Insights from studying the mutation-induced allostery in the M2 proton channel by molecular dynamics

In Silico Design and Synthesis of Targeted Curcumin Derivatives as Xanthine Oxidase Inhibitors

BACKGROUND

Curcumin is a well-known pharmacophore and some of its derivatives are shown to target xanthine oxidase (XO) to alleviate disorders caused by the excess production of uric acid.

OBJECTIVE

Curcumin based derivatives were designed, synthesized and evaluated for their antioxidant and xanthine oxidase inhibitory potential.

METHOD

In this report, we designed and synthesized two series of curcumin derivatives modified by inserting pyrazole and pyrimidine ring to central keto group. The synthesized compounds were evaluated for their antioxidant and xanthine oxidase inhibitory potential.

RESULTS

Results showed that pyrazole analogues of curcumin produced excellent XO inhibitory potency with the IC50 values varying from 06.255 µM to 10.503 µM. Among pyrimidine derivatives compound CU3a1 having ortho nitro substitution exhibited more potent xanthine oxidase inhibitory activity than any other curcumin derivative of this series.

CONCLUSION

Curcumin derivatives CU5b1, CU5b2, CU5b3, and CU3a1 showed a potent inhibitory activity against xanthine oxidase along with good antioxidant potential.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

BACKGROUND

Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics.

OBJECTIVE

In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed.

RESULTS

Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design.

CONCLUSION

This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

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.

Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Beta-lyase Plays in Hyperoxaluria

Hyperoxaluria, excessive urinary oxalate excretion, is a significant health problem worldwide. Disrupted oxalate metabolism has been implicated in hyperoxaluria and accordingly, an enzymatic disturbance in oxalate biosynthesis can result in the primary hyperoxaluria. Alanine glyoxylate aminotransferase-1 and glyoxylate reductase, the enzymes involving glyoxylate (precursor for oxalate) metabolism, have been related to primary hyperoxalurias. Some studies suggest that other enzymes such as glycolate oxidase and alanine glyoxylate aminotransferase-2 might be associated with primary hyperoxaluria as well, but evidence of a definitive link is not strong between the clinical cases and gene mutations. There are still some idiopathic hyperoxalurias, which require a further study for the etiologies. Some aminotransferases, particularly kynurenine aminotransferases, can convert glyoxylate to glycine. Based on biochemical and structural characteristics, expression level, subcellular localization of some aminotransferases, a number of them appear able to catalyze the transamination of glyoxylate to glycine more efficiently than alanine glyoxylate aminotransferase-1. The aim of this minireview is to explore other undermining causes of primary hyperoxaluria and stimulate research toward achieving a comprehensive understanding of underlying mechanisms leading to the disease. Herein, we reviewed all aminotransferases in the liver for their functions in glyoxylate metabolism. Particularly, kynurenine aminotransferase-I and III were carefully discussed regarding their biochemical and structural characteristics, cellular localization, and enzyme inhibition. Kynurenine aminotransferase-III is, so far, the most efficient putative mitochondrial enzyme to transaminate glyoxylate to glycine in mammalian livers, might be an interesting enzyme to look over in hyperoxaluria etiology of primary hyperoxaluria and should be carefully investigated for its involvement in oxalate metabolism.

The Multiple Applications and Possible Mechanisms of the Hyperbaric Oxygenation Therapy

Hyperbaric Oxygenation Therapy (HBOT) is used as an adjunctive method for multiple diseases. The method meets the routine treating and is non-invasive, as well as provides 100% pure oxygen (O2), which is at above-normal atmospheric pressure in a specialized chamber. It is well known that in the condition of O2 deficiency, it will induce a series of adverse events. In order to prevent the injury induced by anoxia, the capability of offering pressurized O2 by HBOT seems involuntary and significant. In recent years, HBOT displays particular therapeutic efficacy in some degree, and it is thought to be beneficial to the conditions of angiogenesis, tissue ischemia and hypoxia, nerve system disease, diabetic complications, malignancies, Carbon monoxide (CO) poisoning and chronic radiation-induced injury. Single and combination HBOT are both applied in previous studies, and the manuscript is to review the current applications and possible mechanisms of HBOT. The applicability and validity of HBOT for clinical treatment remain controversial, even though it is regarded as an adjunct to conventional medical treatment with many other clinical benefits. There also exists a negative side effect of accepting pressurized O2, such as oxidative stress injury, DNA damage, cellular metabolic, activating of coagulation, endothelial dysfunction, acute neurotoxicity and pulmonary toxicity. Then it is imperative to comprehensively consider the advantages and disadvantages of HBOT in order to obtain a satisfying therapeutic outcome.

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.

3D pharmacophore-based virtual screening, docking and density functional theory approach towards the discovery of novel human epidermal growth factor receptor-2 (HER2) inhibitors

Human epidermal growth factor receptor 2 (HER2) is one of the four members of the epidermal growth factor receptor (EGFR) family and is expressed to facilitate cellular proliferation across various tissue types. Therapies targeting HER2, which is a transmembrane glycoprotein with tyrosine kinase activity, offer promising prospects especially in breast and gastric/gastroesophageal cancer patients. Persistence of both primary and acquired resistance to various routine drugs/antibodies is a disappointing outcome in the treatment of many HER2 positive cancer patients and is a challenge that requires formulation of new and improved strategies to overcome the same. Identification of novel HER2 inhibitors with improved therapeutics index was performed with a highly correlating (r=0.975) ligand-based pharmacophore model (Hypo1) in this study. Hypo1 was generated from a training set of 22 compounds with HER2 inhibitory activity and this well-validated hypothesis was subsequently used as a 3D query to screen compounds in a total of four databases of which two were natural product databases. Further, these compounds were analyzed for compliance with Veber's drug-likeness rule and optimum ADMET parameters. The selected compounds were then subjected to molecular docking and Density Functional Theory (DFT) analysis to discern their molecular interactions at the active site of HER2. The findings thus presented would be an important starting point towards the development of novel HER2 inhibitors using well-validated computational techniques.

iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets

Knowledge of protein-protein interactions and their binding sites is indispensable for in-depth understanding of the networks in living cells. With the avalanche of protein sequences generated in the postgenomic age, it is critical to develop computational methods for identifying in a timely fashion the protein-protein binding sites (PPBSs) based on the sequence information alone because the information obtained by this way can be used for both biomedical research and drug development. To address such a challenge, we have proposed a new predictor, called iPPBS-Opt, in which we have used: (1) the K-Nearest Neighbors Cleaning (KNNC) and Inserting Hypothetical Training Samples (IHTS) treatments to optimize the training dataset; (2) the ensemble voting approach to select the most relevant features; and (3) the stationary wavelet transform to formulate the statistical samples. Cross-validation tests by targeting the experiment-confirmed results have demonstrated that the new predictor is very promising, implying that the aforementioned practices are indeed very effective. Particularly, the approach of using the wavelets to express protein/peptide sequences might be the key in grasping the problem's essence, fully consistent with the findings that many important biological functions of proteins can be elucidated with their low-frequency internal motions. To maximize the convenience of most experimental scientists, we have provided a step-by-step guide on how to use the predictor's web server (http://www.jci-bioinfo.cn/iPPBS-Opt) to get the desired results without the need to go through the complicated mathematical equations involved.

Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition

With the explosive growth of protein sequences entering into protein data banks in the post-genomic era, it is highly demanded to develop automated methods for rapidly and effectively identifying the protein-protein binding sites (PPBSs) based on the sequence information alone. To address this problem, we proposed a predictor called iPPBS-PseAAC, in which each amino acid residue site of the proteins concerned was treated as a 15-tuple peptide segment generated by sliding a window along the protein chains with its center aligned with the target residue. The working peptide segment is further formulated by a general form of pseudo amino acid composition via the following procedures: (1) it is converted into a numerical series via the physicochemical properties of amino acids; (2) the numerical series is subsequently converted into a 20-D feature vector by means of the stationary wavelet transform technique. Formed by many individual "Random Forest" classifiers, the operation engine to run prediction is a two-layer ensemble classifier, with the 1st-layer voting out the best training data-set from many bootstrap systems and the 2nd-layer voting out the most relevant one from seven physicochemical properties. Cross-validation tests indicate that the new predictor is very promising, meaning that many important key features, which are deeply hidden in complicated protein sequences, can be extracted via the wavelets transform approach, quite consistent with the facts that many important biological functions of proteins can be elucidated with their low-frequency internal motions. The web server of iPPBS-PseAAC is accessible at http://www.jci-bioinfo.cn/iPPBS-PseAAC , by which users can easily acquire their desired results without the need to follow the complicated mathematical equations involved.

Computational assay of Zanamivir binding affinity with original and mutant influenza neuraminidase 9 using molecular docking

Based upon molecular docking, this study aimed to find notable in silico neuraminidase 9 (NA9) point mutations of the avian influenza A H7N9 virus that possess a Zanamivir resistant property and to determine the lead compound capable of inhibiting these NA9 mutations. Seven amino acids (key residues) at the binding site of neuraminidase 9 responsible for Zanamivir-NA9 direct interactions were identified and 72 commonly occurring mutant NA9 versions were created using the Sybyl-X 2.0 software. The docking scores obtained after Zanamivir was bound to all mutant molecules of NA9 revealed 3 notable mutations R292W, R118P, and R292K that could greatly reduce the binding affinity of the medicine. These 3 mutant NA9 versions were then bound to each of 154 different molecules chosen from 5 groups of compounds to determine which molecule(s) might be capable of inhibiting mutant neuraminidase 9, leading to the discovery of the lead compound of potent mutant NA9 inhibitors. This compound, together with other mutations occurring to NA9 identified in the study, would be used as data for further research regarding neuraminidase inhibitors and synthesizing new viable medications used in the fight against the virus.

Insight into a molecular interaction force supporting peptide backbones and its implication to protein loops and folding

Although not being classified as the most fundamental protein structural elements like α-helices and β-strands, the loop segment may play considerable roles for protein stability, flexibility, and dynamic activity. Meanwhile, the protein loop is also quite elusive; i.e. its interactions with the other parts of protein as well as its own shape-maintaining forces have still remained as a puzzle or at least not quite clear yet. Here, we report a molecular force, the so-called polar hydrogen-π interaction (Hp-π), which may play an important role in supporting the backbones of protein loops. By conducting the potential energy surface scanning calculations on the quasi π-plane of peptide bond unit, we have observed the following intriguing phenomena: (1) when the polar hydrogen atom of a peptide unit is perpendicularly pointing to the π-plane of other peptide bond units, a remarkable Hp-π interaction occurs; (2) the interaction is distance and orientation dependent, acting in a broad space, and belonging to the 'point-to-plane' one. The molecular force reported here may provide useful interaction concepts and insights into better understanding the loop's unique stability and flexibility feature, as well as the driving force of the protein global folding.

Computational investigation of drug-resistant mutant of M2 proton channel (S31N) against rimantadine

M2 proton channel is the target for treating the patients who ere suffering from influenza A infection, which facilitates the spread of virions. Amantadine and rimantadine are adamantadine-based drugs, which target M2 proton channel and inhibit the viral replication. Preferably, rimantadine drug is used more than amantadine because of its fewer side effects. However, S31N mutation in the M2 proton channel was highly resistant to the rimantadine drug. Therefore, in the present study, we focused to understand the drug-resistance mechanism of S31N mutation with the aid of molecular docking and dynamics approach. The docking analysis undoubtedly indicates that affinity for rimantadine with mutant-type M2 proton channel is significantly lesser than the native-type M2 proton channel. In addition, RMSD, RMSF, and principal component analysis suggested that the mutation shows increased flexibility. Furthermore, the intermolecular hydrogen bonds analysis showed that there is a complete loss of hydrogen bonds in the mutant complex. On the whole, we conclude that the intermolecular contact was maintained by D-44, a key residue for stable binding of rimantadine. These findings are certainly helpful for better understanding of drug-resistance mechanism and also helpful for designing new drugs for treating influenza infection against drug-resistance target.

QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors

In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. The correlation coefficient (R), root mean square error (RMSE) and average absolute error (AAE) for LS-SVM are: R=0.988, RMSE=0.207 and AAE=0.145 for the training set, and R=0.965, RMSE=0.403 and AAE=0.338 for the test set. Leave one out cross validation test was used for assessment of the predictive power and validity of models which led to cross-validation correlation coefficient QUOTE of 0.864 and 0.850 and standardized predicted relative error sum of squares (SPRESS) of 0.553 and 0.581 for LS-SVM and MLR models, respectively.

Computational studies on the substrate interactions of influenza A virus PB2 subunit

Influenza virus, which spreads around the world in seasonal epidemics and leads to large numbers of deaths every year, has several ribonucleoproteins in the central core of the viral particle. These viral ribonucleoproteins can specifically bind the conserved 3' and 5' caps of the viral RNAs with responsibility for replication and transcription of the viral RNA in the nucleus of infected cells. A fundamental question of most importance is that how the cap-binding proteins in the influenza virus discriminates between capped RNAs and non-capped ones. To get an answer, we performed molecular dynamics simulations and free energy calculations on the influenza A virus PB2 subunit, an important component of the RNP complexes, with a cap analog m7GTP. Our calculations showed that some key residues in the active site, such as Arg355, His357, Glu361 as well as Gln406, could offer significant hydrogen bonding and hydrophobic interactions with the guanine ring of the cap analog m7GTP to form an aromatic sandwich mechanism for the cap recognition and positioning in the active site. Subsequently, we applied this idea to a virtual screening procedure and identified 5 potential candidates that might be inhibitors against the PB2 subunit. Interestingly, 2 candidates Cpd1 and Cpd2 have been already reported to have inhibitory activities to the influenza virus cap-binding proteins. Further calculation also showed that they had comparatively higher binding affinities to the PB2 subunit than that of m7GTP. We believed that our findings could give an atomic insight into the deeper understanding of the cap recognition and binding mechanism, providing useful information for searching or designing novel drugs against influenza viruses.

Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine

Proteins belonging to different subfamilies of Voltage-gated K(+) channels (VKC) are functionally divergent. The traditional method to classify ion channels is more time consuming. Thus, it is highly desirable to develop novel computational methods for VKC subfamily classification. In this study, a support vector machine based method was proposed to predict VKC subfamilies using amino acid and dipeptide compositions. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife cross-validation, the proposed method (VKCPred) achieved an overall accuracy of 93.09% with 93.22% average sensitivity and 98.34% average specificity, which are superior to that of other two state-of-the-art classifiers. These results indicate that VKCPred can be efficiently used to identify and annotate voltage-gated K(+) channels' subfamilies. The VKCPred software and dataset are freely available at http://cobi.uestc.edu.cn/people/hlin/tools/VKCPred/.

Insights into the mutation-induced HHH syndrome from modeling human mitochondrial ornithine transporter-1

Human mitochondrial ornithine transporter-1 is reported in coupling with the hyperornithinemia-hyperammonemia-homocitrullinuria (HHH) syndrome, which is a rare autosomal recessive disorder. For in-depth understanding of the molecular mechanism of the disease, it is crucially important to acquire the 3D structure of human mitochondrial ornithine transporter-1. Since no such structure is available in the current protein structure database, we have developed it via computational approaches based on the recent NMR structure of human mitochondrial uncoupling protein (Berardi MJ, Chou JJ, et al. Nature 2011, 476:109–113). Subsequently, we docked the ligand L-ornithine into the computational structure to search for the favorable binding mode. It was observed that the binding interaction for the most favorable binding mode is featured by six remarkable hydrogen bonds between the receptor and ligand, and that the most favorable binding mode shared the same ligand-binding site with most of the homologous mitochondrial carriers from different organisms, implying that the ligand-binding sites are quite conservative in the mitochondrial carriers family although their sequences similarity is very low with 20% or so. Moreover, according to our structural analysis, the relationship between the disease-causing mutations of human mitochondrial ornithine transporter-1 and the HHH syndrome can be classified into the following three categories: (i) the mutation occurs in the pseudo-repeat regions so as to change the region of the protein closer to the mitochondrial matrix; (ii) the mutation is directly affecting the substrate binding pocket so as to reduce the substrate binding affinity; (iii) the mutation is located in the structural region closer to the intermembrane space that can significantly break the salt bridge networks of the protein. These findings may provide useful insights for in-depth understanding of the molecular mechanism of the HHH syndrome and developing effective drugs against the disease.

Two birds with one stone? Possible dual-targeting H1N1 inhibitors from traditional Chinese medicine

The H1N1 influenza pandemic of 2009 has claimed over 18,000 lives. During this pandemic, development of drug resistance further complicated efforts to control and treat the widespread illness. This research utilizes traditional Chinese medicine Database@Taiwan (TCM Database@Taiwan) to screen for compounds that simultaneously target H1 and N1 to overcome current difficulties with virus mutations. The top three candidates were de novo derivatives of xylopine and rosmaricine. Bioactivity of the de novo derivatives against N1 were validated by multiple machine learning prediction models. Ability of the de novo compounds to maintain CoMFA/CoMSIA contour and form key interactions implied bioactivity within H1 as well. Addition of a pyridinium fragment was critical to form stable interactions in H1 and N1 as supported by molecular dynamics (MD) simulation. Results from MD, hydrophobic interactions, and torsion angles are consistent and support the findings of docking. Multiple anchors and lack of binding to residues prone to mutation suggest that the TCM de novo derivatives may be resistant to drug resistance and are advantageous over conventional H1N1 treatments such as oseltamivir. These results suggest that the TCM de novo derivatives may be suitable candidates of dual-targeting drugs for influenza.

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.

Exploration of conformational transition in the aryl-binding site of human FXa using molecular dynamics simulations

Human coagulation Factor X (FX), a member of the vitamin K-dependent serine protease family, is a crucial component of the human coagulation cascade. Activated FX (FXa) participates in forming the prothrombinase complex on activated platelets to convert prothrombin to thrombin in coagulation reactions. In the current study, 30-ns MD simulations were performed on both the open and closed states of human FXa. Root mean squares (RMS) fluctuations showed that structural fluctuations concentrated on the loop regions of FXa, and the presence of a ligand in the closed system resulted in larger fluctuations of the gating residues. The open system had a gating distance from 9.23 to 11.33 Å, i.e., significantly larger than that of the closed system (4.69-6.35 Å), which allows diversified substrates of variable size to enter. Although the solvent accessible surface areas (SASA) of FXa remained the same in both systems, the open system generally had a larger total SASA or hydrophobic SASA (or both) for residues surrounding the S4 pocket. Additionally, more hydrogen bonds were formed in the closed state than in the open state of FXa, which is believed to play a significant role in maintaining the closed confirmation of the aryl-binding site. Based on the results of MD simulations, we propose that an induced-fit mechanism governs the functioning of human coagulation FX, which helps provide a better understanding of the interactions between FXa and its substrate, and the mechanism of the conformational changes involved in human coagulation.

A negative cooperativity mechanism of human CYP2E1 inferred from molecular dynamics simulations and free energy calculations

Human cytochrome P450 2E1 (CYP2E1) participates in the metabolism of over 2% of all the oral drugs. A hallmark peculiar feature of this enzyme is that it exhibits a pronounced negative cooperativity in substrate binding. However the mechanism by which the negative cooperativity occurs is unclear. Here, we performed molecular dynamics simulations and free energy calculations on human CYP2E1 to examine the structural differences between the substrate-free and the enzymes with one and two aniline molecules bound. Our results indicate that although the effector substrate does not bind in the active site cavity, it still can directly interact with the active site residues of human CYP2E1. The interaction of the effector substrate with the active site leads to a reorientation of active site residues, which thereby weakens the interactions of the active substrate with this site. We also identify a conserved residue T303 that plays a crucial role in the negative cooperative binding on the short-range effects. This residue is a key factor in the positioning of substrates and in proton delivery to the active site. Additionally, a long-range effect of the effector substrate is identified in which F478 is proposed to play a key role. As located in the interface between the active and effector sites, this residue structurally links the active and effector sites and is found to play a significant role in affecting substrate access and ligand positioning within the active site. In the negative cooperative binding, this residue can decrease the interactions of the active substrate with the active site by π-π stacking which then lowers the hydroxylation activity for the active substrate. These findings are in agreement with previous experimental observations and thus provide detailed atomistic insight into the poorly understood mechanism of the negative cooperativity in human CYP2E1.

Investigation into potent inflammation inhibitors from traditional Chinese medicine

Microsomal prostaglandin E synthase-1 (mPGES-1) is the key enzyme for prostaglandin E2 (PGE2) generation during inflammation and is a potential target for designing anti-inflammatory drugs. Potential inhibitors of m-PGES-1 were selected from traditional Chinese medicine (TCM Database@Taiwan) based on the pharmacophore map generated by the top HypoGen hypothesis and validated using structure- and ligand-based analysis. Key features for potential m-PGES-1 inhibitors include pi-interactions and H-bond donors. TCM compounds, shanciol B, shanciol A, castilliferol, and aurantiamide acetate, contoured to the quantitative structure-activity relationship pharmacophore and exhibited high docking scores and binding stability with m-PGES-1. Bioactivity models multiple linear regression (MLR) and support vector machine also supported activity predictions for the candidate compounds. Our results indicate that the investigated TCM compounds could be of use for development into mPGES-1 inhibitors.

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.

An allosteric mechanism inferred from molecular dynamics simulations on phospholamban pentamer in lipid membranes

Phospholamban functions as a regulator of Ca(2+) concentration of cardiac muscle cells by triggering the bioactivity of sarcoplasmic reticulum Ca(2+)-ATPase. In order to understand its dynamic mechanism in the environment of bilayer surroundings, we performed long time-scale molecular dynamic simulations based on the high-resolution NMR structure of phospholamban pentamer. It was observed from the molecular dynamics trajectory analyses that the conformational transitions between the "bellflower" and "pinwheel" modes were detected for phospholamban. Particularly, the two modes became quite similar to each other after phospholamban was phosphorylated at Ser16. Based on these findings, an allosteric mechanism was proposed to elucidate the dynamic process of phospholamban interacting with Ca(2+)-ATPase.

Insights from modeling the 3D structure of New Delhi metallo-β-lactamse and its binding interactions with antibiotic drugs

New Delhi metallo-beta-lactamase (NDM-1) is an enzyme that makes bacteria resistant to a broad range of beta-lactam antibiotic drugs. This is because it can inactivate most beta-lactam antibiotic drugs by hydrolyzing them. For in-depth understanding of the hydrolysis mechanism, the three-dimensional structure of NDM-1 was developed. With such a structural frame, two enzyme-ligand complexes were derived by respectively docking Imipenem and Meropenem (two typical beta-lactam antibiotic drugs) to the NDM-1 receptor. It was revealed from the NDM-1/Imipenem complex that the antibiotic drug was hydrolyzed while sitting in a binding pocket of NDM-1 formed by nine residues. And for the case of NDM-1/Meropenem complex, the antibiotic drug was hydrolyzed in a binding pocket formed by twelve residues. All these constituent residues of the two binding pockets were explicitly defined and graphically labeled. It is anticipated that the findings reported here may provide useful insights for developing new antibiotic drugs to overcome the resistance problem.