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

A-to-I RNA editing in bacteria increases pathogenicity and tolerance to oxidative stress

Adenosine-to-inosine (A-to-I) RNA editing is an important posttranscriptional event in eukaryotes; however, many features remain largely unexplored in prokaryotes. This study focuses on a serine-to-proline recoding event (S128P) that originated in the mRNA of fliC, which encodes a flagellar filament protein; the editing event was observed in RNA-seq samples exposed to oxidative stress. Using Sanger sequencing, we show that the S128P editing event is induced by H₂O₂. To investigate the in vivo interaction between RNAs and TadA, which is the principal enzyme for A-to-I editing, genome-wide RNA immunoprecipitation–coupled high-throughput sequencing (iRIP-Seq) analysis was performed using HA-tagged TadA from Xanthomonas oryzae pv. oryzicola. We found that TadA can bind to the mRNA of fliC and the binding motif is identical to that previously reported by Bar-Yaacov and colleagues. This editing event increased motility and enhanced tolerance to oxidative stress due to changes in flagellar filament structure, which was modelled in 3D and measured by TEM. The change in filament structure due to the S128P mutant increased biofilm formation, which was measured by the 3D laser scanning confocal microscopy. RNA-seq revealed that a gene cluster that contributes to siderophore biosynthesis and Fe³⁺ uptake was upregulated in S128P compared with WT. Based on intracellular levels of reactive oxygen species and an oxidative stress survival assay, we found that this gene cluster can contribute to the reduction of the Fenton reaction and increases biofilm formation and bacterial virulence. This oxidative stress response was also confirmed in Pseudomonas putida. Overall, our work demonstrates that A-to-I RNA editing plays a role in bacterial pathogenicity and adaptation to oxidative stress.

Adenosine-to-inosine (A-to-I) RNA editing is an important posttranscriptional event in eukaryotes that has only been recently documented in bacteria. In this study, we use multiple ‘omic’ approaches to show that A-to-I RNA editing can occur in fliC, a flagellar filament protein. We show that TadA, which encodes adenosine deaminase, can directly bind to mRNA of target genes through recognition of a GACG motif. This editing event changes a single amino acid residue from serine to proline in FliC, resulting in a structural change in the flagellar filament. This posttranscriptional editing event contributes to virulence and increases tolerance to oxidative stress by enhancing biofilm formation. Our results provide insight into a new mechanism that bacterial pathogens use to adapt to oxidative stress, which can also increase virulence.

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.

Current Biochemical Applications and Future Prospects of Chlorotoxin in Cancer Diagnostics and Therapeutics

Chlorotoxin (CTX) is a minute 4 kDa protein made up of 36 amino acid residues, commonly known for its binding affinity to chloride channels and matrix metalloproteinase-2 (MMP-2) of glioma tumors of the spine and brain. This property and the possibility of conjugating this peptide to nanoparticles have enabled its diverse use in various biotechnological and biomedical applications for cancer treatment, such as in tumor imaging and radiotherapy. Because of the fascinating biological properties CTX possesses, elucidating its mechanism of action may hold promise for the development of new and effective therapeutic drugs, as well as more sensitive and highly specific cancer-screening kits. This article therefore reviews the currently known applications of CTX and suggests diverse ways in which it can be applied for the design of improved drugs and diagnostic tools for cancer.

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.

Exploring the structure and conformational landscape of human leptin. A molecular dynamics approach

Leptin is a hormone that regulates energy homeostasis, inflammation, hematopoiesis and immune response, among other functions (Houseknecht et al., 1998; Zhang et al., 1995; Paz-Filho et al., 2010). To obtain its crystallographic structure, it was necessary to substitute a tryptophan for a glutamic acid at position 100, thus creating a mutant leptin that has been reported to have biological activity comparable to the activity of the wild type but that crystallizes more readily. Here, we report a comparative study of the conformational space of WT and W100E leptin using molecular dynamics simulations performed at 300, 400, and 500 K. We detected differences between the interactions of the two proteins with local and distal effects, resulting in changes in the conformation, accessible surface area, compactness, electrostatic potential and dynamic behavior. Additionally, the series of unfolding events that occur when leptin is subjected to high temperature differs for the two constructs. We observed that both proteins are mostly unstructured after 20 ns of MD simulation at 500 K. However, WT leptin maintains a significant amount of secondary structure in helix α2, while the most stable region of W100E leptin is helix α3. Furthermore, we found that the region between residues 25 and 42 might adopt interconverting secondary structures ranging from α-helices and random coils to β-strand structures. Thus, this region can be considered an intrinsically disordered region. This atomistic description supports our understanding of leptin signaling and consequently might facilitate the use of leptin in treatments for the pathophysiologies in which it is implicated.

A comparative study of structural and conformational properties of casein kinase-1 isoforms: insights from molecular dynamics and principal component analysis

Wnt signaling pathway regulates several developmental processes in human; however recently this pathway has been associated with development of different types of cancers. Casein kinase-1 (CK1) constitutes a family of serine-threonine protein kinase; various members of this family participate in Wnt signal transduction pathway and serve as molecular switch to this pathway. Among the known six isoforms of CK1, in human, at least three isoforms (viz. alpha, delta and epsilon) have been reported as oncogenic. The development of common therapeutics against these kinases is an arduous task; unless we have the detailed information of their tertiary structures and conformational properties. In the present work, the dynamical and conformational properties for each of three isoforms of CK1 are explored through molecular dynamics (MD) simulations. The conformational space distribution of backbone atoms is evaluated using principal component analysis of MD data, which are further validated on the basis of potential energy surface. Based on these analytics, it is suggested that conformational subspace shifts upon binding to ligands and guides the kinase action of CK1 isoforms. Further, this paper as a first effort to concurrently study all the three isoforms of CK1 provides structural basis for development of common anticancer therapeutics against three isoforms of CK1.

Common and distant structural characteristics of feruloyl esterase families from Aspergillus oryzae

Background

Feruloyl esterases (FAEs) are important biomass degrading accessory enzymes due to their capability of cleaving the ester links between hemicellulose and pectin to aromatic compounds of lignin, thus enhancing the accessibility of plant tissues to cellulolytic and hemicellulolytic enzymes. FAEs have gained increased attention in the area of biocatalytic transformations for the synthesis of value added compounds with medicinal and nutritional applications. Following the increasing attention on these enzymes, a novel descriptor based classification system has been proposed for FAEs resulting into 12 distinct families and pharmacophore models for three FAE sub-families have been developed.

Methodology/Principal Findings

The feruloylome of Aspergillus oryzae contains 13 predicted FAEs belonging to six sub-families based on our recently developed descriptor-based classification system. The three-dimensional structures of the 13 FAEs were modeled for structural analysis of the feruloylome. The three genes coding for three enzymes, viz., A.O.2, A.O.8 and A.O.10 from the feruloylome of A. oryzae, representing sub-families with unknown functional features, were heterologously expressed in Pichia pastoris, characterized for substrate specificity and structural characterization through CD spectroscopy. Common feature-based pharamacophore models were developed according to substrate specificity characteristics of the three enzymes. The active site residues were identified for the three expressed FAEs by determining the titration curves of amino acid residues as a function of the pH by applying molecular simulations.

Conclusions/Significance

Our findings on the structure-function relationships and substrate specificity of the FAEs of A. oryzae will be instrumental for further understanding of the FAE families in the novel classification system. The developed pharmacophore models could be applied for virtual screening of compound databases for short listing the putative substrates prior to docking studies or for post-processing docking results to remove false positives. Our study exemplifies how computational predictions can complement to the information obtained through experimental methods.

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.

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.

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.

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

As an essential component of the viral envelope, M2 proton channel plays a central role in the virus replications and has been a key target for drug design against the influenza A viruses. The adamantadine-based drugs, such as amantadine and rimantadine, were developed for blocking the channel so as to suppress the replication of viruses. However, patients, especially those infected by the H1N1 influenza A viruses, are increasingly suffering from the drug-resistance problem. According to the findings revealed recently by the high-resolution NMR studies, the drug-resistance problem is due to the structural allostery caused by some mutations, such as L26F, V27A and S31N, in the four-helix bundle of the channel. In this study, we are to address this problem from a dynamic point of view by conducting molecular dynamics (MD) simulations on both the open and the closed states of the wild-type (WT) and S31N mutant M2 channels in the presence of rimantadine. It was observed from the MD simulated structures that the mutant channel could still keep open even if binding with rimantadine, but the WT channel could not. This was because the mutation would destabilize the helix bundle and trigger it from a compact packing state to a loose one. It is anticipated that the findings may provide useful insights for in-depth understanding the action mechanism of the M2 channel and developing more-effective drugs against influenza A viruses.

Molecular modeling of BAD complex resided in a mitochondrion integrating glycolysis and apoptosis

BAD (Bcl-2 antagonist of cell death) and GK (glucokinase) reside in a mitochondrial complex together with PKA and PP1 catalytic units (PKAc and PP1c) and WAVE-1 that integrates glycolysis and apoptosis. Our research results reveal that BAD is phosphorylated and inactivated on Ser 75 in a BAD-Bcl-xL complex by PKA (targeted to mitochondria through association with WAVE1), resulting in the dissociation of BAD and its binding to GK. Moreover, GK can interact with PP1c and also distinguish WAVE1. On the other hand, BAD is dephosphorylated and activated on Ser75 by PP1c, leading to the separation of PKAc and its binding to the regulatory (R) subunit of PKA which by the dimerization domain of its R subunit connects with WAVE1 linked with GK of the complex. This may be the reason of the complex existing in liver mitochondria, regardless of phosphorylated and dephosphorylated BAD. Additionally, GK like PKA may also prevent Bcl-xL from rebinding to BAD by phosphorylating BAD at Ser 118. The BAD complex model reveals that BAD and GK play key roles because of BAD as a substrate for the PKA-PP1 pair and by BH3 domain directly interacting with GK. This is helpful for our development and research of the molecular mechanism of BAD integrating glycolysis and apoptosis.

Studies on the structural stability of rabbit prion probed by molecular dynamics simulations of its wild-type and mutants

Prion diseases are invariably fatal and highly infectious neurodegenerative diseases that affect humans and animals. Rabbits are the only mammalian species reported to be resistant to infection from prion diseases isolated from other species (Vorberg et al., 2003). Fortunately, the NMR structure of rabbit prion (124-228) (PDB entry 2FJ3), the NMR structure of rabbit prion protein mutation S173N (PDB entry 2JOH) and the NMR structure of rabbit prion protein mutation I214V (PDB entry 2JOM) were released recently. This paper studies these NMR structures by molecular dynamics simulations. Simulation results confirm the structural stability of wild-type rabbit prion, and show that the salt bridge between D177 and R163 greatly contributes to the structural stability of rabbit prion protein.

Molecular modeling studies of L-arabinitol 4-dehydrogenase of Hypocrea jecorina: its binding interactions with substrate and cofactor

L-arabinitol 4-dehydrogenase (LAD1; EC 1.1.1.12) is an enzyme in the L-arabinose catabolic pathway of fungi that catalyzes the conversion of L-arabinitol into L-xylulose. The primary objective of this work is to identify the catalytic and coenzyme binding domains of LAD1 from Hypocrea jecorina in order to provide better insight into the possible catalytic events in these domains. The 3D structure of NAD(+)-dependent LAD1 was developed based on the crystal structure of human sorbitol dehydrogenase as a template. A series of molecular mechanics and dynamics operations were performed to find the most stable binding interaction for the enzyme and its ligands. Using the verified model, a docking study was performed with the substrate L-arabinitol, Zn(2+) and NAD(+). This study found a catalytic Zn(2+) binding domain (Cys66, His91, Glu92 and Glu176) and a cofactor NAD(+) binding domain (Gly202, ILeu204, Gly205, Cys273, Arg229 and Val298) with strong hydrogen bonding contacts with the substrate and cofactor. The binding pockets of the enzyme for l-arabinitol, NAD(+), and Zn(2+) have been explicitly defined. The results from this study should guide future mutagenesis studies and provide useful clues for engineering enzymes to improve the utilization of polyols for rare sugar production.

Reversal of coenzyme specificity and improvement of catalytic efficiency of Pichia stipitis xylose reductase by rational site-directed mutagenesis

A major problem when xylose is used for ethanol production is the intercellular redox imbalance arising from different coenzyme specificities of xylose reductase (XR) and xylitol dehydrogenase. The residue Lys21 in XR from Pichia stipitis was subjected to site-directed mutagenesis to alter its coenzyme specificity. The N272D mutant exhibited improved catalytic efficiency when NADH was the coenzyme. Both K21A and K21A/N272D preferred NADH to NADPH, their catalytic efficiencies for NADPH were almost zero. The catalytic efficiency of K21A/N272D for NADH was almost 9-fold and 2-fold that of K21A and the wild-type enzyme, respectively. Complete reversal of coenzyme specificity toward NADH and improved catalytic efficiency were achieved.

Yeast metabolic engineering for hemicellulosic ethanol production

Efficient fermentation of hemicellulosic sugars is critical for the bioconversion of lignocellulosics to ethanol. Efficient sugar uptake through the heterologous expression of yeast and fungal xylose/glucose transporters can improve fermentation if other metabolic steps are not rate limiting. Rectification of cofactor imbalances through heterologous expression of fungal xylose isomerase or modification of cofactor requirements in the yeast oxidoreductase pathway can reduce xylitol production while increasing ethanol yields, but these changes often occur at the expense of xylose utilization rates. Genetic engineering and evolutionary adaptation to increase glycolytic flux coupled with transcriptomic and proteomic studies have identified targets for further modification, as have genomic and metabolic engineering studies in native xylose fermenting yeasts.

Molecular dynamics studies on the interactions of PTP1B with inhibitors: from the first phosphate-binding site to the second one

Protein tyrosine phosphatases 1B (PTP1B) is a major negative regulator of both insulin and leptin signaling pathways. In view of this, it becomes an important target for drug development against cancers, diabetes and obesity. The aim of the current study is to use the long time-scale molecular dynamics (MD) simulations to investigate the structural and dynamic factors that cause its inhibition by INTA and INTB, the two most potent and highly selective PTP1B inhibitors known so far. In order to investigate the mode of collective motions that is vitally important to the biological function, the covariance matrix of C(alpha) atoms was introduced for performing the dynamic analysis of the inhibition systems. It has been observed that the conformational and dynamic features of WPD-Loop, R-Loop and S-Loop play a key role in providing a smooth entrance for the inhibitors moving into the binding pocket as well as a favorable microenvironment to stabilize them. Furthermore, the hydrogen bonding networks formed around the active site with INTA and INTB may be the main reason of why the inhibition of PTP1B by the two ligands is so potent and selective. All these findings might provide useful insights for developing novel and effective drugs to treat cancer, diabetes and obesity.

Predicting protein fold pattern with functional domain and sequential evolution information

The fold pattern of a protein is one level deeper than its structural classification, and hence is more challenging and complicated for prediction. Many efforts have been made in this regard, but so far all the reported success rates are still under 70%, indicating that it is extremely difficult to enhance the success rate even by 1% or 2%. To address this problem, here a novel approach is proposed that is featured by combining the functional domain information and the sequential evolution information through a fusion ensemble classifier. The predictor thus developed is called PFP-FunDSeqE. Tests were performed for identifying proteins among their 27 fold patterns. Compared with the existing predictors tested by a same stringent benchmark dataset, the new predictor can, for the first time, achieve over 70% success rate. The PFP-FunDSeqE predictor is freely available to the public as a web server at http://www.csbio.sjtu.edu.cn/bioinf/PFP-FunDSeqE/.

Computational studies of the binding modes of A 2A adenosine receptor antagonists

A molecular docking study was performed on several structurally diverse A(2A) AR antagonists, including xanthines, and non-xanthine type antagonists to investigate their binding modes with A(2A) adenosine receptor (AR), one of the four subtypes of AR, which is currently of great interest as a target for therapeutic intervention, in particular for Parkinson's disease. The high-affinity binding site was found to be a hydrophobic pocket with the involvement of hydrogen bonding interactions as well as pi-pi stacking interactions with the ligands. The detailed binding modes for both xanthine and non-xanthine type A(2A) antagonists were compared and the essential features were extracted and converted to database searchable queries for virtual screening study of novel A(2A) AR antagonists. Findings from this study are helpful for elucidating the binding pattern of A(2A) AR antagonists and for the design of novel active ligands.

Analysis of ligand binding to proteins using molecular dynamics simulations

This work aims to explore theoretically the molecular mechanisms of ligand binding to proteins through the use of molecular dynamics simulations. The binding of sodium dodecyl sulfate (SDS) to cobra cardio toxin A3 (CTX A3) and thiourea (TOU) to lysozyme have been chosen as the two model systems. Data acquisitions were made by Gromacs software. To begin with, the collisions of ligand molecules with every residue of CTX A3 and lysozyme were evaluated. With this information in hand, the average numbers of collisions with each residue was defined and then assessed. Next, a measure of the affinity of a residue, P(i), referred to as conformational factor, toward a ligand molecule was established. Based on the results provided, all site-making residues for CTX A3 and lysozyme were identified. The results are in good agreement with the experimental data. Finally, based on this method, all site-making residues of bovine carbonic anhydrase (BCA) toward the SDS ligand were predicted.

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

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

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

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