Polyprotein cleavage mechanism of SARS CoV Mpro and chemical modification of the octapeptide

Computational studies of the binding mechanism of calmodulin with chrysin

Calmodulin (CaM) plays a crucial role in metabolism and physiology of eukaryotes by regulating biological activities. Multiple lines of evidences indicate that the phosphorylated flavonoids possess relatively stronger affinities for proteins by forming non-covalent complexes with them, and that the cellular functions are often triggered by this kind of interactions. Chrysin is one of the phosphorylated flavonoids that exist ubiquitously in plants and have remarkably beneficial pharmacological effects. In this study, the molecular docking tools were utilized to investigate the interactions of CaM with chrysin. Two different favorable binding modes have been observed. To complement the results obtained by the molecular docking study, an in-depth investigation into the binding modes was conducted with the molecular dynamics (MD) simulation to explore the binding profile and energy landscape. Based on the results thus obtained, a clear definition of the binding pocket for each of the two binding modes has been revealed. These findings may shed light upon the binding interactions of CaM with chrysin, providing a solid molecular basis for subset analysis of its pharmacological benefits.

Insights from modeling the 3D structure of NAD(P)H-dependent D-xylose reductase of Pichia stipitis and its binding interactions with NAD and NADP

NAD(P)H-dependent d-xylose reductase is a homodimeric oxidoreductase that belongs to the aldo-keto reductase superfamily. The enzyme has the special function to catalyze the first step in the assimilation of xylose into yeast metabolic pathways. Performing this function via reducing the open chain xylose to xylitol, the xylose reductase of Pichia stipitis is one of the most important enzymes that can be used to construct recombinant Saccharomyces cerevisiae strain for utilizing xylose and producing alcohol. To investigate into the interaction mechanism of the enzyme with its ligand NAD and NADP, the 3D structure was developed for the NAD(P)H-dependent d-xylose reductase from P. stipitis. With the 3D structure, the molecular docking operations were conducted to find the most stable bindings of the enzyme with NAD and NADP, respectively. Based on these results, the binding pockets of the enzyme for NAD and NADP have been explicitly defined. It has been found that the residues in forming the binding pockets for both NAD and NADP are almost the same and mainly hydrophilic. These findings may be used to guide mutagenesis studies, providing useful clues to modify the enzyme to improve the utilization of xylose for producing alcohol. Also, because human aldose reductases have the function to reduce the open chain form of glucose to sorbitol, a process physiologically significant for diabetic patients at the time that their blood glucose levels are elevated, the information gained through this study may also stimulate the development of new strategies for therapeutic treatment of diabetes.

Synthesis and activity of an octapeptide inhibitor designed for SARS coronavirus main proteinase

The outbreak of SARS, a life-threatening disease, has spread over many countries around the world. So far there is no effective drug for the treatment of SARS. Stimulated by the binding mechanism of SARS-CoV Mpro with the octapeptide AVLQSGFR reported recently as well as the "Chou's distorted key" theory, we synthesized the octapeptide AVLQSGFR for conducting various biochemical experiments to investigate the antiviral potential of the octapeptide against SARS coronavirus (BJ-01). The results demonstrate that, compared with other compounds reported so far, AVLQSGFR is the most active in inhibiting replication of the SARS coronavirus, and that no detectable toxicity is observed on Vero cells under the condition of experimental concentration.

From genome to drug lead: identification of a small-molecule inhibitor of the SARS virus

Virtual screening, a fast, computational approach to identify drug leads [Perola, E.; Xu, K.; Kollmeyer, T. M.; Kaufmann, S. H.; Prendergast, F. G. J. Med. Chem.2000, 43, 401; Miller, M. A. Nat. Rev. Drug Disc.2002, 1 220], is limited by a known challenge in crystallographically determining flexible regions of proteins. This approach has not been able to identify active inhibitors of the severe acute respiratory syndrome-associated coronavirus (SARS-CoV) using solely the crystal structures of a SARS-CoV cysteine proteinase with a flexible loop in the active site [Yang, H. T.; Yang, M. J.; Ding, Y.; Liu, Y. W.; Lou, Z. Y. Proc. Natl. Acad. Sci. U.S.A.2003, 100, 13190; Jenwitheesuk, E.; Samudrala, R. Bioorg. Med. Chem. Lett.2003, 13, 3989; Rajnarayanan, R. V.; Dakshanamurthy, S.; Pattabiraman, N. Biochem. Biophys. Res. Commun.2004, 321, 370; Du, Q.; Wang, S.; Wei, D.; Sirois, S.; Chou, K. Anal. Biochem.2005, 337, 262; Du, Q.; Wang, S.; Zhu, Y.; Wei, D.; Guo, H. Peptides2004, 25, 1857; Lee, V.; Wittayanarakul, K.; Remsungenen, T.; Parasuk, V.; Sompornpisut, P. Science (Asia)2003, 29, 181; Toney, J.; Navas-Martin, S.; Weiss, S.; Koeller, A. J. Med. Chem.2004, 47, 1079; Zhang, X. W.; Yap, Y. L. Bioorg. Med. Chem.2004, 12, 2517]. This article demonstrates a genome-to-drug-lead approach that uses terascale computing to model flexible regions of proteins, thus permitting the utilization of genetic information to identify drug leads expeditiously. A small-molecule inhibitor of SARS-CoV, exhibiting an effective concentration (EC50) of 23 microM in cell-based assays, was identified through virtual screening against a computer-predicted model of the cysteine proteinase. Screening against two crystal structures of the same proteinase failed to identify the 23-microM inhibitor. This study suggests that terascale computing can complement crystallography, broaden the scope of virtual screening, and accelerate the development of therapeutics to treat emerging infectious diseases such as SARS and Bird Flu.

Predicting protease types by hybridizing gene ontology and pseudo amino acid composition

Proteases play a vitally important role in regulating most physiological processes. Different types of proteases perform different functions with different biological processes. Therefore, it is highly desired to develop a fast and reliable means to identify the types of proteases according to their sequences, or even just identify whether they are proteases or nonproteases. The avalanche of protein sequences generated in the postgenomic era has made such a challenge become even more critical and urgent. By hybridizing the gene ontology approach and pseudo amino acid composition approach, a powerful predictor called GO-PseAA predictor was introduced to address the problems. To avoid redundancy and bias, demonstrations were performed on a dataset where none of proteins has >/= 25% sequence identity to any other. The overall success rates thus obtained by the jackknife cross-validation test in identifying protease and nonprotease was 91.82%, and that in identifying the protease type was 85.49% among the following five types: (1) aspartic, (2) cysteine, (3) metallo, (4) serine, and (5) threonine. The high jackknife success rates yielded for such a stringent dataset indicate the GO-PseAA predictor is very powerful and might become a useful tool in bioinformatics and proteomics.

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.

Searching for hypothetical proteins: theory and practice based upon original data and literature

A large part of mammalian proteomes is represented by hypothetical proteins (HP), i.e. proteins predicted from nucleic acid sequences only and protein sequences with unknown function. Databases are far from being complete and errors are expected. The legion of HP is awaiting experiments to show their existence at the protein level and subsequent bioinformatic handling in order to assign proteins a tentative function is mandatory. Two-dimensional gel-electrophoresis with subsequent mass spectrometrical identification of protein spots is an appropriate tool to search for HP in the high-throughput mode. Spots are identified by MS or by MS/MS measurements (MALDI-TOF, MALDI-TOF-TOF) and subsequent software as e.g. Mascot or ProFound. In many cases proteins can thus be unambiguously identified and characterised; if this is not the case, de novo sequencing or Q-TOF analysis is warranted. If the protein is not identified, the sequence is being sent to databases for BLAST searches to determine identities/similarities or homologies to known proteins. If no significant identity to known structures is observed, the protein sequence is examined for the presence of functional domains (databases PROSITE, PRINTS, InterPro, ProDom, Pfam and SMART), subjected to searches for motifs (ELM) and finally protein-protein interaction databases (InterWeaver, STRING) are consulted or predictions from conformations are performed. We here provide information about hypothetical proteins in terms of protein chemical analysis, independent of antibody availability and specificity and bioinformatic handling to contribute to the extension/completion of protein databases and include original work on HP in the brain to illustrate the processes of HP identification and functional assignment.

Inhibition of the severe acute respiratory syndrome 3CL protease by peptidomimetic alpha,beta-unsaturated esters

The proteolytic processing of polyproteins by the 3CL protease of severe acute respiratory syndrome coronavirus is essential for the viral propagation. A series of tripeptide alpha,beta-unsaturated esters and ketomethylene isosteres, including AG7088, are synthesized and assayed to target the 3CL protease. Though AG7088 is inactive (IC50 > 100 microM), the ketomethylene isosteres and tripeptide alpha,beta-unsaturated esters containing both P1 and P2 phenylalanine residues show modest inhibitory activity (IC50 = 11-39 microM). The Phe-Phe dipeptide inhibitors 18a-e are designed on the basis of computer modeling of the enzyme-inhibitor complex. The most potent inhibitor 18c with an inhibition constant of 0.52 microM is obtained by condensation of the Phe-Phe dipeptide alpha,beta-unsaturated ester with 4-(dimethylamino)cinnamic acid. The cell-based assays also indicate that 18c is a nontoxic anti-SARS agent with an EC50 value of 0.18 microM.

Predicting membrane protein type by functional domain composition and pseudo-amino acid composition

Given the sequence of a protein, how can we predict whether it is a membrane protein or non-membrane protein? If it is, what membrane protein type it belongs to? Since these questions are closely relevant to the function of an uncharacterized protein, their importance is self-evident. Particularly, with the explosion of protein sequences entering into databanks and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to give a fast answers to these questions. By hybridizing the functional domain (FunD) and pseudo-amino acid composition (PseAA), a new strategy called FunD-PseAA predictor was introduced. To test the power of the predictor, a highly non-homologous data set was constructed where none of proteins has 25% sequence identity to any other. The overall success rates obtained with the FunD-PseAA predictor on such a data set by the jackknife cross-validation test was 85% for the case in identifying membrane protein and non-membrane protein, and 91% in identifying the membrane protein type among the following 5 categories: (1) type-1 membrane protein, (2) type-2 membrane protein, (3) multipass transmembrane protein, (4) lipid chain-anchored membrane protein, and (5) GPI-anchored membrane protein. These rates are much higher than those obtained by the other methods on the same stringent data set, indicating that the FunD-PseAA predictor may become a useful high throughput tool in bioinformatics and proteomics.