Quality assessment software tools | Protein structure data analysis
Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem.
A method for estimating the absolute quality of a single protein structure, i.e. without including additional information from other models or alternative template structures. QMEAN is based on the composite scoring function which evaluates several structural features of proteins. The absolute quality estimate of a model is expressed in terms of how well the model score agrees with the expected values from a representative set of high resolution experimental structures. The resulting QMEAN Z-score is a measure of the ‘degree of nativeness’ of a given protein structure. The Z-scores of the individual components of the composite QMEAN score point to structural descriptors that contribute most to the final score, and thereby indicate potential reasons for ‘bad’ models.
A single-model quality assessment (QA) method. Different from other single-model QA methods, Qprob estimates the prediction error estimation of several different physicochemical, structural and energy feature scores, and use the combination of probability density distribution of the errors for the global quality assessment. We blindly tested our method in the CASP11 experiment, and it was ranked as one of the best single-model QA method based on the CASP official evaluation and our own evaluations. In particular, the good performance of our method on template free targets demonstrates its good capability of selecting models for hard targets.
Validates ‘R-factor’-like protein structure. RPF calculates a discriminating power (DP) score that estimates how well the query structure satisfies the data relative to a statistical random-coil structure. It is able to analyze homodimeric proteins. The tool can be used for large-scale nuclear magnetic resonance (NMR) structure quality assessments. It provides an effective and convention tool for evaluating and validating protein structures derived from NOESY data.
Furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. ProTSAV is capable of evaluating predicted model structures based on some popular online servers and standalone tools. It succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures.
A server which provides global and local prediction of the quality of 3D models of proteins. ModFOLD, is a fast Model Quality Assessment Program (MQAP) used for the global assessment of either single or multiple models. The server produces both machine readable and graphical output, providing users with intuitive visual reports on the quality of predicted protein tertiary structures.
Predicts the quality of protein models. ProQ is a neural-network-based method that identifies correct models from a large subset of incorrect models. It uses a combination of several structural features, which aims to improve the detection of correct models. It can also be combined with the Pcons fold recognition predictor (Pmodeller) with the main advantage of eliminating some incorrect patterns with high scores.
A web server to provide the community with access to all three model quality assessment approaches (i.e. single, clustering and hybrid). Apollo evaluates the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure.
Selects blocks following a reproducible set of conditions. Gblocks is a program that eliminates poorly aligned positions and divergent regions of a DNA or protein alignment so that it becomes more suitable for phylogenetic analysis. It provides a web server that implements important features to make its use as simple as possible without losing the functionality that it is necessary in most of the cases.
Calculates, identifies, graphs, reports and/or evaluates a large number (>30) of key structural parameters both for individual residues and for the entire protein. VADAR is a comprehensive web server for quantitative protein structure evaluation. The web server produces extensive tables and high quality graphs for quantitatively and qualitatively assessing protein structures determined by X-ray crystallography, NMR (Nuclear magnetic resonance) spectroscopy, 3D-threading or homology modelling.
Evaluates and validates protein structures solved by either X-ray crystallography or NMR (Nuclear magnetic resonance) spectroscopy. PROSESS integrates a variety of previously developed, well-known and thoroughly tested methods to evaluate both global and residue-specific: covalent and geometric quality, non-bonded/packing quality, torsion angle quality, chemical shift quality and NOE (Nuclear Overhauser Enhancements) quality. The web application produces detailed tables, explanations, structural images and graphs that summarize the results and compare them to values observed in high-quality or high-resolution protein structures.
Examines the compatibility between the sequence and the structure of a protein by assigning scores to individual residues and their amino acid exchange patterns after considering their local environments. Harmony is a server to assess the compatibility of an amino acid sequence with a proposed three-dimensional structure. Users can submit their protein structure files and, if required, the alignment of homologous sequences. Scores are mapped on the structure for subsequent examination that is useful to also recognize regions of possible local errors in protein structures.
Predicts local and global model quality estimates. ProQ3D employs both the structural information derived from the three-dimensional (3D) structure of the model to be predicted and the evolutionary information derived from the amino acid sequence of this model. It is based on a combination of ProQ2 and two other methods: ProQCenFA and ProQRosCen.
An automated measure for the assessment of protein structure prediction quality, that further builds and extends some of the evaluation methods introduced at CASP3. MaxSub aims at identifying the largest subset of C(alpha) atoms of a model that superimpose 'well' over the experimental structure, and produces a single normalized score that represents the quality of the model.
Consists of a modeling solution for biologics. BioLuminate is a suit that aims to address issues associated with the molecular design of biologics. The software enables protein-protein docking, protein engineering and antibody modeling. Users can also perform advanced computational analyses, including for instance helical stability/melting analysis from molecular dynamics (MD) simulations, or free energy perturbation (FEP) calculations of binding affinity and protein stability. The suite includes AggScore, which identifies aggregation hotspots.
A novel Model Quality Assessment Program that compares 3D models of proteins without the need for CPU intensive structural alignments by utilizing the Q measure for model comparisons. ModFOLDclust carries out clustering of multiple models and provides per-residue local quality assessment. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead.
A quality assessment program based on the consistency between the model structure and the protein’s conservation pattern. ConQuass can identify problematic structural models, and that the scores it assigns to the server models in CASP8 correlate with the similarity of the models to the native structure. When the conservation information is reliable, the method's performance is comparable and complementary to that of the other single-structure quality assessment methods that participated in CASP8 and that do not use additional structural information from homologs.
Uses a support vector machine (SVM) to predict the quality of a membrane protein model by combining structural and sequence-based features calculated from the model. ProQM-resample is a model quality assessment program (MQAP) that was incorporated as scoring function in the Rosetta modelling framework. This gives in one hand full access to the modelling machinery within Rosetta and allows for easy integration with any Rosetta protocol. In particular, ProQM-resample uses the repack protocol to sample side-chain conformations followed by rescoring using ProQM to improve model selection.
Aims to help researchers and students to manipulate and treat PDB files in a high-throughput fashion in order to guarantee high quality data for posterior analyses. It has a user-friendly graphical user interface developed to allows even users with no computing background to download and manipulate theirs PDB files without using command line. Among its several features, PDBest is able to identify and correct formatting errors or inconsistencies, adding hydrogens as well as comprehensively filtering/selecting subsets of atoms, residues or chains.
Predicts TM-score and GDT_TS score based on a feature vector containing statistical potential energy terms and consistency-based terms between the actual structural features (extracted from the three-dimensional coordinates) and predicted values (from primary sequence). SVMQA is a support-vector-machine-based single-model global quality assessment (SVMQA) method. The software can contribute to the 3D modeling of difficult target proteins in terms of model selection.