Biomedical subjects
Publications and source records attributed to Thomas Lengauer.
We performed a genome-wide association study of 19,779 nonsynonymous SNPs in 735 individuals with Crohn disease and 368 controls. A total of 7,159 of these SNPs were informative. We followed up on all 72 SNPs with P 0.4), these data suggest that the underlying biological process may be specific to Crohn disease.
MutLalpha, a heterodimer of MLH1 and PMS2, plays a central role in human DNA mismatch repair. It interacts ATP-dependently with the mismatch detector MutSalpha and assembles and controls further repair enzymes. We tested if the interaction of MutLalpha with DNA-bound MutSalpha is impaired by cancer-associated mutations in MLH1, and identified one mutation (Ala128Pro) which abolished interaction as well as mismatch repair activity. Further examinations revealed three more residues whose mutation interfered with interaction. Homology modelling of MLH1 showed that all residues clustered in a small accessible surface patch, suggesting that the major interaction interface of MutLalpha for MutSalpha is located on the edge of an extensive beta-sheet that backs the MLH1 ATP binding pocket. Bioinformatic analysis confirmed that this patch corresponds to a conserved potential protein-protein interaction interface which is present in both human MLH1 and its E.coli homologue MutL. MutL could be site-specifically crosslinked to MutS from this patch, confirming that the bacterial MutL-MutS complex is established by the corresponding interface in MutL. This is the first study that identifies the conserved major MutLalpha-MutSalpha interaction interface in MLH1 and demonstrates that mutations in this interface can affect interaction and mismatch repair, and thereby can also contribute to cancer development.
BACKGROUND: Disease progression in HIV infection has been associated with switch of viral coreceptor usage from CCR5 to CXCR4. OBJECTIVES: To investigate the relationship between HIV-coreceptor tropism and clinical and virological outcome in 40 heavily pretreated patients over time. METHODS: Coreceptor phenotype was predicted after sequencing the V3 loop of the HIV glycoprotein 120. RESULTS: Coreceptor use was stable during observation time in 87% of patients, and CCR5 tropism was predominant. Viral mutations in the pol gene and clinical parameters were not predictive for coreceptor switching. CONCLUSIONS: Even in patients with repeated HAART failure, CCR5 antagonists might be a valuable treatment option.
BACKGROUND: In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the quality of models. With a given target sequence and template structure, we construct a number of different alignments and corresponding models for the sequence. The quality of these models is scored with an MQAP and used to choose the most promising model. An SVM-based selection scheme is suggested for combining MQAP partial potentials, in order to optimize for improved model selection. RESULTS: The approach has been tested on a representative set of proteins. The ability of the method to improve models was validated by comparing the MQAP-selected structures to the native structures with the model quality evaluation program TM-score. Using the SVM-based model selection, a significant increase in model quality is obtained (as shown with a Wilcoxon signed rank test yielding p-values below 10(-15)). The average increase in TMscore is 0.016, the maximum observed increase in TM-score is 0.29. CONCLUSION: In template-based protein structure prediction alignment is known to be a bottleneck limiting the overall model quality. Here we show that a combination of systematic alignment variation and modern model scoring functions can significantly improve the quality of alignment-based models.
MOTIVATION: The binding of endogenous antigenic peptides to MHC class I molecules is an important step during the immunologic response of a host against a pathogen. Thus, various sequence- and structure-based prediction methods have been proposed for this purpose. The sequence-based methods are computationally efficient, but are hampered by the need of sufficient experimental data and do not provide a structural interpretation of their results. The structural methods are data-independent, but are quite time-consuming and thus not suited for screening of whole genomes. Here, we present a new method, which performs sequence-based prediction by incorporating information obtained from molecular modeling. This allows us to perform large databases screening and to provide structural information of the results. RESULTS: We developed a SVM-trained, quantitative matrix-based method for the prediction of MHC class I binding peptides, in which the features of the scoring matrix are energy terms retrieved from molecular dynamics simulations. At the same time we used the equilibrated structures obtained from the same simulations in a simple and efficient docking procedure. Our method consists of two steps: First, we predict potential binders from sequence data alone and second, we construct protein-peptide complexes for the predicted binders. So far, we tested our approach on the HLA-A0201 allele. We constructed two prediction models, using local, position-dependent (DynaPred(POS)) and global, position-independent (DynaPred) features. The former model outperformed the two sequence-based methods used in our evaluation; the latter shows a much higher generalizability towards other alleles than the position-dependent models. The constructed peptide structures can be refined within seconds to structures with an average backbone RMSD of 1.53 A from the corresponding experimental structures.
The evolution of drug resistance in HIV is characterized by the accumulation of resistance-associated mutations in the HIV genome. Mutagenetic trees, a family of restricted Bayesian tree models, have been applied to infer the order and rate of occurrence of these mutations. Understanding and predicting this evolutionary process is an important prerequisite for the rational design of antiretroviral therapies. In practice, mixtures models of K mutagenetic trees provide more flexibility and are often more appropriate for modelling observed mutational patterns. Here, we investigate the model selection problem for K-mutagenetic trees mixture models. We evaluate several classical model selection criteria including cross-validation, the Bayesian Information Criterion (BIC), and the Akaike Information Criterion. We also use the empirical Bayes method by constructing a prior probability distribution for the parameters of a mutagenetic trees mixture model and deriving the posterior probability of the model. In addition to the model dimension, we consider the redundancy of a mixture model, which is measured by comparing the topologies of trees within a mixture model. Based on the redundancy, we propose a new model selection criterion, which is a modification of the BIC. Experimental results on simulated and on real HIV data show that the classical criteria tend to select models with far too many tree components. Only cross-validation and the modified BIC recover the correct number of trees and the tree topologies most of the time. At the same optimal performance, the runtime of the new BIC modification is about one order of magnitude lower. Thus, this model selection criterion can also be used for large data sets for which cross-validation becomes computationally infeasible.
BACKGROUND: Gene 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. RESULTS: We 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; simRel and 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. CONCLUSION: The 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.
MOTIVATION: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. RESULTS: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.
MOTIVATION: Potassium channels are mainly known for their role in regulating and maintaining the membrane potential. Since this is one of the key mechanisms of signal transduction, malfunction of these potassium channels leads to a wide variety of severe diseases. Thus potassium channels are priority targets of research for new drugs, despite the fact that this protein family is highly variable and closely related to other channels, which makes it very difficult to identify new types of potassium channel sequences. RESULTS: Here we present a new method for identifying potassium channel sequences (PSM, Property Signature Method), which-in contrast to the known methods for protein classification-is directly based on physicochemical properties of amino acids rather than on the amino acids themselves. A signature for the pore region including the selectivity filter has been created, representing the most common physicochemical properties of known potassium channels. This string enables genome-wide screening for sequences with similar features despite a very low degree of amino acid similarity within a protein family.
CpG island methylation plays an important role in epigenetic gene control during mammalian development and is frequently altered in disease situations such as cancer. The majority of CpG islands is normally unmethylated, but a sizeable fraction is prone to become methylated in various cell types and pathological situations. The goal of this study is to show that a computational epigenetics approach can discriminate between CpG islands that are prone to methylation from those that remain unmethylated. We develop a bioinformatics scoring and prediction method on the basis of a set of 1,184 DNA attributes, which refer to sequence, repeats, predicted structure, CpG islands, genes, predicted binding sites, conservation, and single nucleotide polymorphisms. These attributes are scored on 132 CpG islands across the entire human Chromosome 21, whose methylation status was previously established for normal human lymphocytes. Our results show that three groups of DNA attributes, namely certain sequence patterns, specific DNA repeats, and a particular DNA structure, are each highly correlated with CpG island methylation (correlation coefficients of 0.64, 0.66, and 0.49, respectively). We predicted, and subsequently experimentally examined 12 CpG islands from human Chromosome 21 with unknown methylation patterns and found more than 90% of our predictions to be correct. In addition, we applied our prediction method to analyzing Human Epigenome Project methylation data on human Chromosome 6 and again observed high prediction accuracy. In summary, our results suggest that DNA composition of CpG islands (sequence, repeats, and structure) plays a significant role in predisposing CpG islands for DNA methylation. This finding may have a strong impact on our understanding of changes in CpG island methylation in development and disease.
MOTIVATION: Recombination plays an important role in the evolution of many pathogens, such as HIV or malaria. Despite substantial prior work, there is still a pressing need for efficient and effective methods of detecting recombination and analyzing recombinant sequences. RESULTS: We introduce Recco, a novel fast method that, given a multiple sequence alignment, scores the cost of obtaining one of the sequences from the others by mutation and recombination. The algorithm comes with an illustrative visualization tool for locating recombination breakpoints. We analyze the sequence alignment with respect to all choices of the parameter alpha weighting recombination cost against mutation cost. The analysis of the resulting cost curve yields additional information as to which sequence might be recombinant. On random genealogies Recco is comparable in its power of detecting recombination with the algorithm Geneconv (Sawyer, 1989). For specific relevant recombination scenarios Recco significantly outperforms Geneconv.
Resistance (R) proteins in plants confer specificity to the innate immune system. Most R proteins have a centrally located NB-ARC (nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4) domain. For two tomato (Lycopersicon esculentum) R proteins, I-2 and Mi-1, we have previously shown that this domain acts as an ATPase module that can hydrolyze ATP in vitro. To investigate the role of nucleotide binding and hydrolysis for the function of I-2 in planta, specific mutations were introduced in conserved motifs of the NB-ARC domain. Two mutations resulted in autoactivating proteins that induce a pathogen-independent hypersensitive response upon expression in planta. These mutant forms of I-2 were found to be impaired in ATP hydrolysis, but not in ATP binding, suggesting that the ATP- rather than the ADP-bound state of I-2 is the active form that triggers defense signaling. In addition, upon ADP binding, the protein displayed an increased affinity for ADP suggestive of a change of conformation. Based on these data, we propose that the NB-ARC domain of I-2, and likely of related R proteins, functions as a molecular switch whose state (on/off) depends on the nucleotide bound (ATP/ADP).
BACKGROUND: Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available. RESULTS: Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure. CONCLUSION: NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at http://noxclass.bioinf.mpi-inf.mpg.de/.
BACKGROUND: In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results. We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates. The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information. RESULTS: The step of clustering local structures yields an average RMSD quantization error of 1.19 A for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88. CONCLUSION: The described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods.
Highly active antiretroviral therapy (HAART), in which three or more drugs are given in combination, has substantially improved the clinical management of HIV-1 infection. Still, the emergence of drug-resistant variants eventually leads to therapy failure in most patients. In such a scenario, the high diversity of resistance-associated mutational patterns complicates the choice of an optimal follow-up regimen. To support physicians in this task, a range of bioinformatics tools for predicting drug resistance or response to combination therapy from the viral genotype have been developed. With several free and commercial software services available, computational advice is rapidly gaining acceptance as an important element of rational decision-making in the treatment of HIV infection.
We characterized 16 additional mutations in human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) whose role in drug resistance is still unknown by analyzing 1,906 plasma-derived HIV-1 subtype B pol sequences from 551 drug-naïve patients and 1,355 nucleoside RT inhibitor (NRTI)-treated patients. Twelve mutations positively associated with NRTI treatment strongly correlated both in pairs and in clusters with known NRTI resistance mutations on divergent evolutionary pathways. In particular, T39A, K43E/Q, K122E, E203K, and H208Y clustered with the nucleoside analogue mutation 1 cluster (NAM1; M41L+L210W+T215Y). Their copresence in this cluster was associated with an increase in thymidine analogue resistance. Moreover, treatment failure in the presence of K43E, K122E, or H208Y was significantly associated with higher viremia and lower CD4 cell count. Differently, D218E clustered with the NAM2 pathway (D67N+K70R+K219Q+T215F), and its presence in this cluster determined an increase in zidovudine resistance. In contrast, three mutations (V35I, I50V, and R83K) negatively associated with NRTI treatment showed negative correlations with NRTI resistance mutations and were associated with increased susceptibility to specific NRTIs. In particular, I50V negatively correlated with the lamivudine-selected mutation M184V and was associated with a decrease in M184V/lamivudine resistance, whereas R83K negatively correlated with both NAM1 and NAM2 clusters and was associated with a decrease in thymidine analogue resistance. Finally, the association pattern of the F214L polymorphism revealed its propensity for the NAM2 pathway and its strong negative association with the NAM1 pathway. Our study provides evidence of novel RT mutational patterns that regulate positively and/or negatively NRTI resistance and strongly suggests that other mutations beyond those currently known to confer resistance should be considered for improved prediction of clinical response to antiretroviral drugs.
BACKGROUND: Formation of transmembrane ion channels by hepatitis C virus (HCV) p7 and abrogation of channel function by amantadine was demonstrated in vitro. The relevance of HCV p7 amino acid (aa) variations for response to antiviral therapy with amantadine is unknown. METHODS: HCV p7 was sequenced in 86 individuals who were infected with HCV genotype 1. Thirty-six of 86 patients received amantadine within an interferon-alpha (IFN-alpha)-based antiviral therapy. Helical wheel modelling for HCV p7 was performed. RESULTS: No significant correlation of overall aa variations within HCV p7 was observed with response to IFN-alpha-based therapy with amantadine in HCV genotype 1alpha/b infected patients. When analysis was restricted to non-conservative aa variations, a higher number of aa substitutions within complete HCV p7 and transmembrane helix 2 was associated with non-response in HCV-1b-infected patients receiving therapy with amantadine (P=0.015 and P=0.037, respectively), without amantadine (P=0.106 and P=0.118, respectively), and in the total cohort of HCV-1b-infected patients (P=0.00007 and P=0.011, respectively). Furthermore, substitution L20F was observed more often in non-responders than responders with HCV-1b infection and therapy with amantadine (P=0.099). By in silico modelling, aa 20 was located toward the p7 channel lumen. Substitution L20F may impair amantadine action by altering the shape of the ion channel pore. CONCLUSION: Substitution L20F within HCV p7 may be associated with non-response to combination therapy specifically with amantadine in HCV-1b-infected patients. Non-responders with HCV-1b infection showed higher numbers of non-conservative aa variations within HCV p7 than responders, irrespective of the application of amantadine.
Selenoproteins are a diverse group of proteins that contain selenocysteine (Sec), the 21st amino acid. In the genetic code, UGA serves as a termination signal and a Sec codon. This dual role has precluded the automatic annotation of selenoproteins. Recent advances in the computational identification of selenoprotein genes have provided a first glimpse of the size, functions, and phylogenetic diversity of eukaryotic selenoproteomes. Here, we describe the identification of a selenoprotein family named SelJ. In contrast to known selenoproteins, SelJ appears to be restricted to actinopterygian fishes and sea urchin, with Cys homologues only found in cnidarians. SelJ shows significant similarity to the jellyfish J1-crystallins and with them constitutes a distinct subfamily within the large family of ADP-ribosylation enzymes. Consistent with its potential role as a structural crystallin, SelJ has preferential and homogeneous expression in the eye lens in early stages of zebrafish development. A structural role for SelJ would be in contrast to the majority of known selenoenzymes. The unusually highly restricted phylogenetic distribution of SelJ, its specialization, and the comparative analysis of eukaryotic selenoproteomes reveal the diversity and functional plasticity of selenoproteins and point to a mosaic evolution of the use of Sec in proteins.