We set out to analyze this issue and create new tool for predicting S-glutathionylated cysteine residues. RESULTS: One hundred forty proteins with experimentally proven S-glutathionylated cysteine residues were found in the literature and the RedoxDB database. These proteins contain 1018 non-S-glutathionylated cysteines and 235 S-glutathionylated ones. Based on 235 S-glutathionylated cysteines, non-redundant positive dataset of 221 heptapeptide sequences of S-glutathionylated cysteines was made. Based on 221 heptapeptide sequences, a position-specific matrix was created by analyzing the protein sequence near the cysteine residue (three amino acid residues before and three after the cysteine). We propose the method for calculating the glutathionylation propensity score, which utilizes the position-specific matrix and a criterion for predicting glutathionylated peptides. CONCLUSION: Non-S-glutathionylated sites were enriched by cysteines in - 3 and + 3 positions. The proposed prediction method demonstrates 76.6% of correct predictions of S-glutathionylated cysteines. This method can be used for detecting new glutathionylation sites, especially in proteins with an unknown structure.

MOTIVATION: Modelling with multiple servers that use different algorithms for docking results in more reliable predictions of interaction sites. However, the scoring and comparison of all models by an expert is time-consuming and is not feasible for large volumes of data generated by such modelling. RESULTS: Quality ASsessment of DOcking Models (QASDOM) Server is a simple and efficient tool for real-time simultaneous analysis, scoring and ranking of data sets of receptor-ligand complexes built by a range of docking techniques. This meta-server is designed to analyse large data sets of docking models and rank them by scoring criteria developed in this study. It produces two types of output showing the likelihood of specific residues and clusters of residues to be involved in receptor-ligand interactions and the ranking of models. The server also allows visualizing residues that form interaction sites in the receptor and ligand sequence and displays 3D model structures of the receptor-ligand complexes. AVAILABILITY: http://qasdom.eimb.ru. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.