Biomedical subjects

Hans-Peter Lenhof

Publications and source records attributed to Hans-Peter Lenhof.

10 recordsLinked to original sources

PubMed2006

A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors.

BACKGROUND: The development of effective frameworks that permit an accurate diagnosis of tumors, especially in their early stages, remains a grand challenge in the field of bioinformatics. Our approach uses statistical learning techniques applied to multiple antigen tumor antigen markers utilizing the immune system as a very sensitive marker of molecular pathological processes. For validation purposes we choose the intracranial meningioma tumors as model system since they occur very frequently, are mostly benign, and are genetically stable. RESULTS: A total of 183 blood samples from 93 meningioma patients (WHO stages I-III) and 90 healthy controls were screened for seroreactivity with a set of 57 meningioma-associated antigens. We tested several established statistical learning methods on the resulting reactivity patterns using 10-fold cross validation. The best performance was achieved by Naïve Bayes Classifiers. With this classification method, our framework, called Minimally Invasive Multiple Marker (MIMM) approach, yielded a specificity of 96.2%, a sensitivity of 84.5%, and an accuracy of 90.3%, the respective area under the ROC curve was 0.957. Detailed analysis revealed that prediction performs particularly well on low-grade (WHO I) tumors, consistent with our goal of early stage tumor detection. For these tumors the best classification result with a specificity of 97.5%, a sensitivity of 91.3%, an accuracy of 95.6%, and an area under the ROC curve of 0.971 was achieved using a set of 12 antigen markers only. This antigen set was detected by a subset selection method based on Mutual Information. Remarkably, our study proves that the inclusion of non-specific antigens, detected not only in tumor but also in normal sera, increases the performance significantly, since non-specific antigens contribute additional diagnostic information. CONCLUSION: Our approach offers the possibility to screen members of risk groups as a matter of routine such that tumors hopefully can be diagnosed immediately after their genesis. The early detection will finally result in a higher cure- and lower morbidity-rate.

Algorithms↗

PubMed2006

BALLView: an object-oriented molecular visualization and modeling framework.

We present BALLView, an extensible tool for visualizing and modeling bio-molecular structures. It provides a variety of different models for bio-molecular visualization, e.g. ball-and-stick models, molecular surfaces, or ribbon models. In contrast to most existing visualization tools, BALLView also offers rich functionality for molecular modeling and simulation, including molecular mechanics methods (AMBER and CHARMM force fields), continuum electrostatics methods employing a Finite-Difference Poisson Boltzmann solver, and secondary structure calculation. Results of these computations can be exported as publication quality images or as movies. Even unexperienced users have direct access to this functionality through an intuitive graphical user interface, which makes BALLView particularly useful for teaching. For more advanced users, BALLView is extensible in different ways. Owing to its framework design, extension on the level of C per thousand+ per thousand per thousand+ code is very convenient. In addition, an interface to the scripting language Python allows the interactive rapid prototyping of new methods. BALLView is portable and runs on all major platforms (Windows, MacOS X, Linux, most Unix flavors). It is available free of charge under the GNU Public License (GPL) from our website http://www.ballview.org.

Computer Graphics↗

PubMed2005

BALLView: a tool for research and education in molecular modeling.

We present BALLView, a molecular viewer and modeling tool. It combines state-of-the-art visualization capabilities with powerful modeling functionality including implementations of force field methods and continuum electrostatics models. BALLView is a versatile and extensible tool for research in structural bioinformatics and molecular modeling. Furthermore, the convenient and intuitive graphical user interface offers novice users direct access to the full functionality, rendering it ideal for teaching. Through an interface to the object-oriented scripting language Python it is easily extensible.

Computer Graphics↗

PubMed2005

GraBCas: a bioinformatics tool for score-based prediction of Caspase- and Granzyme B-cleavage sites in protein sequences.

Caspases and granzyme B are proteases that share the primary specificity to cleave at the carboxyl terminal of aspartate residues in their substrates. Both, caspases and granzyme B are enzymes that are involved in fundamental cellular processes and play a central role in apoptotic cell death. Although various targets are described, many substrates still await identification and many cleavage sites of known substrates are not identified or experimentally verified. A more comprehensive knowledge of caspase and granzyme B substrates is essential to understand the biological roles of these enzymes in more detail. The relatively high variability in cleavage site recognition sequence often complicates the identification of cleavage sites. As of yet there is no software available that allows identification of caspase and/or granzyme with cleavage sites differing from the consensus sequence. Here, we present a bioinformatics tool 'GraBCas' that provides score-based prediction of potential cleavage sites for the caspases 1-9 and granzyme B including an estimation of the fragment size. We tested GraBCas on already known substrates and showed its usefulness for protein sequence analysis. GraBCas is available at http://wwwalt.med-rz.uniklinik-saarland.de/med_fak/humangenetik/software/index.html.

Caspases↗

PubMed2005

Complex humoral immune response against a benign tumor: frequent antibody response against specific antigens as diagnostic targets.

There are numerous studies on the immune response against malignant human tumors. This study was aimed to address the complexity and specificity of humoral immune response against a benign human tumor. We assembled a panel of 62 meningioma-expressed antigens that show reactivity with serum antibodies of meningioma patients, including 41 previously uncharacterized antigens by screening of a fetal brain expression library. We tested the panel for reactivity with 48 sera, including sera of patients with common-type, atypical, and anaplastic meningioma, respectively. Meningioma sera detected an average of 14.6 antigens per serum and normal sera an average of 7.8 antigens per serum (P = 0.0001). We found a decline of seroreactivity with malignancy with a statistical significant difference between common-type and anaplastic meningioma (P < 0.05). We detected 17 antigens exclusively with patient sera, including 12 sera that were reactive against KIAA1344, 9 against natural killer tumor recognition (NKTR), and 7 against SRY (sex determining region Y)-box2 (SOX2). More than 80% of meningioma patients had antibodies against at least one of the antigens KIAA1344, SC65, SOX2, and C6orf153. Our results show a highly complex but specific humoral immune response against a benign tumor with a distinct serum reactivity pattern and a decline of complexity with malignancy. The frequent antibody response against specific antigens offers new diagnostic and therapeutic targets for meningioma. We developed a statistical learning method to differentiate sera of meningioma patients from sera of healthy donors.

Antibodies, Neoplasm↗

PubMed2004

Integrative analysis of cancer-related data using CAP.

The development of human cancer is a highly complex process and can be considered the result of several combined events, such as genetic alterations, disturbance of signal transduction, or failure of immunological surveillance. Cancer-related databases usually focus on specific fields of research, e.g., cancer genetics or cancer immunology, whereas the complexity of cancer genesis requires an integrated analysis of heterogeneous data from several sources. Here we present the cancer-associated protein database (CAP), a novel analysis system for cancer-related data. CAP integrates data from multiple external databases, augments these data with functional annotations, and offers tools for statistical analysis of these data. We have employed CAP to analyze genes that have been found to cause an autoimmune response in cancer. In particular, we explored the connection between the autoimmune response, mutations, and overexpression of these genes. Our preliminary results suggest that mutations are not significant contributors to raising an antibody response against tumor antigens, whereas overexpression seems to play a more important role. We hereby demonstrate how different types of data can be integrated and analyzed successfully, providing interesting results. As the amount of available data is growing rapidly, a combined analysis will play an important role in exploring the genetic and immunological basis of cancer. CAP is freely available at the following web site: http://www.bioinf.uni-sb.de/CAP/.

Autoimmunity↗

PubMed2004

Computational modeling of the sugar-lectin interaction.

In the last few years numerous experimental studies have shed light onto the details of the lectin-carbohydrate interaction. X-ray crystallography and NMR spectroscopy have been used to elucidate the structures of lectins, sugars, and their complexes. In addition, an increasing number of experimental methods has been employed to determine the thermodynamic and kinetic parameters of the binding process. Based on this experimental data, computational methods have been developed to model and predict these interactions. A plethora of techniques from Molecular Modeling and Computational Chemistry have been applied to the problem and current models achieve high-quality predictions. These successes are based on both new theoretical approaches and reliable experimental data. The aim of the present article is to outline the most relevant computational and experimental methods applied in the field of lectin-carbohydrate interaction and to give an overview of the current state of the art in the modeling of these interactions with a focus on plant lectins.

Carbohydrates↗

PubMed2002

Lectin-sugar interaction. Calculated versus experimental binding energies.

Although a steadily increasing number of protein--ligand docking experiments have been performed successfully, there are only few studies concerning protein--sugar interactions. In this study, we investigate the interaction of wheat germ agglutinin (WGA) with N-acetylglucosamine and a number of its derivatives and predict the binding free energies using flexible docking techniques. To assess the quality of our predictions, we also determined those binding free energies experimentally in cell-binding studies. The predicted binding site, ligand orientation, and details of the binding mode are in perfect agreement with the known crystal structure of WGA with a sialoglycopeptide. Furthermore, we obtained an excellent linear correlation of our predicted binding free energies with both our own data and experimental data from the literature [Monsigny, M., Roche, A.C., Sene, C., Maget Dana, R. & Delmotte, F. (1980) Eur. J. Biochem. 104, 147-153.]. In both cases, predicted energies were within 1.0 kJ x mol(-1) of the experimental value. These results illustrate the usefulness of docking-based methods for the qualitative and quantitative prediction of protein--carbohydrate interactions. The insights gained from such theoretical studies may be used to complement the results from the still scarce crystal structures.

Binding Sites↗

PubMed2002

A combinatorial approach to protein docking with flexible side chains.

Rigid-body docking approaches are not sufficient to predict the structure of a protein complex from the unbound (native) structures of the two proteins. Accounting for side chain flexibility is an important step towards fully flexible protein docking. This work describes an approach that allows conformational flexibility for the side chains while keeping the protein backbone rigid. Starting from candidates created by a rigid-docking algorithm, we demangle the side chains of the docking site, thus creating reasonable approximations of the true complex structure. These structures are ranked with respect to the binding free energy. We present two new techniques for side chain demangling. Both approaches are based on a discrete representation of the side chain conformational space by the use of a rotamer library. This leads to a combinatorial optimization problem. For the solution of this problem, we propose a fast heuristic approach and an exact, albeit slower, method that uses branch-and-cut techniques. As a test set, we use the unbound structures of three proteases and the corresponding protein inhibitors. For each of the examples, the highest-ranking conformation produced was a good approximation of the true complex structure.

Algorithms↗

PubMed2002

Multiple sequence alignment with arbitrary gap costs: computing an optimal solution using polyhedral combinatorics.

Multiple sequence alignment is one of the dominant problems in computational molecular biology. Numerous scoring functions and methods have been proposed, most of which result in NP-hard problems. In this paper we propose for the first time a general formulation for multiple alignment with arbitrary gap-costs based on an integer linear program (ILP). In addition we describe a branch-and-cut algorithm to effectively solve the ILP to optimality. We evaluate the performances of our approach in terms of running time and quality of the alignments using the BAliBase database of reference alignments. The results show that our implementation ranks amongst the best programs developed so far.

Algorithms↗