Biomedical subjects

Gisbert Schneider

Publications and source records attributed to Gisbert Schneider.

33 records · Page 2Linked to original sources

PubMed2003

Properties and prediction of mitochondrial transit peptides from Plasmodium falciparum.

A neural network approach for the prediction of mitochondrial transit peptides (mTPs) from the malaria-causing parasite Plasmodium falciparum is presented. Nuclear-encoded mitochondrial protein precursors of P. falciparum were analyzed by statistical methods, principal component analysis and supervised neural networks, and were compared to those of other eukaryotes. A distinct amino acid usage pattern has been found in protein encoding regions of P. falciparum: glycine, alanine, tryptophan and arginine are under-represented, whereas isoleucine, tyrosine, asparagine and lysine are over-represented compared to the SwissProt average. Similar patterns were observed in mTPs of P. falciparum. Using principal component analysis (PCA), mTPs from P. falciparum were shown to differ considerably from those of other organisms. A neural network system (PlasMit) for prediction of mTPs in P. falciparum sequences was developed, based on the relative amino acid frequency in the first 24 N-terminal amino acids, yielding a Matthews correlation coefficient of 0.74 (90% correct prediction) in a 20-fold cross-validation study. This system predicted 1177 (22%) mitochondrial genes, based on 5334 annotated genes in the P. falciparum genome. A second network with the same topology was trained to give more conservative estimate. This more stringent network yielded a Matthews correlation coefficient of 0.51 (84% correct prediction) in a 10-fold cross-validation study. It predicted 381 (7.1%) mitochondrial genes, based on 5334 annotated genes in the P. falciparum genome.

Amino Acids↗

PubMed2003

Comparison of correlation vector methods for ligand-based similarity searching.

Correlation vector methods were tested for their usefulness in ligand-based virtual screening. Three molecular descriptors--two based on potential pharmacophore points and one on partial atom charges--and three similarity measures--the Manhattan distance, the Euclidian distance and the Tanimoto coefficient--were compared. The alignment-free descriptors seem to be particularly applicable when a course-grain filtering of data sets is required in combination with a high execution speed. Significant enrichment of actives was obtained by retrospective analysis. The cumulative percentages for all three descriptors allow for the retrieval of up to 78% of the active molecules in the first five percent of the reference database. Different descriptors retrieved only weakly overlapping sets of active molecules among the top-ranking compounds. If a single similarity index is to be used, the Manhattan distance seems to be particularly applicable. Generally, none of the three different descriptors tested in this study clearly outperformed the others. The suitability of a descriptor critically depends on the ligand-receptor interaction under investigation. For ligand-based similarity searching it is recommended to exploit several descriptors in parallel.

Algorithms↗

PubMed2003

Improved anaerobic use of arginine by Saccharomyces cerevisiae.

Anaerobic arginine catabolism in Saccharomyces cerevisiae was genetically modified to allow assimilation of all four rather than just three of the nitrogen atoms in arginine. This was accomplished by bypassing normal formation of proline, an unusable nitrogen source in the absence of oxygen, and causing formation of glutamate instead. A pro3 ure2 strain expressing a PGK1 promoter-driven PUT2 allele encoding Delta(1)-pyrroline-5-carboxylate dehydrogenase lacking a mitochondrial targeting sequence produced significant cytoplasmic activity, accumulated twice as much intracellular glutamate, and produced twice as much cell mass as the parent when grown anaerobically on limiting arginine as sole nitrogen source.

Anaerobiosis↗

PubMed2003

Support vector machine applications in bioinformatics.

The support vector machine (SVM) approach represents a data-driven method for solving classification tasks. It has been shown to produce lower prediction error compared to classifiers based on other methods like artificial neural networks, especially when large numbers of features are considered for sample description. In this review, the theory and main principles of the SVM approach are outlined, and successful applications in traditional areas of bioinformatics research are described. Current developments in techniques related to the SVM approach are reviewed which might become relevant for future functional genomics and chemogenomics projects. In a comparative study, we developed neural network and SVM models to identify small organic molecules that potentially modulate the function of G-protein coupled receptors. The SVM system was able to correctly classify approximately 90% of the compounds in a cross-validation study yielding a Matthews correlation coefficient of 0.78. This classifier can be used for fast filtering of compound libraries in virtual screening applications.

Algorithms↗

PubMed2002

A virtual screening method for prediction of the HERG potassium channel liability of compound libraries.

A computer-based method has been developed for prediction of the hERG (human ether-à-go-go related gene) K(+)-channel affinity of low molecular weight compounds. hERG channel blockage is a major concern in drug design, as such blocking agents can cause sudden cardiac death. Various techniques were applied to finding appropriate molecular descriptors for modeling structure-activity relationships: substructure analysis, self-organizing maps (SOM), principal component analysis (PCA), partial least squares fitting (PLS), and supervised neural networks. The most accurate prediction system was based on an artificial neural network. In a validation study, 93 % of the nonblocking agents and 71 % of the hERG channel blockers were correctly classified. This virtual screening method can be used for general compound-library shaping and combinatorial library design.

Cation Transport Proteins↗

PubMed2002

Development of a virtual screening method for identification of "frequent hitters" in compound libraries.

A computer-based method was developed for rapid and automatic identification of potential "frequent hitters". These compounds show up as hits in many different biological assays covering a wide range of targets. A scoring scheme was elaborated from substructure analysis, multivariate linear and nonlinear statistical methods applied to several sets of one and two-dimensional molecular descriptors. The final model is based on a three-layered neural network, yielding a predictive Matthews correlation coefficient of 0.81. This system was able to correctly classify 90% of the test set molecules in a 10-times cross-validation study. The method was applied to database filtering, yielding between 8% (compilation of trade drugs) and 35% (Available Chemicals Directory) potential frequent hitters. This filter will be a valuable tool for the prioritization of compounds from large databases, for compound purchase and biological testing, and for building new virtual libraries.

Databases, Factual↗

PubMed2002

Virtual screening and fast automated docking methods.

Recent advances in high-throughput protein structure determination and in computational chemistry have refocused attention on virtual screening and fast automated docking methods. This review provides a brief introduction to the basic ideas and outlines computational tools currently used. We also provide several examples of where virtual screening has proved successful, highlighting the usefulness of the approach.

Combinatorial Chemistry Techniques↗

PubMed2002

Trends in virtual combinatorial library design.

Recent developments in combinatorial molecular design using virtual screening methods are summarised. These include similarity-based compound clustering techniques, structure-based docking and scoring, and fragment-based de novo design. Three major trends have been identified: i) the design of small target-focused compound libraries yielding activity-enriched sets of molecules; ii) advanced prediction methods for "drug-like" molecular properties complement activity predictions in the library design process, forming a multi-dimensional objective function; iii) "cherry picking" of selected products is increasingly used in lead generation and optimisation compared to purely educt-driven library design methods aiming at maximising structural diversity.

Algorithms↗

PubMedDate not supplied

Ligand-based combinatorial design of selective purinergic receptor (A2A) antagonists using self-organizing maps.

A virtual screening procedure based on a topological pharmacophore similarity metric and self-organizing maps (SOM) was developed and applied to optimizing combinatorial products functioning as P(1) purinergic receptor antagonists. The target was the human A(2A) receptor. A SOM was developed using a set of biologically tested molecules to establish a preliminary structure-activity relationship. A combinatorial library design was performed by projecting virtually assembled new molecules onto the SOM. A small focused library of 17 selected combinatorial products was synthesized and tested. On average, the designed structures yielded a 3-fold smaller binding constant ( approximately 33 vs approximately 100 nM) and 3.5-fold higher selectivity (50 vs 14) than the initial library. The most selective compound obtained revealed a 121-fold relative selectivity for A(2A) with K(i) (A(2A)) = 2.4 nM, and K(i) (A(1)) = 292 nM. This result demonstrates that it was possible to design a small, activity-enriched focused library with an improved property profile using the SOM virtual screening approach. The strategy might be particularly useful in projects in which structure-based design cannot be applied because of a lack of receptor structure information, for example, in the many projects aiming at finding new GPCR modulators.

Adenosine A2 Receptor Antagonists↗

PubMedDate not supplied

Comparison of support vector machine and artificial neural network systems for drug/nondrug classification.

Support vector machine (SVM) and artificial neural network (ANN) systems were applied to a drug/nondrug classification problem as an example of binary decision problems in early-phase virtual compound filtering and screening. The results indicate that solutions obtained by SVM training seem to be more robust with a smaller standard error compared to ANN training. Generally, the SVM classifier yielded slightly higher prediction accuracy than ANN, irrespective of the type of descriptors used for molecule encoding, the size of the training data sets, and the algorithm employed for neural network training. The performance was compared using various different descriptor sets and descriptor combinations based on the 120 standard Ghose-Crippen fragment descriptors, a wide range of 180 different properties and physicochemical descriptors from the Molecular Operating Environment (MOE) package, and 225 topological pharmacophore (CATS) descriptors. For the complete set of 525 descriptors cross-validated classification by SVM yielded 82% correct predictions (Matthews cc = 0.63), whereas ANN reached 80% correct predictions (Matthews cc = 0.58). Although SVM outperformed the ANN classifiers with regard to overall prediction accuracy, both methods were shown to complement each other, as the sets of true positives, false positives (overprediction), true negatives, and false negatives (underprediction) produced by the two classifiers were not identical. The theory of SVM and ANN training is briefly reviewed.

Algorithms↗

PubMedDate not supplied

SVM-based feature selection for characterization of focused compound collections.

Artificial neural networks, the support vector machine (SVM), and other machine learning methods for the classification of molecules are often considered as a "black box", since the molecular features that are most relevant for a given classifier are usually not presented in a human-interpretable form. We report on an SVM-based algorithm for the selection of relevant molecular features from a trained classifier that might be important for an understanding of ligand-receptor interactions. The original SVM approach was extended to allow for feature selection. The method was applied to characterize focused libraries of enzyme inhibitors. A comparison with classical Kolmogorov-Smirnov (KS)-based feature selection was performed. In most of the applications the SVM method showed sustained classification accuracy, thereby relying on a smaller number of molecular features than KS-based classifiers. In one case both methods produced comparable results. Limiting the calculation of descriptors to only the most relevant ones for a certain biological activity can also be used to speed up high-throughput virtual screening.

Algorithms↗

PubMedDate not supplied

A hierarchical clustering approach for large compound libraries.

A modified version of the k-means clustering algorithm was developed that is able to analyze large compound libraries. A distance threshold determined by plotting the sum of radii of leaf clusters was used as a termination criterion for the clustering process. Hierarchical trees were constructed that can be used to obtain an overview of the data distribution and inherent cluster structure. The approach is also applicable to ligand-based virtual screening with the aim to generate preferred screening collections or focused compound libraries. Retrospective analysis of two activity classes was performed: inhibitors of caspase 1 [interleukin 1 (IL1) cleaving enzyme, ICE] and glucocorticoid receptor ligands. The MDL Drug Data Report (MDDR) and Collection of Bioactive Reference Analogues (COBRA) databases served as the compound pool, for which binary trees were produced. Molecules were encoded by all Molecular Operating Environment 2D descriptors and topological pharmacophore atom types. Individual clusters were assessed for their purity and enrichment of actives belonging to the two ligand classes. Significant enrichment was observed in individual branches of the cluster tree. After clustering a combined database of MDDR, COBRA, and the SPECS catalog, it was possible to retrieve MDDR ICE inhibitors with new scaffolds using COBRA ICE inhibitors as seeds. A Java implementation of the clustering method is available via the Internet (http://www.modlab.de).

Algorithms↗

PubMedDate not supplied

Impact of conformational flexibility on three-dimensional similarity searching using correlation vectors.

Many three-dimensional (3D) virtual screening concepts, like automated docking or pharmacophore searching, rely on the calculation of a "bioactive" or "receptor-relevant" conformation of a molecule to assess its biological activity. We investigated the dependence of the presence of conformations near the "bioactive" conformation on three-dimensional similarity searching with pharmacophore-based correlation vectors (CATS3D approach). Cocrystal structures of 11 target classes served as queries for virtual screening of a database of annotated ligands. Different numbers of conformations were calculated. Single 3D structures were obtained using the 3D structure generator CORINA and conformational ensembles by the conformation generation program ROTATE. This approach was able to reproduce conformations for high resolution cocrystal structures. For virtual screening we found that using only the CORINA-generated single conformation already resulted in a significant enrichment of isofunctional molecules having the same biological property profile. This observation was also made for ligand classes with many rotatable bonds. Although more similar conformations were considered to be more similar in the CATS3D description, the impact of using multiple conformations on the enrichment of actives was not as high as expected. CATS3D provides an alignment-free three-dimensional virtual screening approach that is less dependent on the presence of conformations which are close to the "bioactive" conformation of a molecule compared to methods that rely on an explicit three-dimensional alignment of molecules.

Algorithms↗

PubMedDate not supplied

Flux (1): a virtual synthesis scheme for fragment-based de novo design.

It is demonstrated that the fragmentation of druglike molecules by applying simplistic pseudo-retrosynthesis results in a stock of chemically meaningful building blocks for de novo molecule generation. A stochastic search algorithm in conjunction with ligand-based similarity scoring (Flux: fragment-based ligand builder reaxions) facilitated the generation of new molecules using a single known reference compound as a template. This molecule assembly method is applicable in the absence of receptor-structure information. In a case study, we used imantinib (Gleevec) and a Factor Xa inhibitor as the reference structures. The algorithm succeeded in redesigning the templates from scratch and suggested several alternative molecular structures. The resulting designed molecules were chemically reasonable and contained essential substructure motifs. A comparison of molecular descriptors suggests that holographic descriptors might be advantageous over binary fingerprints for ligand-based de novo design.

Algorithms↗

PubMedDate not supplied

NIPALSTREE: a new hierarchical clustering approach for large compound libraries and its application to virtual screening.

A hierarchical clustering algorithm--NIPALSTREE--was developed that is able to analyze large data sets in high-dimensional space. The result can be displayed as a dendrogram. At each tree level the algorithm projects a data set via principle component analysis onto one dimension. The data set is sorted according to this one dimension and split at the median position. To avoid distortion of clusters at the median position, the algorithm identifies a potentially more suited split point left or right of the median. The procedure is recursively applied on the resulting subsets until the maximal distance between cluster members exceeds a user-defined threshold. The approach was validated in a retrospective screening study for angiotensin converting enzyme (ACE) inhibitors. The resulting clusters were assessed for their purity and enrichment in actives belonging to this ligand class. Enrichment was observed in individual branches of the dendrogram. In further retrospective virtual screening studies employing the MDL Drug Data Report (MDDR), COBRA, and the SPECS catalog, NIPALSTREE was compared with the hierarchical k-means clustering approach. Results show that both algorithms can be used in the context of virtual screening. Intersecting the result lists obtained with both algorithms improved enrichment factors while losing only few chemotypes.

Algorithms↗