Summary: Biological sequence diagrams are fundamental for visualizing various functional elements in protein or nucleotide sequences that enable a summarization and presentation of existing information as well as means of intuitive new discoveries. Here, we present a software package called illustrator of biological sequences (IBS) that can be used for representing the organization of either protein or nucleotide sequences in a convenient, efficient and precise manner. Multiple options are provided in IBS, and biological sequences can be manipulated, recolored or rescaled in a user-defined mode. Also, the final representational artwork can be directly exported into a publication-quality figure. Availability and implementation: The standalone package of IBS was implemented in JAVA, while the online service was implemented in HTML5 and JavaScript. Both the standalone package and online service are freely available at http://ibs.biocuckoo.org. Contact:renjian.sysu@gmail.com or xueyu@hust.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

The novel coronavirus pneumonia (COVID-19) is an infectious acute respiratory caused by the novel coronavirus. The virus is a positive-strand RNA virus with high homology to bat coronavirus. In this study, conserved domain analysis, homology modeling, and molecular docking were used to compare the biological roles of specific proteins of the novel coronavirus. The results showed that some viral structural and nonstructural proteins could bind to the porphyrin, respectively. At the same time, orf1ab, ORF10 and ORF3a proteins coordinated to attack heme on the 1-beta chain of hemoglobin, dissociating iron to form porphyrin. Deoxyhemoglobin is more vulnerable to virus attacks than oxidized hemoglobin. The attack will cause less and less hemoglobin that can carry oxygen and carbon dioxide, producing symptoms of respiratory distress. Virus attack damaged many organs and tissues. Lung cells are toxic and inflammatory due to derivatives produced by the attack, which eventually resulted in ground-glass-like lung images. Capillaries easily broken due to inflammation. Proteins such as fibrinogen filled the capillaries' cracks through the coagulation reaction. Therefore, many fibrin and thrombus gathered in the lung tissue of critically ill patients. The mechanism also interfered with the normal heme anabolic pathway of the human body, expecting to result in human disease. This paper is only for academic discussion, the correctness of the theory needs to be confirmed by other experiments. According to the reader's suggestion, the content of the drug-related efficacy analysis has been deleted. Due to the side effects of drugs, please consult a qualified doctor for detailed treatment information, and do not take the drug yourself. We look forward to these discoveries bringing more ideas to people and inspiring people's confidence in defeating the virus.

Currently, the novel coronavirus pneumonia has been widespread globally, and there is no specific medicine. In response to the emergency, we employed bioinformatics methods to investigate the virus's pathogenic mechanism, finding possible control methods. We speculated in previous studies that E protein was associated with viral infectivity. The present study adopted the domain search techniques to analyse the E protein. According to the results, the E protein could bind iron or haem. The iron and haem bound by the E protein came from the attacked haemoglobin and phagocytes. When E protein was attached to haem, it synthesised oxygen and water into superoxide anions, hydrogen peroxide and hydroxyl radicals. When the iron-bound E protein and the haem-bound E protein worked together, they converted superoxide anions and hydrogen peroxide into oxygen and water. These were the “ROS attack” and “ROS escape” of the virus. “ROS attack” damaged the tissues or cells exposed on the surface of the virus, and “ROS escape” decomposed the superoxide anion and hydrogen peroxide that attacked the virus. When NK cells were exposed to infected cells, viruses that had not shed from the infected cells’ surface damaged them through “ROS attack”. In addition, lymphocytes such as T cells and B cells, which could be close to the antigen of the virus surface, were also easily damaged or killed by the "ROS attack", generating a decrease in lymphocytes. When memory B cells were exposed to the virus’s surface antigen, they were also damaged by “ROS attack”, resulting in the patient's re-infection. The virus applied the “ROS escape” to decompose hydrogen peroxide released by phagocytes into oxygen and water. The surrounding cells were replenished with oxygen, and the patient was in a “happy hypoxia” state. When the phagocytes swallowed the virus, the E protein converted superoxide anions into oxygen and water. In this way, the virus parasitized in the vesicles of the phagocyte. While virus was in the lysosome, the E protein generated ROS to damage nearby hydrolases. In this way, the virus parasitized the lysosome. Excessive hydroxyl free radicals destroyed the membrane structure of the lysosome, causing the hydrolase release from lysosome, autophagy of phagocytic cells and subsequent cell death. As a result, the colonizing phagocytes of the virus was associated with asymptomatic infection or retest-positive. Briefly, the virus inhibited the immune system through “ROS escape”, and damaged the immune system by “ROS attack”. The destruction instigated a strong cytokine storm, leading to organ failure and complications.

The novel coronavirus pneumonia (COVID-19) is an infectious acute respiratory caused by the novel coronavirus. The virus is the positive-strand RNA one with high homology to bat coronavirus. The pathogenic mechanism of the new coronavirus is still unclear, which is a significant obstacle to the development of drugs and patients' rescue. In this study, conserved domain analysis, homology modeling, and molecular docking were made to compare the biological roles of specific proteins belonging to the novel coronavirus. The conserved domain analysis showed envelope protein (E), nucleocapsid phosphoprotein (N) and ORF3a had heme linked sites, which Arg134 of ORF3a, Cys44 of E, Ile304 of N were the heme-iron linked site, respectively. ORF3a also possessed the conserved domains of human cytochrome C reductases and bacterial EFeB protein. These three domains were highly overlapping so that ORF3a could dissociate the iron of heme to form porphyrin. Heme linked sites of E protein may be relevant to the high infectivity, and the role of heme linked sites of N protein may be related to the virus replication. The docking results showed that orf1ab, ORF10, and ORF3a proteins coordinated to attack the 1-beta chain of hemoglobin, and some structural and non-structural viral proteins could bind porphyrin. Deoxyhemoglobin was more vulnerable to virus attacks than oxidized hemoglobin. But ORF3a was specific and would not attack blue blood protein, normal cytochrome C, and peroxidase. As for the attack, it would cause increasingly less hemoglobin that could carry oxygen and carbon dioxide, thus producing symptoms of respiratory distress and coagulation reaction, damaging many organs and tissues. The mechanism also interfered with the normal heme anabolic pathway of the human body, expecting to cause human diseases. Based on the small molecule drug library, drugbank, we searched for drugs bound to viral proteins by molecular docking. The results showed that some anticancer drugs could attach to the heme-iron linked site of ORF3a and N. Remdesivir was relatively more obvious than Hydroxychloroquine and Chloroquine in terms of the binding capacity of ORF3a, but the combined role of three drugs to ORF3a was lower. Unfortunately, no drug could bind to the heme-iron linked site of E. Besides, these higher binding energies may prevent all screened drugs from binding firmly to viral proteins. Since there were no clinical data, so inhibitory effects on ORF3a and N were still unclear. This theory is only for academic discussion and needed to be verified by other experiments. Please consult a qualified doctor for treatment details. Due to the toxicity and side effects of drugs, do not use medicines yourself. We expect these discoveries to bring more ideas to people to relieve patients' symptoms and save more lives.

Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (ATG) genes in Saccharomyces cerevisiae and obtained 1,944 confocal images containing > 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 atg knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 fluorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in S. cerevisiae but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy. Abbreviations: Ac: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; ATG: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; MCC: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; Pr: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; Sn: sensitivity; Sp: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.

The novel coronavirus pneumonia (COVID-19) is an infectious acute respiratory infection caused by the novel coronavirus. The virus is a positive-strand RNA virus with high homology to bat coronavirus. In this study, conserved domain analysis, homology modeling, and molecular docking were used to compare the biological roles of certain proteins of the novel coronavirus. The results showed the ORF8 and surface glycoprotein could bind to the porphyrin, respectively. At the same time, orf1ab, ORF10, and ORF3a proteins could coordinate attack the heme on the 1-beta chain of hemoglobin to dissociate the iron to form the porphyrin. The attack will cause less and less hemoglobin that can carry oxygen and carbon dioxide. The lung cells have extremely intense poisoning and inflammatory due to the inability to exchange carbon dioxide and oxygen frequently, which eventually results in ground-glass-like lung images. The mechanism also interfered with the normal heme anabolic pathway of the human body, is expected to result in human disease. According to the validation analysis of these finds, chloroquine could prevent orf1ab, ORF3a, and ORF10 to attack the heme to form the porphyrin, and inhibit the binding of ORF8 and surface glycoproteins to porphyrins to a certain extent, effectively relieve the symptoms of respiratory distress. Since the ability of chloroquine to inhibit structural proteins is not particularly obvious, the therapeutic effect on different people may be different. Favipiravir could inhibit the envelope protein and ORF7a protein bind to porphyrin, prevent the virus from entering host cells, and catching free porphyrins. This paper is only for academic discussion, the correctness needs to be confirmed by other laboratories. Due to the side effects and allergic reactions of drugs such as chloroquine, please consult a qualified doctor for treatment details, and do not take the medicine yourself.