Biomedical subjects

Jan E Gewehr

Publications and source records attributed to Jan E Gewehr.

3 recordsLinked to original sources

PubMed2005

SSEP-Domain: protein domain prediction by alignment of secondary structure elements and profiles.

MOTIVATION: The prediction of protein domains is a crucial task for functional classification, homology-based structure prediction and structural genomics. In this paper, we present the SSEP-Domain protein domain prediction approach, which is based on the application of secondary structure element alignment (SSEA) and profile-profile alignment (PPA) in combination with InterPro pattern searches. SSEA allows rapid screening for potential domain regions while PPA provides us with the necessary specificity for selecting significant hits. The combination with InterPro patterns allows finding domain regions without solved structural templates if sequence family definitions exist. RESULTS: A preliminary version of SSEP-Domain was ranked among the top-performing domain prediction servers in the CASP 6 and CAFASP 4 experiments. Evaluation of the final version shows further improvement over these results together with a significant speed-up. AVAILABILITY: The server is available at http://www.bio.ifi.lmu.de/SSEP/

Algorithms↗

PubMed2005

QUASAR--scoring and ranking of sequence-structure alignments.

SUMMARY: Sequence-structure alignments are a common means for protein structure prediction in the fields of fold recognition and homology modeling, and there is a broad variety of programs that provide such alignments based on sequence similarity, secondary structure or contact potentials. Nevertheless, finding the best sequence-structure alignment in a pool of alignments remains a difficult problem. QUASAR (quality of sequence-structure alignments ranking) provides a unifying framework for scoring sequence-structure alignments that aids finding well-performing combinations of well-known and custom-made scoring schemes. Those scoring functions can be benchmarked against widely accepted quality scores like MaxSub, TMScore, Touch and APDB, thus enabling users to test their own alignment scores against 'standard-of-truth' structure-based scores. Furthermore, individual score combinations can be optimized with respect to benchmark sets based on known structural relationships using QUASAR's in-built optimization routines.

Computational Biology↗

PubMed2004

Binding matrix: a novel approach for binding site recognition.

Recognition of protein-DNA binding sites in genomic sequences is a crucial step for discovering biological functions of genomic sequences. Explosive growth in availability of sequence information has resulted in a demand for binding site detection methods with high specificity. The motivation of the work presented here is to address this demand by a systematic approach based on Maximum Likelihood Estimation. A general framework is developed in which a large class of binding site detection methods can be described in a uniform and consistent way. Protein-DNA binding is determined by binding energy, which is an approximately linear function within the space of sequence words. All matrix based binding word detectors can be regarded as different linear classifiers which attempt to estimate the linear separation implied by the binding energy function. The standard approaches of consensus sequences and profile matrices are described using this framework. A maximum likelihood approach for determining this linear separation leads to a novel matrix type, called the binding matrix. The binding matrix is the most specific matrix based classifier which is consistent with the input set of known binding words. It achieves significant improvements in specificity compared to other matrices. This is demonstrated using 95 sets of experimentally determined binding words provided by the TRANSFAC database.

Algorithms↗