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 neuralnetwork-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.Keywords: Homology modeling; fold recognition; structural information; LiveBench; neural networks; protein model; protein decoys The ability to use an algorithm or energy function to distinguish between correct and incorrect protein models is of importance both for the development of protein structure prediction methods and for a better understanding of the physical principles ruling protein folding. Energy functions can be divided into different categories depending on the background principles and what structural features of a model they use. In order for an energy function to be useful in protein structure predictions, it should not only be able to identify the native protein configuration, but also detect to native-like structures, because it often is not possible to generate the native structure without experimental information. Ideally, the energy function should correlate well with a distance measure from the native structure. Exactly how to define what conformations are native-like is not trivial, but several different measures have been developed (for review, see Cristobal et al. 2001).Many different energy functions for evaluating protein structures have been developed. These focus either on the identification of native, or native-like, protein models from a large set of decoys

The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent Fnat (>0.50) and iRMS (<3.0Å). This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining Fnat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure prediction, and DockQ should be useful in a similar development in the protein docking field. DockQ is available at http://github.com/bjornwallner/DockQ/

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 3 10 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. V oltage-gated ion channels are critical for biological signaling, and they are able to regulate ion flux on a millisecond time scale. To sense changes in membrane voltage, each ion channel is equipped with four voltage-sensor domains (VSDs) connected to a central ion-conducting pore domain. The fourth transmembrane segment (S4) of each VSD carries several positively charged amino-acid residues responsible for VSD gating (1). At least three elementary charges per VSD must traverse outwards through the membrane electric field to open a channel that corresponds to a considerable displacement of the S4 helix ( Fig. 1A) (2-4). The positive charges in S4 make salt bridges with negative countercharges on their move through the VSD (4-8). It has even been proposed that the VSD undergoes a conformational alteration following the opening, when the channel relaxes to an inactivated, that is closed, state (9). In addition to conferring voltage dependence to ion channels, VSDs also regulate enzymes (10), act as voltage-gated proton channels (11,12), are susceptible to disease-causing mutations (13,14), and serve as targets for drugs and toxins (1,(15)(16)(17)(18). Therefore, it is of crucial interest to understand the details underlying voltage sensing by VSDs.Few, if any, segments of membrane proteins have received more attention than the S4 helix of voltage sensors. In addition to their paramount biological importance, they can help us understand fundamental biophysical problems such as why some membrane protein segments can be hydrophilic (19), how charges effectively move through a membrane, or how a potential triggers structural changes on a microsecond time scale. These questions are inherently linked to transient conformations and contacts that can be difficult to capture in a single structure. Although active...

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.