Characterization of water dissociation on α-Al2O3(11[combining macron]02): theory and experiment.

The interaction of water with α-alumina (i.e. α-Al2O3) surfaces is important in a variety of applications and a useful model for the interaction of water with environmentally abundant aluminosilicate phases. Despite its significance, studies of water interaction with α-Al2O3 surfaces other than the (0001) are extremely limited. Here we characterize the interaction of water (D2O) with a well defined α-Al2O3(11[combining macron]02) surface in UHV both experimentally, using temperature programmed desorption and surface-specific vibrational spectroscopy, and theoretically, using periodic-slab density functional theory calculations. This combined approach makes it possible to demonstrate that water adsorption occurs only at a single well defined surface site (the so-called 1-4 configuration) and that at this site the barrier between the molecularly and dissociatively adsorbed forms is very low: 0.06 eV. A subset of OD stretch vibrations are parallel to this dissociation coordinate, and thus would be expected to be shifted to low frequencies relative to an uncoupled harmonic oscillator. To quantify this effect we solve the vibrational Schrödinger equation along the dissociation coordinate and find fundamental frequencies red-shifted by more than 1500 cm(-1). Within the context of this model, at moderate temperatures, we further find that some fraction of surface deuterons are likely delocalized: dissociatively and molecularly absorbed states are no longer distinguishable.

Optically probing Al-O and O-H vibrations to characterize water adsorption and surface reconstruction on α-alumina: an experimental and theoretical study.

Oxide/water interfaces are ubiquitous in a wide variety of applications and the environment. Despite this ubiquity, and attendant decades of study, gaining molecular level insight into water/oxide interaction has proven challenging. In part, this challenge springs from a lack of tools to concurrently characterize changes in surface structure (i.e., water/oxide interaction from the perspective of the solid) and O-H population and local environment (i.e., water/oxide interaction from the water perspective). Here, we demonstrate the application of surface specific vibrational spectroscopy to the characterization of the interaction of the paradigmatic α-Al2O3(0001) surface and water. By probing both the interfacial Al-O (surface phonon) and O-H spectral response, we characterize this interaction from both perspectives. Through electronic structure calculation, we assign the interfacial Al-O response and rationalize its changes on surface dehydroxylation and reconstruction. Because our technique is all-optical and interface specific, it is equally applicable to oxide surfaces in vacuum, ambient atmospheres and at the solid/liquid interface. Application of this approach to additional alumina surfaces and other oxides thus seems likely to significantly expand our understanding of how water meets oxide surfaces and thus the wide variety of phenomena this interaction controls.

Measuring prediction capacity of individual verbs for the identification of protein interactions.

MOTIVATION: The identification of events such as protein-protein interactions (PPIs) from the scientific literature is a complex task. One of the reasons is that there is no formal syntax to denote such relations in the scientific literature. Nonetheless, it is important to understand such relational event representations to improve information extraction solutions (e.g., for gene regulatory events). In this study, we analyze publicly available protein interaction corpora (AIMed, BioInfer, BioCreAtIve II) to determine the scope of verbs used to denote protein interactions and to measure their predictive capacity for the identification of PPI events. Our analysis is based on syntactical language patterns. This restriction has the advantage that the verb mention is used as the independent variable in the experiments enabling comparability of results in the usage of the verbs. The initial selection of verbs has been generated from a systematic analysis of the scientific literature and existing corpora for PPIs. We distinguish modifying interactions (MIs) such as posttranslational modifications (PTMs) from non-modifying interactions (NMIs) and assumed that MIs have a higher predictive capacity due to stronger scientific evidence proving the interaction. We found that MIs are less frequent in the corpus but can be extracted at the same precision levels as PPIs. A significant portion of correct PPI reportings in the BioCreAtIve II corpus use the verb "associate", which semantically does not prove a relation. The performance of every monitored verb is listed and allows the selection of specific verbs to improve the performance of PPI extraction solutions. Programmatic access to the text processing modules is available online (www.ebi.ac.uk/webservices/whatizit/info.jsf) and the full analysis of Medline abstracts will be made through the Web pages of the Rebholz group.

Text processing through Web services: calling Whatizit.

MOTIVATION: Text-mining (TM) solutions are developing into efficient services to researchers in the biomedical research community. Such solutions have to scale with the growing number and size of resources (e.g. available controlled vocabularies), with the amount of literature to be processed (e.g. about 17 million documents in PubMed) and with the demands of the user community (e.g. different methods for fact extraction). These demands motivated the development of a server-based solution for literature analysis. Whatizit is a suite of modules that analyse text for contained information, e.g. any scientific publication or Medline abstracts. Special modules identify terms and then link them to the corresponding entries in bioinformatics databases such as UniProtKb/Swiss-Prot data entries and gene ontology concepts. Other modules identify a set of selected annotation types like the set produced by the EBIMed analysis pipeline for proteins. In the case of Medline abstracts, Whatizit offers access to EBI's in-house installation via PMID or term query. For large quantities of the user's own text, the server can be operated in a streaming mode (http://www.ebi.ac.uk/webservices/whatizit).

EBIMed--text crunching to gather facts for proteins from Medline.

UNLABELLED: To allow efficient and systematic retrieval of statements from Medline we have developed EBIMed, a service that combines document retrieval with co-occurrence-based analysis of Medline abstracts. Upon keyword query, EBIMed retrieves the abstracts from EMBL-EBI's installation of Medline and filters for sentences that contain biomedical terminology maintained in public bioinformatics resources. The extracted sentences and terminology are used to generate an overview table on proteins, Gene Ontology (GO) annotations, drugs and species used in the same biological context. All terms in retrieved abstracts and extracted sentences are linked to their entries in biomedical databases. We assessed the quality of the identification of terms and relations in the retrieved sentences. More than 90% of the protein names found indeed represented a protein. According to the analysis of four protein-protein pairs from the Wnt pathway we estimated that 37% of the statements containing such a pair mentioned a meaningful interaction and clarified the interaction of Dkk with LRP. We conclude that EBIMed improves access to information where proteins and drugs are involved in the same biological process, e.g. statements with GO annotations of proteins, protein-protein interactions and effects of drugs on proteins. AVAILABILITY: Available at http://www.ebi.ac.uk/Rebholz-srv/ebimed

Automatic extraction of mutations from Medline and cross-validation with OMIM.

Mutations help us to understand the molecular origins of diseases. Researchers, therefore, both publish and seek disease-relevant mutations in public databases and in scientific literature, e.g. Medline. The retrieval tends to be time-consuming and incomplete. Automated screening of the literature is more efficient. We developed extraction methods (called MEMA) that scan Medline abstracts for mutations. MEMA identified 24,351 singleton mutations in conjunction with a HUGO gene name out of 16,728 abstracts. From a sample of 100 abstracts we estimated the recall for the identification of mutation-gene pairs to 35% at a precision of 93%. Recall for the mutation detection alone was >67% with a precision rate of >96%. This shows that our system produces reliable data. The subset consisting of protein sequence mutations (PSMs) from MEMA was compared to the entries in OMIM (20,503 entries versus 6699, respectively). We found 1826 PSM-gene pairs to be in common to both datasets (cross-validated). This is 27% of all PSM-gene pairs in OMIM and 91% of those pairs from OMIM which co-occur in at least one Medline abstract. We conclude that Medline covers a large portion of the mutations known to OMIM. Another large portion could be artificially produced mutations from mutagenesis experiments. Access to the database of extracted mutation-gene pairs is available through the web pages of the EBI (refer to http://www.ebi. ac.uk/rebholz/index.html).

Distributed modules for text annotation and IE applied to the biomedical domain.

Biological databases contain facts from scientific literature that have been curated by hand to ensure high quality. Curation is time-consuming and can be supported by information extraction methods. We present a server software infrastructure which allows to easily plug in modules to identify biologically interesting pieces of text to be then presented in a web interface to the curator. There are modules which identify UniProt, UMLS and GO terminology, gene and protein names, mutations and protein-protein interactions. UniProt, UMLS and GO concepts are automatically linked to the original source. The module for mutations is based on syntax patterns and the one for protein-protein interactions relies on chunk parsing. All modules work as separate servers possibly distributed on different machines and can be combined into processing pipelines as necessary. Communication is based on XML annotated text streams, each server processing the XML elements it is designed for, and possibly adding more information in the form of XML annotation. The server and the underlying software are available to the public.

Computer-assisted generation of a protein-interaction database for nuclear receptors.

With the increasing amount of biological data available, automated methods for information retrieval become necessary. We employed computer-assisted text mining to retrieve all protein-protein interactions for nuclear receptors from MEDLINE in a systematic way. A dictionary of protein names and of terms denoting interactions was generated, and trioccurrences of two protein names and one interaction term in one sentence were retrieved. Abstracts containing at least one such trioccurrence were manually checked by biologists to select the relevant interactions out of the automatically extracted data. In total, 4360 abstracts were retrieved containing data on protein interactions for nuclear receptors. The resulting database contains all reported protein interactions involving nuclear receptors from 1966 to September 2001. Remarkably, the annual increase in number of reported interactors for nuclear receptors has been following an exponential growth curve in the years 1991 to 2001. Apparent in the data set is the high complexity of protein interactions for nuclear receptors. The number of interactions correlates with the number of published papers for a given receptor, suggesting that the number of reported interactors is a reflection of the intensity of research dedicated to a given receptor. Indeed, comparison of the retrieved data to a systematic yeast two-hybrid-based interaction analysis suggests that most NRs are similar with respect to the number of interacting proteins. The data set obtained serves as a source for information on NR interactions, as well as a reference data set for the improvement of advanced text-mining methods.

PCorral--interactive mining of protein interactions from MEDLINE.

The extraction of information from the scientific literature is a complex task-for researchers doing manual curation and for automatic text processing solutions. The identification of protein-protein interactions (PPIs) requires the extraction of protein named entities and their relations. Semi-automatic interactive support is one approach to combine both solutions for efficient working processes to generate reliable database content. In principle, the extraction of PPIs can be achieved with different methods that can be combined to deliver high precision and/or high recall results in different combinations at the same time. Interactive use can be achieved, if the analytical methods are fast enough to process the retrieved documents. PCorral provides interactive mining of PPIs from the scientific literature allowing curators to skim MEDLINE for PPIs at low overheads. The keyword query to PCorral steers the selection of documents, and the subsequent text analysis generates high recall and high precision results for the curator. The underlying components of PCorral process the documents on-the-fly and are available, as well, as web service from the Whatizit infrastructure. The human interface summarizes the identified PPI results, and the involved entities are linked to relevant resources and databases. Altogether, PCorral serves curator at both the beginning and the end of the curation workflow for information retrieval and information extraction. Database URL: http://www.ebi.ac.uk/Rebholz-srv/pcorral.

GOAnnotator: linking protein GO annotations to evidence text.

BACKGROUND: Annotation of proteins with gene ontology (GO) terms is ongoing work and a complex task. Manual GO annotation is precise and precious, but it is time-consuming. Therefore, instead of curated annotations most of the proteins come with uncurated annotations, which have been generated automatically. Text-mining systems that use literature for automatic annotation have been proposed but they do not satisfy the high quality expectations of curators. RESULTS: In this paper we describe an approach that links uncurated annotations to text extracted from literature. The selection of the text is based on the similarity of the text to the term from the uncurated annotation. Besides substantiating the uncurated annotations, the extracted texts also lead to novel annotations. In addition, the approach uses the GO hierarchy to achieve high precision. Our approach is integrated into GOAnnotator, a tool that assists the curation process for GO annotation of UniProt proteins. CONCLUSION: The GO curators assessed GOAnnotator with a set of 66 distinct UniProt/SwissProt proteins with uncurated annotations. GOAnnotator provided correct evidence text at 93% precision. This high precision results from using the GO hierarchy to only select GO terms similar to GO terms from uncurated annotations in GOA. Our approach is the first one to achieve high precision, which is crucial for the efficient support of GO curators. GOAnnotator was implemented as a web tool that is freely available at http://xldb.di.fc.ul.pt/rebil/tools/goa/.