Prediction of the tertiary structure of the beta-secretase zymogen

Purification and Characterization of a DegP-Type Protease from the Marine Bacterium Cobetia amphilecti KMM 296

A new member of the DegP-type periplasmic serine endoproteases of the S1C family from the marine bacterium Cobetia amphilecti KMM 296 (CamSP) was expressed in Escherichia coli cells. The calculated molecular weight, number of amino acids, and isoelectric point (pI) of the mature protein CamSP are 69.957 kDa, 666, and 4.84, respectively. The proteolytic activity of the purified recombinant protease CamSP was 2369.4 and 1550.9 U/mg with the use of 1% bovine serum albumin (BSA) and casein as the substrates, respectively. The enzyme CamSP exhibited maximum activity at pH 6.0-6.2, while it was stable over a wide pH range from 5.8 to 8.5. The optimal temperature for the CamSP protease activity was 50 °C. The enzyme required NaCl or KCl at concentrations of 0.3 and 0.5 M, respectively, for its maximum activity. The Michaelis constant (K_m) and V_max for BSA were determined to be 41.7 µg/mL and 0.036 µg/mL min^-1, respectively. The metal ions Zn²⁺, Cu²⁺, Mn²⁺, Li²⁺, Mg²⁺, and Ca²⁺ slightly activated CamSP, while the addition of CoCl₂ to the incubation mixture resulted in a twofold increase in its protease activity. Ethanol, isopropanol, glycerol, and Triton-X-100 increased the activity of CamSP from two- to four-times. The protease CamSP effectively degraded the wheat flour proteins but had no proteolytic activity towards soybean, corn, and the synthetic substrates, α-benzoyl-Arg-p-nitroanilide (BAPNA) and N-Succinyl-L-alanyl-L-alanyl-L-prolyl-L-phenylalanine 4-nitroanilide (SAPNA).

Elucidating the Protonation State of the γ-Secretase Catalytic Dyad

γ-Secretase (GS) is an intramembrane aspartyl protease that participates in the sequential cleavage of C99 to generate different isoforms of the amyloid-β (Aβ) peptides that are associated with the development of Alzheimer's disease. Due to its importance in the proteolytic processing of C99 by GS, we performed pH replica exchange molecular dynamics (pH-REMD) simulations of GS in its apo and substrate-bound forms to sample the protonation states of the catalytic dyad. We found that the catalytic dyad is deprotonated at physiological pH in our apo form, but the presence of the substrate at the active site displaces its monoprotonated state toward physiological pH. Our results show that Asp257 acts as the general base and Asp385 as the general acid during the cleavage mechanism. We identified different amino acids such as Lys265, Arg269, and the PAL motif interacting with the catalytic dyad and promoting changes in its acid-base behavior. Finally, we also found a significant pK_a shift of Glu280 related to the internalization of TM6-CT in the GS-apo form. Our study provides critical mechanistic insight into the GS mechanism and the basis for future research on the genesis of Aβ peptides and the development of Alzheimer's disease.

Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model

Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.

An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule

OBJECTIVE

One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information.

METHODS

To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed.

RESULTS AND CONCLUSION

It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.

RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule

By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook.

Database URL: http://bioinfor.imu.edu.cn/raacbook

The Cooperative Effect between Polybasic Region (PBR) and Polysialyltransferase Domain (PSTD) within Tumor-Target Polysialyltranseferase ST8Sia II

ST8Sia II (STX) is a highly homologous mammalian polysialyltransferase (polyST), which is a validated tumor-target in the treatment of cancer metastasis reliant on tumor cell polysialylation. PolyST catalyzes the synthesis of α2,8-polysialic acid (polySia) glycans by carrying out the activated CMP-Neu5Ac (Sia) to N- and O-linked oligosaccharide chains on acceptor glycoproteins. In this review article, we summarized the recent studies about intrinsic correlation of two polybasic domains, Polysialyltransferase domain (PSTD) and Polybasic region (PBR) within ST8Sia II molecule, and suggested that the critical amino acid residues within the PSTD and PBR motifs of ST8Sia II for polysialylation of Neural cell adhesion molecules (NCAM) are related to ST8Sia II activity. In addition, the conformational changes of the PSTD domain due to point mutations in the PBR or PSTD domain verified an intramolecular interaction between the PBR and the PSTD. These findings have been incorporated into Zhou's NCAM polysialylation/cell migration model, which will provide new perspectives on drug research and development related to the tumor-target ST8Sia II.

A Possible Modulation Mechanism of Intramolecular and Intermolecular Interactions for NCAM Polysialylation and Cell Migration

Polysialic acid (polySia) is a novel glycan that posttranslationally modifies neural cell adhesion molecules (NCAMs) in mammalian cells. Up-regulation of polySia-NCAM expression or NCAM polysialylation is associated with tumor cell migration and progression in many metastatic cancers and neurocognition. It has been known that two highly homologous mammalian polysialyltransferases (polySTs), ST8Sia II (STX) and ST8Sia IV (PST), can catalyze polysialylation of NCAM, and two polybasic domains, polybasic region (PBR) and polysialyltransferase domain (PSTD) in polySTs play key roles in affecting polyST activity or NCAM polysialylation. However, the molecular mechanisms of NCAM polysialylation and cell migration are still not entirely clear. In this minireview, the recent research results about the intermolecular interactions between the PBR and NCAM, the PSTD and cytidine monophosphate-sialic acid (CMP-Sia), the PSTD and polySia, and as well as the intramolecular interaction between the PBR and the PSTD within the polyST, are summarized. Based on these cooperative interactions, we have built a novel model of NCAM polysialylation and cell migration mechanisms, which may be helpful to design and develop new polysialyltransferase inhibitors.

Using Chou's General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou's Five-Step Rule

Laccases are a group of enzymes with a critical activity in the degradation process of both phenolic and non-phenolic compounds. These enzymes present in a diverse array of species, including fungi and bacteria. Since this enzyme is in the market for different usages from industry to medicine, having a better knowledge of its structures and properties from diverse sources will be useful to select the most appropriate candidate for different purposes. In the current study, sequence- and structure-based characteristics of these enzymes from fungi and bacteria, including pseudo amino acid composition (PseAAC), physicochemical characteristics, and their secondary structures, are being compared and classified. Autodock 4 software was used for docking analysis between these laccases and some phenolic and non-phenolic compounds. The results indicated that features including molecular weight, aliphatic, extinction coefficient, and random coil percentage of these protein groups present high degrees of diversity in most cases. Categorization of these enzymes by the notion of PseAAC, showed over 96% accuracy. The binding free energy between fungal laccases and their substrates showed to be considerably higher than those of bacterial ones. According to the outcomes of the current study, data mining methods by using different machine learning algorithms, especially neural networks, could provide valuable information for a fair comparison between fungal and bacterial laccases. These results also suggested an association between efficacy and physicochemical features of laccase enzymes from different sources.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Use of Chou's 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangshen Pingfei San on Idiopathic Pulmonary Fibrosis

BACKGROUND

Shuangshen Pingfei San (SPS) is the derivative from the classic formula Renshen Pingfei San in treating idiopathic pulmonary fibrosis (IPF).

METHODS

In this study, Chou's 5-steps rule was performed to explore the potential active compound and mechanism of SPS on IPF. Compound-target network, target- pathway network, herb-target network and the core gene target interaction network were established and analyzed. A total of 296 compounds and 69 candidate therapeutic targets of SPS in treating IPF were obtained. Network analysis revealed that the main active compounds were flavonoids (such as apigenin, quercetin, naringenin, luteolin), other clusters (such as ginsenoside Rh2, diosgenin, tanshinone IIa), which might also play significant roles. SPS regulated multiple IPF relative genes, which affect fibrosis (PTGS2, KDR, FGFR1, TGFB, VEGFA, MMP2/9) and inflammation (PPARG, TNF, IL13, IL4, IL1B, etc.).

CONCLUSION

In conclusion, anti-pulmonary fibrosis effect of SPS might be related to the regulation of inflammation and pro-fibrotic signaling pathways. These findings revealed that the potential active compounds and mechanisms of SPS on IPF were a benefit to further study.

Inhibition of α-amylase Activity by Zn2+: Insights from Spectroscopy and Molecular Dynamics Simulations

Background:

Inhibition of α-amylase activity is an important strategy in the treatment of diabetes mellitus. An important treatment for diabetes mellitus is to reduce the digestion of carbohydrates and blood glucose concentrations. Inhibiting the activity of carbohydrate-degrading enzymes such as α-amylase and glucosidase significantly decreases the blood glucose level. Most inhibitors of α-amylase have serious adverse effects, and the α-amylase inactivation mechanisms for the design of safer inhibitors are yet to be revealed.

Objective:

In this study, we focused on the inhibitory effect of Zn2+ on the structure and dynamic characteristics of α-amylase from Anoxybacillus sp. GXS-BL (AGXA), which shares the same catalytic residues and similar structures as human pancreatic and salivary α-amylase (HPA and HSA, respectively).

Methods:

Circular dichroism (CD) spectra of the protein (AGXA) in the absence and presence of Zn2+ were recorded on a Chirascan instrument. The content of different secondary structures of AGXA in the absence and presence of Zn2+ was analyzed using the online SELCON3 program. An AGXA amino acid sequence similarity search was performed on the BLAST online server to find the most similar protein sequence to use as a template for homology modeling. The pocket volume measurer (POVME) program 3.0 was applied to calculate the active site pocket shape and volume, and molecular dynamics simulations were performed with the Amber14 software package.

Results:

According to circular dichroism experiments, upon Zn2+ binding, the protein secondary structure changed obviously, with the α-helix content decreasing and β-sheet, β-turn and randomcoil content increasing. The structural model of AGXA showed that His217 was near the active site pocket and that Phe178 was at the outer rim of the pocket. Based on the molecular dynamics trajectories, in the free AGXA model, the dihedral angle of C-CA-CB-CG displayed both acute and planar orientations, which corresponded to the open and closed states of the active site pocket, respectively. In the AGXA-Zn model, the dihedral angle of C-CA-CB-CG only showed the planar orientation. As Zn2+ was introduced, the metal center formed a coordination interaction with H217, a cation-π interaction with W244, a coordination interaction with E242 and a cation-π interaction with F178, which prevented F178 from easily rotating to the open state and inhibited the activity of the enzyme.

Conclusion:

This research may have uncovered a subtle mechanism for inhibiting the activity of α-amylase with transition metal ions, and this finding will help to design more potent and specific inhibitors of α-amylases.

Microbial Proteases Applications

The use of chemicals around the globe in different industries has increased tremendously, affecting the health of people. The modern world intends to replace these noxious chemicals with environmental friendly products for the betterment of life on the planet. Establishing enzymatic processes in spite of chemical processes has been a prime objective of scientists. Various enzymes, specifically microbial proteases, are the most essentially used in different corporate sectors, such as textile, detergent, leather, feed, waste, and others. Proteases with respect to physiological and commercial roles hold a pivotal position. As they are performing synthetic and degradative functions, proteases are found ubiquitously, such as in plants, animals, and microbes. Among different producers of proteases, Bacillus sp. are mostly commercially exploited microbes for proteases. Proteases are successfully considered as an alternative to chemicals and an eco-friendly indicator for nature or the surroundings. The evolutionary relationship among acidic, neutral, and alkaline proteases has been analyzed based on their protein sequences, but there remains a lack of information that regulates the diversity in their specificity. Researchers are looking for microbial proteases as they can tolerate harsh conditions, ways to prevent autoproteolytic activity, stability in optimum pH, and substrate specificity. The current review focuses on the comparison among different proteases and the current problems faced during production and application at the industrial level. Deciphering these issues would enable us to promote microbial proteases economically and commercially around the world.

Identification and characterization of WD40 superfamily genes in peach

The WD40 transcription factor family is a superfamily found in all eukaryotes that plays important roles in regulating growth and development. To our knowledge, to date, WD40 superfamily genes have been identified and characterized in several plant species, but little information is available on the WD40 superfamily genes in peach. In this study, we identified 220 members of the WD40 superfamily in the peach genome, and these members were further classified into five subfamilies based on phylogenetic comparison with those in Arabidopsis. The members within each subfamily had conserved motifs and gene structures. The WD40 genes were unevenly distributed on chromosomes 1 to 8 of the peach genome. Additionally, 58 pairs of paralog WD40 members were found on eight chromosomes in peach, and 242 pairs of orthologous WD40 genes in peach and Arabidopsis were matched. The 54 selected putative WD40 genes in peach had diverse expression patterns in red-fleshed and white-fleshed peach fruits at five developmental stages. Prupe.6G211800.1 was located only on the cytomembrane, while Prupe.1G428200.1 and Prupe.I003200.1 were located on both the cytomembrane and in the nucleus; Prupe.1G558700.1 was densely localized around the nuclear rim but relatively faintly localized in the nucleoplasm; Prupe.5G116300.1 was located in the nucleus and cytomembrane with strong signals but showed weak signals in the cytoplasm; and Prupe.8G212400.1 and Prupe.1G053600.1 were located mainly in the nuclear envelope and cytomembrane but relatively faintly in the nucleoplasm. This study provides a foundation for the further functional verification of WD40 genes in peach.

Mechanistic Insights of Chemicals and Drugs as Risk Factors for Systemic Lupus Erythematosus

Systemic Lupus Erythematosus (SLE) is a chronic and relapsing heterogenous autoimmune disease that primarily affects women of reproductive age. Genetic and environmental risk factors are involved in the pathogenesis of SLE, and susceptibility genes have recently been identified. However, as gene therapy is far from clinical application, further investigation of environmental risk factors could reveal important therapeutic approaches. We systematically explored two groups of environmental risk factors: chemicals (including silica, solvents, pesticides, hydrocarbons, heavy metals, and particulate matter) and drugs (including procainamide, hydralazine, quinidine, Dpenicillamine, isoniazid, and methyldopa). Furthermore, the mechanisms underlying risk factors, such as genetic factors, epigenetic change, and disrupted immune tolerance, were explored. This review identifies novel risk factors and their underlying mechanisms. Practicable measures for the management of these risk factors will benefit SLE patients and provide potential therapeutic strategies.

Hydrogen bond analysis of the EGFR-ErbB3 heterodimer related to non-small cell lung cancer and drug resistance

Lung cancer is the predominant cause of cancer deaths on a worldwide scale. A mutation in the epidermal growth factor receptor (EGFR) can cause non-small cell lung cancer (NSCLC). The L858R one-point mutation in exon 21 in EGFR is the most prevalent in NSCLC. For over 60% of EGFR-muted NSCLC, another mutation T790M can cause drug resistance. In this paper, we consider EGFR and ErbB3 heterodimers involving three structures of EGFR, wild-type, with L858R mutation, and with L858R and T790M mutations. We perform molecular dynamics (MD) simulations to analyze hydrogen bonds in all three instances. The hydrogen bonds contribute to the conformational stability of the protein and molecular recognition. Several other parameters are also investigated in the present study, which reveals significant changes in the dimer at different levels of mutation. The knowledge and results obtained from this study lead to useful insight into the mechanism of NSCLC drug resistance.

Exploration of synthetic multifunctional amides as new therapeutic agents for Alzheimer's disease through enzyme inhibition, chemoinformatic properties, molecular docking and dynamic simulation insights

A new series of multifunctional amides has been synthesized having moderate enzyme inhibitory potentials and mild cytotoxicity. 2-Furyl(1-piperazinyl)methanone (1) was coupled with 3,5-dichloro-2-hydroxybenzenesulfonyl chloride (2) to form {4-[(3,5-dichloro-2-hydroxyphenyl)sulfonyl]-1-piperazinyl}(2-furyl)methanone (3). Different elecrophiles were synthesized by the reaction of various un/substituted anilines (4a-o) with 2-bromoacetylbromide (5), 2‑bromo‑N-(un/substituted-phenyl)acetamides (6a-o). Further, equimolar ratios of 3 and 6a-o were allowed to react in the presence of K₂CO₃ in acetonitrile to form desired multifunctional amides (7a-o). The structural confirmation of all the synthesized compounds was carried out by their EI-MS, IR, ¹H NMR and ¹³C NMR spectral data. Enzyme inhibition activity was performed against acetyl and butyrylcholinestrase enzymes, whereby 7e showed very good activity having IC₅₀ value of 5.54 ± 0.03 and 9.15 ± 0.01 μM, respectively, relative to eserine, a reference standard. Hemolytic activity of the molecules was checked to asertain their cytotoxicity towards red blood cell membrance and it was observed that most of the compounds were not toxic up to certain range. Moreover, chemoinformatic protepties and docking simulation results also showed the significance of 7e as compared to other compounds. Based on in vitro and in silico analysis 7e could be used as a template for the development of new drugs against Alzheimer's disease.

iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition

DNA replication, occurring in all living organisms and being the basis for biological inheritance, is the process of producing two identical replicas from one original DNA molecule. To in-depth understand such an important biological process and use it for developing new strategy against genetics diseases, the knowledge of duplication origin sites in DNA is indispensible. With the explosive growth of DNA sequences emerging in the postgenomic age, it is highly desired to develop high throughput tools to identify these regions purely based on the sequence information alone. In this paper, by incorporating the dinucleotide position-specific propensity information into the general pseudo nucleotide composition and using the random forest classifier, a new predictor called iROS-gPseKNC was proposed. Rigorously cross-validations have indicated that the proposed predictor is significantly better than the best existing method in sensitivity, specificity, overall accuracy, and stability. Furthermore, a user-friendly web-server for iROS-gPseKNC has been established at http://www.jci-bioinfo.cn/iROS-gPseKNC, by which users can easily get their desired results without the need to bother the complicated mathematics, which were presented just for the integrity of the methodology itself.

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier.

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.

The effect of glycosylation on the transferrin structure: A molecular dynamic simulation analysis

Transferrins have been defined by the highly cooperative binding of iron and a carbonate anion to form a Fe-CO3-Tf ternary complex. As such, the layout of the binding site residues affects transferrin function significantly; In contrast to N-lobe, C-lobe binding site of the transferrin structure has been less characterized and little research which surveyed the interaction of carbonate with transferrin in the C-lobe binding site has been found. In the present work, molecular dynamic simulation was employed to gain access into the molecular level understanding of carbonate binding site and their interactions in each lobe. Residues responsible for carbonate binding of transferrin structure were pointed out. In addition, native human transferrin is a glycoprotein that two N-linked complex glycan chains located in the C-lobe. Usually, in the molecular dynamic simulation for simplifying, glycan is removed from the protein structure. Here, we explore the effect of glycosylation on the transferrin structure. Glycosylation appears to have an effect on the layout of the binding site residue and transferrin structure. On the other hand, sometimes the entire transferrin formed by separated lobes that it allows the results to be interpreted in a straightforward manner rather than more parameters required for full length protein. But, it should be noted that there are differences between the separated lobe and full length transferrin, hence, a comparative analysis by the molecular dynamic simulation was performed to investigate such structural variations. Results revealed that separation in C-lobe caused a significant structural variation in comparison to N-lobe. Consequently, the separated lobes and the full length one are different, showing the importance of the interlobe communication and the impact of the lobes on each other in the transferrin structure.

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.

Structure of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser of Aβ-peptide with phospholipase A2 from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Å resolution

Alzheimer’s disease (AD) is one of the most significant social and health burdens of the present century. Plaques formed by extracellular deposits of amyloid β (Aβ) are the prime player of AD’s neuropathology. Studies have implicated the varied role of phospholipase A₂ (PLA₂) in brain where it contributes to neuronal growth and inflammatory response. Overall contour and chemical nature of the substrate-binding channel in the low molecular weight PLA₂s are similar. This study involves the reductionist fragment-based approach to understand the structure adopted by N-terminal fragment of Alzheimer’s Aβ peptide in its complex with PLA₂. In the current communication, we report the structure determined by X-ray crystallography of N-terminal sequence Asp-Ala-Glu-Phe-Arg-His-Asp-Ser (DAEFRHDS) of Aβ-peptide with a Group I PLA₂ purified from venom of Andaman Cobra sub-species Naja naja sagittifera at 2.0 Å resolution (Protein Data Bank (PDB) Code: 3JQ5). This is probably the first attempt to structurally establish interaction between amyloid-β peptide fragment and hydrophobic substrate binding site of PLA₂ involving H bond and van der Waals interactions. We speculate that higher affinity between Aβ and PLA₂ has the therapeutic potential of decreasing the Aβ–Aβ interaction, thereby reducing the amyloid aggregation and plaque formation in AD.

iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking

Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. 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 this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.

A rare de novo duplication of chromosome 21q22.12 → q22.3 with other concomitant deletion and duplication of small fragments in 21q associated with Down syndrome: Prenatal diagnosis, molecular cytogenetic characterization

Background

Karyotyping is considered the gold standard for the genome-wide detection of genomic imbalances in prenatal diagnosis, but it has a number of inherent limitations, namely the time required to culture cell and the limited resolution(5 ~ 10 Mb). Although fluorescence in situ hybridization (FISH) can also be used as a rapid prenatal diagnosis for common aneuploidies, it is labor intensive, requires prior knowledge of the regions of interest, and can only be used to diagnose one or a few genomic regions simultaneously. Array comparative genomic hybridization (aCGH) can overcome the resolution, the locus-specific, and the time limitations of the karyotyping and FISH techniques and is currently the most powerful method for detecting chromosomal alterations in pre and postnatal clinical cases. Several investigations have suggested that the aCGH testing should be considered a first-tier test for the diagnosis of cytogenetic aberrations in the fetus.

Results

This study used karyotyping, FISH, sequence-tagged site (STS) analysis and aCGH to diagnose a case of de novo duplication of chromosome 21q22.12 → q22.3 with other concomitant deletion and duplication of small fragments in 21q associated with Down syndrome prenatally.

Conclusions

FISH, aCGH and STS analysis are useful in prenatal investigation of the nature of de novo alterations of small fragments of the chromosome.

β-Secretases, Alzheimer's Disease, and Down Syndrome

Individuals with Down Syndrome (DS), or trisomy 21, develop Alzheimer's disease (AD) pathology by approximately 40 years of age. Chromosome 21 harbors several genes implicated in AD, including the amyloid precursor protein and one homologue of the β-site APP cleaving enzyme, BACE2. Processing of the amyloid precursor protein by β-secretase (BACE) is the rate-limiting step in the production of the pathogenic Aβ peptide. Increased amounts of APP in the DS brain result in increased amounts of Aβ and extracellular plaque formation beginning early in life. BACE dysregulation potentially represents an overlapping biological mechanism with sporadic AD and a common therapeutic target. As the lifespan for those with DS continues to increase, age-related concerns such as obesity, depression, and AD are of growing concern. The ability to prevent or delay the progression of neurodegenerative diseases will promote healthy aging and improve quality of life for those with DS.

A novel canonical dual computational approach for prion AGAAAAGA amyloid fibril molecular modeling

Many experimental studies have shown that the prion AGAAAAGA palindrome hydrophobic region (113-120) has amyloid fibril forming properties and plays an important role in prion diseases. However, due to the unstable, noncrystalline and insoluble nature of the amyloid fibril, to date structural information on AGAAAAGA region (113-120) has been very limited. This region falls just within the N-terminal unstructured region PrP (1-123) of prion proteins. Traditional X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy experimental methods cannot be used to get its structural information. Under this background, this paper introduces a novel approach of the canonical dual theory to address the 3D atomic-resolution structure of prion AGAAAAGA amyloid fibrils. The novel and powerful canonical dual computational approach introduced in this paper is for the molecular modeling of prion AGAAAAGA amyloid fibrils, and that the optimal atomic-resolution structures of prion AGAAAAGA amyloid fibils presented in this paper are useful for the drive to find treatments for prion diseases in the field of medicinal chemistry. Overall, this paper presents an important method and provides useful information for treatments of prion diseases.

Is BACE1 a suitable therapeutic target for the treatment of Alzheimer's disease? Current strategies and future directions

Alzheimer's disease (AD) is characterized by the extracellular deposition of the β-amyloid protein (Aβ). Aβ is a fragment of a much larger precursor protein, the amyloid precursor protein (APP). Sequential proteolytic cleavage of APP by β-secretase and γ-secretase liberates Aβ from APP. The aspartyl protease BACE1 (β-site APP-cleaving enzyme 1) catalyses the rate-limiting step in the production of Aβ, and as such it is considered to be a major target for drug development in Alzheimer's disease. However, the development of a BACE1 inhibitor therapy is problematic for two reasons. First, BACE1 has been found to have important physiological roles. Therefore, inhibition of the enzyme could have toxic consequences. Second, the active site of BACE1 is relatively large, and many of the bulky compounds that are needed to inhibit BACE1 activity are unlikely to cross the blood-brain barrier. This review focuses on the structure BACE1, current therapeutic strategies based on developing active-site inhibitors, and new approaches to therapy involving targeting the expression or post-translational regulation of BACE1.

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.

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.

Why does β-secretase zymogen possess catalytic activity? Molecular modeling and molecular dynamics simulation studies

Beta-secretase is a potential target for inhibitory drugs against Alzheimer's disease as it cleaves amyloid precursor protein (APP) to form insoluble amyloid plaques and vascular deposits in the brain. Beta-secretase is matured from its precursor protein, called beta-secretase zymogen, which, different from most of other zymogens, is also partially active in cleaving APP. Hence, it is important to study on the mechanism of the zymogen's activation process. This study was to model the 3-D structure of the zymogen, followed by intensive molecular dynamics (MD) simulations to identify the most probable 3-D model and to study the dynamic structural behavior of the zymogen for understanding the effects of pro-segment on the function of the enzyme. The results revealed that the dropping in catalytic activity of the beta-secretase zymogen could be attributed to the occupation of the entrance of the catalytic site of the zymogen by its pro-segment. On the other hand, the partial catalytic activity of the zymogen could be explained by high fluctuation of the pro-segment in comparison with that of other zymogens, resulting in the occasionally exposure of the catalytic site for access its substrate APP. Indeed, steered MD (SMD) simulation revealed a weak pulling force at quasi-equilibrium state for the pro-segment of the zymogen leaving from the entrance, indicating that this swinging process could take place spontaneously. Furthermore, MM-PBSA calculation revealed a small change of free energy of 10.56 kal/mol between the initial and final states of the process of pro-segment swung outside the binding pocket of beta-secretase zymogen. These results not only account for the partial catalytic activity of beta-secretase zymogen, but also provide useful clues for discovering new potent ligands, as new type of drug leads for curing Alzheimer's disease, to prevent the pro-segment of the zymogen from leaving its catalytic site.