In silico prediction of Severe Acute Respiratory Syndrome Coronavirus 2 main protease cleavage sites.

One of the emerging subjects to combat the SARS-CoV-2 virus is to design accurate and efficient drug such as inhibitors against the viral protease to stop the viral spread. In addition to laboratory investigation of the viral protease, which is fundamental, the in silico research of viral protease such as the protease cleavage site prediction is critically important and urgent. However, this problem has yet to be addressed. This article has, for the first time, investigated this problem using the pattern recognition approaches. The article has shown that the pattern recognition approaches incorporating a specially tailored kernel function for dealing with amino acids has the outstanding performance in the accuracy of cleavage site prediction and the discovery of the prototype cleavage peptides.

Global Analysis of Genes Essential for Francisella tularensis Schu S4 Growth In Vitro and for Fitness during Competitive Infection of Fischer 344 Rats.

The highly virulent intracellular pathogen Francisella tularensis is a Gram-negative bacterium that has a wide host range, including humans, and is the causative agent of tularemia. To identify new therapeutic drug targets and vaccine candidates and investigate the genetic basis of Francisella virulence in the Fischer 344 rat, we have constructed an F. tularensis Schu S4 transposon library. This library consists of more than 300,000 unique transposon mutants and represents a transposon insertion for every 6 bp of the genome. A transposon-directed insertion site sequencing (TraDIS) approach was used to identify 453 genes essential for growth in vitro Many of these essential genes were mapped to key metabolic pathways, including glycolysis/gluconeogenesis, peptidoglycan synthesis, fatty acid biosynthesis, and the tricarboxylic acid (TCA) cycle. Additionally, 163 genes were identified as required for fitness during colonization of the Fischer 344 rat spleen. This in vivo selection screen was validated through the generation of marked deletion mutants that were individually assessed within a competitive index study against the wild-type F. tularensis Schu S4 strain.IMPORTANCE The intracellular bacterial pathogen Francisella tularensis causes a disease in humans characterized by the rapid onset of nonspecific symptoms such as swollen lymph glands, fever, and headaches. F. tularensis is one of the most infectious bacteria known and following pulmonary exposure can have a mortality rate exceeding 50% if left untreated. The low infectious dose of this organism and concerns surrounding its potential as a biological weapon have heightened the need for effective and safe therapies. To expand the repertoire of targets for therapeutic development, we initiated a genome-wide analysis. This study has identified genes that are important for F. tularensis under in vitro and in vivo conditions, providing candidates that can be evaluated for vaccine or antibacterial development.

Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data.

Recently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.

Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs.

BACKGROUND: Identification of synthetic lethal interactions in cancer cells could offer promising new therapeutic targets. Large-scale functional genomic screening presents an opportunity to test large numbers of cancer synthetic lethal hypotheses. Methods enriching for candidate synthetic lethal targets in molecularly defined cancer cell lines can steer effective design of screening efforts. Loss of one partner of a synthetic lethal gene pair creates a dependency on the other, thus synthetic lethal gene pairs should never show simultaneous loss-of-function. We have developed a computational approach to mine large multi-omic cancer data sets and identify gene pairs with mutually exclusive loss-of-function. Since loss-of-function may not always be genetic, we look for deleterious mutations, gene deletion and/or loss of mRNA expression by bimodality defined with a novel algorithm BiSEp. RESULTS: Applying this toolkit to both tumour cell line and patient data, we achieve statistically significant enrichment for experimentally validated tumour suppressor genes and synthetic lethal gene pairings. Notably non-reliance on genetic loss reveals a number of known synthetic lethal relationships otherwise missed, resulting in marked improvement over genetic-only predictions. We go on to establish biological rationale surrounding a number of novel candidate synthetic lethal gene pairs with demonstrated dependencies in published cancer cell line shRNA screens. CONCLUSIONS: This work introduces a multi-omic approach to define gene loss-of-function, and enrich for candidate synthetic lethal gene pairs in cell lines testable through functional screens. In doing so, we offer an additional resource to generate new cancer drug target and combination hypotheses. Algorithms discussed are freely available in the BiSEp CRAN package at http://cran.r-project.org/web/packages/BiSEp/index.html .

HIV infection and sexual behaviors among non-commercial men who have sex with men at different venues.

The prevalence of HIV infection among men who have sex with men (MSM) has increased rapidly in China. Previous studies suggested that some venue-specific characteristics could significantly affect MSM's sexual behaviors that were related to HIV transmission. Thus, to compare the HIV infection rates and related risky sexual behaviors among MSM at different venues, we conducted a cross-sectional study with time-location sampling in Shenzhen, China. Among the 801 MSM recruited in the study, 7.0 % (n = 56) were found to be HIV positive, with 0.9 % of MSM at bars (BMSM), 3.5 % of MSM at suburban recreational centers (RMSM), 8.1 % of MSM at saunas (SMSM), 9.3 % of MSM at parks (PMSM), and 10.1 % of MSM at dorm-based venues (DMSM). HIV infection was significantly more prevalent in MSM in dorm-based venues, parks, and saunas than in other venues. Compared to MSM in other venues, BMSM were more likely to be single, drug and alcohol users, but less likely to be HIV and syphilis positive. More PMSM reported having unprotected anal intercourse with other men while more SMSM reported having multiple male sex partners and more RMSM had a low level of HIV-related knowledge. The results indicated that MSM frequenting different venues were inconsistent with regards to demographic characteristics, HIV and syphilis infection rates, and risky sexual behaviors. Greater efforts are needed to develop intervention strategies that target specific venues and risky behaviors.

A comparison of HIV infection and related risk factors between money boys and noncommercial men who have sex with men in Shenzhen, China.

BACKGROUND: HIV transmission among men who have sex with men (MSM) has become an increasing concern in China. Money boys (MBs) are a subgroup of MSM who sell sex to men. Direct comparison of HIV prevalence and related risk factors between MB and noncommercial MSM (ncMSM) has rarely been done. This study was conducted to make the comparison. METHODS: Eight hundred fifty MBs and 801 ncMSM were parallel recruited in Shenzhen by time-location sampling. Their behavioral and serologic data on HIV and syphilis were collected and compared. Multiple logistic regression analysis was performed to evaluate the determinants for HIV risk in MBs and ncMSM, respectively. RESULTS: The prevalence of HIV was 4.5% in MBs and 7.0% in ncMSM. Although MBs tended to have more male partners than ncMSM, they were more likely to report a consistent condom use in male anal intercourse, especially in commercial sex. Noncommercial MSM were more likely to visit parks and saunas instead of bars, massage centers, recreational centers, and home-based venues. Syphilis infection and recruitment venue were associated with HIV infection in both MBs and ncMSM. Being from a hometown with a high HIV prevalence and without a male partner from Hong Kong were found to be risk factors for HIV infection in MBs, and early sexual debut was a risk factor in ncMSM. CONCLUSION: Money boys differed from ncMSM in the rate of HIV infection and some sexual characteristics and behaviors. Formatting separate interventions specifically targeting the 2 subgroups may be necessary.

[The influence of T lymphocyte activation on HIV-1 susceptibility of Han Chinese].

OBJECTIVE: To explore the influence of T lymphocyte activation on HIV-1 susceptibility of Han Chinese. METHODS: In 2008, 37 HIV-1 highly exposed persistently seronegative individuals (ESNs) and 101 healthy controls were screened from Shenzhen. Flow cytometer was used to assay the expression difference of HIV-1 infection related co-receptor, the difference between the two groups were analyzed by Mann-Whitney U statistics methods. RESULTS: T cell HLA-DR(+) CD4 T cells and HLA-DR(+) expression of ESNs (12.64 (5.94 - 21.90), 21.12 (10.74 - 30.21)) were all significantly lower than that of healthy controls (22.52 (7.91 - 58.60), 32.28 (14.72 - 67.82)) (P values all < 0.05). T cell CD45RA-RO(+), CCR5(+)CD4 expression of ESNs (58.68 (49.06 - 72.44), 21.93 (15.84 - 25.89)) were all significantly higher than that of healthy controls (53.17 (42.63 - 63.21), 16.14 (11.94 - 21.98)) (P values all < 0.05). T cell CXCR4(+)CD4 T cells expression of ESNs (93.67 (92.17 - 94.96)) was significantly lower than that of healthy controls (95.16 (92.99 - 96.77)) (P values all < 0.05). Healthy controls and ESNs could be divided into low expression group and high expression group according to HLA-DR(+)CD8 T cells bimodal distribution. A total of 89.2% (33/37) ESNs fell into HLA-DR + CD8 low expression group, and 58.4% (59/101) of the healthy controls located in low expression group (P < 0.05). CONCLUSION: To Han Chinese, the low activation status of T lymphocyte has significant correlation with HIV-1 low susceptibility.

A comparison of HIV infection and related risks among male sex workers in different venues in Shenzhen, China.

Different risks of HIV infection have been reported among different types of male sex workers (MSW). In order to compare the prevalence of HIV infection and related risk behaviors of MSW in different venues in Shenzhen, China, a time-location sampling survey was conducted in 2008. 5.1% of the 394 MSWs were tested positive for HIV, with 6.9% in those working in parks (PMSW), 11.3% in small family clubs (FMSW) and 1.7% in entertainment venues. PMSWs and FMSWs reported a higher proportion of self-identified homosexual/gay. Moreover, FMSWs reported a lower coverage of HIV-related education and services and were more likely to self-report coming from provinces with higher HIV prevalence. The results indicated that MSWs in small venues and parks were comparatively at higher risk of being infected and suggested that current HIV preventive intervention needs to be expanded to the small venues in Shenzhen.

Trends of HIV/Syphilis/HSV-2 seropositive rate and factors associated with HSV-2 infection in men who have sex with men in Shenzhen, China: A retrospective study.

OBJECTIVES: To analyze the trends of HIV/syphilis/HSV-2 seropositive rate and explore the related factors with HSV-2 infection to provide the basis for adjusting STD intervention strategies and formulating prevention and control measures among MSM in Shenzhen. METHODS: Time-location sampling was conducted among MSM in Shenzhen in 2012, 2014, 2016, and 2018. Data on demographics, sexual behaviors and the laboratory test results of HIV, syphilis, HSV-2 were collected. The χ2 trend test was used to analyze the trends of HIV/syphilis/HSV-2 seropositive rate. The binary logistic regression model was used to explore the factors associated with HSV-2 infection. RESULTS: The seropositive rate of HIV fell significantly from 15.9% in 2012 to 8.7% in 2018 (Ptrend = 0.003), syphilis seropositive rate was significantly decreased from 20.4% in 2012 to 14.8% in 2018 (Ptrend = 0.025), HSV-2 seropositive rate had no significant change (16.7% in 2012 to 14.0% in 2018; Ptrend = 0.617). In principal component logistic regression analysis showed that FAC1_1 (X1 = Ever had sex with female, X2 = Gender of first sexual partner, X3 = Marital status, X4 = Age group), FAC2_1 (X5 = Education, X6 = Monthly income (RMB), X7 = Frequency of condom use in anal sex with men in the past 6 months), and FAC4_1 (X9 = History of STDs) were significantly associated with HSV-2 infection. CONCLUSIONS: The seropositive rates of HIV and syphilis have dropped significantly but are still high. HSV-2 seropositive rate had no significant change and maintained a high level. It is necessary to continue strengthening HIV and syphilis interventions among MSM in Shenzhen. HSV-2 detection and intervention are urgently required for MSM, which might be another effective biological strategy further to control the HIV epidemic among MSM in Shenzhen.

A genetic programming approach for Burkholderia pseudomallei diagnostic pattern discovery.

MOTIVATION: Finding diagnostic patterns for fighting diseases like Burkholderia pseudomallei using biomarkers involves two key issues. First, exhausting all subsets of testable biomarkers (antigens in this context) to find a best one is computationally infeasible. Therefore, a proper optimization approach like evolutionary computation should be investigated. Second, a properly selected function of the antigens as the diagnostic pattern which is commonly unknown is a key to the diagnostic accuracy and the diagnostic effectiveness in clinical use. RESULTS: A conversion function is proposed to convert serum tests of antigens on patients to binary values based on which Boolean functions as the diagnostic patterns are developed. A genetic programming approach is designed for optimizing the diagnostic patterns in terms of their accuracy and effectiveness. During optimization, it is aimed to maximize the coverage (the rate of positive response to antigens) in the infected patients and minimize the coverage in the non-infected patients while maintaining the fewest number of testable antigens used in the Boolean functions as possible. The final coverage in the infected patients is 96.55% using 17 of 215 (7.4%) antigens with zero coverage in the non-infected patients. Among these 17 antigens, BPSL2697 is the most frequently selected one for the diagnosis of Burkholderia Pseudomallei. The approach has been evaluated using both the cross-validation and the Jack-knife simulation methods with the prediction accuracy as 93% and 92%, respectively. A novel approach is also proposed in this study to evaluate a model with binary data using ROC analysis.

HIV prevalence and related risk factors among male sex workers in Shenzhen, China: results from a time-location sampling survey.

BACKGROUND: HIV transmission among men who have sex with men has recently become a major concern in China. Little is known, however, about HIV transmission among male sex workers (MSW). This study aimed to investigate HIV infection prevalence and risk factors among MSW in Shenzhen, China. MATERIALS AND METHODS: Following formative research, a cross-sectional study was conducted using time-location sampling among MSW in Shenzhen, from April to July 2008. Behavioural and serological data on HIV and syphilis were collected. The risk factors for HIV infection were analysed using a logistic regression model. RESULTS: In total, 394 MSW were recruited for the survey. The prevalence of HIV and syphilis among these workers was 5.3% and 14.3%, respectively. Only a quarter of the MSW self-identified as homosexual. More than 70% had sex with both men and women. HIV-related knowledge levels were high regardless of HIV serostatus. Consistent condom use was low (37.1%) and varied by type of sexual partner. Factors including more non-commercial male partners, working in small home-based family clubs, being drunk before sexual intercourse, having a history of HIV tests, syphilis infection and a short period of residence in Shenzhen were associated with an increased risk of HIV infection. CONCLUSIONS: High-risk sexual practices were common among MSW regardless of their high level of HIV awareness. The working venues were associated with HIV infection and a recent test for HIV was a potential predictor of HIV infection. The time-location sampling method was found to be an appropriate way of recruiting MSW for this study, especially those without fixed working places.

Recurrence of positive SARS-CoV-2 viral RNA in recovered COVID-19 patients during medical isolation observation.

Recently, the recurrence of positive SARS-CoV-2 viral RNA in recovered COVID-19 patients is receiving more attention. Herein we report a cohort study on the follow-up of 182 recovered patients under medical isolation observation. Twenty (10.99%) patients out of the 182 were detected to be SARS-CoV-2 RNA positive (re-positives), although none showed any clinical symptomatic recurrence, indicating that COVID-19 responds well to treatment. Patients aged under 18 years had higher re-positive rates than average, and none of the severely ill patients re-tested positive. There were no significant differences in sex between re-positives and non-re-positives. Notably, most of the re-positives turned negative in the following tests, and all of them carried antibodies against SARS-CoV-2. This indicates that they might not be infectious, although it is still important to perform regular SARS-CoV-2 RNA testing and follow-up for assessment of infectivity. The findings of this study provide information for improving the management of recovered patients, and for differentiating the follow-up of recovered patients with different risk levels.

Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy.

BACKGROUND: Tyrosine sulfation is one of the most important posttranslational modifications. Due to its relevance to various disease developments, tyrosine sulfation has become the target for drug design. In order to facilitate efficient drug design, accurate prediction of sulfotyrosine sites is desirable. A predictor published seven years ago has been very successful with claimed prediction accuracy of 98%. However, it has a particularly low sensitivity when predicting sulfotyrosine sites in some newly sequenced proteins. RESULTS: A new approach has been developed for predicting sulfotyrosine sites using the random forest algorithm after a careful evaluation of seven machine learning algorithms. Peptides are formed by consecutive residues symmetrically flanking tyrosine sites. They are then encoded using an amino acid hydrophobicity scale. This new approach has increased the sensitivity by 22%, the specificity by 3%, and the total prediction accuracy by 10% compared with the previous predictor using the same blind data. Meanwhile, both negative and positive predictive powers have been increased by 9%. In addition, the random forest model has an excellent feature for ranking the residues flanking tyrosine sites, hence providing more information for further investigating the tyrosine sulfation mechanism. A web tool has been implemented at http://ecsb.ex.ac.uk/sulfotyrosine for public use. CONCLUSION: The random forest algorithm is able to deliver a better model compared with the Hidden Markov Model, the support vector machine, artificial neural networks, and others for predicting sulfotyrosine sites. The success shows that the random forest algorithm together with an amino acid hydrophobicity scale encoding can be a good candidate for peptide classification.

Peptide bioinformatics: peptide classification using peptide machines.

Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research such as functional site prediction, protein structure identification, and protein function recognition. In these applications, we normally need to assign a peptide to one of the given categories using a computer model. They are therefore referred to as peptide classification applications. Among various machine learning approaches, including neural networks, peptide machines have demonstrated excellent performance in many applications. This chapter discusses the basic concepts of peptide classification, commonly used feature extraction methods, three peptide machines, and some important issues in peptide classification.

A probabilistic peptide machine for predicting hepatitis C virus protease cleavage sites.

Although various machine learning approaches have been used for predicting protease cleavage sites, constructing a probabilistic model for these tasks is still challenging. This paper proposes a novel algorithm termed as a probabilistic peptide machine where estimating probability density functions and constructing a classifier for predicting protease cleavage sites are combined into one process. The simulation based on experimentally determined Hepatitis C virus (HCV) protease cleavage data has demonstrated the success of this new algorithm.

A Noise Trimming and Positional Significance of Transposon Insertion System to Identify Essential Genes in Yersinia pestis.

Massively parallel sequencing technology coupled with saturation mutagenesis has provided new and global insights into gene functions and roles. At a simplistic level, the frequency of mutations within genes can indicate the degree of essentiality. However, this approach neglects to take account of the positional significance of mutations - the function of a gene is less likely to be disrupted by a mutation close to the distal ends. Therefore, a systematic bioinformatics approach to improve the reliability of essential gene identification is desirable. We report here a parametric model which introduces a novel mutation feature together with a noise trimming approach to predict the biological significance of Tn5 mutations. We show improved performance of essential gene prediction in the bacterium Yersinia pestis, the causative agent of plague. This method would have broad applicability to other organisms and to the identification of genes which are essential for competitiveness or survival under a broad range of stresses.

Prediction of signal peptides using bio-basis function neural networks and decision trees.

Signal peptide identification is of immense importance in drug design. Accurate identification of signal peptides is the first critical step to be able to change the direction of the targeting proteins and use the designed drug to target a specific organelle to correct a defect. Because experimental identification is the most accurate method, but is expensive and time-consuming, an efficient and affordable automated system is of great interest. In this article, we propose using an adapted neural network, called a bio-basis function neural network, and decision trees for predicting signal peptides. The bio-basis function neural network model and decision trees achieved 97.16% and 97.63% accuracy respectively, demonstrating that the methods work well for the prediction of signal peptides. Moreover, decision trees revealed that position P(1'), which is important in forming signal peptides, most commonly comprises either leucine or alanine. This concurs with the (P(3)-P(1)-P(1')) coupling model.

Predicting hepatitis C virus protease cleavage sites using generalized linear indicator regression models.

This paper discusses how to predict hepatitis C virus protease cleavage sites in proteins using generalized linear indicator regression models. The mutual information is used for model-size optimization. Two simulation strategies are adopted, i.e., building a model based on published peptides and building a model based on the published peptides plus newly collected sequences. It is found that the latter outperforms the former significantly. The simulation also shows that the generalized linear indicator regression model far outperforms the multilayer perceptron model.

A bio-basis function neural network for protein peptide cleavage activity characterisation.

This paper presents a novel neural learning algorithm for analysing protein peptides which comprise amino acids as non-numerical attributes. The algorithm is derived from the radial basis function neural networks (RBFNNs) and is referred to as a bio-basis function neural network (BBFNN). The basic principle is to replace the radial basis function used by RBFNNs with a bio-basis function. Each basis in BBFNN is supported by a peptide. The bases collectively form a feature space, in which each basis represents a feature dimension. A linear classifier is constructed in the feature space for characterising a protein peptide in terms of functional status. The theoretical basis of BBFNN is that peptides, which perform the same function will have similar compositions of amino acids. Because of this, the similarity between peptides can have statistical significance for modelling while the proposed bio-basis function can well code this information from data. The application to two real cases shows that BBFNN outperformed multi-layer perceptrons and support vector machines.

A novel radial basis function neural network for discriminant analysis.

A novel radial basis function neural network for discriminant analysis is presented in this paper. In contrast to many other researches, this work focuses on the exploitation of the weight structure of radial basis function neural networks using the Bayesian method. It is expected that the performance of a radial basis function neural network with a well-explored weight structure can be improved. As the weight structure of a radial basis function neural network is commonly unknown, the Bayesian method is, therefore, used in this paper to study this a priori structure. Two weight structures are investigated in this study, i.e., a single-Gaussian structure and a two-Gaussian structure. An expectation-maximization learning algorithm is used to estimate the weights. The simulation results showed that the proposed radial basis function neural network with a weight structure of two Gaussians outperformed the other algorithms.