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.

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.

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.

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.

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.

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 .

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.

Interleukin-27 is differentially associated with HIV viral load and CD4+ T cell counts in therapy-naïve HIV-mono-infected and HIV/HCV-co-infected Chinese.

Human Immunodeficiency Virus (HIV) infection and the resultant Acquired Immunodeficiency Syndrome (AIDS) epidemic are major global health challenges; hepatitis C virus (HCV) co-infection has made the HIV/AIDS epidemic even worse. Interleukin-27 (IL-27), a cytokine which inhibits HIV and HCV replication in vitro, associates with HIV infection and HIV/HCV co-infection in clinical settings. However, the impact of HIV and HCV viral loads on plasma IL-27 expression levels has not been well characterized. In this study, 155 antiretroviral therapy-naïve Chinese were recruited. Among them 80 were HIV- and HCV-negative healthy controls, 45 were HIV-mono-infected and 30 were HIV/HCV-co-infected. Plasma level HIV, HCV, IL-27 and CD4+ number were counted and their correlation, regression relationships were explored. We show that: plasma IL-27 level was significantly upregulated in HIV-mono-infected and HIV/HCV-co-infected Chinese; HIV viral load was negatively correlated with IL-27 titer in HIV-mono-infected subjects whereas the relationship was opposite in HIV/HCV-co-infected subjects; and the relationships between HIV viral loads, IL-27 titers and CD4+ T cell counts in the HIV mono-infection and HIV/HCV co-infection groups were dramatically different. Overall, our results suggest that IL-27 differs in treatment-naïve groups with HIV mono-infections and HIV/HCV co-infections, thereby providing critical information to be considered when caring and treating those with HIV mono-infection and HIV/HCV co-infection.

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.

Transcriptomic assay of CD8+ T cells in treatment-naïve HIV, HCV-mono-infected and HIV/HCV-co-infected Chinese.

BACKGROUND: Co-infection with HIV and HCV is very common. It is estimated that over 5 million people are co-infected with HIV and HCV worldwide. Accumulated evidence shows that each virus alters the course of infection of the other one. CD8+ T cells play a crucial role in the eradication of viruses and infected target cells. To the best of our knowledge, no one has investigated the gene expression profiles in HIV/HCV-co-infected individuals. METHODOLOGY: Genome-wide transcriptomes of CD8+ T cells from HIV/HCV-co-infected or mono-infected treatment-naïve individuals were analyzed by microarray assays. Pairwise comparisons were performed and differentially expressed genes were identified followed by quantitative real-time PCR (qRT-PCR) validation. Directed Acyclic Graphs (DAG) from Web-based Gene SeT AnaLysis Toolkit (WebGestalt) and DAVID bioinformatics resources 6.7 (the Database for Annotation, Visualization, and Integrated Discovery) were used to discover the Gene Ontology (GO) categories with significantly enriched gene numbers. The enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were also obtained by using WebGestalt software. RESULTS AND CONCLUSIONS: A total of 110, 24 and 72 transcript IDs were shown to be differentially expressed (> 2-fold and p<0.05) in comparisons between HCV- and HIV-mono-infected groups, HIV/HCV-co-infected and HIV-mono-infected groups, and HIV/HCV-co-infected and HCV-mono-infected groups, respectively. In qRT-PCR assay, most of the genes showed similar expressing profiles with the observation in microarray assays. Further analysis revealed that genes involved in cell proliferation, differentiation, transcriptional regulation and cytokine responses were significantly altered. These data offer new insights into HIV/HCV co-infections, and may help to identify new markers for the management and treatment of HIV/HCV co-infections.

[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.

An ultra-fast metabolite prediction algorithm.

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.

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.

Neural networks.

Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement. In bioinformatics applications, neural networks have played an important role for classification, function approximation, knowledge discovery, and data visualisation. This chapter will focus on supervised neural networks and discuss their applications to bioinformatics.

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.

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.

[Expression and localization of strong epitope hAFP(542-550); on eukaryotic cytoplasmic membrane].

AIM: To construct the recombinant plasmid of pGPC3-EGFP containing human AFP(542-550) gene, EGFP gene and GPC3 gene to express fusion protein GPC3-hAFP(542-550)-EGFP and to discover its localization on cytoplasmic membrane. METHODS: GPC3 gene was obtained from total RNA of human placental tissues by RT-PCR; After the enhanced green fluorescent protein (EGFP) gene was amplified from pEGFP-N1 plasmid and the gene segment of-KOZAK-GPCN + afp(542-550)-was chemically synthesized, the recombinant plasmid pcDNA3.1(+)/GPCN+afp(542-550)-EGFP-GPCC (pGPC3-EGFP) containing three chimeric genes of strong epitope hAFP(542-550), GPI-anchored protein GPC3 and EGFP was constructed. The fusion protein (GPC3-hAFP(542-550)-EGFP) was detected on RNA and protein levels at 24 h and 48 h after pGPC3-EGFP was transfected into HepG2 (GPC3(+)AFP(+)) via lipofectamine 2000. EGFP expression was observed by fluorescent microscopy after pGPC3-EGFP was transfected into HepG2 using pEGFP-N1 plasmid transfection as a positive control. The fusion protein in both membrane proteins and soluble proteins extracted from the transfected 293 cells (GPC3(-)AFP(-)) was detected by Western blot using GPC3 monoclonal antibody as primary antibody. RESULTS: The recombinant plasmid pGPC3-EGFP was successfully constructed through restriction endonuclease digestion and sequencing; pGPC3-EGFP expression in HepG2 cells was detected not only by RT-PCR using specific primers (GPCN-F and EGFP-r) but also by Western blot using GFP polyclonal antibody and GPC3 monoclonal antibody. Green fluoresce was mainly found around pGPC3-EGFP transfected HepG2 cell periphery beside sporadic distribution in cytoplasm, but that of pEGFP-N1 transfected HepG2 cell was evenly distributed in the whole cell. Moreover, the fusion protein was not detected in soluble proteins but membrane proteins extracted from transfected 293 cells. CONCLUSION: The recombinant plasmid of pGPC3-EGFP based on protein engineering theory can express fusion protein (GPC3-hAFP(542-550)-EGFP) in eukaryotic cells. Furthermore, the fusion protein is still located on cytoplasmic membrane, which is a characteristic of GPI-anchored membrane protein, and is a new GPI-reanchored protein.

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.

Predict prokaryotic proteins through detecting N-formylmethionine residues in protein sequences using support vector machine.

Identifying prokaryotes in silico is commonly based on DNA sequences. In experiments where DNA sequences may not be immediately available, we need to have a different approach to detect prokaryotes based on RNA or protein sequences. N-formylmethionine (fMet) is known as a typical characteristic of prokaryotes. A web tool has been implemented here for predicting prokaryotes through detecting the N-formylmethionine residues in protein sequences. The predictor is constructed using support vector machine. An online predictor has been implemented using Python. The implemented predictor is able to achieve the total prediction accuracy 80% with the specificity 80% and the sensitivity 81%.