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

The substrate specificity of SARS coronavirus 3C-like proteinase

The 3C-like proteinase of severe acute respiratory syndrome coronavirus (SARS) has been proposed to be a key target for structural based drug design against SARS. We have designed and synthesized 34 peptide substrates and determined their hydrolysis activities. The conserved core sequence of the native cleavage site is optimized for high hydrolysis activity. Residues at position P4, P3, and P3′ are critical for substrate recognition and binding, and increment of β-sheet conformation tendency is also helpful. A comparative molecular field analysis (CoMFA) model was constructed. Based on the mutation data and CoMFA model, a multiply mutated octapeptide S24 was designed for higher activity. The experimentally determined hydrolysis activity of S24 is the highest in all designed substrates and is close to that predicted by CoMFA. These results offer helpful information for the research on the mechanism of substrate recognition of coronavirus 3C-like proteinase.

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.

Heuristic molecular lipophilicity potential (HMLP): A 2D-QSAR study to LADH of molecular family pyrazole and derivatives

The quantum chemical and structure-based technique heuristic molecular lipophilicity potential (HMLP) is used in the liver alcohol dehydrogenase (LADH) study of molecular family pyrazole and derivatives. The molecular lipophilic index LM, molecular hydrophilic index HM, lipophilic indices lss, and hydrophilic indices hss of the substitutes (fragments), and atomic lipophilicity indices las are constructed and used in QSAR study. The HMLP indices are correlated with bioactivities of 18 pyrazole derivatives according to the 2D QSAR procedure. The multiple linear regression equation between the bioactivities of pyrazole derivatives and HMLP indices are built using partial least square (PLS) with the optimal statistical quantity (r=0.987, s=0.479, F=47.19). The inhibition mechanism of LADH of the pyrazole derivatives is explained according to the physical meaning of HMLP indices. During the HMLP calculations for the 2D QSAR, the only input parameters are the atomic van der Waals radius without the need to resort to any empirical parameters. Accordingly, HMLP can provide a rigorous theoretical approach with a crystal clear physical meaning for the 2D QSAR.

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.

Characterization of trans- and cis-cleavage activity of the SARS coronavirus 3CLpro protease: basis for the in vitro screening of anti-SARS drugs

Severe acute respiratory syndrome (SARS) has been globally reported. A novel coronavirus (CoV), SARS-CoV, was identified as the etiological agent of the disease. SARS-CoV 3C-like protease (3CLpro) mediates the proteolytic processing of replicase polypeptides 1a and 1ab into functional proteins, playing an important role in viral replication. In this study, we demonstrated the expression of the SARS-CoV 3CLpro in Escherichia coli and Vero cells, and then characterized the in vitro trans-cleavage and the cell-based cis-cleavage by the 3CLpro. Mutational analysis of the 3CLpro demonstrated the importance of His41, Cys145, and Glu166 in the substrate-binding subsite S1 for keeping the proteolytic activity. In addition, alanine substitution of the cleavage substrates indicated that Gln-(P1) in the substrates mainly determined the cleavage efficiency. Therefore, this study not only established the quantifiable and reliable assay for the in vitro and cell-based measurement of the 3CLpro activity, but also characterized the molecular interaction of the SARS-CoV 3CLpro with the substrates. The results will be useful for the rational development of the anti-SARS drugs.

Coronavirus 3CLpro proteinase cleavage sites: possible relevance to SARS virus pathology

Background

Despite the passing of more than a year since the first outbreak of Severe Acute Respiratory Syndrome (SARS), efficient counter-measures are still few and many believe that reappearance of SARS, or a similar disease caused by a coronavirus, is not unlikely. For other virus families like the picornaviruses it is known that pathology is related to proteolytic cleavage of host proteins by viral proteinases. Furthermore, several studies indicate that virus proliferation can be arrested using specific proteinase inhibitors supporting the belief that proteinases are indeed important during infection. Prompted by this, we set out to analyse and predict cleavage by the coronavirus main proteinase using computational methods.

Results

We retrieved sequence data on seven fully sequenced coronaviruses and identified the main 3CL proteinase cleavage sites in polyproteins using alignments. A neural network was trained to recognise the cleavage sites in the genomes obtaining a sensitivity of 87.0% and a specificity of 99.0%. Several proteins known to be cleaved by other viruses were submitted to prediction as well as proteins suspected relevant in coronavirus pathology. Cleavage sites were predicted in proteins such as the cystic fibrosis transmembrane conductance regulator (CFTR), transcription factors CREB-RP and OCT-1, and components of the ubiquitin pathway.

Conclusions

Our prediction method NetCorona predicts coronavirus cleavage sites with high specificity and several potential cleavage candidates were identified which might be important to elucidate coronavirus pathology. Furthermore, the method might assist in design of proteinase inhibitors for treatment of SARS and possible future diseases caused by coronaviruses. It is made available for public use at our website: .

Molecular advances in severe acute respiratory syndrome-associated coronavirus (SARS-CoV)

The sudden outbreak of severe acute respiratory syndrome (SARS) in 2002 prompted the establishment of a global scientific network subsuming most of the traditional rivalries in the competitive field of virology. Within months of the SARS outbreak, collaborative work revealed the identity of the disastrous pathogen as SARS-associated coronavirus (SARS-CoV). However, although the rapid identification of the agent represented an important breakthrough, our understanding of the deadly virus remains limited. Detailed biological knowledge is crucial for the development of effective countermeasures, diagnostic tests, vaccines and antiviral drugs against the SARS-CoV. This article reviews the present state of molecular knowledge about SARS-CoV, from the aspects of comparative genomics, molecular biology of viral genes, evolution, and epidemiology, and describes the diagnostic tests and the anti-viral drugs derived so far based on the available molecular information.