Hans-Peter Lenhof

Bio: Hans-Peter Lenhof is an academic researcher from Saarland University. The author has contributed to research in topics: Macromolecular docking & Autoantibody. The author has an hindex of 44, co-authored 164 publications receiving 6046 citations. Previous affiliations of Hans-Peter Lenhof include Max Planck Society.

Calculated versus experimental binding energies

Abstract: 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 energiesexperimentallyincell-bindingstudies.Thepredicted 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 experi

Abstract analysis of pathways using the BN++ software framework

Abstract: analysis of pathways using the BN++ software framework Jan Küntzer*1, Benny Kneissl1, Oliver Kohlbacher2 and Hans-Peter Lenhof1 Address: 1Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany and 2Center for Bioinformatics/Wilhelm Schickard Institute for Computer Science, Eberhard-Karls-Universität Tübingen, 72076 Tübingen, Germany Email: Jan Küntzer* kuentzer@bioinf.uni-sb.de * Corresponding author Introduction Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a vast amount of life science data, including pathway information such as metabolic and regulatory pathways. The rapid increase of these data for various organisms offers the possibility to perform analyses on the networks for single organisms (intra-species) as well as across different organisms (inter-species). However, the sheer amount and heterogeneity of the data pose a major challenge and call for an integrative system, allowing to manage all this information. With BN++, especially its C++ framework, we presented such a system [2]. In contrast to databases (e.g. KEGG, Reactome, IntAct,...), which offer only predefined analyses such as minimal connected component or pathway detection from a start to an end compound, the mathematical graph representation in BN++ allows additionally the implementation of own routines. The analysis of biochemical pathway information has different applications, e.g. in the process of target identification, drug design and in the search for causes of genetic diseases. Therefore, nodes or edges are removed and alternative pathways in an organism need to be identified. In basic research these networks can be used for the comparison of metabolic processes of different organisms. For example the information on the metabolism of one organism can be used to understand the newly sequenced genome (and, hence the metabolic pathways) of another organism as presented in [1]. Background To understand the mathematical graph representation, we first need to define some concepts: We define G(V, E) to be a mathematical graph, where V denotes a finite set of nodes of G and E = (V × V) denotes a set of pairs of nodes, called the edges of the G. A graph G(V, E) is defined as bipartite, iff V = V1 ∪ V2 can be partitioned into two sets V1 and V2 such that V1 V2 = ∅ and (u, v) ∈ E = ((V1 × V2) ∪ (V2 × V1)) implies either u ∈ V1 and v ∈ V2, or u ∈ V2 and v ∈ V1. The modeling of a biochemical pathway as a mathematical graph can be done in different ways, differing in the interpretation of the nodes and edges. Hence, various data models have been developed over the last years. The mostly used models are presented in [3]. We will define three different models in the following: First, we define a bipartite reaction graph to be a bipartite graph, where V1 contains all events and V2 all compounds. A node A is connected with a directed edge to node B, iff compound A plays the role of an educt in event B or if compound B plays the role of a product in event A. Second, a compound graph is defined as a mathematical graph, where the nodes are the chemical compounds. A is connected with a directed edge to B, iff compound A plays the role of an educt and compound B plays the role of a product in the same event. Third, a event graph defines a mathematical graph, where the nodes are the events. A node A is connected with a directed edge to B, iff a compound Y plays the role of an educt in the event A and the role of a product in the event B. from BioSysBio 2007: Systems Biology, Bioinformatics and Synthetic Biology Manchester, UK. 11–13 January 2007 Published: 8 May 2007 BMC Systems Biology 2007, 1(Suppl 1):P24 doi:10.1186/1752-0509-1-S1-P24 John Cumbers, Xu Gu, Jong Sze Wong Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/pdf/1752-0509-1-S1-info.pdf This abstract is available from: http://www.biomedcentral.com/1752-0509/1?issue=S1 © 2007 Küntzer et al; licensee BioMed Central Ltd.

GeneTrail: A Framework for the Analysis of High-Throughput Profiles.

Abstract: Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms. GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de.

New contact measures for the protein docking problem

Abstract: We have developed and implemented a parallel distributed algorithm for the rigid-body protein docking problem. The algorithm is based on a new fitness fuJ1ction for evaluating the surface mat,ching of a given conformation. The fitJless function is defined as the weighted sum of two contact. measures, tt1e geometric contact measurr and the chemical c~ontact mensuw. 1‘t 1e rometric contact measure measures g the “size” of the contact, area of two molecules. It. is a potential fJmctioJ1 that counts the “van der Waals contacts” t)et,ween t,he atoms of t,he two molecules (the algorithm does JJot c0mput.e the I,ennard-Jones potential). The chemical contact measure is also based on the “van der Waals contacts” prirlciple: We consider all atom pairs that have a ‘

A diagnostic in-vitro method or assay for diagnosing chronic obstructive pulmonary disease (COPD)

Abstract: The present invention refers to an in-vitro diagnostic assay or method for diagnosing chronic obstructive pulmonary disease (COPD), comprising screening for the presence of antibodies in a blood or serum sample, wherein the antibodies are directed to MCM3 (SEQ ID NO: 1).

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

Abstract: Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria

Abstract: New evidence and consensus has led to further revision of the McDonald Criteria for diagnosis of multiple sclerosis. The use of imaging for demonstration of dissemination of central nervous system lesions in space and time has been simplified, and in some circumstances dissemination in space and time can be established by a single scan. These revisions simplify the Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use.

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.

Abstract: A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Abstract: Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.