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ProteinCCD / Protein Crystallisation Construct Designer
Helps deciding how to choose promising constructs for protein expression and crystallisation. ProteinCCD functions as a meta-server that collects information from prediction servers concerning secondary structure, disorder, coiled coils, transmembrane segments, domains and domain linkers. The server displays a condensed view of all results against the protein sequence. The user can study the output and choose interactively possible starts and ends for suitable protein constructs.
An integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of Crysalis includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties.
A web server for prediction of protein crystallizability. The prediction is made by comparing several features of the protein with distributions of these features in TargetDB and combining the results into an overall probability of crystallization. XtalPred provides: (1) a detailed comparison of the protein's features to the corresponding distribution from TargetDB; (2) a summary of protein features and predictions that indicate problems that are likely to be encountered during protein crystallization; (3) prediction of ligands; and (4) (optional) lists of close homologs from complete microbial genomes that are more likely to crystallize.
fDETECT / fast Determination of Eligibility of TargEts for CrysTallization
Detects propensity for material production failure (MF), purification failure (PF), crystallization failure (CF) and successful diffraction-quality crystallization (CR). fDETECT is a web application that is an extension of a method permitting to investigate crystallization propensity and the resulting coverage by X-ray structures. It allows the batch prediction of up to 1000 protein sequences. It also offers an option to run the PPCpred method.
A Parzen Window approach to estimate a protein's propensity to produce diffraction-quality crystals. The Protein Data Bank (PDB) provided training data whilst the databases TargetDB and PepcDB were used to define feature selection data as well as test data independent of feature selection and training. ParCrys outperforms the OB-Score, SECRET and CRYSTALP on the data examined, with accuracy and Matthews correlation coefficient values of 79.1% and 0.582, respectively (74.0% and 0.227, respectively, on data with a 'real-world' ratio of positive:negative examples).
Predicts propensity for production of diffraction-quality crystals, production of crystals, purification and production of the protein material. PPCpred utilizes comprehensive set of inputs based on energy and hydrophobicity indices, composition of certain amino acid types, predicted disorder, secondary structure and solvent accessibility, and content of certain buried and exposed residues. PPCpred is a novel approach that alleviates drawbacks of the existing methods by using a recent dataset and improved protocol to annotate progress along the crystallization process.
Permits to select feasible target proteins from UniProt for structural determination. Crysf predicts crystallization propensities (CPs) based on user-submitted protein sequences of interest. It exploits the UniProt-derived functional annotations to predict the protein CP. The tool can guide structural biologists to select feasible proteins from UniProt. The predictive performance of the Crysf predictor is dependent on the quality and reliability of the functional annotations derived from the database.
A Z-score scale to rank potential targets by their predicted propensity to produce diffraction-quality crystals. The OB-Score is derived from a matrix of predicted isoelectric point and hydrophobicity values for nonredundant PDB entries solved to <or=3.0 A against a background of UniRef50. A highly significant difference was found between the OB-Scores for TargetDB test datasets. A wide range of OB-Scores was observed across 241 proteomes and within 7868 PfamA families; 73.4% of PfamA families contain >or=1 member with a high OB-Score, presenting favourable candidates for structural studies.
daq / DA+ data acquisition
Consists of distributed services and components which communicate via messaging and streaming technologies to analyze data acquisition of modern macromolecular crystallography (MX). daq provides an easy and intuitive graphical user interface (GUI) allowing straightforward experiment control. This tool supports standard, as well as advanced data acquisition protocols, such as multi-orientation Single-wavelength Anomalous Dispersion (SAD), energy interleaved Multi-wavelength Anomalous Dispersion (MAD), raster scan and serial crystallography.
An ensemble method for prediction of protein crystallization based on a scoring card method (SCM) with the sequence features of p-collocated amino acid pairs. The SCM classifier determines the crystallization of a sequence based on a weighted-sum score. The weights are the composition of the p-collocated amino acid pairs, and the propensity scores of the amino acid pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. Not like existing prediction methods in pursuit of high accuracy, the SCM-based prediction method aims to maximize both the simplicity and interpretability of used features and classification method. The experimental results show that the SCM-based methods are comparable to the SVM-based methods in terms of accuracy for single and ensemble classifiers.
SECRET / SEquence-based CRystallizability EvaluaTor
A Web server for protein crystallizability prediction. SECRET is a machine-learning approach to sequence-based prediction of protein crystallizability in which we exploit subtle differences between proteins whose structures were solved by X-ray analysis [or by both X-ray and nuclear magnetic resonance (NMR) spectroscopy] and those proteins whose structures were solved by NMR spectroscopy alone. Due to the application of metamethods for cost sensitivity, our method is able to handle real datasets with unbalanced class representation.
cctbx / Computational Crystallography Toolbox
Contains fundamental algorithms for computational crystallography that are designed for integration into highly automated software systems, but are also suitable for smaller systems, including educational software. cctbx provides three main modules. The eltbx (element toolbox) is a collection of tables of various X-ray and neutron scattering factors, element names, atomic numbers, atomic weights, ionic radii, and characteristic X-ray wavelengths. The uctbx (unit-cell toolbox) provides tools for the description and manipulation of unit cells, and is organized around the UnitCell class. The sgtbx (space-group toolbox) where a large variety of space-group symbols can be used to derive the corresponding symmetry operations. The entire community is invited to actively participate in the development of the code base.
Enables classification of new crystal structures. KinConform is a comprehensive machine learning based on classification of protein kinase active/inactive conformations. It takes any number of input structures, separates their chains and generates a fasta file of sequences. This fasta file is aligned (using MAPGAPS) to kinase profiles, identifying which chains are kinases. A series of measurements are then taken for each kinase chain and used as input to a machine learning classifier.
A kernel-based method that predicts the propensity of a given protein sequence to produce diffraction-quality crystals. CRYSTALP2 utilizes the composition and collocation of amino acids, isoelectric point, and hydrophobicity, as estimated from the primary sequence, to generate predictions. It extends its predecessor, CRYSTALP, by enabling predictions for sequences of unrestricted size and provides improved prediction quality. Test on several independent datasets show that CRYSTALP2 outperforms several existing methods such as SECRET, CRYSTALP and the OB-Score, and provides comparable and complementary results to the ParCrys and XtalPred methods.
Proposes applications to analyze and provide optimal boundary information for query sequences and to visualize the data. PDPredictor website hosts high-throughput structure determination pipelines developed by structural genomics programs that offer a unique opportunity for data mining. One important question is how protein properties derived from a primary sequence correlate with the protein’s propensity to yield X-ray quality crystals (crystallizability) and 3D X-ray structures. A set of protein properties were computed for over 1300 proteins that expressed well but were insoluble, and for ~720 unique proteins that resulted in X-ray structures. The correlation of the protein’s iso-electric point and grand average hydropathy (GRAVY) with crystallizability was analyzed for full length and domain constructs of protein targets. In a second step, several additional properties that can be calculated from the protein sequence were added and evaluated.
A sequence-based protein crystallization predictor by fusing multi-view protein features with two-layered SVM (2L-SVM). TargetCrys was compared with existing sequence-based protein crystallization predictors and demonstrated that the proposed method outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. TargetCrys achieved much better prediction performance than the other five considered protein crystallization predictors, OB-score, XtalPred, CRYSTALP2, ParCrys and MetaPPCP.
PredPPCrys / Prediction of Procedures Propensity for protein Crystallization
An accurate prediction of sequence clone, protein production, purification and crystallization propensity from sequence by multi-step heterogeneous feature fusion and selection. PredPPCrys uses a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy. The predicted crystallization targets of currently non-crystallizable proteins were provided as compendium data, which are anticipated to facilitate target selection and design for the worldwide structural genomics consortium.
Reduces the need for expensive and time-consuming phasing experiments. CaspR executes an optimized molecular replacement procedure using a combination of well-established stand-alone software tools. It provides a progress report in the form of hierarchically organized summary sheets that describe the different stages of the computation with an increasing level of detail. The tool is useful in simple molecular replacement cases by automatically replacing the amino acid sequence of the template by those of the molecule of interest, thus accelerating the tedious refinement process.
Provides biological interfaces in protein crystal structures. PreBI is a web application that automatically generate all of the possible interfaces, according to the symmetry operations given in the coordinate file. It analyses the complementarities of the electrostatic potential, hydrophobicity and shape of the interfaces. The most probable biological interface is identified according to the combination of the degree of complementarity derived from the database analyses and the area of the interface.
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