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Biomedical subjects

Daniel Fischer

Publications and source records attributed to Daniel Fischer.

8 recordsLinked to original sources
PubMed2003

3D-Jury: a simple approach to improve protein structure predictions.

MOTIVATION: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins. RESULTS: The 3D-Jury system generates meta-predictions from sets of models created using variable methods. It is not necessary to know prior characteristics of the methods. The system is able to utilize immediately new components (additional prediction providers). The accuracy of the system is comparable with other well-tuned prediction servers. The algorithm resembles methods of selecting models generated using ab initio folding simulations. It is simple and offers a portable solution to improve the accuracy of other protein structure prediction protocols. AVAILABILITY: The 3D-Jury system is available via the Structure Prediction Meta Server (http://BioInfo.PL/Meta/) to the academic community. SUPPLEMENTARY INFORMATION: 3D-Jury is coupled to the continuous online server evaluation program, LiveBench (http://BioInfo.PL/LiveBench/)

Algorithms↗
PubMed2003

3D-SHOTGUN: a novel, cooperative, fold-recognition meta-predictor.

To gain a better understanding of the biological role of proteins encoded in genome sequences, knowledge of their three-dimensional (3D) structure and function is required. The computational assignment of folds is becoming an increasingly important complement to experimental structure determination. In particular, fold-recognition methods aim to predict approximate 3D models for proteins bearing no sequence similarity to any protein of known structure. However, fully automated structure-prediction methods can currently produce reliable models for only a fraction of these sequences. Using a number of semiautomated procedures, human expert predictors are often able to produce more and better predictions than automated methods. We describe a novel, fully automatic, fold-recognition meta-predictor, named 3D-SHOTGUN, which incorporates some of the strategies human predictors have successfully applied. This new method is reminiscent of the so-called cooperative algorithms of Computer Vision. The input to 3D-SHOTGUN are the top models predicted by a number of independent fold-recognition servers. The meta-predictor consists of three steps: (i) assembly of hybrid models, (ii) confidence assignment, and (iii) selection. We have applied 3D-SHOTGUN to an unbiased test set of 77 newly released protein structures sharing no sequence similarity to proteins previously released. Forty-six correct rank-1 predictions were obtained, 30 of which had scores higher than that of the first incorrect prediction-a significant improvement over the performance of all individual servers. Furthermore, the predicted hybrid models were, on average, more similar to their corresponding native structures than those produced by the individual servers. This opens the possibility of generating more accurate, full-atom homology models for proteins with no sequence similarity to proteins of known structure. These improvements represent a step forward toward the wider applicability of fully automated structure-prediction methods at genome scales.

Algorithms↗
PubMed2003

Twenty thousand ORFan microbial protein families for the biologist?

The genomes of most newly sequenced organisms contain a significant fraction of ORFs (open reading frames) that match no other sequence in the databases. We refer to these singleton ORFs as sequence ORFans. Because little can be learned about ORFans by homology, the origin and functions of ORFans remain a mystery. However, in this era of full genome sequencing, it seems that ORFans have been underemphasized. In this minireview, we draw attention to the increasing number of ORFans and to the consequences of this growth to biological research in the postgenomic era.

Animals↗
PubMed2003

The 2002 Olympic Games of protein structure prediction.

The summer of every even year is considered by the protein structure prediction community as the Olympic Games season, because in addition to a number of continuous benchmarking experiments such as LiveBench, much effort is invested in the blind prediction experiments CASP and CAFASP. Here we report the major advances registered in the field since the last Games of 2000, as measured by the recently completed LiveBench-4 experiment. These results provide a timely measure of the capabilities of current methods and of their expected performance in the upcoming CASP-5 and CAFASP-3 experiments. We also describe the initiation of the two new, community-wide experiments, PDB-CAFASP and MR-CAFASP. These new experiments extend the scope of previous efforts and may have important implications for structural genomics.

Computational Biology↗
PubMedDate not supplied

Microbial desizing using starch as model compound: enzyme properties and desizing efficiency.

A film of sizing agents protects yarn during weaving. Its removal in a subsequent washing process causes 50% of the organic effluent load of textile finishing processes and requires large amounts of auxiliary chemicals (e.g., surfactants). Microbial desizing is a new bioprocess that uses the acidifying culture of a two-phase anaerobic digestion plant for the removal and partial degradation (acidification) of the sizing agent. Soluble starch is used in this study to characterize the enzymatic properties in the supernatant of the desizing culture and to link them to desizing efficiencies. The supernatant of the culture (grown at 37 degrees C, pH 5.5) displayed the highest enzymatic activity between pH 4 and 5 and in a broad temperature range (20-80 degrees C). Highest metabolization rates were determined with the substrate amylose. Short chain dextrins (average of 5 and 10 glucose units) and amylopectin were converted significantly more slowly. At 37 degrees C the half-life time of the enzymatic activity in the supernatant was 45 h. In a desizing test a decisive reduction of the chain length was found already after 1 h (allowing starch solubilization). A microbial desizing experiment with dyed, native maize starch demonstrated the efficiency of the proposed bioprocess.

Bacteria, Anaerobic↗