Biomedical subjects

Björn Wallner

Publications and source records attributed to Björn Wallner.

8 recordsLinked to original sources

PubMed2006

Identification of correct regions in protein models using structural, alignment, and consensus information.

In this study we present two methods to predict the local quality of a protein model: ProQres and ProQprof. ProQres is based on structural features that can be calculated from a model, while ProQprof uses alignment information and can only be used if the model is created from an alignment. In addition, we also propose a simple approach based on local consensus, Pcons-local. We show that all these methods perform better than state-of-the-art methodologies and that, when applicable, the consensus approach is by far the best approach to predict local structure quality. It was also found that ProQprof performed better than other methods for models based on distant relationships, while ProQres performed best for models based on closer relationship, i.e., a model has to be reasonably good to make a structural evaluation useful. Finally, we show that a combination of ProQprof and ProQres (ProQlocal) performed better than any other nonconsensus method for both high- and low-quality models. Additional information and Web servers are available at: http://www.sbc.su.se/~bjorn/ProQ/.

Algorithms↗

PubMed2005

Pcons5: combining consensus, structural evaluation and fold recognition scores.

MOTIVATION: The success of the consensus approach to the protein structure prediction problem has led to development of several different consensus methods. Most of them only rely on a structural comparison of a number of different models. However, there are other types of information that might be useful such as the score from the server and structural evaluation. RESULTS: Pcons5 is a new and improved version of the consensus predictor Pcons. Pcons5 integrates information from three different sources: the consensus analysis, structural evaluation and the score from the fold recognition servers. We show that Pcons5 is better than the previous version of Pcons and that it performs better than using only the consensus analysis. In addition, we also present a version of Pmodeller based on Pcons5, which performs significantly better than Pcons5. AVAILABILITY: Pcons5 is the first Pcons version available as a standalone program from http://www.sbc.su.se/~bjorn/Pcons5. It should be easy to implement in local meta-servers.

Algorithms↗

PubMed2005

Tertiary windowing to detect positive diversifying selection.

As a protein-encoding gene evolves, different selective pressures act on the gene temporally and spatially. An examination of the ratio of nonsynonymous-to-synonymous nucleotide substitution rate ratios (K(a)/K(s)) has proven to be a valuable method to examine selective pressures on protein encoding genes, including detecting positive diversifying selection. To gain power over averaging all sites in a gene together, examination of sites in primary sequence windows has frequently been employed. However, selection acts on folded proteins and sites that are close in tertiary space may not be close in primary sequence. A new method for the examination of K(a)/K(s) ratios based upon windows in tertiary structure is introduced and applied to the leptin gene family in mammals. Tertiary sequence windowing detects new sites under positive diversifying selection and detects positive diversifying selection with a more significant signal along various branches of the leptin gene family tree.

Animals↗

PubMed2005

All are not equal: a benchmark of different homology modeling programs.

Modeling a protein structure based on a homologous structure is a standard method in structural biology today. In this process an alignment of a target protein sequence onto the structure of a template(s) is used as input to a program that constructs a 3D model. It has been shown that the most important factor in this process is the correctness of the alignment and the choice of the best template structure(s), while it is generally believed that there are no major differences between the best modeling programs. Therefore, a large number of studies to benchmark the alignment qualities and the selection process have been performed. However, to our knowledge no large-scale benchmark has been performed to evaluate the programs used to transform the alignment to a 3D model. In this study, a benchmark of six different homology modeling programs- Modeller, SegMod/ENCAD, SWISS-MODEL, 3D-JIGSAW, nest, and Builder-is presented. The performance of these programs is evaluated using physiochemical correctness and structural similarity to the correct structure. From our analysis it can be concluded that no single modeling program outperform the others in all tests. However, it is quite clear that three modeling programs, Modeller, nest, and SegMod/ ENCAD, perform better than the others. Interestingly, the fastest and oldest modeling program, SegMod/ ENCAD, performs very well, although it was written more than 10 years ago and has not undergone any development since. It can also be observed that none of the homology modeling programs builds side chains as well as a specialized program (SCWRL), and therefore there should be room for improvement.

Amino Acid Sequence↗

PubMed2004

Profile-profile methods provide improved fold-recognition: a study of different profile-profile alignment methods.

To improve the detection of related proteins, it is often useful to include evolutionary information for both the query and target proteins. One method to include this information is by the use of profile-profile alignments, where a profile from the query protein is compared with the profiles from the target proteins. Profile-profile alignments can be implemented in several fundamentally different ways. The similarity between two positions can be calculated using a dot-product, a probabilistic model, or an information theoretical measure. Here, we present a large-scale comparison of different profile-profile alignment methods. We show that the profile-profile methods perform at least 30% better than standard sequence-profile methods both in their ability to recognize superfamily-related proteins and in the quality of the obtained alignments. Although the performance of all methods is quite similar, profile-profile methods that use a probabilistic scoring function have an advantage as they can create good alignments and show a good fold recognition capacity using the same gap-penalties, while the other methods need to use different parameters to obtain comparable performances.

Models, Chemical↗

PubMed2004

Using evolutionary information for the query and target improves fold recognition.

In this study, we show that it is possible to increase the performance over PSI-BLAST by using evolutionary information for both query and target sequences. This information can be used in three different ways: by sequence linking, profile-profile alignments, and by combining sequence-profile and profile-sequence searches. If only PSI-BLAST is used, 16% of superfamily-related protein domains can be detected at 90% specificity, but if a sequence-profile and a profile-sequence search are combined, this is increased to 20%, profile-profile searches detects 19%, whereas a linking procedure identifies 22% of these proteins. All three methods show equal performance, but the best combination of speed and accuracy seems to be obtained by the combined searches, because this method shows a good performance even at high specificity and the lowest computational cost. In addition, we show that the E-values reported by all these methods, including PSI-BLAST, underestimate the true rate of false positives. This behavior is seen even if a very strict E-value cutoff and a limited number of iterations are used. However, the difference is more pronounced with a looser E-value cutoff and more iterations.

Computational Biology↗

PubMed2003

Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller.

CASP provides a unique opportunity to compare the performance of automatic fold recognition methods with the performance of manual experts who might use these methods. Here, we show that a novel automatic fold recognition server, Pmodeller, is getting close to the performance of manual experts. Although a small group of experts still perform better, most of the experts participating in CASP5 actually performed worse even though they had full access to all automatic predictions. Pmodeller is based on Pcons (Lundström et al., Protein Sci 2001; 10(11):2354-2365) the first "consensus" predictor that uses predictions from many other servers. Therefore, the success of Pmodeller and other consensus servers should be seen as a tribute to the collective of all developers of fold recognition servers. Furthermore we show that the inclusion of another novel method, ProQ2, to evaluate the quality of the protein models improves the predictions.

Algorithms↗

PubMed2003

Can correct protein models be identified?

The ability to separate correct models of protein structures from less correct models is of the greatest importance for protein structure prediction methods. Several studies have examined the ability of different types of energy function to detect the native, or native-like, protein structure from a large set of decoys. In contrast to earlier studies, we examine here the ability to detect models that only show limited structural similarity to the native structure. These correct models are defined by the existence of a fragment that shows significant similarity between this model and the native structure. It has been shown that the existence of such fragments is useful for comparing the performance between different fold recognition methods and that this performance correlates well with performance in fold recognition. We have developed ProQ, a neural-network-based method to predict the quality of a protein model that extracts structural features, such as frequency of atom-atom contacts, and predicts the quality of a model, as measured either by LGscore or MaxSub. We show that ProQ performs at least as well as other measures when identifying the native structure and is better at the detection of correct models. This performance is maintained over several different test sets. ProQ can also be combined with the Pcons fold recognition predictor (Pmodeller) to increase its performance, with the main advantage being the elimination of a few high-scoring incorrect models. Pmodeller was successful in CASP5 and results from the latest LiveBench, LiveBench-6, indicating that Pmodeller has a higher specificity than Pcons alone.

Caspases↗