Kazuhiko Fukui

Energy based approach for understanding the recognition mechanism in protein-protein complexes.

Protein-protein interactions play an essential role in the regulation of various cellular processes. Understanding the recognition mechanism of protein-protein complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-protein complexes. The new approach is different from the traditional distance based contacts in which the repulsive interactions are treated as binding sites as well as the contacts within a specific cutoff have been treated in the same way. We found that the residues and residue-pairs with charged and aromatic side chains are important for binding. These residues influence to form cation-, electrostatic and aromatic interactions. Our observation has been verified with the experimental binding specificity of protein-protein complexes and found good agreement with experiments. Based on these results we have proposed a novel mechanism for the recognition of protein-protein complexes: the charged and aromatic residues in receptor and ligand initiate recognition by making suitable interactions between them; the neighboring hydrophobic residues assist the stability of complex along with other hydrogen bonding partners by the polar residues. Further, the propensity of residues in the binding sites of receptors and ligands, atomic contributions and the influence on secondary structure will be discussed.

TMFunction: database for functional residues in membrane proteins

We have developed the database TMFunction, which is a collection of more than 2900 experimentally observed functional residues in membrane proteins. Each entry includes the numerical values for the parameters IC50 (measure of the effectiveness of a compound in inhibiting biological function), Vmax (maximal velocity of transport), relative activity of mutants with respect to wild-type protein, binding affinity, dissociation constant, etc., which are important for understanding the sequence–structure–function relationship of membrane proteins. In addition, we have provided information about name and source of the protein, Uniprot and Protein Data Bank codes, mutational and literature information. Furthermore, TMFunction is linked to related databases and other resources. We have set up a web interface with different search and display options so that users have the ability to get the data in several ways. TMFunction is freely available at http://tmbeta-genome.cbrc.jp/TMFunction/.

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

BackgroundProtein-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.MethodsWe 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.ResultsWe 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.ConclusionsThe results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.

Computationally and experimentally derived general rules for fragmentation of various glycosyl bonds in sodium adduct oligosaccharides.

Mechanisms of fragmentation of glycosyl bond linkages in various saccharides were investigated by using computational calculations to find general rules of fragmentation of sodiated oligosaccharides in mass spectrometry. The calculations revealed that alpha-Glc, alpha-Gal, beta-Man, alpha-Fuc, beta-GlcNAc, and beta-GalNAc linkages were cleaved more easily than beta-Glc, beta-Gal, and alpha-Man linkages because the transition states of the former were stabilized by the anomeric effect. The 1-6 linkage was more stable than the others, since saccharides with flexible 1-6 linkages were more stabilized in energy than the other linkages by the sodium cation. The sialyl linkage was the most labile of all the linkages investigated. Comparison of activation energies and binding affinities to the sodium cation revealed an increase in activation energy in proportion to the increment in binding affinity. The calculated stabilities of glycosyl bonds were: alpha-Man (Manalpha1-3Man, Manalpha1-4Man, Manalpha1-6Man) > beta-Gal (Galbeta1-4Gal) > alpha-GalNAc (GalNAcalpha1-4GalNAc) > beta-Man (Manbeta1-4GlcNAc) > alpha-Gal (Galalpha1-3Gal, Galalpha1-4Gal, Galalpha1-6Gal) > beta-Man (Manbeta1-4Man) > beta-GalNAc (GalNAcbeta1-4GalNAc) > alpha-Fuc (Fucalpha1-6GlcNAc) > alpha-Fuc (Fucalpha1-4GlcNAc) > beta-GlcNAc (GlcNAcbeta1-4GlcNAc) > alpha-Fuc (Fucalpha1-3GlcNAc) > alpha-NeuNAc (NeuNAcalpha2-3Gal, NeuNAcalpha2-6Gal); this result was close to the experimentally deduced trend. These theoretically and experimentally derived general rules for fragmentation should be useful for analyzing the experimentally obtained mass spectra of oligosaccharides.

Scoring Function Based Approach for Locating Binding Sites and Understanding Recognition Mechanism of Protein−DNA Complexes

Protein-DNA recognition plays an essential role in the regulation of gene expression. Understanding the recognition mechanism of protein-DNA complexes is a challenging task in molecular and computational biology. In this work, a scoring function based approach has been developed for identifying the binding sites and delineating the important residues for binding in protein-DNA complexes. This approach considers both the repulsive interactions and the effect of distance between atoms in protein and DNA. The results showed that positively charged, polar, and aromatic residues are important for binding. These residues influence the formation of electrostatic, hydrogen bonding, and stacking interactions. Our observation has been verified with experimental binding specificity of protein-DNA complexes and found to be in good agreement with experiments. The comparison of protein-RNA and protein-DNA complexes reveals that the contribution of phosphate atoms in DNA is twice as large as in protein-RNA complexes. Furthermore, we observed that the positively charged, polar, and aromatic residues serve as hotspot residues in protein-RNA complexes, whereas other residues also altered the binding specificity in protein-DNA complexes. Based on the results obtained in the present study and related reports, a plausible mechanism has been proposed for the recognition of protein-DNA complexes.

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.

Theoretical Investigation on the Binding Specificity of Sialyldisaccharides with Hemagglutinins of Influenza A Virus by Molecular Dynamics Simulations

Background: Recognition of terminal sialyldisaccharides by influenza A hemagglutinin initiates the infection process of influenza. Results: MD simulations on sialyldisaccharide-hemagglutinin (H1, H3, H5, and H9) complexes reveal the molecular basis of specific recognition. Conclusion: The order of the binding specificity of Neu5Acα(2–3)Gal and Neu5Acα(2–6)Gal is H3 > H5 > H9 > H1 and H1 > H3 > H5 > H9, respectively. Significance: The insights from this study will help in designing carbohydrate-based therapeutics against influenza viral infections. 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 novel natural inhibitor for NorM – a multidrug and toxic compound extrusion transporter – an insilico molecular modeling and simulation studies

The emergence of bacterial multidrug resistance is an increasing problem in treatment of infectious diseases. An important cause for the multidrug resistance of bacteria is the expression of multidrug efflux transporters. The multidrug and toxic compound extrusion (MATE) transporters are most recently recognized as unique efflux system for extrusion of antimicrobials and therapeutic drugs due to energy stored in either Na+ or H+ electrochemical gradient. In the present study, high throughput virtual screening of natural compound collections against NorM – a MATE transporter from Neisseria gonorrhea (NorM-NG) has been carried out followed by flexible docking. The molecular simulation in membrane environment has been performed for understanding the stability and binding energetic of top lead compounds. Results identified a compound from the Indian medicinal plant “Terminalia chebula” which has good binding free energy compared to substrates (rhodamine 6 g, ethidium) and more favorable interactions with the central cavity forming active site residues. The compound has restricted movement in TM7, TM8, and TM1, thus blocking the disruption of Na+ – coordination along with equilibrium state bias towards occlude state of NorM transporter. Thus, this compound blocks the effluxing pathway of antimicrobial drugs and provides as a natural bioactive lead inhibitor against NorM transporter in drug-resistant gonorrhea.

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.

Improving the Accuracy of an Affinity Prediction Method by Using Statistics on Shape Complementarity between Proteins

To elucidate the partners in protein-protein interactions (PPIs), we previously proposed an affinity prediction method called affinity evaluation and prediction (AEP), which is based on the shape complementarity characteristics between proteins. The structures of the protein complexes obtained in our shape complementarity evaluation were selected by a newly developed clustering method called grouping. Our previous experiments showed that AEP gave accuracies that differed with the data composition and scale. In this study, we set a data scale (84 x 84 = 7056 protein pairs) including 84 biologically relevant complexes and then designed 225 parameter sets based on four key parameters related to the grouping and the calculation of affinity scores. As a result of receiver operating characteristic analysis, we obtained 27.4% sensitivity (= recall), 91.0% specificity, 3.5% precision, 90.2% accuracy, 6.3% F-measure(max), and an area under the curve of 0.585. Chiefly by optimization of the grouping, AEP was able to provide prediction accuracy for a maximum F-measure that statistically distinguished 23 target complexes among 84 protein pairs. Moreover, the active sites of these complexes were successfully predicted with high accuracy (i.e., 2.37 angstroms in 1CGI and 2.38 angstroms in 1PPE) of interface RMSD. To assess the improvement in accuracy we compared the results of AEP of different data sets and of tentative methods using ZDOCK 3.0.1 or ZRANK scores.