WMSA 2: a multiple DNA/RNA sequence alignment tool implemented with accurate progressive mode and a fast win-win mode combining the center star and progressive strategies.

Multiple sequence alignment is widely used for sequence analysis, such as identifying important sites and phylogenetic analysis. Traditional methods, such as progressive alignment, are time-consuming. To address this issue, we introduce StarTree, a novel method to fast construct a guide tree by combining sequence clustering and hierarchical clustering. Furthermore, we develop a new heuristic similar region detection algorithm using the FM-index and apply the k-banded dynamic program to the profile alignment. We also introduce a win-win alignment algorithm that applies the central star strategy within the clusters to fast the alignment process, then uses the progressive strategy to align the central-aligned profiles, guaranteeing the final alignment's accuracy. We present WMSA 2 based on these improvements and compare the speed and accuracy with other popular methods. The results show that the guide tree made by the StarTree clustering method can lead to better accuracy than that of PartTree while consuming less time and memory than that of UPGMA and mBed methods on datasets with thousands of sequences. During the alignment of simulated data sets, WMSA 2 can consume less time and memory while ranking at the top of Q and TC scores. The WMSA 2 is still better at the time, and memory efficiency on the real datasets and ranks at the top on the average sum of pairs score. For the alignment of 1 million SARS-CoV-2 genomes, the win-win mode of WMSA 2 significantly decreased the consumption time than the former version. The source code and data are available at https://github.com/malabz/WMSA2.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

A "Plug-and-Display" Nanoparticle Vaccine Platform Based on Outer Membrane Vesicles Displaying SARS-CoV-2 Receptor-binding Domain.

Biomimetic nanoparticles obtained from bacteria or viruses have attracted substantial interest in vaccine research and development. Outer membrane vesicles (OMVs) are mainly secreted by gram-negative bacteria during average growth, with a nano-sized diameter and self-adjuvant activity, which may be ideal for vaccine delivery. OMVs have functioned as a multifaceted delivery system for proteins, nucleic acids, and small molecules. To take full advantage of the biological characteristics of OMVs, bioengineered Escherichia coli-derived OMVs were utilized as a carrier and SARS-CoV-2 receptor-binding domain (RBD) as an antigen to construct a "Plug-and-Display" vaccine platform. The SpyCatcher (SC) and SpyTag (ST) domains in Streptococcus pyogenes were applied to conjugate OMVs and RBD. The Cytolysin A (ClyA) gene was translated with the SC gene as a fusion protein after plasmid transfection, leaving a reactive site on the surface of the OMVs. After mixing RBD-ST in a conventional buffer system overnight, covalent binding was formed between the OMVs and RBD. Thus, a multivalent-displaying OMV vaccine was achieved. By replacing with diverse antigens, the OMVs vaccine platform can efficiently display a variety of heterogeneous antigens, thereby potentially rapidly preventing infectious disease epidemics. This protocol describes a precise method for constructing the OMV vaccine platform, including production, purification, bioconjugation, and characterization.

Novel Synthetic Lipopeptides as Potential Mucosal Adjuvants Enhanced SARS-CoV-2 rRBD-Induced Immune Response.

As TLR2 agonists, several lipopeptides had been proved to be candidate vaccine adjuvants. In our previous study, lipopeptides mimicking N-terminal structures of the bacterial lipoproteins were also able to promote antigen-specific immune response. However, the structure-activity relationship of lipopeptides as TLR2 agonists is still unclear. Here, 23 synthetic lipopeptides with the same lipid moiety but different peptide sequences were synthesized, and their TLR2 activities in vitro and mucosal adjuvant effects to OVA were evaluated. LP1-14, LP1-30, LP1-34 and LP2-2 exhibited significantly lower cytotoxicity and stronger TLR2 activity compared with Pam2CSK4, the latter being one of the most potent TLR2 agonists. LP1-34 and LP2-2 assisted OVA to induce more profound specific IgG in sera or sIgA in BALF than Pam2CSK4. Furthermore, the possibility of LP1-34, LP2-2 and Pam2CSK4 as the mucosal adjuvant for the SARS-CoV-2 recombinant RBD (rRBD) was investigated. Intranasally immunized with rRBD plus either the novel lipopeptide or Pam2CSK4 significantly increased the levels of specific serum and respiratory mucosal IgG and IgA, while rRBD alone failed to induce specific immune response due to its low immunogenicity. The novel lipopeptides, especially LP2-2, significantly increased levels of rRBD-induced SARS-CoV-2 neutralizing antibody in sera, BALF and nasal wash. Finally, Support vector machine (SVM) results suggested that charged residues in lipopeptides might be beneficial to the agonist activity, while lipophilic residues might adversely affect the agonistic activity. Figuring out the relationship between peptide sequence in the lipopeptide and its TLR2 activity may lay the foundation for the rational design of novel lipopeptide adjuvant for COVID-19 vaccine.

Single-cell RNA analysis reveals the potential risk of organ-specific cell types vulnerable to SARS-CoV-2 infections.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease 2019 (COVID-19) since December 2019 that has led to more than 160 million confirmed cases, including 3.3 million deaths. To understand the mechanism by which SARS-CoV-2 invades human cells and reveal organ-specific susceptible cell types for COVID-19, we conducted comprehensive bioinformatic analysis using public single-cell RNA sequencing datasets. Utilizing the expression information of six confirmed COVID-19 receptors (ACE2, TMPRSS2, NRP1, AXL, FURIN and CTSL), we demonstrated that macrophages are the most likely cells that may be associated with SARS-CoV-2 pathogenesis in lung. Besides the widely reported 'chemokine storm', we identified ribosome related pathways that may also be potential therapeutic target for COVID-19 lung infection patients. Moreover, cell-cell communication analysis and trajectory analysis revealed that M1-like macrophages showed the highest relation to severe COVID-19 patients. And we also demonstrated that up-regulation of chemokine pathways generally lead to severe symptoms, while down-regulation of ribosome and RNA activity related pathways are more likely to be mild. Other organ-specific susceptible cell type analyses could also provide potential targets for COVID-19 therapy. This work can provide clues for understanding the pathogenesis of COVID-19 and contribute to understanding the mechanism by which SARS-CoV-2 invades human cells.

VPTMdb: a viral posttranslational modification database.

In viruses, posttranslational modifications (PTMs) are essential for their life cycle. Recognizing viral PTMs is very important for a better understanding of the mechanism of viral infections and finding potential drug targets. However, few studies have investigated the roles of viral PTMs in virus-human interactions using comprehensive viral PTM datasets. To fill this gap, we developed the first comprehensive viral posttranslational modification database (VPTMdb) for collecting systematic information of PTMs in human viruses and infected host cells. The VPTMdb contains 1240 unique viral PTM sites with 8 modification types from 43 viruses (818 experimentally verified PTM sites manually extracted from 150 publications and 422 PTMs extracted from SwissProt) as well as 13 650 infected cells' PTMs extracted from seven global proteomics experiments in six human viruses. The investigation of viral PTM sequences motifs showed that most viral PTMs have the consensus motifs with human proteins in phosphorylation and five cellular kinase families phosphorylate more than 10 viral species. The analysis of protein disordered regions presented that more than 50% glycosylation sites of double-strand DNA viruses are in the disordered regions, whereas single-strand RNA and retroviruses prefer ordered regions. Domain-domain interaction analysis indicating potential roles of viral PTMs play in infections. The findings should make an important contribution to the field of virus-human interaction. Moreover, we created a novel sequence-based classifier named VPTMpre to help users predict viral protein phosphorylation sites. VPTMdb online web server (http://vptmdb.com:8787/VPTMdb/) was implemented for users to download viral PTM data and predict phosphorylation sites of interest.