Systematically annotating function of enzymes that belong to large protein families encoded in a single eukaryotic genome is a very challenging task. We carried out such an exercise to annotate function for serine-protease family of the trypsin fold in Drosophila melanogaster, with an emphasis on annotating serine-protease homologues (SPHs) that may have lost their catalytic function. Our approach involves data mining and data integration to provide function annotations for 190 Drosophila gene products containing serine-protease-like domains, of which 35 are SPHs. This was accomplished by analysis of structure-function relationships, gene-expression profiles, large-scale protein-protein interaction data, literature mining and bioinformatic tools. We introduce functional residue clustering (FRC), a method that performs hierarchical clustering of sequences using properties of functionally important residues and utilizes correlation co-efficient as a quantitative similarity measure to transfer in vivo substrate specificities to proteases. We show that the efficiency of transfer of substrate-specificity information using this method is generally high. FRC was also applied on Drosophila proteases to assign putative competitive inhibitor relationships (CIRs). Microarray gene-expression data were utilized to uncover a large-scale and dual involvement of proteases in development and in immune response. We found specific recruitment of SPHs and proteases with CLIP domains in immune response, suggesting evolution of a new function for SPHs. We also suggest existence of separate downstream protease cascades for immune response against bacterial/fungal infections and parasite/parasitoid infections. We verify quality of our annotations using information from RNAi screens and other evidence types. Utilization of such multi-fold approaches results in 10-fold increase of function annotation for Drosophila serine proteases and demonstrates value in increasing annotations in multiple genomes.

In a genome-wide analysis, we have identified 85 human genes encoding 103 protein isoforms that resemble retroviral Gag proteins. These genes were domesticated from retrotransposons in at least five independent events during vertebrate evolution and were subsequently duplicated further in mammals. Structural insights into the mammalian proteins can be inferred by homology to Gag from viruses such as HIV; in turn, the cellular roles of the mammalian Gag homologs, such as apoptosis-related functions and binding to ubiquitin ligases, might hint at further functionality of viral Gag itself.

Hereditary non-polyposis colorectal cancer (HNPCC) syndrome is caused by heterozygous germline mutations in DNA mismatch repair genes (MMR), (MSH2, MLH1, MSH6, and PMS2) and it is inherited in an autosomal dominant pattern with high penetrance. Several patients have been reported carrying bi-allelic MMR gene mutations and whose phenotype resembled a syndrome with childhood malignancies including hematological malignancies, brain, and colorectal tumors. This phenotype is similar to the tumor spectrum of MMR knockout mice. Herein we describe two brothers of healthy consanguineous parents from Pakistan, who had developed two and three colorectal cancers at the ages of 11 and 12 years, respectively, and less than 30 polyps. Tumor specimens were microsatellite instable (MSI-H), and expression of MSH2 and MSH6 was lost. Mutation analyses of DNA samples from both patients revealed a novel homozygous c.2006-5T > A mutation in intron 12 of the MSH2 gene. This phenotype of the brothers is unusual as they neither develop hematological malignancies nor brain tumors at an older age of presentation than other patients with homozygous MSH2 mutations. The milder phenotype may be due to the expression of low amounts of MSH2 protein with reduced activity. (c) 2005 Wiley-Liss, Inc.

MOTIVATION: Generation of alternative transcripts from the same gene in is an important biological event due to their contribution in creating functional diversity in eukaryotes. In this work, we choose the task of extracting information around this complex topic using a two step procedure involving machine learning and information extraction. RESULTS: In the first step, we trained a classifier that inductively learns to identify sentences about physiological transcript diversity from the MEDLINE abstracts. Using a large hand-built corpus, we compared the sentence classification performance of various text categorization methods. Support vector machines (SVM) followed by the maximum entropy classifier outperformed other methods for the sentence classification task. The SVM with the radial basis function kernel and optimized parameters achieved Fbeta-measure of 91% during the four-fold cross validation and of 74% when applied to all sentences in more than 12 million abstracts of MEDLINE. In the second step, we identified eight frequently present semantic categories in the sentences and performed a limited amount of semantic role labeling. The role labeling step also achieved very high Fbeta-measure for all eight categories. AVAILABILITY: The results of our two-step procedure are summarized in the LSAT database of alternative transcripts. LSAT is available at http://www.bork.embl.de/LSAT/.

Structures for protein domains have increased rapidly in recent years owing to advances in structural biology and structural genomics projects. New structures are often similar to those solved previously, and such similarities can give insights into function by linking poorly understood families to those that are better characterized. They also allow the possibility of combing information to find still more proteins adopting a similar structure and sometimes a similar function, and to reprioritize families in structural genomics pipelines. We explore this possibility here by preparing merged profiles for pairs of structurally similar, but not necessarily sequence-similar, domains within the SMART and Pfam database by way of the Structural Classification of Proteins (SCOP). We show that such profiles are often able to successfully identify further members of the same superfamily and thus can be used to increase the sensitivity of database searching methods like HMMer and PSI-BLAST. We perform detailed benchmarks using the SMART and Pfam databases with four complete genomes frequently used as annotation benchmarks. We quantify the associated increase in structural information in Swissprot and discuss examples illustrating the applicability of this approach to understand functional and evolutionary relationships between protein families.

Transcript diversity generated by alternative splicing and associated mechanisms contributes heavily to the functional complexity of biological systems. The numerous examples of the mechanisms and functional implications of these events are scattered throughout the scientific literature. Thus, it is crucial to have a tool that can automatically extract the relevant facts and collect them in a knowledge base that can aid the interpretation of data from high-throughput methods. We have developed and applied a composite text-mining method for extracting information on transcript diversity from the entire MEDLINE database in order to create a database of genes with alternative transcripts. It contains information on tissue specificity, number of isoforms, causative mechanisms, functional implications, and experimental methods used for detection. We have mined this resource to identify 959 instances of tissue-specific splicing. Our results in combination with those from EST-based methods suggest that alternative splicing is the preferred mechanism for generating transcript diversity in the nervous system. We provide new annotations for 1,860 genes with the potential for generating transcript diversity. We assign the MeSH term "alternative splicing" to 1,536 additional abstracts in the MEDLINE database and suggest new MeSH terms for other events. We have successfully extracted information about transcript diversity and semiautomatically generated a database, LSAT, that can provide a quantitative understanding of the mechanisms behind tissue-specific gene expression. LSAT (Literature Support for Alternative Transcripts) is publicly available at http://www.bork.embl.de/LSAT/.

BACKGROUND: The exploitation of information extraction (IE), a technology aiming to provide instances of structured representations from free-form text, has been rapidly growing within the molecular biology (MB) research community to keep track of the latest results reported in literature. IE systems have traditionally used shallow syntactic patterns for matching facts in sentences but such approaches appear inadequate to achieve high accuracy in MB event extraction due to complex sentence structure. A consensus in the IE community is emerging on the necessity for exploiting deeper knowledge structures such as through the relations between a verb and its arguments shown by predicate-argument structure (PAS). PAS is of interest as structures typically correspond to events of interest and their participating entities. For this to be realized within IE a key knowledge component is the definition of PAS frames. PAS frames for non-technical domains such as newswire are already being constructed in several projects such as PropBank, VerbNet, and FrameNet. Knowledge from PAS should enable more accurate applications in several areas where sentence understanding is required like machine translation and text summarization. In this article, we explore the need to adapt PAS for the MB domain and specify PAS frames to support IE, as well as outlining the major issues that require consideration in their construction. RESULTS: We introduce PASBio by extending a model based on PropBank to the MB domain. The hypothesis we explore is that PAS holds the key for understanding relationships describing the roles of genes and gene products in mediating their biological functions. We chose predicates describing gene expression, molecular interactions and signal transduction events with the aim of covering a number of research areas in MB. Analysis was performed on sentences containing a set of verbal predicates from MEDLINE and full text journals. Results confirm the necessity to analyze PAS specifically for MB domain. CONCLUSIONS: At present PASBio contains the analyzed PAS of over 30 verbs, publicly available on the Internet for use in advanced applications. In the future we aim to expand the knowledge base to cover more verbs and the nominal form of each predicate.

Entries in biological databases are usually linked to scientific references. To generate those links and to keep them up-to-date, database maintainers have to continuously scan the scientific literature to select references that are relevant for each single database entry. The continuous growth of both the corpus of scientific literature and the size of biological databases makes this task very hard. We present a protocol intended to assist the updating of an existing set of literature (abstract) links from a single database entry with new references. It consists of taking the set of MEDLINE neighbour references of the existing linked abstracts and evaluating their relevance according to the existing set of abstracts. To test the applicability of the algorithm, we did a simple benchmark of the system using the references associated with the entries of a protein domain database. Human experts found the references that the algorithm scored highly were more relevant to the database entry than those scored lowly, suggesting that the algorithm was useful.

BACKGROUND: To date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant. RESULTS: In this work we addressed those questions showing that the keyword content of the different sections of a standard scientific article (abstract, introduction, methods, results, and discussion) is very heterogeneous. CONCLUSIONS: Although the abstract contains the best ratio of keywords per total of words, other sections of the article may be a better source of biologically relevant data.