RNAapt3D: RNA aptamer 3D-structural modeling database.

RNA aptamers are oligonucleotides with high binding affinity and specificity for target molecules and are expected to be a new generation of therapeutic molecules and targeted delivery materials. The tertiary structure of RNA molecules and RNA-protein interaction sites are increasingly important as potential targets for new drugs. The pathological mechanisms of diseases must be understood in detail to guide drug design. In developing RNA aptamers as drugs, information about the interaction mechanisms and structures of RNA aptamer-target protein complexes are useful. We constructed a database, RNA aptamer 3D-structural modeling (RNAapt3D), consisting of RNA aptamer data that are potential drug candidates. The database includes RNA sequences and computationally predicted RNA tertiary structures based on secondary structures and implements methods that can be used to predict unknown structures of RNA aptamer-target molecule complexes. RNAapt3D should enable the design of RNA aptamers for target molecules and improve the efficiency and productivity of candidate drug selection. RNAapt3D can be accessed at https://rnaapt3d.medals.jp.

Assessing the activation/inhibition of tyrosine kinase-related pathways with a newly developed platform.

The phosphorylation of cellular proteins plays a crucial role in the transduction of various signals from outside the cell into the nucleus. The signals are transduced by phosphorylation chain reactions within multiple pathways; however, determining which pathways are responsible for each defined signal has proven challenging. To estimate the activity of each pathway, we developed a phosphorylation array platform comprising a protein array with 1200 proteins belonging to 376 signalling pathways and an analytical method to estimate pathway activity based on the phosphorylation levels of proteins. The performance of our system was assessed by reconstructing kinase-substrate relationships, as well as by estimating pathway activity upon epidermal growth factor (EGF) stimulation and the pharmacological inhibition of epidermal growth factor receptor (EGFR). As a result, kinase-substrate relationships were reliably reconstructed based on the precise measurement of phosphorylation levels of constituent proteins on the array. Furthermore, the pathway activities associated with EGF stimulation and EGFR inhibition were successfully traced through the related pathways from the outer membrane to the nucleus along a time course. Thus, our phosphorylation array system can effectively assess the activity of specific signalling pathways that are perturbed by extracellular stimuli, such as various drugs.

Promotion of the Warburg effect is associated with poor benefit from adjuvant chemotherapy in colorectal cancer.

Metabolic reprogramming, including the Warburg effect, is a hallmark of cancer. Indeed, the diversity of cancer metabolism leads to cancer heterogeneity, but accurate assessment of metabolic properties in tumors has not yet been undertaken. Here, we performed absolute quantification of the expression levels of 113 proteins related to carbohydrate metabolism and antioxidant pathways, in stage III colorectal cancer surgical specimens from 70 patients. The Warburg effect appeared in absolute protein levels between tumor and normal mucosa specimens demonstrated. Notably, the levels of proteins associated with the tricarboxylic citric acid cycle were remarkably reduced in the malignant tumors which had relapsed after surgery and treatment with 5-fluorouracil-based adjuvant therapy. In addition, the efficacy of 5-fluorouracil also decreased in the cultured cancer cell lines with promotion of the Warburg effect. We further identified nine and eight important proteins, which are closely related to the Warburg effect, for relapse risk and 5-fluorouracil benefit, respectively, using a biomarker exploration procedure. These results provide us a clue for bridging between metabolic protein expression profiles and benefit from 5-fluorouracil adjuvant chemotherapy.

A large-scale targeted proteomics assay resource based on an in vitro human proteome.

Targeted proteomics approaches are of value for deep and accurate quantification of protein abundance. Extending such methods to quantify large numbers of proteins requires the construction of predefined targeted assays. We developed a targeted proteomics platform-in vitro proteome-assisted multiple reaction monitoring (MRM) for protein absolute quantification (iMPAQT)-by using >18,000 human recombinant proteins, thus enabling protein absolute quantification on a genome-wide scale. Our platform comprises experimentally confirmed MRM assays of mass tag (mTRAQ)-labeled peptides to allow for rapid and straightforward measurement of the absolute abundance of predefined sets of proteins by mass spectrometry. We applied iMPAQT to delineate the quantitative metabolic landscape of normal and transformed human fibroblasts. Oncogenic transformation gave rise to relatively small but global changes in metabolic pathways resulting in aerobic glycolysis (Warburg effect) and increased rates of macromolecule synthesis. iMPAQT should facilitate quantitative biology studies based on protein abundance measurements.

ToGo-WF: prediction of RNA tertiary structures and RNA-RNA/protein interactions using the KNIME workflow.

Recent progress in molecular biology has revealed that many non-coding RNAs regulate gene expression or catalyze biochemical reactions in tumors, viruses and several other diseases. The tertiary structure of RNA molecules and RNA-RNA/protein interaction sites are of increasing importance as potential targets for new medicines that treat a broad array of human diseases. Current RNA drugs are split into two groups: antisense RNA molecules and aptamers. In this report, we present a novel workflow to predict RNA tertiary structures and RNA-RNA/protein interactions using the KNIME environment, which enabled us to assemble a combination of RNA-related analytical tools and databases. In this study, three analytical workflows for comprehensive structural analysis of RNA are introduced: (1) prediction of the tertiary structure of RNA; (2) prediction of the structure of RNA-RNA complexes and analysis of their interactions; and (3) prediction of the structure of RNA-protein complexes and analysis of their interactions. In an RNA-protein case study, we modeled the tertiary structure of pegaptanib, an aptamer drug, and performed docking calculations of the pegaptanib-vascular endothelial growth factor complex using a fragment of the interaction site of the aptamer. We also present molecular dynamics simulations of the RNA-protein complex to evaluate the affinity of the complex by mutating bases at the interaction interface. The results provide valuable information for designing novel features of aptamer-protein complexes.

A crosslinker-based identification of redox relay targets.

Thiol-based redox control is among the most important mechanisms for maintaining cellular redox homeostasis, with essential participation of cysteine thiols of oxidoreductases. To explore cellular redox regulatory networks, direct interactions among active cysteine thiols of oxidoreductases and their targets must be clarified. We applied a recently described thiol-ene crosslinking-based strategy, named divinyl sulfone (DVSF) method, enabling identification of new potential redox relay partners of the cytosolic oxidoreductases thioredoxin (TXN) and thioredoxin domain containing 17 (TXNDC17). Applying multiple methods, including classical substrate-trapping techniques, will increase understanding of redox regulatory mechanisms in cells.

Redox Sensitivities of Global Cellular Cysteine Residues under Reductive and Oxidative Stress.

The protein cysteine residue is one of the amino acids most susceptible to oxidative modifications, frequently caused by oxidative stress. Several applications have enabled cysteine-targeted proteomics analysis with simultaneous detection and quantitation. In this study, we employed a quantitative approach using a set of iodoacetyl-based cysteine reactive isobaric tags (iodoTMT) and evaluated the transient cellular oxidation ratio of free and reversibly modified cysteine thiols under DTT and hydrogen peroxide (H2O2) treatments. DTT treatment (1 mM for 5 min) reduced most cysteine thiols, irrespective of their cellular localizations. It also caused some unique oxidative shifts, including for peroxiredoxin 2 (PRDX2), uroporphyrinogen decarboxylase (UROD), and thioredoxin (TXN), proteins reportedly affected by cellular reactive oxygen species production. Modest H2O2 treatment (50 µM for 5 min) did not cause global oxidations but instead had apparently reductive effects. Moreover, with H2O2, significant oxidative shifts were observed only in redox active proteins, like PRDX2, peroxiredoxin 1 (PRDX1), TXN, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Overall, our quantitative data illustrated both H2O2- and reduction-mediated cellular responses, whereby while redox homeostasis is maintained, highly reactive thiols can potentiate the specific, rapid cellular signaling to counteract acute redox stress.

Functional profiling of asymmetrically-organized human CCT/TRiC chaperonin.

Molecular organization of the eukaryote chaperonin known as CCT/TRiC complex was recently clarified. Eight distinct subunits are uniquely organized, providing a favorable folding cavity for specific client proteins such as tubulin and actin. Because of its heterogeneous subunit composition, CCT complex has polarized inner faces, which may underlie an essential part of its chaperonin function. In this study, we structurally characterized the closed and open states of CCT complex, using molecular dynamics analyses. Our results showed that the inter-subunit interaction energies were asymmetrically distributed and were remodeled during conformational changes of CCT complex. In addition, exploration of redox related characteristics indicated changes in inner surface properties, including electrostatic potential, pKa and exposure of inner cysteine thiol groups, between the closed and open states. Cysteine activation events were experimentally verified by interaction analyses, using tubulin as a model substrate. Our data highlighted the importance of dynamics-based structural profiling of asymmetrically oriented chaperonin function.

Theoretical investigation on the glycan-binding specificity of Agrocybe cylindracea galectin using molecular modeling and molecular dynamics simulation studies.

Galectins are ß-galactoside binding proteins which have the ability to serve as potent antitumor, cancer biomarker, and induce tumor cell apoptosis. Agrocybe cylindracea galectin (ACG) is a fungal galectin which specifically recognizes α(2,3)-linked sialyllactose at the cell surface that plays extensive roles in the biological recognition processes. To investigate the change in glycan-binding specificity upon mutations, single point and double point site-directed in silico mutations are performed at the binding pocket of ACG. Molecular dynamics (MD) simulation studies are carried out for the wild-type (ACG) and single point (ACG1) and double point (ACG2) mutated ACGs to investigate the dynamics of substituted mutants and their interactions with the receptor sialyllactose. Plausible binding modes are proposed for galectin-sialylglycan complexes based on the analysis of hydrogen bonding interactions, total pair-wise interaction energy between the interacting binding site residues and sialyllactose and binding free energy of the complexes using molecular mechanics-Poisson-Boltzmann surface area. Our result shows that high contribution to the binding in different modes is due to the direct and water-mediated hydrogen bonds. The binding specificity of double point mutant Y59R/N140Q of ACG2 is found to be high, and it has 26 direct and water-mediated hydrogen bonds with a relatively low-binding free energy of -47.52 ± 5.2 kcal/mol. We also observe that the substituted mutant Arg59 is crucial for glycan-binding and for the preference of α(2,3)-linked sialyllactose at the binding pocket of ACG2 galectin. When compared with the wild-type and single point mutant, the double point mutant exhibits enhanced affinity towards α(2,3)-linked sialyllactose, which can be effectively used as a model for biological cell marker in cancer therapeutics.

Insights into the binding specificity of wild type and mutated wheat germ agglutinin towards Neu5Acα(2-3)Gal: a study by in silico mutations and molecular dynamics simulations.

Wheat germ agglutinin (WGA) is a plant lectin, which specifically recognizes the sugars NeuNAc and GlcNAc. Mutated WGA with enhanced binding specificity can be used as biomarkers for cancer. In silico mutations are performed at the active site of WGA to enhance the binding specificity towards sialylglycans, and molecular dynamics simulations of 20 ns are carried out for wild type and mutated WGAs (WGA1, WGA2, and WGA3) in complex with sialylgalactose to examine the change in binding specificity. MD simulations reveal the change in binding specificity of wild type and mutated WGAs towards sialylgalactose and bound conformational flexibility of sialylgalactose. The mutated polar amino acid residues Asn114 (S114N), Lys118 (G118K), and Arg118 (G118R) make direct and water mediated hydrogen bonds and hydrophobic interactions with sialylgalactose. An analysis of possible hydrogen bonds, hydrophobic interactions, total pair wise interaction energy between active site residues and sialylgalactose and MM-PBSA free energy calculation reveals the plausible binding modes and the role of water in stabilizing different binding modes. An interesting observation is that the binding specificity of mutated WGAs (cyborg lectin) towards sialylgalactose is found to be higher in double point mutation (WGA3). One of the substituted residues Arg118 plays a crucial role in sugar binding. Based on the interactions and energy calculations, it is concluded that the order of binding specificity of WGAs towards sialylgalactose is WGA3 > WGA1 > WGA2 > WGA. On comparing with the wild type, double point mutated WGA (WGA3) exhibits increased specificity towards sialylgalactose, and thus, it can be effectively used in targeted drug delivery and as biological cell marker in cancer therapeutics.

Tertiary structure prediction of RNA-RNA complexes using a secondary structure and fragment-based method.

A method has been developed for predicting the tertiary structures of RNA-RNA complex structures using secondary structure information and a fragment assembly algorithm. The linker base pair and secondary structure potential derived from the secondary structure information are particularly useful for prediction. Application of this method to several kinds of RNA-RNA complex structures, including kissing loops, hammerhead ribozymes, and other functional RNAs, produced promising results. Use of the secondary structure potential effectively restrained the conformational search space, leading to successful prediction of kissing loop structures, which mainly consist of common structural elements. The failure to predict more difficult targets had various causes but should be overcome through such measures as tuning the balance of the energy contributions from the Watson-Crick and non- Watson-Crick base pairs, by obtaining knowledge about a wider variety of RNA structures.

Phosphorylated protein chip combined with artificial intelligence tools for precise drug screening.

We have developed a protein array system, named "Phospho-Totum", which reproduces the phosphorylation state of a sample on the array. The protein array contains 1471 proteins from 273 known signaling pathways. According to the activation degrees of tyrosine kinases in the sample, the corresponding groups of substrate proteins on the array are phosphorylated under the same conditions. In addition to measuring the phosphorylation levels of the 1471 substrates, we have developed and performed the artificial intelligence-assisted tools to further characterize the phosphorylation state and estimate pathway activation, tyrosine kinase activation, and a list of kinase inhibitors that produce phosphorylation states similar to that of the sample. The Phospho-Totum system, which seamlessly links and interrogates the measurements and analyses, has the potential to not only elucidate pathophysiological mechanisms in diseases by reproducing the phosphorylation state of samples, but also be useful for drug discovery, particularly for screening targeted kinases for potential drug kinase inhibitors.

Theoretical investigation on the binding specificity of sialyldisaccharides with hemagglutinins of influenza A virus by molecular dynamics simulations.

Recognition of cell-surface sialyldisaccharides by influenza A hemagglutinin (HA) triggers the infection process of influenza. The changes in glycosidic torsional linkage and the receptor conformations may alter the binding specificity of HAs to the sialylglycans. In this study, 10-ns molecular dynamics simulations were carried out to examine the structural and dynamic behavior of the HAs bound with sialyldisaccharides Neu5Acα(2-3)Gal (N23G) and Neu5Acα(2-6)Gal (N26G). The analysis of the glycosidic torsional angles and the pair interaction energy between the receptor and the interacting residues of the binding site reveal that N23G has two binding modes for H1 and H5 and a single binding mode for H3 and H9. For N26G, H1 and H3 has two binding modes, and H5 and H9 has a single binding mode. The direct and water-mediated hydrogen bonding interactions between the receptors and HAs play dominant roles in the structural stabilization of the complexes. It is concluded from pair interaction energy and Molecular Mechanic-Poisson-Boltzmann Surface Area calculations that N26G is a better receptor for H1 when compared with N23G. N23G is a better receptor for H5 when compared with N26G. However, H3 and H9 can recognize N23G and N26G in equal binding specificity due to the marginal energy difference (≈2.5 kcal/mol). The order of binding specificity of N23G is H3 > H5 > H9 > H1 and N26G is H1 > H3 > H5 > H9, respectively. The proposed conformational models will be helpful in designing inhibitors for influenza virus.

Identification of efflux proteins using efficient radial basis function networks with position-specific scoring matrices and biochemical properties.

Efflux proteins are membrane proteins, which are involved in the transportation of multidrugs. The annotation of efflux proteins in genomic sequences would aid to understand the function. Although the percentage of membrane proteins in genomes is estimated to be 25-30%, there is no information about the content of efflux proteins. For annotating such class of proteins it is necessary to develop a reliable method to identify efflux proteins from amino acid sequence information. In this work, we have developed a method based on radial basis function networks using position specific scoring matrices (PSSM) and amino acid properties. We noticed that the C-terminal domain of efflux proteins contain vital information for discrimination. Our method showed an accuracy of 78 and 92% in discriminating efflux proteins from transporters and membrane proteins, respectively using fivefold cross-validation. We utilized our method for annotating the genomes E. coli and P. aeruginosa and it predicted 8.7 and 9.2% of proteins as efflux proteins in these genomes, respectively. The predicted efflux proteins have been compared with available experimental data and we observed a very good agreement between them. Further, we developed a web server for classifying efflux proteins and it is freely available at http://rbf.bioinfo.tw/∼sachen/EFFLUXpredict/Efflux-RBF.php. We suggest that our method could be an effective tool for annotating efflux proteins in genomic sequences.

Relationship between amino acid properties and functional parameters in olfactory receptors and discrimination of mutants with enhanced specificity.

BACKGROUND: Olfactory receptors are key components in signal transduction. Mutations in olfactory receptors alter the odor response, which is a fundamental response of organisms to their immediate environment. Understanding the relationship between odorant response and mutations in olfactory receptors is an important problem in bioinformatics and computational biology. In this work, we have systematically analyzed the relationship between various physical, chemical, energetic and conformational properties of amino acid residues, and the change of odor response/compound's potency/half maximal effective concentration (EC50) due to amino acid substitutions. RESULTS: We observed that both the characteristics of odorant molecule (ligand) and amino acid properties are important for odor response and EC50. Additional information on neighboring and surrounding residues of the mutants enhanced the correlation between amino acid properties and EC50. Further, amino acid properties have been combined systematically using multiple regression techniques and we obtained a correlation of 0.90-0.98 with odor response/EC50 of goldfish, mouse and human olfactory receptors. In addition, we have utilized machine learning methods to discriminate the mutants, which enhance or reduce EC50 values upon mutation and we obtained an accuracy of 93% and 79% for self-consistency and jack-knife tests, respectively. CONCLUSIONS: Our analysis provides deep insights for understanding the odor response of olfactory receptor mutants and the present method could be used for identifying the mutants with enhanced specificity.

Infrared multiple photon dissociation spectroscopy and computational studies of O-glycosylated peptides.

The infrared multiple photon dissociation (IRMPD) spectra of O-glycosylated peptides in the gas phase were studied in the IR scanning range of 5.7-9.5 µm. Fragmentation of protonated and sodiated O-glycopeptides was investigated using electrospray ionization (ESI) Fourier-transform ion cyclotron resonance (FTICR) mass spectrometry (MS) with a free electron laser (FEL). FEL is used in the IRMPD technique as a tunable IR light source. In the IRMPD spectroscopic analysis of the protonated O-glycopeptide, fragment ions of the b/y and B/Y types were observed in the range of 5.7-9.5 µm, corresponding to the cleavage of the backbone in the parent amino acid sequence and glycosyl bonds, whereas the spectra of the sodiated glycopeptide showed major peaks of photoproducts of the B/Y type in the range of 8.4-9.5 µm. The IRMPD spectra of the O-glycopeptides were compared with simulated IR spectra for the structures obtained from the molecular dynamics.

Prospects for tertiary structure prediction of RNA based on secondary structure information.

We developed a method, called RNA Assembler using Secondary Structure Information Effectively (RASSIE), for predicting RNA tertiary structures using known secondary structure information. We attempted a fragment assembly-based method that uses a secondary structure-based fragment library. For several typical target structures such as stem-loops, bulge-loops, and 2-way junctions, our method provided numerous good quality candidate structures in less computational time than previously proposed methods. By using a high-resolution potential energy function, we were able to select good predicted structures from candidate structures. This method of efficient conformational search and detailed structure evaluation using high-resolution potential is potentially useful for the tertiary structure prediction of RNA.

Phosprof: pathway analysis database of drug response based on phosphorylation activity measurements.

Protein phosphorylation plays a fundamental role in many cellular processes. Proteins are phosphorylated by kinases, which have been studied as drug targets for the treatment of various diseases, particularly cancer. Because kinases have multiple roles in interconnected molecular pathways, their specific regulation is required to enhance beneficial and reduce adversarial effects of drugs. Using our previously developed platform, we measured phosphorylation profiles of MCF7 and K562 cells treated with 94 clinical drugs. These phosphorylation profiles can provide insights into pathway activities and biological functions. Here, we introduce Phosprof, a novel database of drug response based on phosphorylation activity. Phosprof is able to present up- or downregulated phosphorylated signature proteins on pathway maps, significant pathways on the hierarchal tree in signal transduction and commonly perturbed pathways affected by the selected drugs. It also serves as a useful web interface for new or known drug profile search based on their molecular similarity with the 94 drugs. Phosprof can be helpful for further investigation of drug responses in terms of phosphorylation by utilizing the various approved drugs whose target phenotypes are known. DATABASE URL: https://phosprof.medals.jp/.

GRIPDB - G protein coupled Receptor Interaction Partners DataBase.

The G protein Coupled Receptor (GPCR) superfamily is one of the most important pharmaceutical targets. Studies of GPCRs have long been performed under the assumption that GPCRs function as monomers. However, recent studies have revealed that many GPCRs function as homo- and/or hetero-dimers or higher-order oligomeric molecular complexes. As a result, information about GPCR oligomerization is rapidly accumulating, although the molecular mechanisms of oligomerization are not fully understood. A comprehensive collection of information about oligomerization would accelerate investigations of the molecular mechanisms of GPCRs' oligomerization and involvement in signaling. Hence, we have developed a database, G protein coupled Receptor Interaction Partners DataBase (GRIPDB), which provides information about GPCR oligomerization. The entries in the database are divided into two sections: (I) Experiment Information section and (II) Prediction Information section. The Experiment Information section contains (I-i) experimentally indentified GPCR oligomers and their annotations, and (I-ii) experimentally suggested interfaces for the oligomerization. Since the number of experimentally suggested interfaces is limited, the entries in the Prediction Information section have been introduced to provide information about the oligomerization interfaces predicted by our computational method. The experimentally suggested or computationally predicted interfaces are displayed by 3D graphics, using GPCRs with available coordinates. The information in the GRIPDB, especially that about the interfaces, is useful to investigate the molecular mechanisms of signal transduction via GPCR oligomerization. The GRIPDB is available on the web at the following URL: http://grip.cbrc.jp/GDB/index.html .

Sequence and structural features of binding site residues in protein-protein complexes: comparison with protein-nucleic acid complexes.

BACKGROUND: Protein-protein interactions are important for several cellular processes. Understanding the mechanism of protein-protein recognition and predicting the binding sites in protein-protein complexes are long standing goals in molecular and computational biology. METHODS: We have developed an energy based approach for identifying the binding site residues in protein-protein complexes. The binding site residues have been analyzed with sequence and structure based parameters such as binding propensity, neighboring residues in the vicinity of binding sites, conservation score and conformational switching. RESULTS: We observed that the binding propensities of amino acid residues are specific for protein-protein complexes. Further, typical dipeptides and tripeptides showed high preference for binding, which is unique to protein-protein complexes. Most of the binding site residues are highly conserved among homologous sequences. Our analysis showed that 7% of residues changed their conformations upon protein-protein complex formation and it is 9.2% and 6.6% in the binding and non-binding sites, respectively. Specifically, the residues Glu, Lys, Leu and Ser changed their conformation from coil to helix/strand and from helix to coil/strand. Leu, Ser, Thr and Val prefer to change their conformation from strand to coil/helix. CONCLUSIONS: The results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.