Chioko Nagao

Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.

Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.

Crystal structure of FtsA from Staphylococcus aureus

The bacterial cell‐division protein FtsA anchors FtsZ to the cytoplasmic membrane. But how FtsA and FtsZ interact during membrane division remains obscure. We have solved 2.2 Å resolution crystal structure for FtsA from Staphylococcus aureus. In the crystals, SaFtsA molecules within the dimer units are twisted, in contrast to the straight filament of FtsA from Thermotoga maritima, and the half of S12–S13 hairpin regions are disordered. We confirmed that SaFtsZ and SaFtsA associate in vitro, and found that SaFtsZ GTPase activity is enhanced by interaction with SaFtsA.

Creation of lysine-deficient mutant lymphotoxin-α with receptor selectivity by using a phage display system

The cytokine lymphotoxin-alpha (LT alpha) activates various biological functions through its three receptor subtypes, tumor necrosis factor receptor 1 (TNFR1), TNFR2 and herpes virus entry mediator (HVEM), but the relative contribution of each receptor to each function is unclear. Therefore it is important to create mutant LT alpha with receptor selectivity for optimized cancer therapy and the analysis of receptor function. Here, we attempted to create a lysine-deficient mutant LT alpha with TNFR1-selective bioactivity using a phage display technique. We obtained the TNFR1-selective mutant LT alpha R1selLT, which contained the mutations K19N, K28Q, K39S, K84Q, K89V, and K119H. Compared with wild-type LT alpha (wtLT alpha), R1selLT showed several-fold higher bioactivity via TNFR1 but 40-fold lower bioactivity via TNFR2. Kinetic association-dissociation parameters of R1selLT with TNFR2 were higher than those of wtLT alpha, whereas these parameters of R1selLT with TNFR1 were lower than those of wtLT alpha, suggesting that destabilization of the R1selLT-TNFR2 complex causes the decreased bioactivity of R1selLT on TNFR2. We also showed that the K84Q mutation contributed to the enhanced activity via TNFR1, and K39S lowered activity via TNFR2. R1selLT likely will be useful in cancer therapy and in analysis of the LT alpha structure-function relationship.

Relationships between functional subclasses and information contained in active-site and ligand-binding residues in diverse superfamilies.

To investigate the relationships between functional subclasses and sequence and structural information contained in the active‐site and ligand‐binding residues (LBRs), we performed a detailed analysis of seven diverse enzyme superfamilies: aldolase class I, TIM‐barrel glycosidases, α/β‐hydrolases, P‐loop containing nucleotide triphosphate hydrolases, collagenase, Zn peptidases, and glutamine phosphoribosylpyrophosphate, subunit 1, domain 1. These homologous superfamilies, as defined in CATH, were selected from the enzyme catalytic‐mechanism database. We defined active‐site and LBRs based solely on the literature information and complex structures in the Protein Data Bank. From a structure‐based multiple sequence alignment for each CATH homologous superfamily, we extracted subsequences consisting of the aligned positions that were used as an active‐site or a ligand‐binding site by at least one sequence. Using both the subsequences and full‐length alignments, we performed cluster analysis with three sequence distance measures. We showed that the cluster analysis using the subsequences was able to detect functional subclasses more accurately than the clustering using the full‐length alignments. The subsequences determined by only the literature information and complex structures, thus, had sufficient information to detect the functional subclasses. Detailed examination of the clustering results provided new insights into the mechanism of functional diversification for these superfamilies. Proteins 2010.

Sagace: A web-based search engine for biomedical databases in Japan

BackgroundIn the big data era, biomedical research continues to generate a large amount of data, and the generated information is often stored in a database and made publicly available. Although combining data from multiple databases should accelerate further studies, the current number of life sciences databases is too large to grasp features and contents of each database.FindingsWe have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data) and biological resource banks (such as mouse models of disease and cell lines). With Sagace, users can search more than 300 databases in Japan. Sagace offers features tailored to biomedical research, including manually tuned ranking, a faceted navigation to refine search results, and rich snippets constructed with retrieved metadata for each database entry.ConclusionsSagace will be valuable for experts who are involved in biomedical research and drug development in both academia and industry. Sagace is freely available athttp://sagace.nibio.go.jp/en/.

Dextran sulfate-resistant A/Puerto Rico/8/34 influenza virus is associated with the emergence of specific mutations in the neuraminidase glycoprotein.

Dextran sulfate (DS) is a negatively charged sulfated polysaccharide that suppresses the replication of influenza A viruses. The suppression was thought to be associated with inhibition of the hemagglutinin-dependent fusion activity. However, we previously showed that suppression by DS was observed not only at the initial stage of viral infection, but also later when virus is released from infected cells due to inhibition of neuraminidase (NA) activity. In the present study, we isolated DS-resistant A/Puerto Rico/8/34 (PR8) influenza viruses and analyzed the inhibition by DS. We found six mutations in NA genes of five independent resistant PR8 viruses and each resistant NA gene had two mutations. All mutations were from basic to acidic or neutral amino acids. In addition, R430L, K432E or K435E in the 430-435 region was a common mutation in all resistant NA genes. To determine which amino acid(s) are responsible for this resistance, a panel of recombinant viruses containing a PR8 and A/WSN/33(WSN) chimeric NA gene or an NA gene with different mutation(s) was generated using reverse genetics. Using recombinant viruses containing a PR8/WSN chimeric NA, we showed that one third of the C-terminal region of PR8 NA was responsible for DS-sensitivity. Recombinant viruses with a single mutation in NA replicated better than wild-type PR8 in the presence of DS, but were still DS-sensitive. However, replication of recombinant viruses with double mutations from the resistant viruses was not affected by the presence or absence of DS. In addition, resistant recombinant viruses were found to be sensitive to the NA inhibitor, oseltamivir and the oseltamivir-resistant recombinant virus was sensitive to DS. These results suggested that DS is an NA inhibitor with a different mechanism of action from the currently used NA inhibitors and that DS could be used in combination with these inhibitors to treat influenza virus infections.

Computational design, construction, and characterization of a set of specificity determining residues in protein-protein interactions.

Proteins interact with different partners to perform different functions and it is important to elucidate the determinants of partner specificity in protein complex formation. Although methods for detecting specificity determining positions have been developed previously, direct experimental evidence for these amino acid residues is scarce, and the lack of information has prevented further computational studies. In this article, we constructed a dataset that is likely to exhibit specificity in protein complex formation, based on available crystal structures and several intuitive ideas about interaction profiles and functional subclasses. We then defined a “structure‐based specificity determining position (sbSDP)” as a set of equivalent residues in a protein family showing a large variation in their interaction energy with different partners. We investigated sequence and structural features of sbSDPs and demonstrated that their amino acid propensities significantly differed from those of other interacting residues and that the importance of many of these residues for determining specificity had been verified experimentally. Proteins 2012;.

Detecting subtle functional differences in ketopantoate reductase and related enzymes using a rule-based approach with sequence-structure homology recognition scores

Ketopatoate reductase (KPR) is the second enzyme in the pantothenate (vitamin B(5)) biosynthesis pathway, an essential metabolic pathway identified as a potential target for new antimicrobials. The sequence similarity among putative KPRs is limited and KPR itself belongs to a large superfamily of 6-phosphogluconate dehydrogenases. Therefore, it is necessary to discriminate between true and other enzymes. In this paper, we describe a systematic analysis of putative KPRs in the context of this superfamily. Detailed structural analysis allowed us to define key residues for KPR activity and we classified eight structural genomics structures of the KPR family into four functional subclasses. We proposed a semi-automatic protocol, using sequence-structure homology recognition scores, for assigning KPR and related proteins to these subclasses and applied it to a representative set of 103 completely sequenced bacterial genomes. A similar approach can be applied to other enzyme families, which would aid the correct identification of drug targets and help design novel specific inhibitors.

Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges

Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public databases, but these databases typically contain data measured using various protocols in different laboratories, raising the issue of data quality. In this study, we retrieved the intrinsic clearance (CLint) measurements from an open database and performed extensive manual curation. Then, chemical descriptors were calculated using freely available software, and prediction models were built using machine learning algorithms. The models trained on the curated data showed better performance than those trained on the non‐curated data and achieved performance comparable to previously published models, showing the importance of manual curation in data preparation. The curated data were made available, to make our models fully reproducible.