Takatsugu Hirokawa

SOSUI: classification and secondary structure prediction system for membrane proteins.

UNLABELLED The system SOSUI for the discrimination of membrane proteins and soluble ones together with the prediction of transmembrane helices was developed, in which the accuracy of the classification of proteins was 99% and the corresponding value for the transmembrane helix prediction was 97%. AVAILABILITY The system SOSUI is available through internet access: http://www.tuat.ac.jp/mitaku/sosui/. CONTACT sosui@biophys.bio.tuat. ac.jp.

Ubiquitin is phosphorylated by PINK1 to activate parkin

PINK1 (PTEN induced putative kinase 1) and PARKIN (also known as PARK2) have been identified as the causal genes responsible for hereditary recessive early-onset Parkinsonism. PINK1 is a Ser/Thr kinase that specifically accumulates on depolarized mitochondria, whereas parkin is an E3 ubiquitin ligase that catalyses ubiquitin transfer to mitochondrial substrates. PINK1 acts as an upstream factor for parkin and is essential both for the activation of latent E3 parkin activity and for recruiting parkin onto depolarized mitochondria. Recently, mechanistic insights into mitochondrial quality control mediated by PINK1 and parkin have been revealed, and PINK1-dependent phosphorylation of parkin has been reported. However, the requirement of PINK1 for parkin activation was not bypassed by phosphomimetic parkin mutation, and how PINK1 accelerates the E3 activity of parkin on damaged mitochondria is still obscure. Here we report that ubiquitin is the genuine substrate of PINK1. PINK1 phosphorylated ubiquitin at Ser 65 both in vitro and in cells, and a Ser 65 phosphopeptide derived from endogenous ubiquitin was only detected in cells in the presence of PINK1 and following a decrease in mitochondrial membrane potential. Unexpectedly, phosphomimetic ubiquitin bypassed PINK1-dependent activation of a phosphomimetic parkin mutant in cells. Furthermore, phosphomimetic ubiquitin accelerates discharge of the thioester conjugate formed by UBCH7 (also known as UBE2L3) and ubiquitin (UBCH7∼ubiquitin) in the presence of parkin in vitro, indicating that it acts allosterically. The phosphorylation-dependent interaction between ubiquitin and parkin suggests that phosphorylated ubiquitin unlocks autoinhibition of the catalytic cysteine. Our results show that PINK1-dependent phosphorylation of both parkin and ubiquitin is sufficient for full activation of parkin E3 activity. These findings demonstrate that phosphorylated ubiquitin is a parkin activator.

Structural Basis for a Broad But Selective Ligand Spectrum of a Mouse Olfactory Receptor: Mapping the Odorant-Binding Site

The olfactory receptor (OR) superfamily provides a basis for the remarkable ability to recognize and discriminate a large number of odorants. In mice, the superfamily includes ∼1000 members, and they recognize overlapping sets of odorants with distinct affinities and specificities. To address the molecular basis of odor discrimination by the mammalian OR superfamily, we performed functional analysis on a series of site-directed mutants and performed ligand docking simulation studies to define the odorant-binding site of a mouse OR. Our results indicate that several amino acids in the transmembrane domains formed a ligand-binding pocket. Although other G-protein-coupled receptors (GPCRs) recognize biogenic ligands mainly with ionic or hydrogen bonding interactions, ORs recognize odorants mostly via hydrophobic and van der Waals interactions. This accounts for the broad but selective binding by ORs as well as their relatively low ligand-binding affinities. Furthermore, we succeeded in rational receptor design, inserting point mutations in the odorant-binding site that resulted in predicted changes in ligand specificity and antagonist activity. This ability to rationally design the receptor validated the binding site structure that was deduced with our mutational and ligand docking studies. Such broad and specific sensitivity suggests an evolutionary process during which mutations in the active site led to an enormous number of ORs with a wide range of ligand specificity. The current study reveals the molecular environment of the odorant-binding site, and it further advances the understanding of GPCR pharmacology.

Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane–water interfaces

MOTIVATION An amphiphilicity index of amino acid residues was developed for improving the method of transmembrane helix prediction. RESULTS The transfer energy of a hydrocarbon stem group beyond the gamma-carbon was calculated from the accessible surface area, and used to index the amphiphilicity of the residue. Non-zero amphiphilicity index values were obtained for lysine, arginine, histidine, glutamic acid, glutamine, tyrosine and tryptophan. Those residues were found to be abundant in the end regions of transmembrane helices, indicating their preference for the membrane-water interface. The moving average of the amphiphilicity index actually showed significant peaks in the end regions of most transmembrane helices. A dispersion diagram of average amphiphilicity index versus average hydrophobicity index was devised to facilitate discrimination of transmembrane helices. AVAILABILITY The amphiphilicity index has been incorporated into a system, SOSUI, for the discrimination of membrane proteins and the prdiction of tranmembrane helical regions (http://sosui.proteome.bio.tuat.ac.jp/sosuiframe0.html).

Alterations of structure and hydrolase activity of parkinsonism-associated human ubiquitin carboxyl-terminal hydrolase L1 variants

Ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1) is a neuron-specific ubiquitin recycling enzyme. A mutation at residue 93 and polymorphism at residue 18 within human UCH-L1 are linked to familial Parkinsons disease and a decreased Parkinsons disease risk, respectively. Thus, we constructed recombinant human UCH-L1 variants and examined their structure (using circular dichroism) and hydrolase activities. We confirmed that an I93M substitution results in a decrease in kcat (45.6%) coincident with an alteration in alpha-helical content. These changes may contribute to the pathogenesis of Parkinsons disease. In contrast, an S18Y substitution results in an increase in kcat (112.6%) without altering the circular dichroistic spectrum. These data suggest that UCH-L1 hydrolase activity may be inversely correlated with Parkinsons disease risk and that the hydrolase activity is protective against the disease. Furthermore, we found that oxidation of UCH-L1 by 4-hydroxynonenal, a candidate for endogenous mediator of oxidative stress-induced neuronal cell death, results in a loss of hydrolase activity. Taken together, these results suggest that further studies of altered UCH-L1 hydrolase function may provide new insights into a possible common pathogenic mechanism between familial and sporadic Parkinsons disease.

Multiple functions of precursor BDNF to CNS neurons: negative regulation of neurite growth, spine formation and cell survival

BackgroundProneurotrophins and mature neurotrophins elicit opposite effects via the p75 neurotrophin receptor (p75NTR) and Trk tyrosine kinase receptors, respectively; however the molecular roles of proneurotrophins in the CNS are not fully understood.ResultsBased on two rare single nucleotide polymorphisms (SNPs) of the human brain-derived neurotrophic factor (BDNF) gene, we generated R125M-, R127L- and R125M/R127L-BDNF, which have amino acid substitution(s) near the cleavage site between the pro- and mature-domain of BDNF. Western blot analyses demonstrated that these BDNF variants are poorly cleaved and result in the predominant secretion of proBDNF. Using these cleavage-resistant proBDNF (CR-proBDNF) variants, the molecular and cellular roles of proBDNF on the CNS neurons were examined. First, CR-proBDNF showed normal intracellular distribution and secretion in cultured hippocampal neurons, suggesting that inhibition of proBDNF cleavage does not affect intracellular transportation and secretion of BDNF. Second, we purified recombinant CR-proBDNF and tested its biological effects using cultured CNS neurons. Treatment with CR-proBDNF elicited apoptosis of cultured cerebellar granule neurons (CGNs), while treatment with mature BDNF (matBDNF) promoted cell survival. Third, we examined the effects of CR-proBDNF on neuronal morphology using more than 2-week cultures of basal forebrain cholinergic neurons (BFCNs) and hippocampal neurons. Interestingly, in marked contrast to the action of matBDNF, which increased the number of cholinergic fibers and hippocampal dendritic spines, CR-proBDNF dramatically reduced the number of cholinergic fibers and hippocampal dendritic spines, without affecting the survival of these neurons.ConclusionThese results suggest that proBDNF has distinct functions in different populations of CNS neurons and might be responsible for specific physiological cellular processes in the brain.

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.

Proportion of membrane proteins in proteomes of 15 single-cell organisms analyzed by the SOSUI prediction system.

A software system, SOSUI, was previously developed for discriminating between soluble and membrane proteins and predicting transmembrane regions (Hirokawa et al., Bioinformatics, 14 (1998) 378-379). The performance of the system was 99% for the discrimination between two types of proteins and 96% for the prediction of transmembrane helices. When all of the amino acid sequences from 15 single-cell organisms were analyzed by SOSUI, the proportion of predicted polytopic membrane proteins showed an almost constant value of 15-20%, irrespective of the total genome size. However, single-cell organisms appeared to be categorized in terms of the preference of the number of transmembrane segments: species with small genomes were characterized by a significant peak at a helix number of approximately six or seven; species with large genomes showed a peak at 10 or 11 helices; and species with intermediate genome sizes showed a monotonous decrease of the population of membrane proteins against the number of transmembrane helices.

GRIFFIN: a system for predicting GPCR–G-protein coupling selectivity using a support vector machine and a hidden Markov model

We describe a novel system, GRIFFIN (G-protein and Receptor Interaction Feature Finding INstrument), that predicts G-protein coupled receptor (GPCR) and G-protein coupling selectivity based on a support vector machine (SVM) and a hidden Markov model (HMM) with high sensitivity and specificity. Based on our assumption that whole structural segments of ligands, GPCRs and G-proteins are essential to determine GPCR and G-protein coupling, various quantitative features were selected for ligands, GPCRs and G-protein complex structures, and those parameters that are the most effective in selecting G-protein type were used as feature vectors in the SVM. The main part of GRIFFIN includes a hierarchical SVM classifier using the feature vectors, which is useful for Class A GPCRs, the major family. For the opsins and olfactory subfamilies of Class A and other minor families (Classes B, C, frizzled and smoothened), the binding G-protein is predicted with high accuracy using the HMM. Applying this system to known GPCR sequences, each binding G-protein is predicted with high sensitivity and specificity (>85% on average). GRIFFIN () is freely available and allows users to easily execute this reliable prediction of G-proteins.

Protein structure prediction using a variety of profile libraries and 3D verification

This study is intended to construct a useful method for fold recognition, regardless of whether the proteins to be compared are evolutionarily related. We developed several descendants of our profile–profile comparison method to make use of known structural information for protein structure prediction. Our prediction strategy in CASP6 is simple. For every CASP6 target, we derived target–template alignments from several different versions of profile–profile comparisons. We then constructed and exhaustively evaluated 3D models based on those alignments. Subsequently, we selected proper model(s) among them. We specifically addressed the validation of our simple approach for protein structure prediction through CASP6 because the fold recognition results of CASP5 revealed areas of improvement in the selection of good models. Consequently, we applied a more stringent method for 3D model evaluation this time. All generated models were evaluated based on a structural quality score calculated by both Verify3D and Prosa2003 programs. It turns out that the prediction results of our human group were supported by the results of three servers. The pipeline that we constructed for our human group prediction and human intervention were also greatly effective in improving prediction models, but the efficacy of our scheme for 3D model evaluation was obscure. Proteins 2005;Suppl 7:114–121.