Mizuki Morita

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.

Highly accurate method for ligand-binding site prediction in unbound state (apo) protein structures.

This article describes a new method for predicting ligand‐binding sites of proteins. The method involves calculating the van der Waals interaction energy between a protein and probes placed on the protein surface, and then clustering the probes with attractive interaction to find the energetically most favorable locus. In 80% (28/35) of the test cases, the ligand‐binding site was successfully predicted on a ligand‐bound protein structure, and in 77% (27/35) was successfully predicted on an unbound structure. Our method was used to successfully predict ligand‐binding sites unaffected by induced‐fit as long as its scales were not very large, and it contributed to a significant improvement in prediction with unbound state protein structures. This represents a significant advance over conventional methods in detecting ligand‐binding sites on uncharacterized proteins. Moreover, our method can predict ligand‐binding sites with a narrower locus than those achieved using conventional methods. Proteins 2008.

Toxygates: interactive toxicity analysis on a hybrid microarray and linked data platform

MOTIVATION In early stage drug development, it is desirable to assess the toxicity of compounds as quickly as possible. Biomarker genes can help predict whether a candidate drug will adversely affect a given individual, but they are often difficult to discover. In addition, the mechanism of toxicity of many drugs and common compounds is not yet well understood. The Japanese Toxicogenomics Project provides a large database of systematically collected microarray samples from rats (liver, kidney and primary hepatocytes) and human cells (primary hepatocytes) after exposure to 170 different compounds in different dosages and at different time intervals. However, until now, no intuitive user interface has been publically available, making it time consuming and difficult for individual researchers to explore the data. RESULTS We present Toxygates, a user-friendly integrated analysis platform for this database. Toxygates combines a large microarray dataset with the ability to fetch semantic linked data, such as pathways, compound-protein interactions and orthologs, on demand. It can also perform pattern-based compound ranking with respect to the expression values of a set of relevant candidate genes. By using Toxygates, users can freely interrogate the transcriptomes response to particular compounds and conditions, which enables deep exploration of toxicity mechanisms.

Prediction of carbohydrate-binding proteins from sequences using support vector machines.

Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs). We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC) curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

Implementation of linked data in the life sciences at BioHackathon 2011

BackgroundLinked Data has gained some attention recently in the life sciences as an effective way to provide and share data. As a part of the Semantic Web, data are linked so that a person or machine can explore the web of data. Resource Description Framework (RDF) is the standard means of implementing Linked Data. In the process of generating RDF data, not only are data simply linked to one another, the links themselves are characterized by ontologies, thereby allowing the types of links to be distinguished. Although there is a high labor cost to define an ontology for data providers, the merit lies in the higher level of interoperability with data analysis and visualization software. This increase in interoperability facilitates the multi-faceted retrieval of data, and the appropriate data can be quickly extracted and visualized. Such retrieval is usually performed using the SPARQL (SPARQL Protocol and RDF Query Language) query language, which is used to query RDF data stores. For the database provider, such interoperability will surely lead to an increase in the number of users.ResultsThis manuscript describes the experiences and discussions shared among participants of the week-long BioHackathon 2011 who went through the development of RDF representations of their own data and developed specific RDF and SPARQL use cases. Advice regarding considerations to take when developing RDF representations of their data are provided for bioinformaticians considering making data available and interoperable.ConclusionsParticipants of the BioHackathon 2011 were able to produce RDF representations of their data and gain a better understanding of the requirements for producing such data in a period of just five days. We summarize the work accomplished with the hope that it will be useful for researchers involved in developing laboratory databases or data analysis, and those who are considering such technologies as RDF and Linked Data.

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/.

Blind prediction of quaternary structures of homo-oligomeric proteins from amino acid sequences based on templates

Abstract Background: Prediction of protein tertiary and quaternary structures helps us to understand protein functionality. While tertiary structure prediction techniques have been much improved over the last two

Lipid recognition propensities of amino acids in membrane proteins from atomic resolution data

BackgroundProtein-lipid interactions play essential roles in the conformational stability and biological functions of membrane proteins. However, few of the previous computational studies have taken into account the atomic details of protein-lipid interactions explicitly.ResultsTo gain an insight into the molecular mechanisms of the recognition of lipid molecules by membrane proteins, we investigated amino acid propensities in membrane proteins for interacting with the head and tail groups of lipid molecules. We observed a common pattern of lipid tail-amino acid interactions in two different data sources, crystal structures and molecular dynamics simulations. These interactions are largely explained by general lipophilicity, whereas the preferences for lipid head groups vary among individual proteins. We also found that membrane and water-soluble proteins utilize essentially an identical set of amino acids for interacting with lipid head and tail groups.ConclusionsWe showed that the lipophilicity of amino acid residues determines the amino acid preferences for lipid tail groups in both membrane and water-soluble proteins, suggesting that tightly-bound lipid molecules and lipids in the annular shell interact with membrane proteins in a similar manner. In contrast, interactions between lipid head groups and amino acids showed a more variable pattern, apparently constrained by each proteins specific molecular function.

BUDDY-system: A web site for constructing a dataset of protein pairs between ligand-bound and unbound states

BackgroundElucidating molecular recognition by proteins, such as in enzyme-substrate and receptor-ligand interactions, is a key to understanding biological phenomena. To delineate these protein interactions, it is important to perform structural bioinformatics studies relevant to molecular recognition. Such studies require a dataset of protein structure pairs between ligand-bound and unbound states. In many studies, the same well-designed and high-quality dataset has been used repeatedly, which has spurred the development of subsequent relevant research. Using previously constructed datasets, researchers are able to fairly compare obtained results with those of other studies; in addition, much effort and time is saved. Therefore, it is important to construct a refined dataset that will appeal to many researchers. However, constructing such datasets is not a trivial task.FindingsWe have developed the BUDDY-system, a web site designed to support the building of a dataset comprising pairs of protein structures between ligand-bound and unbound states, which are widely used in various areas associated with molecular recognition. In addition to constructing a dataset, the BUDDY-system also allows the user to search for ligand-bound protein structures by its unbound state or by its ligand; and to search for ligands by a particular receptor protein.ConclusionsThe BUDDY-system receives input from the user as a single entry or a dataset consisting of a list of ligand-bound state protein structures, unbound state protein structures, or ligands and returns to the user a list of protein structure pairs between the ligand-bound and the corresponding unbound states. This web site is designed for researchers who are involved not only in structural bioinformatics but also in experimental studies. The BUDDY-system is freely available on the web.