Biomedical subjects

Clemens Gröpl

Publications and source records attributed to Clemens Gröpl.

3 recordsLinked to original sources

PubMed2006

Absolute myoglobin quantitation in serum by combining two-dimensional liquid chromatography-electrospray ionization mass spectrometry and novel data analysis algorithms.

To measure myoglobin, a marker for myocardial infarction, directly in human serum, two-dimensional liquid chromatography in combination with electrospray ionization mass spectrometry was applied as an analytical method. High-abundant serum proteins were depleted by strong anion-exchange chromatography. The myoglobin fraction was digested and injected onto a 60 mm x 0.2 mm i.d. monolithic capillary column for quantitation of selected peptides upon mass spectrometric detection. The addition of known amounts of myoglobin to the serum sample was utilized for calibration, and horse myoglobin was added as an internal standard to improve reproducibility. Calibration graphs were linear and facilitated the reproducible and accurate determination of the myoglobin amount present in serum. Manual data evaluation using integrated peak areas and an automated multistage algorithm fitting two-dimensional models of peptide elution profiles and isotope patterns to the mass spectrometric raw data were compared. When the automated method was applied, a myoglobin concentration of 460 pg/microL serum was determined with a maximum relative deviation from the theoretical value of 10.1% and a maximum relative standard deviation of 13.4%.

Algorithms↗

PubMed2006

High-accuracy peak picking of proteomics data using wavelet techniques.

A new peak picking algorithm for the analysis of mass spectrometric (MS) data is presented. It is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In an optional third stage, the resulting fit can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques (e.g. SNAP, Apex) our algorithm is able to separate overlapping peaks of multiply charged peptides in ESI-MS data of low resolution. Its improved accuracy with respect to peak positions makes it a valuable preprocessing method for MS-based identification and quantification experiments. The method has been validated on a number of different annotated test cases, where it compares favorably in both runtime and accuracy with currently established techniques. An implementation of the algorithm is freely available in our open source framework OpenMS.

Algorithms↗

PubMed2003

Accelerating screening of 3D protein data with a graph theoretical approach.

MOTIVATION: The Dictionary of Interfaces in Proteins (DIP) is a database collecting the 3D structure of interacting parts of proteins that are called patches. It serves as a repository, in which patches similar to given query patches can be found. The computation of the similarity of two patches is time consuming and traversing the entire DIP requires some hours. In this work we address the question of how the patches similar to a given query can be identified by scanning only a small part of DIP. The answer to this question requires the investigation of the distribution of the similarity of patches. RESULTS: The score values describing the similarity of two patches can roughly be divided into three ranges that correspond to different levels of spatial similarity. Interestingly, the two iso-score lines separating the three classes can be determined by two different approaches. Applying a concept of the theory of random graphs reveals significant structural properties of the data in DIP. These can be used to accelerate scanning the DIP for patches similar to a given query. Searches for very similar patches could be accelerated by a factor of more than 25. Patches with a medium similarity could be found 10 times faster than by brute-force search.

Algorithms↗