ZCURVE_CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes

Identification and characterization of novel alphacoronaviruses in Tadarida brasiliensis (Chiroptera, Molossidae) from Argentina: insights into recombination as a mechanism favoring bat coronavirus cross-species transmission

Bats are reservoirs of various coronaviruses that can jump between bat species or other mammalian hosts, including humans. This article explores coronavirus infection in three bat species (Tadarida brasiliensis, Eumops bonariensis, and Molossus molossus) of the family Molossidae from Argentina using whole viral metagenome analysis. Fecal samples of 47 bats from three semiurban or highly urbanized areas of the province of Santa Fe were investigated. After viral particle enrichment, total RNA was sequenced using the Illumina NextSeq 550 instrument; the reads were assembled into contigs and taxonomically and phylogenetically analyzed. Three novel complete Alphacoronavirus (AlphaCoV) genomes (Tb1-3) and two partial sequences were identified in T. brasiliensis (Tb4-5), and an additional four partial sequences were identified in M. molossus (Mm1-4). Phylogenomic analysis showed that the novel AlphaCoV clustered in two different lineages distinct from the 15 officially recognized AlphaCoV subgenera. Tb2 and Tb3 isolates appeared to be variants of the same virus, probably involved in a persistent infectious cycle within the T. brasiliensis colony. Using recombination analysis, we detected a statistically significant event in Spike gene, which was reinforced by phylogenetic tree incongruence analysis, involving novel Tb1 and AlphaCoVs identified in Eptesicus fuscus (family Vespertilionidae) from the U.S. The putative recombinant region is in the S1 subdomain of the Spike gene, encompassing the potential receptor-binding domain of AlphaCoVs. This study reports the first AlphaCoV genomes in molossids from the Americas and provides new insights into recombination as an important mode of evolution of coronaviruses involved in cross-species transmission. IMPORTANCE This study generated three novel complete AlphaCoV genomes (Tb1, Tb2, and Tb3 isolates) identified in individuals of Tadarida brasiliensis from Argentina, which showed two different evolutionary patterns and are the first to be reported in the family Molossidae in the Americas. The novel Tb1 isolate was found to be involved in a putative recombination event with alphacoronaviruses identified in bats of the genus Eptesicus from the U.S., whereas isolates Tb2 and Tb3 were found in different collection seasons and might be involved in persistent viral infections in the bat colony. These findings contribute to our knowledge of the global diversity of bat coronaviruses in poorly studied species and highlight the different evolutionary aspects of AlphaCoVs circulating in bat populations in Argentina.

Alphacoronaviruses Are Common in Bats in the Upper Midwestern United States

Bats are a reservoir for coronaviruses (CoVs) that periodically spill over to humans, as evidenced by severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2. A collection of 174 bat samples originating from South Dakota, Minnesota, Iowa, and Nebraska submitted for rabies virus testing due to human exposure were analyzed using a pan-coronavirus PCR. A previously partially characterized CoV, Eptesicus bat CoV, was identified in 12 (6.9%) samples by nested RT-PCR. Six near-complete genomes were determined. Genetic analysis found a high similarity between all CoV-positive samples, Rocky Mountain bat CoV 65 and alphacoronavirus HCQD-2020 recently identified in South Korea. Phylogenetic analysis of genome sequences showed EbCoV is closely related to bat CoV HKU2 and swine acute diarrhea syndrome CoV; however, topological incongruences were noted for the spike gene that was more closely related to porcine epidemic diarrhea virus. Similar to some alphaCoVs, a novel gene, ORF7, was discovered downstream of the nucleocapsid, whose protein lacked similarity to known proteins. The widespread circulation of EbCoV with similarities to bat viruses that have spilled over to swine warrants further surveillance.

Genomic Characterization of a Novel Alphacoronavirus Isolated from Bats, Korea, 2020

Coronavirus, an important zoonotic disease, raises concerns of future pandemics. The bat is considered a source of noticeable viruses resulting in human and livestock infections, especially the coronavirus. Therefore, surveillance and genetic analysis of coronaviruses in bats are essential in order to prevent the risk of future diseases. In this study, the genome of HCQD-2020, a novel alphacoronavirus detected in a bat (Eptesicus serotinus), was assembled and described using next-generation sequencing and bioinformatics analysis. The comparison of the whole-genome sequence and the conserved amino acid sequence of replicated proteins revealed that the new strain was distantly related with other known species in the Alphacoronavirus genus. Phylogenetic construction indicated that this strain formed a separated branch with other species, suggesting a new species of Alphacoronavirus. Additionally, in silico prediction also revealed the risk of cross-species infection of this strain, especially in the order Artiodactyla. In summary, this study provided the genetic characteristics of a possible new species belonging to Alphacoronavirus.

Accurate prediction of human essential genes using only nucleotide composition and association information

Motivation

Previously constructed classifiers in predicting eukaryotic essential genes integrated a variety of features including experimental ones. If we can obtain satisfactory prediction using only nucleotide (sequence) information, it would be more promising. Three groups recently identified essential genes in human cancer cell lines using wet experiments and it provided wonderful opportunity to accomplish our idea. Here we improved the Z curve method into the λ-interval form to denote nucleotide composition and association information and used it to construct the SVM classifying model.

Results

Our model accurately predicted human gene essentiality with an AUC higher than 0.88 both for 5-fold cross-validation and jackknife tests. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. We re-predicted the negative dataset by our Pheg server and 118 genes were additionally predicted as essential. Among them, 20 were found to be homologues in mouse essential genes, indicating that some of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the λ-interval Z curve method could be effectively extended to classification issues of other DNA elements.

Availability and Implementation

http://cefg.uestc.edu.cn/Pheg.

Contact

fbguo@uestc.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots

Pseudo dinucleotide composition (PseDNC) and Z curve showed excellent performance in the classification issues of nucleotide sequences in bioinformatics. Inspired by the principle of Z curve theory, we improved PseDNC to give the phase-specific PseDNC (psPseDNC). In this study, we used the prediction of recombination spots as a case to illustrate the capability of psPseDNC and also PseDNC fused with Z curve theory based on a novel machine learning method named large margin distribution machine (LDM). We verified that combining the two widely used approaches could generate better performance compared to only using PseDNC with a support vector machine based (SVM-based) model. The best Mathew's correlation coefficient (MCC) achieved by our LDM-based model was 0.7037 through the rigorous jackknife test and improved by ∼6.6%, ∼3.2%, and ∼2.4% compared with three previous studies. Similarly, the accuracy was improved by 3.2% compared with our previous iRSpot-PseDNC web server through an independent data test. These results demonstrate that the joint use of PseDNC and Z curve enhances performance and can extract more information from a biological sequence. To facilitate research in this area, we constructed a user-friendly web server for predicting hot/cold spots, HcsPredictor, which can be freely accessed from . In summary, we provided a united algorithm by integrating Z curve with PseDNC. We hope this united algorithm could be extended to other classification issues in DNA elements.

Genetic diversity of coronaviruses in Miniopterus fuliginosus bats

Coronaviruses, such as severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus, pose significant public health threats. Bats have been suggested to act as natural reservoirs for both these viruses, and periodic monitoring of coronaviruses in bats may thus provide important clues about emergent infectious viruses. The Eastern bent-wing bat Miniopterus fuliginosus is distributed extensively throughout China. We therefore analyzed the genetic diversity of coronaviruses in samples of M. fuliginosus collected from nine Chinese provinces during 2011–2013. The only coronavirus genus found was Alphacoronavirus. We established six complete and five partial genomic sequences of alphacoronaviruses, which revealed that they could be divided into two distinct lineages, with close relationships to coronaviruses in Miniopterus magnater and Miniopterus pusillus. Recombination was confirmed by detecting putative breakpoints of Lineage 1 coronaviruses in M. fuliginosus and M. pusillus (Wu et al., 2015), which supported the results of topological and phylogenetic analyses. The established alphacoronavirus genome sequences showed high similarity to other alphacoronaviruses found in other Miniopterus species, suggesting that their transmission in different Miniopterus species may provide opportunities for recombination with different alphacoronaviruses. The genetic information for these novel alphacoronaviruses will improve our understanding of the evolution and genetic diversity of coronaviruses, with potentially important implications for the transmission of human diseases.

A Brief Review: The Z-curve Theory and its Application in Genome Analysis

In theoretical physics, there exist two basic mathematical approaches, algebraic and geometrical methods, which, in most cases, are complementary. In the area of genome sequence analysis, however, algebraic approaches have been widely used, while geometrical approaches have been less explored for a long time. The Z-curve theory is a geometrical approach to genome analysis. The Z-curve is a three-dimensional curve that represents a given DNA sequence in the sense that each can be uniquely reconstructed given the other. The Z-curve, therefore, contains all the information that the corresponding DNA sequence carries. The analysis of a DNA sequence can then be performed through studying the corresponding Z-curve. The Z-curve method has found applications in a wide range of areas in the past two decades, including the identifications of protein-coding genes, replication origins, horizontally-transferred genomic islands, promoters, translational start sides and isochores, as well as studies on phylogenetics, genome visualization and comparative genomics. Here, we review the progress of Z-curve studies from aspects of both theory and applications in genome analysis.

A rebuttal to the comments on the genome order index and the Z-curve

Background

Elhaik, Graur and Josic recently commented on the genome order index (S) and the Z-curve (Elhaik et al. Biol Direct 2010, 5: 10). S is a quantity defined as S = a² + c² + g² + t², where a, c, g and t denote corresponding base frequencies. The Z-curve is a three dimensional curve that represents a DNA sequence in the manner that each can be uniquely reconstructed given the other. Elhaik et al. made 4 major claims. 1) In the previous mapping system with the regular tetrahedron, calculation of the radius of the inscribed sphere is "a mathematical error". 2) S follows an exponential distribution and is narrowly distributed with a range of (0.25 - 0.33). 3) Based on the Chargaff's second parity rule (PR2), "S is equivalent to H [Shannon entropy]" and they are derivable from each other. 4) Z-curve "suffers from over dimensionality", because based on the analysis of 235 bacterial genomes, x and y components contributed only less than 1% of the variance and therefore "would be of little use".

Results

1) Elhaik et al. mistakenly neglected the parameter 4/3 when calculating the radius of the inscribed sphere. 2) The exponential distribution of S is a restatement of our previous conclusion, and the range of (0.25 - 0.33) only paraphrases the previously suggested S range (0.25 -1/3). 3) Elhaik et al. incorrectly disregard deviations from PR2 by treating the deviations as 0 altogether, reduce S and H, both having 4 variables, a, c, g and t, into functions of one single variable, a only, and apply this treatment to all DNA sequences as the basis of their "demonstration", which is therefore invalid. 4) Elhaik et al. confuse numeral smallness with biological insignificance, and disregard the distributions of purine/pyrimidine and amino/keto bases (x and y components), the variations of which, although can be less than that of GC content, contain rich information that is important and useful, such as in locating replication origins of bacterial and archaeal genomes, and in studies of gene recognition in various species.

Conclusion

Elhaik et al. confuse S (a single number) with Z-curve (a series of 3D coordinates), which are distinct. To use S as a case study of Z-curve, by itself, is invalid. S and H are neither equivalent nor derivable from each other. The criticisms of Elhaik, Graur and Josic are wrong.

Reviewers

This article was reviewed by Erik van Nimwegen.

VIGOR, an annotation program for small viral genomes

Background

The decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of publicly available genomes. Among the types of genomes being rapidly sequenced are those of microbial and viral genomes responsible for infectious diseases. However, accurate gene prediction is a challenge that persists for decoding a newly sequenced genome. Therefore, accurate and efficient gene prediction programs are highly desired for rapid and cost effective surveillance of RNA viruses through full genome sequencing.

Results

We have developed VIGOR (Viral Genome ORF Reader), a web application tool for gene prediction in influenza virus, rotavirus, rhinovirus and coronavirus subtypes. VIGOR detects protein coding regions based on sequence similarity searches and can accurately detect genome specific features such as frame shifts, overlapping genes, embedded genes, and can predict mature peptides within the context of a single polypeptide open reading frame. Genotyping capability for influenza and rotavirus is built into the program. We compared VIGOR to previously described gene prediction programs, ZCURVE_V, GeneMarkS and FLAN. The specificity and sensitivity of VIGOR are greater than 99% for the RNA viral genomes tested.

Conclusions

VIGOR is a user friendly web-based genome annotation program for five different viral agents, influenza, rotavirus, rhinovirus, coronavirus and SARS coronavirus. This is the first gene prediction program for rotavirus and rhinovirus for public access. VIGOR is able to accurately predict protein coding genes for the above five viral types and has the capability to assign function to the predicted open reading frames and genotype influenza virus. The prediction software was designed for performing high throughput annotation and closure validation in a post-sequencing production pipeline.

Genomic characterizations of bat coronaviruses (1A, 1B and HKU8) and evidence for co-infections in Miniopterus bats

We previously reported the detection of bat coronaviruses (bat CoVs 1A, 1B, HKU7, HKU8 and bat-severe acute respiratory syndrome coronavirus) in Miniopterus spp. that cohabit a cave in Hong Kong. Here, we report the full genomic sequences of bat CoVs 1A, 1B and HKU8. Bat CoVs 1A and 1B, which are commonly found in the Miniopterus, are phylogenetically closely related. Using species-specific RT-PCR assays, bat CoVs 1A and 1B were confirmed to have distinct host specificities to Miniopterus magnater and Miniopterus pusillus, respectively. Interestingly, co-infections of bat CoVs 1B and HKU8 in M. pusillus are detected in seven of 38 virus-positive specimens collected from 2004 to 2006. These findings highlight that co-infections of some coronaviruses might be common events in nature. The biological basis for the host restriction of bat coronaviruses, however, is yet to be determined.

Anti-SARS drug screening by molecular docking

Starting from a collection of 1386 druggable compounds obtained from the 3D pharmacophore search, we performed a similarity search to narrow down the scope of docking studies. The template molecule is KZ7088 (Chou et al., 2003, Biochem Biophys Res Commun 308: 148-151). The MDL MACCS keys were used to fingerprint the molecules. The Tanimoto coefficient is taken as the metric to compare fingerprints. If the similarity threshold was 0.8, a set of 50 unique hits and 103 conformers were retrieved as a result of similarity search. The AutoDock 3.011 was used to carry out molecular docking of 50 ligands to their macromolecular protein receptors. Three compounds, i.e., C(28)H(34)O(4)N(7)Cl, C(21)H(36)O(5)N(6), and C(21)H(36)O(5)N(6), were found that may be promising candidates for further investigation. The main feature shared by these three potential inhibitors as well as the information of the involved side chains of SARS Cov Mpro may provide useful insights for the development of potent inhibitors against SARS enzyme.

ZCURVE_V: a new self-training system for recognizing protein-coding genes in viral and phage genomes

Background

It necessary to use highly accurate and statistics-based systems for viral and phage genome annotations. The GeneMark systems for gene-finding in virus and phage genomes suffer from some basic drawbacks. This paper puts forward an alternative approach for viral and phage gene-finding to improve the quality of annotations, particularly for newly sequenced genomes.

Results

The new system ZCURVE_V has been run for 979 viral and 212 phage genomes, respectively, and satisfactory results are obtained. To have a fair comparison with the currently available software of similar function, GeneMark, a total of 30 viral genomes that have not been annotated by GeneMark are selected to be tested. Consequently, the average specificity of both systems is well matched, however the average sensitivity of ZCURVE_V for smaller viral genomes (< 100 kb), which constitute the main parts of viral genomes sequenced so far, is higher than that of GeneMark. Additionally, for the genome of Amsacta moorei entomopoxvirus, probably with the lowest genomic GC content among the sequenced organisms, the accuracy of ZCURVE_V is much better than that of GeneMark, because the later predicts hundreds of false-positive genes. ZCURVE_V is also used to analyze well-studied genomes, such as HIV-1, HBV and SARS-CoV. Accordingly, the performance of ZCURVE_V is generally better than that of GeneMark. Finally, ZCURVE_V may be downloaded and run locally, particularly facilitating its utilization, whereas GeneMark is not downloadable. Based on the above comparison, it is suggested that ZCURVE_V may serve as a preferred gene-finding tool for viral and phage genomes newly sequenced. However, it is also shown that the joint application of both systems, ZCURVE_V and GeneMark, leads to better gene-finding results. The system ZCURVE_V is freely available at: .

Conclusion

ZCURVE_V may serve as a preferred gene-finding tool used for viral and phage genomes, especially for anonymous viral and phage genomes newly sequenced.

Coronavirus phylogeny based on a geometric approach

A novel coronavirus has been identified as the cause of the outbreak of severe acute respiratory syndrome (SARS). Previous phylogenetic analyses based on sequence alignments show that SARS-CoVs form a new group distantly related to the other three groups of previously characterized coronaviruses. In this paper, a geometric approach based on the Z-curve representation of the whole genome sequence is proposed to analyze the phylogenetic relationships of coronaviruses. The evolutionary distances are obtained through measuring the differences among the three-dimensional Z-curves. The Z-curve is approximately described by its geometric center and the associated three eigenvectors, which indicate the center position and the trend of the Z-curve, respectively. Although some information is lost due to the approximate description of the Z-curve, the phylogenetic tree constructed based on these parameters is consistent with those of previous analyses. The present method has the merits of simplicity and intuitiveness, but it is still in its premature stage. Because the phylogenetic relationships are inferred from the whole genome, instead of some individual genes, the present method represents a new direction of phylogeny study in the post-genome era.

Molecular modeling and chemical modification for finding peptide inhibitor against severe acute respiratory syndrome coronavirus main proteinase

Severe acute respiratory syndrome (SARS) is a respiratory disease caused by a newly found virus, called SARS coronavirus. In this study, the cleavage mechanism of the SARS coronavirus main proteinase (M^pro or 3CL^pro) on the octapeptide NH₂-AVLQ ↓ SGFR-COOH was investigated using molecular mechanics and quantum mechanics simulations based on the experimental structure of the proteinase. It has been observed that the catalytic dyad (His-41/Cys-145) site between domains I and II attracts the π electron density from the peptide bond Gln–Ser, increasing the positive charge on C(CO) of Gln and the negative charge on N(NH) of Ser, so as to weaken the Gln–Ser peptide bond. The catalytic functional group is the imidazole group of His-41 and the S in Cys-145. N_δ1 on the imidazole ring plays the acid–base catalytic role. Based on the “distorted key theory” [K.C. Chou, Anal. Biochem. 233 (1996) 1–14], the possibility to convert the octapeptide to a competent inhibitor has been studied. It has been found that the chemical bond between Gln and Ser will become much stronger and no longer cleavable by the SARS enzyme after either changing the carbonyl group CO of Gln to CH₂ or CF₂ or changing the NH of Ser to CH₂ or CF₂. The octapeptide thus modified might become an effective inhibitor or a potential drug candidate against SARS.

A novel fingerprint map for detecting SARS-CoV

Spike (S) protein is the most important membrane protein on the surface of severe acute respiratory syndrome coronavirus (SARS-CoV). It associates with cellular receptors to mediate infection of their target cells. Inspired by such a mechanism, an in-depth investigation into the genome sequences of S protein of SARS-CoV and its receptor are conducted thru a mathematical transformation and graphic approach. As an outcome, a novel method for visualizing the characteristic of SARS-CoV is suggested. An extensive comparison among a large number of genome sequences has proved that the characteristic thus revealed is unique for SARS-CoV. As such, the characteristic can be regarded as the fingerprint map of SARS-CoV for diagnostic usage. Moreover, the conclusion has been further supported in a real case in Guangdong province of China. The fingerprint map proposed here has the merits of clear visibility and reliability that can serve as a complementary clinical tool for detecting SARS-CoV, particularly for the cases where the results obtained by the conventional methods are uncertain or conflicted with each other.

Characterization of viral proteins encoded by the SARS-coronavirus genome

A new disease, termed severe acute respiratory syndrome (SARS), emerged at the end of 2002 and caused profound disturbances in over 30 countries worldwide in 2003. A novel coronavirus was identified as the aetiological agent of SARS and the 30 kb viral genome was deciphered with unprecedented speed in a coordinated manner by the global community. Since then, much progress has been made in the virological and molecular characterization of the proteins encoded by SARS-coronavirus (SARS-CoV) genome, which contains 14 potential open reading frames (ORFs). These investigations can be broadly classified into three groups: (a) studies on the replicase 1a/1b gene products which are important for viral replication, (b) studies on the structural proteins, spike, nucleocapsid, membrane and envelope, which have homologues in all coronaviruses, and are important for viral assembly and (c) expression and functional studies of the “accessory” proteins that are specifically encoded by SARS-CoV. A comparison of the properties of these three groups of SARS-CoV proteins with the knowledge that coronavirologists have generated over more than 30 years of research can help us in the prevention and treatment of SARS in the event of the re-emergence of this new infectious disease.

Cleavage Site Analysis Using Rule Extraction from Neural Networks

In this paper, we demonstrate that the machine learning approach of rule extraction from a trained neural network can be successfully applied to SARS-coronavirus cleavage site analysis. The extracted rules predict cleavage sites better than consensus patterns. Empirical experiments are also shown.

Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction

Cleavage site prediction is an important issue in molecular biology. We present a new method that generates prediction rules for SARS-CoV protease cleavage sites. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution to improve its quality. Experimental results show that the method could generate new rules for cleavage site prediction, which are more general and accurate than consensus patterns.

Profiles of antibody responses against severe acute respiratory syndrome coronavirus recombinant proteins and their potential use as diagnostic markers

A new coronavirus (severe acute respiratory syndrome coronavirus [SARS-CoV]) has been identified to be the etiological agent of severe acute respiratory syndrome. Given the highly contagious and acute nature of the disease, there is an urgent need for the development of diagnostic assays that can detect SARS-CoV infection. For determination of which of the viral proteins encoded by the SARS-CoV genome may be exploited as diagnostic antigens for serological assays, the viral proteins were expressed individually in mammalian and/or bacterial cells and tested for reactivity with sera from SARS-CoV-infected patients by Western blot analysis. A total of 81 sera, including 67 from convalescent patients and seven pairs from two time points of infection, were analyzed, and all showed immunoreactivity towards the nucleocapsid protein (N). Sera from some of the patients also showed immunoreactivity to U274 (59 of 81 [73%]), a protein that is unique to SARS-CoV. In addition, all of the convalescent-phase sera showed immunoreactivity to the spike (S) protein when analyzed by an immunofluorescence method utilizing mammalian cells stably expressing S. However, samples from the acute phase (2 to 9 days after the onset of illness) did not react with S, suggesting that antibodies to N may appear earlier than antibodies to S. Alternatively, this could be due to the difference in the sensitivities of the two methods. The immunoreactivities to these recombinant viral proteins are highly specific, as sera from 100 healthy donors did not react with any of them. These results suggest that recombinant N, S, and U274 proteins may be used as antigens for the development of serological assays for SARS-CoV.

Potential targets for anti-SARS drugs in the structural proteins from SARS related coronavirus

This is a further study on the severe acute respiratory syndrome (SARS) using the probabilistic models. The purpose was to define the potential targets for anti-SARS drugs in the structural proteins from human SARS related coronavirus (SARS-CoV) while knowing little about the functional sites and possible mutations in these proteins. From a probabilistic viewpoint, we can theoretically select the amino acid pairs as potential candidates for anti-SARS drugs. These candidates have a greater chance of colliding with anti-SARS drugs, are more likely to link with the protein functions and are less vulnerable to mutations.

Identification of a new human coronavirus

Three human coronaviruses are known to exist: human coronavirus 229E (HCoV-229E), HCoV-OC43 and severe acute respiratory syndrome (SARS)-associated coronavirus (SARS-CoV). Here we report the identification of a fourth human coronavirus, HCoV-NL63, using a new method of virus discovery. The virus was isolated from a 7-month-old child suffering from bronchiolitis and conjunctivitis. The complete genome sequence indicates that this virus is not a recombinant, but rather a new group 1 coronavirus. The in vitro host cell range of HCoV-NL63 is notable because it replicates on tertiary monkey kidney cells and the monkey kidney LLC-MK2 cell line. The viral genome contains distinctive features, including a unique N-terminal fragment within the spike protein. Screening of clinical specimens from individuals suffering from respiratory illness identified seven additional HCoV-NL63-infected individuals, indicating that the virus was widely spread within the human population.

Prediction of amino acid pairs sensitive to mutations in the spike protein from SARS related coronavirus

In this study, we analyzed the amino acid pairs affected by mutations in two spike proteins from human coronavirus strains 229E and OC43 by means of random analysis in order to gain some insight into the possible mutations in the spike protein from SARS-CoV. The results demonstrate that the randomly unpredictable amino acid pairs are more sensitive to the mutations. The larger is the difference between actual and predicted frequencies, the higher is the chance of mutation occurring. The effect induced by mutations is to reduce the difference between actual and predicted frequencies. The amino acid pairs whose actual frequencies are larger than their predicted frequencies are more likely to be targeted by mutations, whereas the amino acid pairs whose actual frequencies are smaller than their predicted frequencies are more likely to be formed after mutations. These findings are identical to our several recent studies, i.e. the mutations represent a process of degeneration inducing human diseases.

Prediction of proteinase cleavage sites in polyproteins of coronaviruses and its applications in analyzing SARS-CoV genomes

Recently, we have developed a coronavirus-specific gene-finding system, ZCURVE_CoV 1.0. In this paper, the system is further improved by taking the prediction of cleavage sites of viral proteinases in polyproteins into account. The cleavage sites of the 3C-like proteinase and papain-like proteinase are highly conserved. Based on the method of traditional positional weight matrix trained by the peptides around cleavage sites, the present method also sufficiently considers the length conservation of non-structural proteins cleaved by the 3C-like proteinase and papain-like proteinase to reduce the false positive prediction rate. The improved system, ZCURVE_CoV 2.0, has been run for each of the 24 completely sequenced coronavirus genomes in GenBank. Consequently, all the non-structural proteins in the 24 genomes are accurately predicted. Compared with known annotations, the performance of the present method is satisfactory. The software ZCURVE_CoV 2.0 is freely available at http://tubic.tju.edu.cn/sars/.

Genome organization of the SARS-CoV

Annotation of the genome sequence of the SARS-CoV (severe acute respiratory syndrome-associated coronavirus) is indispensable to understand its evolution and pathogenesis. We have performed a full annotation of the SARS-CoV genome sequences by using annotation programs publicly available or developed by ourselves. Totally, 21 open reading frames (ORFs) of genes or putative uncharacterized proteins (PUPs) were predicted. Seven PUPs had not been reported previously, and two of them were predicted to contain transmembrane regions. Eight ORFs partially overlapped with or embedded into those of known genes, revealing that the SARS-CoV genome is a small and compact one with overlapped coding regions. The most striking discovery is that an ORF locates on the minus strand. We have also annotated non-coding regions and identified the transcription regulating sequences (TRS) in the intergenic regions. The analysis of TRS supports the minus strand extending transcription mechanism of coronavirus. The SNP analysis of different isolates reveals that mutations of the sequences do not affect the prediction results of ORFs.