I. Halil Kavakli

Purification and characterization of three members of the photolyase/cryptochrome family blue-light photoreceptors from vibrio cholerae

The sequence of Vibrio cholerae genome revealed three genes belonging to the photolyase/cryptochrome blue-light photoreceptor family. The proteins encoded by the three genes were purified and characterized. All three proteins contain folate and flavin cofactors and have absorption peaks in the range of 350-500 nm. Only one of the three, VcPhr, is a photolyase specific for cyclobutane pyrimidine dimers. The other two are cryptochromes and were designated VcCry1 and VcCry2, respectively. Mutation of phr abolishes photoreactivation of UV-induced killing, whereas mutations in cry1 and cry2 do not affect photorepair activity. VcCry1 exhibits some unique features. Of all cryptochromes characterized to date, it is the only one that contains stoichiometric amounts of both chromophores and retains its flavin cofactor in the two-electron reduced FADH2 form. In addition, VcCry1 exhibits RNA binding activity and co-purifies with an RNA of 60-70 nucleotides in length.

Optimization Based Tumor Classification from Microarray Gene Expression Data

Background An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. Methodology/Principal Findings We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. Conclusions/Significance The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.

MEMS biosensor for detection of Hepatitis A and C viruses in serum

Resonant microcantilever arrays are developed for the purpose of label-free and real-time analyte monitoring and biomolecule detection. MEMS cantilevers made of electroplated nickel are functionalized with Hepatitis antibodies. Hepatitis A and C antigens at different concentrations are introduced in undiluted bovine serum. All preparation and measurement steps are carried out in the liquid within a specifically designed flowcell without ever drying the cantilevers throughout the experiment. Both actuation and sensing are done remotely and therefore the MEMS cantilevers have no electrical connections, allowing for easily disposable sensor chips. Actuation is achieved using an electromagnet and the interferometric optical sensing is achieved using laser illumination and embedded diffraction gratings at the tip of each cantilever. Resonant frequency of the cantilevers in dynamic motion is monitored using a self-sustaining closed-loop control circuit and a frequency counter. Specificity is demonstrated by detecting both Hepatitis A and Hepatitis C antigens and their negative controls. This is the first report of Hepatitis antigen detection by resonant cantilevers exposed to undiluted serum. A dynamic range in excess of 1000 and with a minimum detectable concentration limit of 0.1ng/ml (1.66pM) is achieved for both Hepatitis A and C. This result is comparable to labeled detection methods such as ELISA.

Purification and Characterization of a Type III Photolyase from Caulobacter crescentus

The photolyase/cryptochrome family is a large family of flavoproteins that encompasses DNA repair proteins, photolyases, and cryptochromes that regulate blue-light-dependent growth and development in plants, and light-dependent and light-independent circadian clock setting in animals. Phylogenetic analysis has revealed a new class of the family, named type III photolyase, which cosegregates with plant cryptochromes. Here we describe the isolation and characterization of a type III photolyase from Caulobacter crescentus. Spectroscopic analysis shows that the enzyme contains both the methenyl tetrahydrofolate photoantenna and the FAD catalytic cofactor. Biochemical analysis shows that it is a bona fide photolyase that repairs cyclobutane pyrimidine dimers. Mutation of an active site Trp to Arg disrupts FAD binding with no measurable effect on MTHF binding. Using enzyme preparations that contain either both chromophores or only folate, we were able to determine the efficiency and rate of transfer of energy from MTHF to FAD.

Classification of cytochrome P450 inhibitors with respect to binding free energy and pIC50 using common molecular descriptors.

Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure based drug discovery. However, the relationship between binding free energies and biological activities (pIC50) of drug candidates is still an unsolved issue that limits the efficiency and speed of drug development processes. In this study, the relationship between them is investigated based on a common molecular descriptor set for human cytochrome P450 enzymes (CYPs). CYPs play an important role in drug-drug interactions, drug metabolism, and toxicity. Therefore, in silico prediction of CYP inhibition by drug candidates is one of the major considerations in drug discovery. The combination of partial least-squares regression (PLSR) and a variety of classification algorithms were employed by considering this relationship as a classification problem. Our results indicate that PLSR with classification is a powerful tool to predict more than one output such as binding free energy and pIC50 simultaneously. PLSR with mixed-integer linear programming based hyperboxes predicts the binding free energy and pIC50 with a mean accuracy of 87.18% (min: 81.67% max: 97.05%) and 88.09% (min: 79.83% max: 92.90%), respectively, for the cytochrome p450 superfamily using the common 6 molecular descriptors with a 10-fold cross-validation.

Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method.

BackgroundA priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules.ResultsWe first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC50 values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naïve Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy.ConclusionOur results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.

Detection of human κ-opioid antibody using microresonators with integrated optical readout

Label-free detection of the interaction between hexahistidine-tagged human κ-opioid receptor membrane protein and anti-His antibody is demonstrated in liquid by an optical microelectromechanical system utilizing electromagnetically actuated microresonators. Shift in resonance frequency due to accretion of mass on the sensitive surface of microresonators is monitored via an integrated optical readout. A frequency resolution of 2Hz is obtained. Together with a sensitivity of 7 ppm/(ng/ml) this leads to a minimum detectable antibody concentration of 5.7 ng/ml for a 50-kHz device. The measurement principle is shown to impart immunity to environmental noise, facilitate operation in liquid media and bring about the prospect for further miniaturization of actuator and readout leading to a portable biochemical sensor.

Enhanced heterotetrameric assembly of potato ADP-glucose pyrophosphorylase using reverse genetics.

ADP-glucose pyrophosphorylase (AGPase) is a key allosteric enzyme in plant starch biosynthesis. Plant AGPase is a heterotetrameric enzyme that consists of large (LS) and small subunits (SS), which are encoded by two different genes. Computational and experimental studies have revealed that the heterotetrameric assembly of AGPase is thermodynamically weak. Modeling studies followed by the mutagenesis of the LS of the potato AGPase identified a heterotetramer-deficient mutant, LS(R88A). To enhance heterotetrameric assembly, LS(R88A) cDNA was subjected to error-prone PCR, and second-site revertants were identified according to their ability to restore glycogen accumulation, as assessed with iodine staining. Selected mutations were introduced into the wild-type (WT) LS and co-expressed with the WT SS in Escherichia coli glgC(-). The biochemical characterization of revertants revealed that LS(I90V)SS(WT), LS(Y378C)SS(WT) and LS(D410G)SS(WT) mutants displayed enhanced heterotetrameric assembly with the WT SS. Among these mutants, LS(Y378C)SS(WT) AGPase displayed increased heat stability compared with the WT enzyme. Kinetic characterization of the mutants indicated that the LS(I90V)SS(WT) and LS(Y378C)SS(WT) AGPases have comparable allosteric and kinetic properties. However, the LS(D410G)SS(WT) mutant exhibited altered allosteric properties of being less responsive and more sensitive to 3-phosphoglyceric acid activation and inorganic phosphate inhibition. This study not only enhances our understanding of the interaction between the SS and the LS of AGPase but also enables protein engineering to obtain enhanced assembled heat-stable variants of AGPase, which can be used for the improvement of plant yields.

Reduced Glucose Sensation Can Increase the Fitness of Saccharomyces cerevisiae Lacking Mitochondrial DNA.

Damage to the mitochondrial genome (mtDNA) can lead to diseases for which there are no clearly effective treatments. Since mitochondrial function and biogenesis are controlled by the nutrient environment of the cell, it is possible that perturbation of conserved, nutrient-sensing pathways may successfully treat mitochondrial disease. We found that restricting glucose or otherwise reducing the activity of the protein kinase A (PKA) pathway can lead to improved proliferation of Saccharomyces cerevisiae cells lacking mtDNA and that the transcriptional response to mtDNA loss is reduced in cells with diminished PKA activity. We have excluded many pathways and proteins from being individually responsible for the benefits provided to cells lacking mtDNA by PKA inhibition, and we found that robust import of mitochondrial polytopic membrane proteins may be required in order for cells without mtDNA to receive the full benefits of PKA reduction. Finally, we have discovered that the transcription of genes involved in arginine biosynthesis and aromatic amino acid catabolism is altered after mtDNA damage. Our results highlight the potential importance of nutrient detection and availability on the outcome of mitochondrial dysfunction.

Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of SIRTUIN6

Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure-based drug discovery. However, virtual screening of chemical libraries with millions of compounds requires a lot of time for computing and data analysis. A priori classification of compounds in the libraries as low- and high-binding free energy sets decreases the number of compounds for virtual screening experiments. This classification also reduces the required computational time and resources. Data analysis is demanding since a compound can be described by more than one thousand attributes that make any data analysis very challenging. In this paper, we use the hyperbox classification method in combination with partial least squares regression to determine the most relevant molecular descriptors of the drug molecules for an efficient classification. The effectiveness of the approach is illustrated on a target protein, SIRT6. The results indicate that the proposed approach outperforms other approaches reported in the literature with 83.55% accuracy using six common molecular descriptors (SC-5, SP-6, SHBd, minHaaCH, maxwHBa, FMF). Additionally, the top 10 hit compounds are determined and reported as the candidate inhibitors of SIRT6 for which no inhibitors have so far been reported in the literature.