Andreas Schlicker

A new measure for functional similarity of gene products based on Gene Ontology.

BackgroundGene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role.ResultsWe present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; simReland funSim. One measure (simRel) is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim) is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families.ConclusionThe approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.

The implications of alternative splicing in the ENCODE protein complement.

Alternative premessenger RNA splicing enables genes to generate more than one gene product. Splicing events that occur within protein coding regions have the potential to alter the biological function of the expressed protein and even to create new protein functions. Alternative splicing has been suggested as one explanation for the discrepancy between the number of human genes and functional complexity. Here, we carry out a detailed study of the alternatively spliced gene products annotated in the ENCODE pilot project. We find that alternative splicing in human genes is more frequent than has commonly been suggested, and we demonstrate that many of the potential alternative gene products will have markedly different structure and function from their constitutively spliced counterparts. For the vast majority of these alternative isoforms, little evidence exists to suggest they have a role as functional proteins, and it seems unlikely that the spectrum of conventional enzymatic or structural functions can be substantially extended through alternative splicing.

Improving disease gene prioritization using the semantic similarity of Gene Ontology terms

Motivation: Many hereditary human diseases are polygenic, resulting from sequence alterations in multiple genes. Genomic linkage and association studies are commonly performed for identifying disease-related genes. Such studies often yield lists of up to several hundred candidate genes, which have to be prioritized and validated further. Recent studies discovered that genes involved in phenotypically similar diseases are often functionally related on the molecular level. Results: Here, we introduce MedSim, a novel approach for ranking candidate genes for a particular disease based on functional comparisons involving the Gene Ontology. MedSim uses functional annotations of known disease genes for assessing the similarity of diseases as well as the disease relevance of candidate genes. We benchmarked our approach with genes known to be involved in 99 diseases taken from the OMIM database. Using artificial quantitative trait loci, MedSim achieved excellent performance with an area under the ROC curve of up to 0.90 and a sensitivity of over 70% at 90% specificity when classifying gene products according to their disease relatedness. This performance is comparable or even superior to related methods in the field, albeit using less and thus more easily accessible information. Availability: MedSim is offered as part of our FunSimMat web service (http://www.funsimmat.de). Contact: mario.albrecht@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

FunSimMat: a comprehensive functional similarity database

Functional similarity based on Gene Ontology (GO) annotation is used in diverse applications like gene clustering, gene expression data analysis, protein interaction prediction and evaluation. However, there exists no comprehensive resource of functional similarity values although such a database would facilitate the use of functional similarity measures in different applications. Here, we describe FunSimMat (Functional Similarity Matrix, http://funsimmat.bioinf.mpi-inf.mpg.de/), a large new database that provides several different semantic similarity measures for GO terms. It offers various precomputed functional similarity values for proteins contained in UniProtKB and for protein families in Pfam and SMART. The web interface allows users to efficiently perform both semantic similarity searches with GO terms and functional similarity searches with proteins or protein families. All results can be downloaded in tab-delimited files for use with other tools. An additional XML–RPC interface gives automatic online access to FunSimMat for programs and remote services.

Molecular basis of telaprevir resistance due to V36 and T54 mutations in the NS3-4A protease of the hepatitis C virus

BackgroundThe inhibitor telaprevir (VX-950) of the hepatitis C virus (HCV) protease NS3-4A has been tested in a recent phase 1b clinical trial in patients infected with HCV genotype 1. This trial revealed residue mutations that confer varying degrees of drug resistance. In particular, two protease positions with the mutations V36A/G/L/M and T54A/S were associated with low to medium levels of drug resistance during viral breakthrough, together with only an intermediate reduction of viral replication fitness. These mutations are located in the protein interior and far away from the ligand binding pocket.ResultsBased on the available experimental structures of NS3-4A, we analyze the binding mode of different ligands. We also investigate the binding mode of VX-950 by protein-ligand docking. A network of non-covalent interactions between amino acids of the protease structure and the interacting ligands is analyzed to discover possible mechanisms of drug resistance. We describe the potential impact of V36 and T54 mutants on the side chain and backbone conformations and on the non-covalent residue interactions. We propose possible explanations for their effects on the antiviral efficacy of drugs and viral fitness. Molecular dynamics simulations of T54A/S mutants and rotamer analysis of V36A/G/L/M side chains support our interpretations. Experimental data using an HCV V36G replicon assay corroborate our findings.ConclusionT54 mutants are expected to interfere with the catalytic triad and with the ligand binding site of the protease. Thus, the T54 mutants are assumed to affect the viral replication efficacy to a larger degree than V36 mutants. Mutations at V36 and/or T54 result in impaired interaction of the protease residues with the VX-950 cyclopropyl group, which explains the development of viral breakthrough variants.

Mining GO Annotations for Improving Annotation Consistency

Despite the structure and objectivity provided by the Gene Ontology (GO), the annotation of proteins is a complex task that is subject to errors and inconsistencies. Electronically inferred annotations in particular are widely considered unreliable. However, given that manual curation of all GO annotations is unfeasible, it is imperative to improve the quality of electronically inferred annotations. In this work, we analyze the full GO molecular function annotation of UniProtKB proteins, and discuss some of the issues that affect their quality, focusing particularly on the lack of annotation consistency. Based on our analysis, we estimate that 64% of the UniProtKB proteins are incompletely annotated, and that inconsistent annotations affect 83% of the protein functions and at least 23% of the proteins. Additionally, we present and evaluate a data mining algorithm, based on the association rule learning methodology, for identifying implicit relationships between molecular function terms. The goal of this algorithm is to assist GO curators in updating GO and correcting and preventing inconsistent annotations. Our algorithm predicted 501 relationships with an estimated precision of 94%, whereas the basic association rule learning methodology predicted 12,352 relationships with a precision below 9%.

FunSimMat update: new features for exploring functional similarity

Quantifying the functional similarity of genes and their products based on Gene Ontology annotation is an important tool for diverse applications like the analysis of gene expression data, the prediction and validation of protein functions and interactions, and the prioritization of disease genes. The Functional Similarity Matrix (FunSimMat, http://www.funsimmat.de) is a comprehensive database providing various precomputed functional similarity values for proteins in UniProtKB and for protein families in Pfam and SMART. With this update, we significantly increase the coverage of FunSimMat by adding data from the Gene Ontology Annotation project as well as new functional similarity measures. The applicability of the database is greatly extended by the implementation of a new Gene Ontology-based method for disease gene prioritization. Two new visualization tools allow an interactive analysis of the functional relationships between proteins or protein families. This is enhanced further by the introduction of an automatically derived hierarchy of annotation classes. Additional changes include a revised user front-end and a new RESTlike interface for improving the user-friendliness and online accessibility of FunSimMat.

GOTax: investigating biological processes and biochemical activities along the taxonomic tree

We describe GOTax, a comparative genomics platform that integrates protein annotation with protein family classification and taxonomy. User-defined sets of proteins, protein families, annotation terms or taxonomic groups can be selected and compared, allowing for the analysis of distribution of biological processes and molecular activities over different taxonomic groups. In particular, a measure of functional similarity is available for comparing proteins and protein families, establishing functional relationships independent of evolution.