Identifying Drug Candidates for COVID-19 with Large-Scale Drug Screening.

Papain-like protease (PLpro) is critical to COVID-19 infection. Therefore, it is a significant target protein for drug development. We virtually screened a 26,193 compound library against the PLpro of SARS-CoV-2 and identified several drug candidates with convincing binding affinities. The three best compounds all had better estimated binding energy than those of the drug candidates proposed in previous studies. By analyzing the docking results for the drug candidates identified in this and previous studies, we demonstrate that the critical interactions between the compounds and PLpro proposed by the computational approaches are consistent with those proposed by the biological experiments. In addition, the predicted binding energies of the compounds in the dataset showed a similar trend as their IC50 values. The predicted ADME and drug-likeness properties also suggested that these identified compounds can be used for COVID-19 treatment.

DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.

It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction methods of DNA binding sites have relatively poor accuracy. Using 3D coordinates and the atom-type of surface protein atom as the input, we trained and tested a deep learning model to predict how likely a voxel on the protein surface is to be a DNA-binding site. Based on three different evaluation datasets, the results show that our model not only outperforms several previous methods on two commonly used datasets, but also demonstrates its robust performance to be consistent among the three datasets. The visualized prediction outcomes show that the binding sites are also mostly located in correct regions. We successfully built a deep learning model to predict the DNA binding sites on target proteins. It demonstrates that 3D protein structures plus atom-type information on protein surfaces can be used to predict the potential binding sites on a protein. This approach should be further extended to develop the binding sites of other important biological molecules.

Polyphenols as Potential Inhibitors of SARS-CoV-2 RNA Dependent RNA Polymerase (RdRp).

An increasing number of studies have demonstrated the antiviral nature of polyphenols, and many polyphenols have been proposed to inhibit SARS-CoV or SARS-CoV-2. Our previous study revealed the inhibitory mechanisms of polyphenols against DNA polymerase α and HIV reverse transcriptase to show that polyphenols can block DNA elongation by competing with the incoming NTPs. Here we applied computational approaches to examine if some polyphenols can also inhibit RNA polymerase (RdRp) in SARS-CoV-2, and we identified some better candidates than remdesivir, the FDA-approved drug against RdRp, in terms of estimated binding affinities. The proposed compounds will be further examined to develop new treatments for COVID-19.

Structural and Functional Enrichment Analyses for Antimicrobial Peptides.

Whether there is any inclination between structures and functions of antimicrobial peptides (AMPs) is a mystery yet to be unraveled. AMPs have various structures associated with many different antimicrobial functions, including antibacterial, anticancer, antifungal, antiparasitic and antiviral activities. However, none has yet reported any antimicrobial functional tendency within a specific category of protein/peptide structures nor any structural tendency of a specific antimicrobial function with respect to AMPs. Here, we examine the relationships between structures categorized by three structural classification methods (CATH, SCOP, and TM) and seven antimicrobial functions with respect to AMPs using an enrichment analysis. The results show that antifungal activities of AMPs were tightly related to the two-layer sandwich structure of CATH, the knottin fold of SCOP, and the first structural cluster of TM. The associations with knottin and TM Cluster 1 even sustained through the AMPs with a low sequence identity. Moreover, another significant mutual enrichment was observed between the third cluster of TM and anti-Gram-positive-bacterial/anti-Gram-negative-bacterial activities. The findings of the structure-function inclination further our understanding of AMPs and could help us design or discover new therapeutic potential AMPs.

Peptidomic analysis of type 1 diabetes associated HLA-DQ molecules and the impact of HLA-DM on peptide repertoire editing.

HLA-DM and class II associated invariant chain (Ii) are key cofactors in the MHC class II (MHCII) antigen processing pathway. We used tandem mass spectrometry sequencing to directly interrogate the global impact of DM and Ii on the repertoire of MHCII-bound peptides in human embryonic kidney 293T cells expressing HLA-DQ molecules in the absence or presence of these cofactors. We found that Ii and DM have a major impact on the repertoire of peptides presented by DQ1 and DQ6, with the caveat that this technology is not quantitative. The peptide repertoires of type 1 diabetes (T1D) associated DQ8, DQ2, and DQ8/2 are altered to a lesser degree by DM expression, and these molecules share overlapping features in their peptide binding motifs that are distinct from control DQ1 and DQ6 molecules. Peptides were categorized into DM-resistant, DM-dependent, or DM-sensitive groups based on the mass spectrometry data, and representative peptides were tested in competitive binding assays and peptide dissociation rate experiments with soluble DQ6. Our data support the conclusion that high intrinsic stability of DQ-peptide complexes is necessary but not sufficient to confer resistance to DM editing, and provide candidate parameters that may be useful in predicting the sensitivity of T-cell epitopes to DM editing.

Large-Scale Analysis of Antimicrobial Activities in Relation to Amphipathicity and Charge Reveals Novel Characterization of Antimicrobial Peptides.

It has been unclear to which antimicrobial activities (e.g., anti-gram-positive bacterial, anti-gram-negative bacterial, antifungal, antiparasitic, and antiviral activities) of antimicrobial peptides (AMPs) a given physiochemical property matters most. This is the first computational study using large-scale AMPs to examine the relationships between antimicrobial activities and two major physiochemical properties of AMPs-amphipathicity and net charge. The results showed that among all kinds of antimicrobial activities, amphipathicity and net charge best differentiated between AMPs with and without anti-gram-negative bacterial activities. In terms of amphipathicity and charge, all the AMPs whose activities were significantly associated with amphipathicity and net charge were alike except those with anti-gram-positive bacterial activities. Furthermore, the higher the amphipathic value, the greater the proportion of AMPs possessing both antibacterial and antifungal activities. This dose-response-like pattern suggests a possible causal relationship-dual antibacterial and antifungal activities of AMPs may be attributable to amphipathicity. These novel findings could be useful for identifying potent AMPs computationally.

DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression.

BACKGROUND AND OBJECTIVE: Not everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible. METHOD: To examine this hypothesis, we developed DeepFlu using the state-of-the-art deep learning approach on the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to forecast who would catch the flu prior to an exposure to IAV. RESULTS: The results indicated that such forecast is possible and, in other words, gene expression could reflect the strength of host immunity. In the leave-one-person-out cross-validation, DeepFlu based on deep neural network outperformed the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. In the external validation, DeepFlu also reached 71.4% accuracy, 0.700 AUROC, and 0.723 AUPR for H1N1 and 73.5% accuracy, 0.732 AUROC, and 0.749 AUPR for H3N2, surpassing the KLRD1 biomarker. In addition, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. DeepFlu is available at https://github.com/ntou-compbio/DeepFlu. CONCLUSIONS: DeepFlu is a prognostic tool that can moderately recognize individuals susceptible to the flu and may help prevent the spread of IAV.

Polyphenols as alternative treatments of COVID-19.

Although scientists around the world have put lots of effort into the development of new treatments for COVID-19 since the outbreak, no drugs except Veklury (remdesivir) have been approved by FDA. There is an urgent need to discover some alternative antiviral treatment for COVID-19. Because polyphenols have been shown to possess antiviral activities, here we conducted a large-scale virtual screening for more than 400 polyphenols. Several lead compounds such as Petunidin 3-O-(6″-p-coumaroyl-glucoside) were identified to have promising binding affinities and convincing binding mechanisms. Analyzing the docking results and ADME properties sheds light on the potential efficacy of the top-ranked drug candidates and pinpoints the key residues on the target proteins for the future of drug development.

Prediction of HLA-DQ8beta cell peptidome using a computational program and its relationship to autoreactive T cells.

The goal was to identify HLA-DQ8-bound beta cell epitopes important in the T cell response in autoimmune diabetes. We first identified HLA-DQ8 (DQA1*0301/DQB1*0302) beta cell epitopes using a computational approach and then related their identification to CD4 T cell responses. The computational program (TEA-DQ8) was adapted from one previously developed for identifying peptides bound to the I-A(g7) molecule and based on a library of naturally processed peptides bound to HLA-DQ8 molecules of antigen-presenting cells. We then examined experimentally the response of NOD.DQ8 mice immunized with peptides derived from the Zinc transporter 8 protein. Log-of-odds scores on peptides were experimentally validated as an indicator of peptide binding to HLA-DQ8 molecules. We also examined previously published data on diabetic autoantigens, including glutamic acid decarboxylase-65, insulin and insulinoma-associated antigen-2, all tested in NOD.DQ8 transgenic mice. In all examples, many peptides identified with a favorable binding motif generated an autoimmune T cell response, but importantly many did not. Moreover, some peptides with weak-binding motifs were immunogenic. These results indicate the benefits and limitations in predicting autoimmune T cell responses strictly from MHC-binding data. TEA-DQ8 performed significantly better than other prediction programs.

In silico identification of drug candidates against COVID-19.

The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M pro ), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M pro . Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M pro . Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M pro . Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design.

Trends in regional cancer mortality in Taiwan 1992-2014.

BACKGROUND: Although the cancer mortality rate in Taiwan has been declining in recent years, no study has yet reported any regional differences in cancer mortality rates in Taiwan. We hypothesized that regional cancer mortality rates in Taiwan, an ethnically homogeneous society, exhibited no significant variations. METHODS: We investigated the trends in Taiwan regional cancer mortality between 1992 and 2014. We analyzed regional age-standardized cancer mortality rates for lung, liver, colon, stomach, oral, breast, and prostate cancers using the Taiwan Longitudinal Health Insurance Database and Demographic Database. Furthermore, we applied Joinpoint regression analysis to evaluate the trends across different regions. RESULTS: There are clear regional variations in mortality rates for liver, stomach, and oral cancers, but not for lung, colon, breast, and prostate cancers. The regional death rates of oral cancer, especially for eastern Taiwan, not only elevate the fastest (APC = 14.78% per year, P < 0.001) but also show the largest disparities between men and women. Regional death rates for stomach cancer, which declined most rapidly, are converging in both general and gender groups. Liver cancer is the only one with regional variations whose trends do not all go in the same direction. We also demonstrated that northern Taiwan has significant regional advantages with respect to cancer mortality. CONCLUSIONS: Some but not all cancers in Taiwan show regional disparities. Liver, stomach, and oral cancers in Taiwan exhibit clear regional variations in mortality rates. In particular, the regional variations in oral cancer mortality rates are consistent with those in alcohol consumption.

Associations between Water Quality Measures and Chronic Kidney Disease Prevalence in Taiwan.

To determine the relationships between exposure to environmental contaminants in water and chronic kidney disease (CKD), we investigated the associations of 61 water attributes with the prevalence of CKD and End-Stage Renal Disease (ESRD) using data from 2005 to 2011 from all 22 counties and cities in the main island of Taiwan. We acquired patient information from the Taiwan Longitudinal Health Insurance Database to calculate the age-standardized CKD and ESRD prevalence rates and linked the patients' residences to the water quality monitoring data, which were sampled periodically for a total of over 45,000 observations obtained from the Taiwan Environmental Water Quality Information Database. The association analysis adjusting for gender, age, and annual effects showed that the zinc (Zn), ammonia, chemical oxygen demand (COD), and dissolved oxygen in rivers were weakly correlated with CKD (τ = 0.268/0.250/0.238/-0.267, p = 6.01×10-6/2.52×10-5/6.05×10-5/3.30×10-5, respectively), but none for ESRD. The importances of Zn and COD in rivers were also demonstrated in a CKD regression model. Moreover, an unusually high CKD prevalence was related to arsenic contamination in groundwater. A further prospective cohort study would improve our understanding of what level of environmental water with risky properties could affect the development of CKD.

A large-scale structural classification of antimicrobial peptides.

Antimicrobial peptides (AMPs) are potent drug candidates against microbial organisms such as bacteria, fungi, parasites, and viruses. AMPs have abundant sequences and structures, two fundamental resources for bioinformatics researches, but analyses on how they associate with each other are either nonexistent or limited to partial classification and data. We thus present A Database of Anti-Microbial peptides (ADAM), which contains 7,007 unique sequences and 759 structures, to systematically establish comprehensive associations between AMP sequences and structures through structural folds and to provide an easy access to view their relationships. 30 distinct AMP structural fold clusters with more than one structure are detected and about a thousand AMPs are associated with at least one structural fold cluster. According to ADAM, AMP structural folds are limited-AMPs only cover about 3% of the overall protein fold space.

Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields.

Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew's correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.

Identification and Phylogeny of the First T Cell Epitope Identified from a Human Gut Bacteroides Species.

Host T cell reactivity toward gut bacterial epitopes has been recognized as part of disease pathogenesis. However, the specificity of T cells that recognize this vast number of epitopes has not yet been well described. After colonizing a C57BL/6J germ-free mouse with the human gut symbiotic bacteria Bacteroides thetaiotaomicron, we isolated a T cell that recognized these bacteria in vitro. Using this T cell, we mapped the first known non-carbohydrate T cell epitope within the phylum Bacteroidetes. The T cell also reacted to two other additional Bacteroides species. We identified the peptide that stimulated the T cell by using a genetic approach. Genomic data from the epitope-positive and epitope-negative bacteria explain the cross-reactivity of the T cell to multiple species. This epitope degeneracy should shape our understanding of the T cell repertoire stimulated by the complex microbiome residing in the gastrointestinal tract in both healthy and disease states.

Analysis and prediction of highly effective antiviral peptides based on random forests.

The goal of this study was to examine and predict antiviral peptides. Although antiviral peptides hold great potential in antiviral drug discovery, little is done in antiviral peptide prediction. In this study, we demonstrate that a physicochemical model using random forests outperform in distinguishing antiviral peptides. On the experimental benchmark, our physicochemical model aided with aggregation and secondary structural features reaches 90% accuracy and 0.79 Matthew's correlation coefficient, which exceeds the previous models. The results suggest that aggregation could be an important feature for identifying antiviral peptides. In addition, our analysis reveals the characteristics of the antiviral peptides such as the importance of lysine and the abundance of α-helical secondary structures.

Predicting peptides bound to I-Ag7 class II histocompatibility molecules using a novel expectation-maximization alignment algorithm.

The useful structural features of class II MHC molecules are rarely integrated into T-cell epitope predictions. We propose an approach that applies a novel expectation-maximization algorithm to align the naturally processed peptides selected by the class II MHC I-A(g7) molecule - focusing on the five MHC-specific anchor positions. Based on the alignment profile, log of odds (LOD) scores supplemented with the Laplace plus-one pseudocounts method are applied to identify the potential T-cell epitopes. In addition, an innovative computational concept of hindering residues using statistical and structural information is developed to refine the prediction. Performance analysis by receiver operating characteristics statistics and the experimental validation of the LOD scores demonstrate the accuracy of our predictive model. Furthermore, our model successfully predicts T-cell epitopes of hen egg-white lysozyme protein antigen. Our study provides a framework for predicting T-cell epitopes in class II MHC molecules.

Evidence for active maintenance of inverted repeat structures identified by a comparative genomic approach.

Inverted repeats have been found to occur in both prokaryotic and eukaryotic genomes. Usually they are short and some have important functions in various biological processes. However, long inverted repeats are rare and can cause genome instability. Analyses of C. elegans genome identified long, nearly-perfect inverted repeat sequences involving both divergently and convergently oriented homologous gene pairs and complete intergenic sequences. Comparisons with the orthologous regions from the genomes of C. briggsae and C. remanei show that the inverted repeat structures are often far more conserved than the sequences. This observation implies that there is an active mechanism for maintaining the inverted repeat nature of the sequences.