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Builds a protein model into an electron density map. ARP/wARP facilitates model building by initially interpreting a density map with free atoms of unknown chemical identity; all structural information for such chemically unassigned atoms is discarded. It consists of a number of tasks that are performed in an iterative fashion. Free atoms are used to obtain better electron density maps through refinement. The tool is based on the paradigm of viewing model building and refinement as one unified procedure for optimizing phase estimates.
CCP4 / Collaborative Computational Project 4
A collection of programs, associated data and software libraries which can be used for macromolecular structure determination by X-ray crystallography. CCP4 is designed to be flexible, allowing users a number of methods of achieving their aims. The programs are from a wide variety of sources but are connected by a common infrastructure provided by standard file formats, data objects and graphical interfaces. Structure solution by macromolecular crystallo­graphy is becoming increasingly automated and the CCP4 suite includes several automation pipelines. A method for experimental X-ray data analysis called AUSPEX was integrated in CCP4.
CNS / Crystallography & NMR System
Provides a flexible multi-level hierachical approach for the most commonly used algorithms in macromolecular structure determination. CNS allows heavy atom searching, experimental phasing (including MAD and MIR), density modification, crystallographic refinement with maximum likelihood targets, and NMR structure calculation using NOEs, J-coupling, chemical shift, and dipolar coupling data. CNS is the result of an international collaborative effort among several research groups.
Provides a suite of methods important for the prediction of protein structural and functional features. predictProtein is a web server that incorporates over 30 tools. This software searches up-to-date public sequence databases, creates alignments, and predicts aspects of protein structure and function. It can help when little is known about the protein in question. For medium-to-high throughput analyses, downloadable software packages and the PredictProtein Machine Image (PPMI) are available.
Predicts oligomerization, functional sites, and conformational changes in transmembrane proteins. EVfold_membrane applies a maximum entropy approach to infer evolutionary co-variation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. The method predicts the structures of 11 transmembrane proteins of unknown structure, including six pharmacological targets. It appears to achieve a useful level of accuracy.
SPIDER / Sequence-based Prediction of Local and Nonlocal Structural Features for Proteins
Predicts different sets of structural protein properties. SPIDER is an iterative deep-learning neutral network. It obtains secondary structure, torsion angles, Cα−atom based angles and dihedral angles, and solvent accessible surface area. It utilises both local and nonlocal structural information in iterations. At each iteration, SPIDER employs a deep-learning neural network to predict a structural property based on structural properties predicted in the previous iteration.
Combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The MULTICOM protein structure prediction pipeline stands ready to meet the needs of the research community and is accessible via a web service. The method uses a multi-level combination technique to combine multiple protein structure templates and sources of structural information to generate models and then employs a number of model refinement and selection tools to return the best possible predicted structure. The MULTICOM system is capable of using both template-based and template-free modeling to handle the full spectrum of protein modeling and generate predictions for all protein structure prediction tasks from the relatively easy to difficult.
I-TASSER / Iterative Threading ASSEmbly Refinement
Allows automated protein structure prediction and structure-based function annotation. I-TASSER constructs, starting from the amino acid sequence, 3D structural models by reassembling fragments excised from threading templates. I-TASSER servers provides a confidence score (C-score) to estimate the models’ global accuracy. The I-TASSER Suite pipeline was tested in community-wide structure and function prediction experiments, including CASP10 and CAMEO.
DENSS / DENsity from Solution Scattering
Measures electron density via a solution scattering data. DENSS is based on an iterative structure factor retrieval algorithm to rebuild the object density from the low-information limiting case of biological small-angle scattering. This software can also reconstruct complex shapes including several different particle densities without modeling. It avoids assumptions implicit to existing modeling algorithms that restrains the resolution given by envelope reconstructions.
Phyre / Protein Homology/analogy Recognition Engine
Predicts 3D structure of a protein sequence. Phyre is a web application that investigates known homologues, builds a hidden Markov model (HMM) of the targeted sequence based on the detected homologues and scans it against a database of HMMs of known protein structures. It also provides advanced features such as a batch submission of a large number of protein sequences for modelling or Phyre Investigator, that allows users to analyze model quality, function and effects of mutations.
A protein structure prediction server excelling at predicting 3D structures for protein sequences without close homologs in the Protein Data Bank (PDB). Given an input sequence, RaptorX predicts its secondary and tertiary structures as well as solvent accessibility and disordered regions. RaptorX also assigns the following confidence scores to indicate the quality of a predicted 3D model: P-value for the relative global quality, GDT (global distance test) and uGDT (un-normalized GDT) for the absolute global quality, and RMSD for the absolute local quality of each residue in the model.
Employs a mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy to investigate allosterism in biological systems. WebPSN allows the user to easily setup the calculation, perform post-processing analyses and both visualize and download numerical and 3D representations of the output. Speed and accuracy make this server suitable to investigate structural communication, including allosterism, in large sets of bio-macromolecular systems.
Predicts the tertiary and secondary structure of a protein, given its amino acid sequence. Protinfo enables users to submit a protein sequence and to request a prediction of the three dimensional (tertiary) structure based on comparative modeling, fold generation and de novo methods developed by the authors. In addition, users can submit NMR chemical shift data and request protein secondary structure assignment that is based on using neural networks to combine the chemical shifts with secondary structure predictions. It serves as a complement to Bioverse framework.
T-RMSD / Tree-based on Root-Mean-Square Deviation
A fine-grained structural clustering method available within the T-Coffee web server. Given a set of structurally related proteins or protein families, T-RMSD will generate a supported structural clustering. The tree is supported by accuracy estimates analog to bootstrap values in phylogenetic reconstruction. Given a set of homologous sequences with known structures, the T-RMSD is a method designed to turn the multiple sequence alignment of these sequences into a structure-based clustering. This clustering is estimated through the systematic comparison of intramolecular distances, in a way similar to the DALI algorithm.
Allows high-throughput protein surface comparison, analysis, and visualization. 3D-SURFER is a web-based platform that compares the protein surface of a single chain, a single domain, or a single complex against databases of protein chains, domains, complexes, or a combination of all three. The software provides two options for protein surface representation: from all surface atoms or using only backbone atoms. Users can additionally specify two types of filters: a CATH and a length filter.
PanDDA / Pan-Dataset Density Analysis
Analyses the data resulting from crystallographic fragment screening. PanDDA is a package that comprises the characterization of a set of related crystallographic data sets of the same crystal form, the identification of (binding) events, and the subtraction of ground state density to reveal clear density for events. This method is applicable and effective at any resolution, though at lower resolutions, as maps become less precise, higher occupancies of changed states will in general be required for them to be detected by Z-score.
Offers a platform for determining protein structural features and tertiary structures. SCRATCH is a web application including ten modules for determining three and eight class: (1) secondary structure, (2) relative solvent accessibility, (3) domain boundaries, (4) disordered regions, (5) disulfide bridges, (6) the effect of single amino acid mutation on stability, (7) residue-residue contact maps, and (8) tertiary structures as well as contacts with other residues compared to average.
Predicts protein 3D structure by using single template homology model. CPHmodels was created to make a front-end that was easy to understand for users without any prior knowledge of homology modelling. It provides a result that is as accurate as possible. The tool is based on an optimized alignment scoring function and employs a double-sided Z-score to rank individual template hits. One of its major feature is the speed: for most queries the response time of the server is inferior to 20 minutes.
A comparative modeling web-server for protein structure modelling closely connected to ModBase. ModWeb accepts one or many sequences in the FASTA format and calculates their models using ModPipe based on the best available templates from the Protein Data Bank (PDB). Alternatively, ModWeb also accepts a protein structure as input and calculates models for all identifiable sequence homologs in the UniProt database. The latter mode is a useful tool for structural genomics efforts to assess the impact of a newly determined protein structure on the modeling of sequences of unknown structure. It is also used to identify new members of sequence superfamilies with at least one member of known structure. The results of ModWeb calculations are available through the ModBase interface as private datasets protected with passwords.
PELE / Protein Energy Landscape Exploration
This technology based on protein structure prediction algorithms and a Monte Carlo sampling, is capable of modelling the all-atom protein-ligand dynamical interactions in an efficient and fast manner, with two orders of magnitude reduced computational cost when compared with traditional molecular dynamics techniques. PELE's heuristic approach generates trial moves based on protein and ligand perturbations followed by side chain sampling and global/local minimization. The web server is designed to make the whole process of running simulations easier and more practical by minimizing input file demand, providing user-friendly interface and producing abstract outputs (e.g. interactive graphs and tables).
SABBAC / Structural Alphabet-based protein BackBone reconstruction from Alpha-Carbon trace
Assists users in reconstruction of protein backbone. SABBAC is an online tool that relies on an approach to fragment selection and assembly. It uses the encoding of the alpha-carbon trace using a hidden Markov model derived structural alphabet. It selects at each position in the structure a small set of candidates among a complete set of over 150 candidate fragments describing all the letters of the structural alphabet.
A web server predicting structure property of a protein sequence without using any templates. RaptorX-Property outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels.
VLDP / Voronoi Laguerre Delaunay Protein
A set of accurate tools, for analysing protein structures, based on the reliable method of Voronoi-Laguerre tessellations. The Voronoi Laguerre Delaunay Protein web server (VLDPws) computes the Laguerre tessellation on a whole given system first embedded in solvent. Through this fine description, VLDPws gives the following data: (i) Amino acid volumes evaluated with high precision, as confirmed by good correlations with experimental data. (ii) A novel definition of inter-residue contacts within the given protein. (iii) A measure of the residue exposure to solvent that significantly improves the standard notion of accessibility in some cases.
An accurate and sensitive superfamily discrimination, combining information from both sequence and structure to produce highly accurate domain alignments. The method employs the same underlying threading algorithm as pGenTHREADER, however it aligns sequences to a domain-based template library rather than a chain-based template library. The use of smaller regions of structure for templates means that different features of the alignments are required for optimal scoring. The final prediction score results from an SVM trained on a combination of 5 different feature inputs; template coverage, alignment score, template length, solvation and pairwise potentials.
Generates protein structure cartoons. Pro-Origami web server allows diagrams to be generated from any Protein Data Bank (PDB) file. The resulting diagram may be either downloaded or viewed as an SVG or bitmap file. For browsers that support JavaScript and SVG, the diagram is interactive, showing the residue at the part of the diagram over which the mouse pointer is positioned. The cartoons are intended to make protein structure easy to interpret by laying out the secondary and super-secondary structure in two dimensions in a manner that makes the structure clear.
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