Small molecular floribundiquinone B derived from medicinal plants inhibits acetylcholinesterase activity.

Being a neurodegenerative disorder, Alzheimer's disease (AD) is the one of the most terrible diseases. And acetylcholinesterase (AChE) is considered as an important target for treating AD. Acetylcholinesterase inhibitors (AChEI) are considered to be one of the effective drugs for the treatment of AD. The aim of this study is to find a novel potential AChEI as a drug for the treatment of AD. In this study, instead of using the synthetic compounds, we used those extracted from plants to investigate the interaction between floribundiquinone B (FB) and AChE by means of both the experimental approach such as fluorescence spectra, ultraviolet-visible (UV-vis) absorption spectrometry, circular dichroism (CD) and the theoretical approaches such as molecular docking. The findings reported here have provided many useful clues and hints for designing more effective and less toxic drugs against Alzheimer's disease.

Chlorella vulgaris Induces Apoptosis of Human Non-Small Cell Lung Carcinoma (NSCLC) Cells.

OBJECTIVE: Chlorella vulgaris (C. vulgaris), a unicellular green microalga, has been widely used as a food supplement and reported to have antioxidant and anticancer properties. The current study was designed to assess the cytotoxic, apoptotic, and DNA-damaging effects of C. vulgaris growth factor (CGF), hot water C. vulgaris extracts, inlung tumor A549 and NCI-H460 cell lines. METHODS: A549 cells, NCI-H460 cells, and normal human fibroblasts were treated with CGF at various concentrations (0-300 µg/ml) for 24 hr. The comet assay and γH2AX assay showed DNA damage in A549 and NCI-H460 cells upon CGF exposure. Evaluation of apoptosis by the TUNEL assay and DNA fragmentation analysis by agarose gel electrophoresis showed that CGF induced apoptosis in A549 and NCI-H460 cells. RESULTS: Chlorella vulgaris hot water extract induced apoptosis and DNA damage in human lung carcinoma cells. CONCLUSION: CGF can thus be considered a potential cytotoxic or genotoxic drug for treatment of lung carcinoma.

pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.

MOTIVATION: Sumoylation is a post-translational modification (PTM) process, in which small ubiquitin-related modifier (SUMO) is attaching by covalent bonds to substrate protein. It is critical to many different biological processes such as replicating genome, expressing gene, localizing and stabilizing proteins; unfortunately, it is also involved with many major disorders including Alzheimer's and Parkinson's diseases. Therefore, for both basic research and drug development, it is important to identify the sumoylation sites in proteins. RESULTS: To address such a problem, we developed a predictor called pSumo-CD by incorporating the sequence-coupled information into the general pseudo-amino acid composition (PseAAC) and introducing the covariance discriminant (CD) algorithm, in which a bias-adjustment term, which has the function to automatically adjust the errors caused by the bias due to the imbalance of training data, had been incorporated. Rigorous cross-validations indicated that the new predictor remarkably outperformed the existing state-of-the-art prediction method for the same purpose. AVAILABILITY AND IMPLEMENTATION: For the convenience of most experimental scientists, a user-friendly web-server for pSumo-CD has been established at http://www.jci-bioinfo.cn/pSumo-CD, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. CONTACT: jjia@gordonlifescience.org, xxiao@gordonlifescience.org or kcchou@gordonlifescience.orgSupplementary information: Supplementary data are available at Bioinformatics online.

iPTM-mLys: identifying multiple lysine PTM sites and their different types.

MOTIVATION: Post-translational modification, abbreviated as PTM, refers to the change of the amino acid side chains of a protein after its biosynthesis. Owing to its significance for in-depth understanding various biological processes and developing effective drugs, prediction of PTM sites in proteins have currently become a hot topic in bioinformatics. Although many computational methods were established to identify various single-label PTM types and their occurrence sites in proteins, no method has ever been developed for multi-label PTM types. As one of the most frequently observed PTMs, the K-PTM, namely, the modification occurring at lysine (K), can be usually accommodated with many different types, such as 'acetylation', 'crotonylation', 'methylation' and 'succinylation'. Now we are facing an interesting challenge: given an uncharacterized protein sequence containing many K residues, which ones can accommodate two or more types of PTM, which ones only one, and which ones none? RESULTS: To address this problem, a multi-label predictor called IPTM-MLYS: has been developed. It represents the first multi-label PTM predictor ever established. The novel predictor is featured by incorporating the sequence-coupled effects into the general PseAAC, and by fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations via a set of multi-label metrics indicate that the first multi-label PTM predictor is very promising and encouraging. AVAILABILITY AND IMPLEMENTATION: For the convenience of most experimental scientists, a user-friendly web-server for iPTM-mLys has been established at http://www.jci-bioinfo.cn/iPTM-mLys, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. CONTACT: wqiu@gordonlifescience.org, xxiao@gordonlifescience.org, kcchou@gordonlifescience.orgSupplementary information: Supplementary data are available at Bioinformatics online.

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.

iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.

Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called "hotspots", while with lower probability in so-called "coldspots". With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called "iRSpot-TNCPseAAC", in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew's correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations.

iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.

Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had [Formula: see text] pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.

Novel inhibitor design for hemagglutinin against H1N1 influenza virus by core hopping method.

The worldwide spread of H1N1 avian influenza and the increasing reports about its resistance to the current drugs have made a high priority for developing new anti-influenza drugs. Owing to its unique function in assisting viruses to bind the cellular surface, a key step for them to subsequently penetrate into the infected cell, hemagglutinin (HA) has become one of the main targets for drug design against influenza virus. To develop potent HA inhibitors, the ZINC fragment database was searched for finding the optimal compound with the core hopping technique. As a result, the Neo6 compound was obtained. It has been shown through the subsequent molecular docking studies and molecular dynamic simulations that Neo6 not only assumes more favorable conformation at the binding pocket of HA but also has stronger binding interaction with its receptor. Accordingly, Neo6 may become a promising candidate for developing new and more powerful drugs for treating influenza. Or at the very least, the findings reported here may provide useful insights to stimulate new strategy in this area.

Roles of L5-7 loop in the structure and chaperone function of SsHSP14.1.

The small heat shock protein SsHSP14.1 from the hyper-thermophilic archeaon, Sulfolobus solfataricus (S. solfataricus) was able to protect proteins from thermal aggregation and prevent enzymes from heat induced inactivation. According to the 3D (dimensional) structural model of SsHSP14.1 developed by us before, the region L5-7 (ß5-ß7, 68-82 residues) plays an important role for the oligomerization of SsHSP14.1 and its chaperone function. Here, to validate the findings, an in-depth investigation was conducted of both the wild type SsHSP14.1 and its deletion mutant DEL75-79. With E. coli proteins and bromelain as substrate, the deletion mutant DEL75-79 can protect them from thermo-aggregating as effective as the wild protein. Interestingly, unlike the wild protein, DEL75-79 was unable to prevent bromelain and EcoRI from thermo-inactivating. Results of size exclusion HPLC showed that the oligomerization state was changed in mutant protein. This was in accordance with the changed structure and lower hydrophobicity of DEL75-79. These outcomes proved that the L5-7 loop did play a role for the oligomerizing SsHSP14.1, and that the residues 75-79 were indispensable for its function of prevent enzymes from thermo-inactivating.

Predicting biological functions of compounds based on chemical-chemical interactions.

Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions.

Binding mechanism of H5N1 influenza virus neuraminidase with ligands and its implication for drug design.

To simulate new strategies for designing effective drugs against bird flu, we have carried out extensive studies by using various computer-aided drug design tools. Molecule AG7088 was first docked to the active site of H5N1 avian influenza neuraminidase (PBD code: 2HTY). The results thus obtained were compared with those by docking zanamivir (Relenza) and oseltamivir (Tamiflu) to the same receptor, respectively. It has been found that the compound AG7088 has better binding energy than zanamivir and oseltamivir. Thus, it was adopted as a template to perform the similarity search of 392,698 druggable compounds in order to find the leading candidates for the next step of modeling studies. Nine analogs of AG7088 were singled out through a series of docking studies. Finally, the molecular dynamics simulation technique was utilized to investigate into the binding interactions between the H5N1 receptor and the nine analogs, with a focus on the binding pocket, intermolecular surfaces and hydrogen bonds. This study may be used as a guide for mutagenesis studies for designing new inhibitors against H5N1.

Possible drug candidates for Alzheimer's disease deduced from studying their binding interactions with alpha7 nicotinic acetylcholine receptor.

Dysfunction in alpha7 nicotinic acetylcholine receptor (nAChR), a member of the Cys-loop ligand-gated ion channel superfamily, is responsible for attentional and cognitive deficits in Alzheimer's disease (AD). To provide useful information for finding drug candidates for the treatment of AD, a study was carried out according to the following procedures. (1) DMXBA, a partial agonist of the alpha7 nAChR, was used as a template molecule. (2) To reduce the number of compounds to be considered, the similarity search and flexible alignment were conducted to exclude those molecules which did not match the template. (3) The molecules thus obtained were docked to alpha7 nAChR. (4) To gain more structural information, the molecular dynamics (MD) simulations were carried out for 9 most favorable agonists obtained by the aforementioned docking studies. (5) By analyzing the hydrogen bond interaction and hydrophobic/hydrophilic interaction, the following seven compounds were singled out as possible drug candidates for AD therapy: gx-50, gx-51, gx-52, gx-180, open3d-99008, open3d-51265, open3d-60247.

Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks.

One limitation of almost all antiviral Quantitative Structure-Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug-drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug-drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg.Med.Chem.2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg.Med.Chem.2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg.Med.Chem.2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug-drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity=84.62%) and 119 of 139 non-active compounds (sensitivity=85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the model in practice, we develop a virtual screening recognizing the model as active 92.7%, 102 of 110 antivirus compounds. These compounds were never use in training or predicting series. Next, we obtained and compared the topology of the CNs and their respective GCs based on Euclidean, Manhattan, Chebychey, Pearson and other similarity measures. The GC of the Manhattan network showed the more interesting features for drug-drug similarity search. We also give the procedure for the construction of Back-Projection Maps for the contribution of each drug sub-structure to the antiviral activity against different species.

Drug candidates from traditional chinese medicines.

Good progress has been made to modernize traditional Chinese medicines by obtaining active components from natural herbs. In this review, some recent works on procuring active components and modernizing traditional Chinese medicines will be covered. In addition, some recent works on drug design using modern drug design tools have been described. With some well defined targets, the traditional Chinese medicine databases have been screened so as to identify those compounds for which the potential as a drug candidate was not known before. Among these studies, two have been selected as examples to be discussed in details. First, new anti-HIV candidates have been detected, namely leucovorin and agaritine derivatives. Subsequently, GTS-21 is proved to be a good candidate for Alzheimer's disease. All these findings may provide useful information for finding effective drug candidates with lower cost.

Agaritine and its derivatives are potential inhibitors against HIV proteases.

Agaritine, or beta-N-[gamma-L(+)-glutamyl]-4-hydroxymethylphenylhydrazine, is a Chinese herbal medicine, known having the antiviral and anticancer function. However, so far no reports whatsoever have been made for its potential as an anti-HIV agent. It was observed by docking experiments for more than 9,000 compounds extracted from various Chinese medicines that the compound agaritine distinguished itself from all the others in binding to the HIV protease with the most favorable free energy. Based on this, a series of derivatives were generated by modifying agaritine. It has been observed thru an extensive docking study that some of agaritine derivatives had markedly stronger binding interaction with the HIV protease than agaritine, suggesting that these derivatives might be good candidates for developing drugs for AIDS therapy.

Molecular insights of SAH enzyme catalysis and implication for inhibitor design.

Biological transmethylation reaction is a key step in the duplication of virus life cycle, in which S-adenosylmethionine plays as the methyl donor. The product of this reactions, S-adenosylhomocysteine (AdoHcy) inhibits the transmethylation process. AdoHcy is hydrolysed to adenosine and L-homocysteine by the action of S-adenosylhomocysteine hydrolase (SAH). Thus the virus life cycle should be cut off once the action of SAH is inhibited. Our study was focussed on the discovery of potential inhibitor against SAH. We performed a similarity search in Traditional Chinese Medicine Database and retrieved 17 hits with high similarity. After that we virtually docked the 17 compounds as well as the natural substrates to the hydrolase using Autodock 3.0.1 software. Then we discussed about the mechanism of the inhibition reaction, followed by proposing the potential inhibitors by comparing best docked solutions and possible modification for the best inhibitors.

Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition.

Cell membranes are vitally important to the life of a cell. Although the basic structure of biological membrane is provided by the lipid bilayer, membrane proteins perform most of the specific functions. Membrane proteins are putatively classified into five different types. Identification of their types is currently an important topic in bioinformatics and proteomics. In this paper, based on the concept of representing protein samples in terms of their pseudo-amino acid composition, the fuzzy K-nearest neighbors (KNN) algorithm has been introduced to predict membrane protein types, and high success rates were observed. It is anticipated that, the current approach, which is based on a branch of fuzzy mathematics and represents a new strategy, may play an important complementary role to the existing methods in this area. The novel approach may also have notable impact on prediction of the other attributes, such as protein structural class, protein subcellular localization, and enzyme family class, among many others.

Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition.

Membrane proteins are generally classified into the following five types: (1) type I membrane proteins, (2) type II membrane proteins, (3) multipass transmembrane proteins, (4) lipid chain-anchored membrane proteins and (5) GPI-anchored membrane proteins. Prediction of membrane protein types has become one of the growing hot topics in bioinformatics. Currently, we are facing two critical challenges in this area: first, how to take into account the extremely complicated sequence-order effects, and second, how to deal with the highly uneven sizes of the subsets in a training dataset. In this paper, stimulated by the concept of using the pseudo-amino acid composition to incorporate the sequence-order effects, the spectral analysis technique is introduced to represent the statistical sample of a protein. Based on such a framework, the weighted support vector machine (SVM) algorithm is applied. The new approach has remarkable power in dealing with the bias caused by the situation when one subset in the training dataset contains many more samples than the other. The new method is particularly useful when our focus is aimed at proteins belonging to small subsets. The results obtained by the self-consistency test, jackknife test and independent dataset test are encouraging, indicating that the current approach may serve as a powerful complementary tool to other existing methods for predicting the types of membrane proteins.