Prediction of the tertiary structure and substrate binding site of caspase-8

iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition

Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.

Human proteins characterization with subcellular localizations

Proteins are responsible for performing the vast majority of cellular functions which are critical to a cell's survival. The knowledge of the subcellular localization of proteins can provide valuable information about their molecular functions. Therefore, one of the fundamental goals in cell biology and proteomics is to analyze the subcellular localizations and functions of these proteins. Recent large-scale human genomics and proteomics studies have made it possible to characterize human proteins at a subcellular localization level. In this study, according to the annotation in Swiss-Prot, 8842 human proteins were classified into seven subcellular localizations. Human proteins in the seven subcellular localizations were compared by using topological properties, biological properties, codon usage indices, mRNA expression levels, protein complexity and physicochemical properties. All these properties were found to be significantly different in the seven categories. In addition, based on these properties and pseudo-amino acid compositions, a machine learning classifier was built for the prediction of protein subcellular localization. The study presented here was an attempt to address the aforementioned properties for comparing human proteins of different subcellular localizations. We hope our findings presented in this study may provide important help for the prediction of protein subcellular localization and for understanding the general function of human proteins in cells.

iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking

Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints

Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (three-dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most protein ion channels are still unknown. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop the sequence-based computational method to address this problem. To take up the challenge, we developed a new predictor called iCDI-PseFpt, in which the protein ion-channel sample is formulated by the PseAAC (pseudo amino acid composition) generated with the gray model theory, the drug compound by the 2D molecular fingerprint, and the operation engine is the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iCDI-PseFpt via the jackknife cross-validation was 87.27%, which is remarkably higher than that by any of the existing predictors in this area. As a user-friendly web-server, iCDI-PseFpt is freely accessible to the public at the website http://www.jci-bioinfo.cn/iCDI-PseFpt/. Furthermore, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in the paper just for its integrity. It has not escaped our notice that the current approach can also be used to study other drug-target interaction networks.

Identification of ligand binding site on RXRγ using molecular docking and dynamics methods

Retinoid X receptors (RXRα, β and γ) are recently known to be cancer chemotherapies targets. The ligand binding domains of RXRs have been crystallized, but the information of RXRγ ligand binding site is not yet available due to the lack of liganded complex. A thorough understanding of the ligand binding sites is essential to study RXRs and may result in cancer therapeutic breakthrough. Thus we aimed to study the RXRγ ligand binding site and find out the differences between the three subtypes. Alignment and molecular simulation were carried out for identifying the RXRγ ligand binding site, characterizing the RXRγ ligand binding mode and comparing the three RXRs. The result has indicated that the RXRγ ligand binding site is defined by helices H5, H10, β-sheet s1 and the end loop. Besides hydrophobic interactions, the ligand 9-cis retinoic acid interacts with RXRγ through a hydrogen bond with Ala106, a salt bridge with Arg95 and the π-π interactions with Phe217 and Phe218. The binding modes exhibit some similarities among RXRs, such as the interactions with Arg95 and Ala106. Nonetheless, owing to the absence of Ile47, Cys48, Ala50, Ala51 and residues 225∼237 in the active site, the binding pocket in RXRγ is two times larger than those of RXRα and RXRβ. Meanwhile, spatial effects of Trp84, Arg95, Ala106, Phe217 and Phe218 help to create a differently shaped binding pocket as compared to those of RXRα and RXRβ. Consequently, the ligand in RXRγ undergoes a "standing" posing which is distinct from the other two RXRs.

Designing inhibitors of M2 proton channel against H1N1 swine influenza virus

Background

M2 proton channel of H1N1 influenza A virus is the target protein of anti-flu drugs amantadine and rimantadine. However, the two once powerful adamantane-based drugs lost their 90% bioactivity because of mutations of virus in recent twenty years. The NMR structure of the M2 channel protein determined by Schnell and Chou (Nature, 2008, 451, 591–595) may help people to solve the drug-resistant problem and develop more powerful new drugs against H1N1 influenza virus.

Methodology

Docking calculation is performed to build the complex structure between receptor M2 proton channel and ligands, including existing drugs amantadine and rimantadine, and two newly designed inhibitors. The computer-aided drug design methods are used to calculate the binding free energies, with the computational biology techniques to analyze the interactions between M2 proton channel and adamantine-based inhibitors.

Conclusions

1) The NMR structure of M2 proton channel provides a reliable structural basis for rational drug design against influenza virus. 2) The channel gating mechanism and the inhibiting mechanism of M2 proton channel, revealed by the NMR structure of M2 proton channel, provides the new ideas for channel inhibitor design. 3) The newly designed adamantane-based inhibitors based on the modeled structure of H1N1-M2 proton channel have two pharmacophore groups, which act like a “barrel hoop”, holding two adjacent helices of the H1N1-M2 tetramer through the two pharmacophore groups outside the channel. 4) The inhibitors with such binding mechanism may overcome the drug resistance problem of influenza A virus to the adamantane-based drugs.

Dynamic modeling of cellular response to DNA damage based on p53 stress response networks

Under acute perturbations from the outside, cells can trigger self-defensive mechanisms to fight against genome stress. To investigate the cellular response to continuous ion radiation (IR), a dynamic model for p53 stress response networks at the cellular level is proposed. The model can successfully be used to simulate the dynamic processes of double-strand breaks (DSBs) generation and their repair, switch-like ataxia telangiectasia mutated (ATM) activation, oscillations occurring in the p53-MDM2 feedback loop, as well as toxins elimination triggered by p53 stress response networks. Especially, the model can predict the plausible outcomes of cellular response under different IR dose regimes.

Prediction of subcellular localization of apoptosis protein using Chou's pseudo amino acid composition

Apoptosis proteins play an essential role in regulating a balance between cell proliferation and death. The successful prediction of subcellular localization of apoptosis proteins directly from primary sequence is much benefited to understand programmed cell death and drug discovery. In this paper, by use of Chou's pseudo amino acid composition (PseAAC), a total of 317 apoptosis proteins are predicted by support vector machine (SVM). The jackknife cross-validation is applied to test predictive capability of proposed method. The predictive results show that overall prediction accuracy is 91.1% which is higher than previous methods. Furthermore, another dataset containing 98 apoptosis proteins is examined by proposed method. The overall predicted successful rate is 92.9%.

Insights from investigating the interactions of adamantane-based drugs with the M2 proton channel from the H1N1 swine virus

The M2 proton channel is one of indispensable components for the influenza A virus that plays a vital role in its life cycle and hence is an important target for drug design against the virus. In view of this, the three-dimensional structure of the H1N1-M2 channel was developed based on the primary sequence taken from a patient recently infected by the H1N1 (swine flu) virus. With an explicit water-membrane environment, molecular docking studies were performed for amantadine and rimantadine, the two commercial drugs generally used to treat influenza A infection. It was found that their binding affinity to the H1N1-M2 channel is significantly lower than that to the H5N1-M2 channel, fully consistent with the recent report that the H1N1 swine virus was resistant to the two drugs. The findings and the relevant analysis reported here might provide useful structural insights for developing effective drugs against the new swine flu virus.

Molecular modeling of human BAD and its interaction with PKAc or PP1c

To build up the structure of human BAD (Bcl-2 antagonist of cell death), subsequently combined with PKAc or PP1c (protein phosphatase 1), to investigate the interaction relationship between BAD and its kinase/PTPese at the molecular level. Additionally, it is concerned with the search for all optimal positions and orientations of a set of amino acid residues of BAD, while its binding sites include N-termini (Glu19, Ala27, and Ser34-Lys35), BH3-located helical domain (Arg98-Lys126), and C-termini (Trp154-Ser163 and Ser167-Gln168). The related sites of PKAc are mainly assembled in C-terminal alpha/beta-domain of PKAc, which comprises the KTL motif (47-49), Glu203 residue, a helical region (Asp241-Arg256), and the span from 328 to 333; while the interaction sites with BAD converge at C-terminal beta-domain of PP1c, which includes the DEK motif (166-168), the stretch from 179 to 197 including a helix (Glu184-Arg188), Glu230-Asp242 segment containing Val232-His237 helix, and Glu287-Leu289 loop. In conclusion, analysis of the complex between BAD and PKAc or PP1c provides a novel viewpoint on the structural origins of molecular recognition. And the complex models suggest that BH3 domain of BAD interact with PKAc or PP1c by electrostatic, van der Waals contacts, hydrogen bond and salt bridge. This is helpful for our development and research of some new drugs, especially mimetic BH3 peptides and inspires scientists with BAD complex and molecular mechanism of its integrating glycolysis and apoptosis.

Identification of proteases and their types

Called by many as biology's version of Swiss army knives, proteases cut long sequences of amino acids into fragments and regulate most physiological processes. They are vitally important in the life cycle. Different types of proteases have different action mechanisms and biological processes. With the avalanche of protein sequences generated during the postgenomic age, it is highly desirable for both basic research and drug design to develop a fast and reliable method for identifying the types of proteases according to their sequences or even just for whether they are proteases or not. In this article, three recently developed identification methods in this regard are discussed: (i) FunD-PseAAC, (ii) GO-PseAAC, and (iii) FunD-PsePSSM. The first two were established by hybridizing the FunD (functional domain) approach and the GO (gene ontology) approach, respectively, with the PseAAC (pseudo amino acid composition) approach. The third method was established by fusing the FunD approach with the PsePSSM (pseudo position-specific scoring matrix) approach. Of these three methods, only FunD-PsePSSM has provided a server called ProtIdent (protease identifier), which is freely accessible to the public via the website at http://www.csbio.sjtu.edu.cn/bioinf/Protease. For the convenience of users, a step-by-step guide on how to use ProtIdent is illustrated. Meanwhile, the caveat in using ProtIdent and how to understand the success expectancy rate of a statistical predictor are discussed. Finally, the essence of why ProtIdent can yield a high success rate in identifying proteases and their types is elucidated.

Evaluation of caspase-3 and caspase-8 deregulation in tongue squamous cell carcinoma, based on immunohistochemistry and computerised image analysis

AIMS

To investigate the potential role of caspase-3 and caspase-8 protein expression in the biological behaviour of tongue squamous cell carcinoma.

MATERIALS AND METHODS

We conducted immunohistochemical analyses of 87 specimens of primary tongue squamous cell carcinoma, using monoclonal anti-caspase-3 and anti-caspase-8 antibodies. A digital image analysis assay was also performed in order to evaluate the results.

RESULTS

Reduced expression of caspase-8 and -3 proteins was observed in 30/87 (34.5 per cent) and 79/87 (90.5 per cent) cases, respectively. Cox regression analysis showed no prognostic significance for the association between overall protein expression of either marker and survival probability (p = 0.174 for caspase-3; p = 0.608 for caspase-8). Interestingly, the size of the examined tumours was strongly correlated with survival status (p = 0.024).

CONCLUSIONS

Simultaneous deregulation of caspase-8 and -3 is a frequent event in tongue squamous cell carcinoma. Activation of caspase-3, which is predominantly down-regulated, may be a crucial process for induction of apoptosis and response to therapeutic strategies.

HIV protease inhibitors impact on apoptosis

HIV protease inhibitors are the backbone of HIV therapy. In addition to blocking intracellular HIV protease and dramatically decreasing viral burden, the protease inhibitors also regulate apoptosis. A growing body of data has confirmed the immunomodulatory effects of HIV protease inhibitors which block CD4+ and CD8+ T cell death in models of HIV infection. The mechanism of this apoptosis inhibition is still under active investigation and supported by several proposed hypothesis for how they alter the fate of the cell. More recently, the anti-apoptotic effects of the HIV protease inhibitors has been extended to the non-HIV, non-immune cell, whereby protease inhibitors prevent apoptosis, and disease, in animal models of sepsis, hepatitis and stroke. Interestingly, when HIV protease inhibitors are used at supra-therapeutic concentrations, they exert pro-apoptotic effects. This has been demonstrated in a number of tumor models. Although it is unclear how HIV protease inhibitors can induce apoptosis at increased concentrations, future research will define the targets of the immunomodulation and reveal the full clinical potential of this intriguing class of drugs.

Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine

Apoptosis proteins play an important role in the development and homeostasis of an organism. The accurate prediction of subcellular location for apoptosis proteins is very helpful for understanding the mechanism of apoptosis and their biological functions. However, most of the existing predictive methods are designed by utilizing a single classifier, which would limit the further improvement of their performances. In this paper, a novel predictive method, which is essentially a multi-classifier system, has been proposed by combing a dual-layer support vector machine (SVM) with multiple compositions including amino acid composition (AAC), dipeptide composition (DPC) and amphiphilic pseudo amino acid composition (Am-Pse-AAC). As a demonstration, the predictive performance of our method was evaluated on two datasets of apoptosis proteins, involving the standard dataset ZD98 generated by Zhou and Doctor, and a larger dataset ZW225 generated by Zhang et al. With the jackknife test, the overall accuracies of our method on the two datasets reach 94.90% and 88.44%, respectively. The promising results indicate that our method can be a complementary tool for the prediction of subcellular location.

Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influenza A virus

This is the continuation of our studies on the prediction of mutation engineered by randomness in proteins from influenza A virus. In our previous studies, we have demonstrated that randomness plays a role in engineering mutations because the measures of randomness in protein are different before and after mutations. Thus we built a cause-mutation relationship to count the mutation engineered by randomness, and conducted several concept-initiated studies to predict the mutations in proteins from influenza A virus, which demonstrated the possibility of prediction of mutations along this line of thought. On the other hand, these concept-initiated studies indicate the directions forwards the enhancement of predictability, of which we need to use the neural network instead of logistic regression that was used in those concept-initiated studies to enhance the predictability. In this proof-of-concept study, we attempt to apply the neural network to modeling the cause-mutation relationship to predict the possible mutation positions, and then we use the amino acid mutating probability to predict the would-be-mutated amino acids at predicted positions. The results confirm the possibility of use of internal cause-mutation relationship with neural network model to predict the mutation positions and use of amino acid mutating probability to predict the would-be-mutated amino acids.

Prediction of mutations engineered by randomness in H5N1 neuraminidases from influenza A virus

In this proof-of-concept study, we attempt to determine whether the cause-mutation relationship defined by randomness is protein dependent by predicting mutations in H5N1 neuraminidases from influenza A virus, because we have recently conducted several concept-initiated studies on the prediction of mutations in hemagglutinins from influenza A virus. In our concept-initiated studies, we defined the randomness as a cause for mutation, upon which we built a cause-mutation relationship, which is then switched into the classification problem because the occurrence and non-occurrence of mutations can be classified as unity and zero. Thereafter, we used the logistic regression and neural network to solve this classification problem to predict the mutation positions in hemagglutinins, and then used the amino acid mutating probability to predict the would-be-mutated amino acids. As the previous results were promising, we extend this approach to other proteins, such as H5N1 neuraminidase in this study, and further address various issues raised during the development of this approach. The result of this study confirms that we can use this cause-mutation relationship to predict the mutations in H5N1 neuraminidases.

A dynamic model for the p53 stress response networks under ion radiation

P53 controls the cell cycle arrest and cell apoptosis through interaction with the downstream genes and their signal pathways. To stimulate the investigation into the complicated responses of p53 under the circumstance of ion radiation (IR) in the cellular level, a dynamic model for the p53 stress response networks is proposed. The model can be successfully used to simulate the dynamic processes of generating the double-strand breaks (DSBs) and their repairing, ataxia telangiectasia mutated (ATM) activation, as well as the oscillations occurring in the p53-MDM2 feedback loop.

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.

3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug design

Cytochrome P450 2C19 (CYP2C19) is a member of the cytochrome P-450 enzyme superfamily and plays an important role in the metabolism of drugs. In order to gain insights for developing personalized drugs, the 3D (dimensional) structure of CYP2C19 has been developed based on the crystal structure of CYP2C9 (PDB code 1R90), and its structure-activity relationship with the ligands of CEC, Fluvoxamine, Lescol, and Ticlopidine investigated through the structure-activity relationship approach. By means of a series of docking studies, the binding pockets of CYP2C19 for the four compounds are explicitly defined that will be very useful for conducting mutagenesis studies, providing insights into personalization of drug treatments and stimulating novel strategies for finding desired personalized drugs.

Prediction of the subcellular location of apoptosis proteins

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. Based on the concept that the subcellular location of an apoptosis protein is mainly determined by its amino acid sequence, a new algorithm for prediction of the subcellular location of an apoptosis protein is proposed. By using of a distinctive set of information parameters derived from the primary sequence of 317 apoptosis proteins, the increment of diversity (ID), the sole prediction parameter, is calculated. The higher predictive success rates than the previous other algorithms is obtained by the jackknife tests using the expanded dataset. Our prediction results show that the local compositions of twin amino acids and hydropathy distribution are very useful to predict subcellular location of protein.

Computational studies for the structure and function of mRPE65

The mRPE65 protein is one form of the RPE65 protein and plays a very important role in the visual cycle. However, its 3D structure and detailed mechanism of function are still unclear because of difficulties with isolation and crystallization. This computational study reports a model for the mRPE65 protein structure derived from a model for sRPE65. The natural substrate for RPE65 has been shown to be a retinyl ester and, by utilizing the Autodock and the Ligplot programs, the interactions between the ester and the protein as well as the effects of several mutations on these interactions are studied. Finally, the position of the binding site is proposed based on an iterative process and the effects of the mutations on the binding site are also discussed.

Insights from modeling the 3D structure of H5N1 influenza virus neuraminidase and its binding interactions with ligands

The highly pathogenic H5N1 influenza virus, which is rapidly mutating and becoming increasingly drug-resistant, was investigated by means of structure-activity relationship between NA (neuraminidase) and three inhibitors, i.e., DANA (2,3-didehydro-2-deoxy-N-acetylneuraminic acid), zanamivir, and oseltamivir. A homology model of the H5N1-NA from the highly pathogenic chicken H5N1 A viruses isolated during the 2003-2004 influenza outbreaks in Japan was built based on the crystal structure of N9-NA complexed with DANA (PDB code: 1F8B). It was found that the traditional constituent residues around the active site of NA family are highly conserved in the H5N1-NA. However, a partially lipophilic pocket composed by Ala248 and Thr249 in N9-NA becomes a hydrophilic pocket because the two residues in the H5N1-NA are replaced by hydrophilic residues Ser227 and Asn228, respectively. On the other hand, two hydrophilic residues Asn347 and Asn348 in the N9-NA are replaced by two lipophilic residues Ala323 and Tyr324 in the H5N1-NA, respectively, leading to the formation of a new lipophilic pocket. This kind of subtle variation not only destroys the original lipophilic environment but also changes the complement interaction between the H5N1-NA and DANA. Such a finding might provide insights into the secret why some of H5N1 strains bear high resistance for existing NA inhibitors, and stimulate new strategies for designing new drugs against these viruses.

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.

Theoretical studies of Alzheimer’s disease drug candidate 3-[(2,4-dimethoxy)benzylidene]-anabaseine (GTS-21) and its derivatives

Theoretical and molecular modeling studies have been conducted for understanding the details of how 3-[(2,4-dimethoxy)benzylidene]-anabaseine dihydrochloride (GTS-21) and its metabolism derivatives bind with the receptor of alpha7 nicotinic acetylcholine dimer. Good accordance with experimental results has been achieved. It was found that the van der Waals repulsion makes the dominant contribution to the binding energy. GTS-21 and its metabolites are apparently too large for the binding sites of the alpha7 dimer. To improve the effectiveness of the drug, a possible approach is to reduce its volume while maintaining the presence of the active groups. Our studies, in combination with experimental studies, will lead to a promising basis for practical drug design against Alzheimer's disease.

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.

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.

Modeling the tertiary structure of human cathepsin-E

Cathepsin-E is an endolysosomal aspartic proteinase and is predominantly expressed in immune system cells. Deficiency of cathepsin-E is associated with the development of atopic dermatitis, a pruritic inflammatory skin disease, which has put us to face a high selectivity challenge in the development of drugs for the therapy of Alzheimer's disease or breast cancer. This is because BACE1 (also known as beta-secretase) and cathepsin-D, both belonging to the family of aspartic proteinases, might interact with the same compound as cathepsin-E does. BACE1 is a putative prime therapeutic target for the treatment of Alzheimer's disease, and cathepsin-D a potential target for breast cancer. Accordingly, in the course of finding drugs against Alzheimer's disease or breast cancer by inhibiting BACE1 or cathepsin-D, the desired drugs should selectively inhibit only BACE1 or cathepsin-D, but definitely not cathepsin-E. To realize this, it is indispensable to find out the structural difference of the three enzymes. Since the crystal structures of BACE1 and cathepsin-D are already known, the lack of three-dimensional structure of cathepsin-E has become the bottleneck in this regard. In view of this, the three-dimensional structure of cathepsin-E has been developed. Although the overall structures of the three enzymes are quite similar to each other, some subtle difference around their active sites that distinguishes cathepsin-E from cathepsin-D and BACE1 has been revealed through an analysis of hydrogen bond network and microenvironment. The computed three-dimensional structure of cathepsin-E and the relevant findings might provide useful insights for designing inhibitors with the desired selectivity.

Support vector machines for predicting apoptosis proteins types

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death, and their function is related to their types. According to the classification scheme by Zhou and Doctor (2003), the apoptosis proteins are categorized into the following four types: (1) cytoplasmic protein; (2) plasma membrane-bound protein; (3) mitochondrial inner and outer proteins; (4) other proteins. A powerful learning machine, the Support Vector Machine, is applied for predicting the type of a given apoptosis protein by incorporating the sqrt-amino acid composition effect. High success rates were obtained by the re-substitute test (98/98 = 100 %) and the jackknife test (89/98 = 90.8%).

Assessment of chemical libraries for their druggability

High throughput virtual screening is acknowledged as the initial means for identifying hit compounds that will be eventually transformed to leads or drug candidates. To improve quality of screening, it is essential to have powerful methods for the analysis of the compound databases. For this purpose, we have developed a novel and practical scoring function to assess the druggability of compounds. The proposed function consists of 12 metrics that take into account physical, chemical and structural properties as well as the presence of undesirable functional groups. We have applied this 12-metric scoring function to 44 different databases that include more than 3.8 million compounds, which are commercially available. The overall quality of each database was evaluated according to the score and rank measured by our 12-metric function. Our findings suggest that, the majority of compounds that do not satisfy druggable rules do so due to high molecular weight, high logP values and the presence of reactive functional groups.

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

The cleavage mechanism of severe acute respiratory syndrome (SARS) coronavirus main proteinase (M(pro) or 3CL(pro)) for the octapeptide AVLQSGFR is studied using molecular mechanics (MM) and quantum mechanics (QM). The catalytic dyad His-41 and Cys-145 in the active pocket between domain I and II seem to polarize the pi-electron density of the peptide bond between Gln and Ser in the octapeptide, leading to an increase of positive charge on C(CO) of Gln and negative charge on N(NH) of Ser. The possibility of enhancing the chemical bond between Gln and Ser based on the "distorted key" theory [Anal. Biochem. 233 (1996) 1] is examined. The scissile peptide bond between Gln and Ser is found to be solidified through "hybrid peptide bond" by changing the carbonyl group CO of Gln to CH(2) or CF(2). This leads to a break of the pi-bond system for the peptide bond, making the octapeptide (AVLQSGFR) a "distorted key" and a potential starting system for the design of anti SARS drugs.

Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor

Based on the crystal structure of acetylcholine-binding protein, the three-dimensional structures of the extracellular domain, or the ligand-binding domains, of the monomer, homodimer, and homopentamer of the alpha7 nicotinic acetylcholine receptor were derived. The interface between two subunits, where the ligand-binding site is located, was investigated. Furthermore, an explicit definition of the ligand-binding pocket was illustrated that might provide useful clues for conducting various mutagenesis studies for finding drugs against schizophrenia and Alzheimer's disease.

Identify catalytic triads of serine hydrolases by support vector machines

The core of an enzyme molecule is its active site from the viewpoints of both academic research and industrial application. To reveal the structural and functional mechanism of an enzyme, one needs to know its active site; to conduct structure-based drug design by regulating the function of an enzyme, one needs to know the active site and its microenvironment as well. Given the atomic coordinates of an enzyme molecule, how can we predict its active site? To tackle such a problem, a distance group approach was proposed and the support vector machine algorithm applied to predict the catalytic triad of serine hydrolase family. The success rate by jackknife test for the 139 serine hydrolases was 85%, implying that the method is quite promising and may become a useful tool in structural bioinformatics.

Modelling extracellular domains of GABA-A receptors: subtypes 1, 2, 3, and 5

GABA is the main inhibitory neurotransmitter in the mammalian central nervous system. When GABA binds to the ubiquitous GABA-A receptors on neurons, chloride channels are activated leading to a rapid increase in chloride conductance that depresses excitatory depolarization. The GABA-A receptors are targets for many clinically important drugs, such as the benzodiazepines, general anaesthetics, and barbiturates. All of these drugs enhance the chloride current activated by GABA. Of the GABA-A receptor family, the subtype 2 is critical for the treatment of anxiety spectrum disorders. To avoid unwanted side effects, it is necessary to find highly selective drugs that interact only with subtype 2 but not with the related receptors such as subtypes 1, 3, and 5. To realize such a goal, it is important to have not only the 3D (dimensional) structure of subtype 2 but also the 3D structures of subtypes 1, 3, and 5. In this study, the 3D structures of all the four subtypes of GABA-A receptors have been derived. The computer-modeled heteropentameric structures bear the following features: (1) each of the five subunits in the pentamer has an intrachain disulfide bond, a hallmark of ligand-gated pentameric channels; (2) those residues which are sensitive to the binding of the benzodiazepine site ligands are grouped around the alpha1,2,3,5/gamma2 interfaces; and (3) those residues which are sensitive to the binding of GABA molecules are grouped around the alpha1,2,3,5/beta2 interfaces. All these findings are fully consistent with experimental observations. Meanwhile, for those sensitive or key residues, a close look at their subtle difference among the four subtypes has been provided through a highlighted superposition picture. In addition to providing the atomic coordinates, the predicted structures have further clarified some ambiguities that could not been uniquely determined by the existing experimental data, such as the directionality of the subunit arrangement in the heteropentamers. The 3D models may provide a reasonable structural frame or footing for designing highly selective drugs. The present models might be also useful in understanding the basic mechanism of operation of the GABA-A receptors, stimulating novel strategies for developing more specific drugs and better treatments.

Caspase inhibitors as anti-inflammatory and antiapoptotic agents

The striking efficacy of Z-VAD-fmk in the various animal models presented above may reflect its ability to inhibit multiple enzymes including caspases. In accord with this, more selective, reversible inhibitors usually show low efficacy in multifactorial models such as ischaemia, but may offer some protection against NMDA-induced excitotoxicity and hepatitis. Importantly, caspase inhibitors may exhibit significant activity in vivo even when they are applied post insult. As far as the CNS is concerned, the first systemically active inhibitors have emerged. Functional recovery could be achieved in some ischaemia models, but long-term protection by caspase inhibitors is still being questioned. Recent developments in drug design enabled the first caspase inhibitors to enter the clinic. Although initially directed towards peripheral indications such as rheumatoid arthritis, caspase inhibitors will no doubt eventually be used to target CNS disorders. For this purpose the peptidic character of current inhibitors will have to be further reduced. Small molecule, nonpeptidic caspase inhibitors, which have appeared recently, indicate that this goal can be accomplished. Unfortunately, many fundamental questions still remain to be addressed. In particular, the necessary spectrum of inhibitory activity required to achieve the desired effect needs to be determined. There is also a safety aspect associated with prolonged administration. Therefore, the next therapeutic areas for broader-range caspase inhibitors are likely to involve acute treatment. Recent results with synergistic effects between MK-801 and caspase inhibitors in ischaemia suggest that caspase inhibitors may need to be used in conjunction with other drugs. It can be expected that, in the near future, research on caspases and their inhibitors will remain a rapidly developing area of biology and medicinal chemistry. More time, however, may be needed for the first caspase inhibitors to appear on the market.

Subcellular location prediction of apoptosis proteins

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. Many efforts in pharmaceutical research have been aimed at understanding their structure and function. Unfortunately, thus far, very few apoptosis protein structures have been determined. In contrast, many apoptosis protein sequences are known, and many more are expected to come in the near future. Because of the extremely unbalanced state, it would be worthwhile to develop a fast sequence-based method to identify their subcellular location so as to gain some insight about their biological function. In view of this, a study was initiated in an attempt to identify the subcellular location of apoptosis proteins according to their sequences by means of the covariant discriminant function, which was established based on the Mahalanobis distance and Chou's invariance theorem (Chou, Proteins 1995;21:319-344). The results were quite promising, indicating that the subcellular location of apoptosis proteins are predictable to a considerably accurate extent if a good training data set can be established. It is expected that, with a continuous improvement of the training data set by incorporating more and more new data, the current method might eventually become a useful tool in this area because the function of an apoptosis protein is closely related to its subcellular location.

Prediction of the tertiary structure of the beta-secretase zymogen

beta-Secretase, also known as BACE, is a transmembrane aspartyl protease, which generates the N terminus of Alzheimer's disease amyloid beta-peptide. The activity of beta-secretase is the rate-limiting step of brain plaques production in vivo, and hence is a potential target for disease modifying drugs for Alzheimer's disease. To better understand the mechanism of action of beta-secretase and help explore novel strategies for drug discovery for Alzheimer's disease, it is important to elucidate the three-dimensional structure of its zymogen. Based on the X-ray structure of the enzyme's protease domain and the X-ray structure of pepsinogen, a model of the three-dimensional structure of the beta-secretase zymogen has been constructed. Comparison of the computed structure of pro-BACE with X-ray structures of pepsinogen and progastricsin (two other pro-aspartyl proteases) reveals a significant difference in the relationship of the pro-segment to the catalytic aspartates. In both pepsinogen and progastricsin a lysine side-chain in the pro-segment forms a salt bridge to the two catalytic aspartates, occupying the position normally occupied by a catalytic water. In the pro-BACE model there is no salt bridge, and the corresponding residue-a proline-does not interact at all with the catalytic residues. These findings can be used to elucidate the recent observations that the pro-domain of beta-secretase does not suppress activity as in a strict zymogen but does appear to facilitate proper folding of an active protease domain. The predicted three-dimensional structure of beta-secretase zymogen and the relevant findings might also provide useful insights for rational design of effective drugs against Alzheimer's disease.

Caspases: key players in programmed cell death

Research in apoptosis has established a central role for caspases. The recent determination of structures of caspase-1, caspase-3 and caspase-8, together with biochemical studies, has greatly enhanced our understanding of the structure, function and specificity of these enzymes. This provides a basis for the further elucidation of the biological role of caspases and a guide to the design of selective inhibitors to treat caspase-mediated diseases.

Prediction of the tertiary structure of a caspase-9/inhibitor complex

Apoptosis, or programmed cell death, plays a central role in the development and homeostasis of an organism. The breakdown of cellular proteins in apoptosis is mediated by caspases, which comprise a highly conserved family of cysteine proteases with specificity for aspartic acid residues at the P1 positions of their substrates. Multiple lines of evidence show that caspase-9 is critical for an apoptosis pathway mediated via the mitochondria. In this study, the three-dimensional structure of the catalytic domain of caspase-9 and its interaction with the inhibitor acetyl-Asp-Val-Ala-Asp fluoromethyl ketone (Ac-DVAD-fmk) have been predicted by a segment matching modeling procedure. As expected, the predicted caspase-9 structure shows both a high similarity in the overall folding topology and remarkable differences in the surface loop regions as compared to other caspase family members such as caspase-1, -3 and -8, for which crystal structures have been determined. This kind of comparative analysis reflects the convergence-divergence duality among the caspases. Moreover, some subtle differences have been observed between caspase-9 and caspase-3 in the subsite contacts with the covalently linked inhibitor Ac-DVAD-fmk. Based on the X-ray structural analysis of caspase-8, a main chain carbonyl oxygen appears to be involved in a catalytic triad with the active site Cys and His residues. The corresponding carbonyl oxygen in caspase-9, together with other expected features of the catalytic apparatus, appears in our model. The predicted structure of caspase-9 can serve as a reference for subsite analysis relative to rational design of highly selective caspase inhibitors for therapeutic application.

The three-dimensional structure of caspase-8: an initiator enzyme in apoptosis

BACKGROUND

In the initial stages of Fas-mediated apoptosis the cysteine protease caspase-8 is recruited to the cell receptor as a zymogen (procaspase-8) and is incorporated into the death-signalling complex. Procaspase-8 is subsequently activated leading to a cascade of proteolytic events, one of them being the activation of caspase-3, and ultimately resulting in cell destruction. Variations in the substrate specificity of different caspases have been reported.

RESULTS

We report here the crystal structure of a complex of the activated human caspase-8 (proteolytic domain) with the irreversible peptidic inhibitor Z-Glu-Val-Asp-dichloromethylketone at 2.8 A resolution. This is the first structure of a representative of the long prodomain initiator caspases and of the group III substrate specificity class. The overall protein architecture resembles the caspase-1 and caspase-3 folds, but shows distinct structural differences in regions forming the active site. In particular, differences observed in subsites S(3), S(4) and the loops involved in inhibitor interactions explain the preference of caspase-8 for substrates with the sequence (Leu/Val)-Glu-X-Asp.

CONCLUSIONS

The structural differences could be correlated with the observed substrate specificities of caspase-1, caspase-3 and caspase-8, as determined from kinetic experiments. This information will help us to understand the role of the various caspases in the propagation of the apoptotic signal. The information gained from this investigation should be useful for the design of specific inhibitors.