Björn Wallner

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.

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.

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/.

Prediction of membrane protein structures with complex topologies using limited constraints

Reliable structure-prediction methods for membrane proteins are important because the experimental determination of high-resolution membrane protein structures remains very difficult, especially for eukaryotic proteins. However, membrane proteins are typically longer than 200 aa and represent a formidable challenge for structure prediction. We have developed a method for predicting the structures of large membrane proteins by constraining helix–helix packing arrangements at particular positions predicted from sequence or identified by experiments. We tested the method on 12 membrane proteins of diverse topologies and functions with lengths ranging between 190 and 300 residues. Enforcing a single constraint during the folding simulations enriched the population of near-native models for 9 proteins. In 4 of the cases in which the constraint was predicted from the sequence, 1 of the 5 lowest energy models was superimposable within 4 Å on the native structure. Near-native structures could also be selected for heme-binding and pore-forming domains from simulations in which pairs of conserved histidine-chelating hemes and one experimentally determined salt bridge were constrained, respectively. These results suggest that models within 4 Å of the native structure can be achieved for complex membrane proteins if even limited information on residue-residue interactions can be obtained from protein structure databases or experiments.

Using multiple templates to improve quality of homology models in automated homology modeling

When researchers build high‐quality models of protein structure from sequence homology, it is today common to use several alternative target‐template alignments. Several methods can, at least in theory, utilize information from multiple templates, and many examples of improved model quality have been reported. However, to our knowledge, thus far no study has shown that automatic inclusion of multiple alignments is guaranteed to improve models without artifacts. Here, we have carried out a systematic investigation of the potential of multiple templates to improving homology model quality. We have used test sets consisting of targets from both recent CASP experiments and a larger reference set. In addition to Modeller and Nest, a new method (Pfrag) for multiple template‐based modeling is used, based on the segment‐matching algorithm from Levitts SegMod program. Our results show that all programs can produce multi‐template models better than any of the single‐template models, but a large part of the improvement is simply due to extension of the models. Most of the remaining improved cases were produced by Modeller. The most important factor is the existence of high‐quality single‐sequence input alignments. Because of the existence of models that are worse than any of the top single‐template models, the average model quality does not improve significantly. However, by ranking models with a model quality assessment program such as ProQ, the average quality is improved by ∼5% in the CASP7 test set.

Tracking a complete voltage-sensor cycle with metal-ion bridges

Voltage-gated ion channels open and close in response to changes in membrane potential, thereby enabling electrical signaling in excitable cells. The voltage sensitivity is conferred through four voltage-sensor domains (VSDs) where positively charged residues in the fourth transmembrane segment (S4) sense the potential. While an open state is known from the Kv1.2/2.1 X-ray structure, the conformational changes underlying voltage sensing have not been resolved. We present 20 additional interactions in one open and four different closed conformations based on metal-ion bridges between all four segments of the VSD in the voltage-gated Shaker K channel. A subset of the experimental constraints was used to generate Rosetta models of the conformations that were subjected to molecular simulation and tested against the remaining constraints. This achieves a detailed model of intermediate conformations during VSD gating. The results provide molecular insight into the transition, suggesting that S4 slides at least 12 Å along its axis to open the channel with a 310 helix region present that moves in sequence in S4 in order to occupy the same position in space opposite F290 from open through the three first closed states.

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. Proteins 2004.

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.

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, ProQ 2 , to evaluate the quality of the protein models improves the predictions. Proteins 2003;53:534–541.

Improved model quality assessment using ProQ2

BackgroundEmploying methods to assess the quality of modeled protein structures is now standard practice in bioinformatics. In a broad sense, the techniques can be divided into methods relying on consensus prediction on the one hand, and single-model methods on the other. Consensus methods frequently perform very well when there is a clear consensus, but this is not always the case. In particular, they frequently fail in selecting the best possible model in the hard cases (lacking consensus) or in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement.ResultsHere, we present a new single-model method, ProQ2, based on ideas from its predecessor, ProQ. ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local.ConclusionsProQ2 is significantly better than its predecessors at detecting high quality models, improving the sum of Z-scores for the selected first-ranked models by 20% and 32% compared to the second-best single-model method in CASP8 and CASP9, respectively. The absolute quality assessment of the models at both local and global level is also improved. The Pearson’s correlation between the correct and local predicted score is improved from 0.59 to 0.70 on CASP8 and from 0.62 to 0.68 on CASP9; for global score to the correct GDT_TS from 0.75 to 0.80 and from 0.77 to 0.80 again compared to the second-best single methods in CASP8 and CASP9, respectively. ProQ2 is available at http://proq2.wallnerlab.org.