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.
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.
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.
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 different sets of structural protein properties. SPIDER is an iterative deep-learning neural 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.
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.
Offers a component-based architecture that allows users to add new functionality in the form of plug-in modules. geWorkbench includes many computational resources permitting to delete many steps that require programming skills. It simplifies the utilization of multi-module analysis pipelines. The tool’s modules consist of wrapped versions of pre-existing third-party software tools.
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.
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.
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.
Allows users to determine quasi-atomic-resolution structures of molecular machines. M3 employs complementary and orthogonal experimental information such as interatomic distances and molecular shapes. It is able to conserve description of physical forces at the atomic level. This tool can rearrange structures at the atomic level and returns an account on the adequacy of the input data.
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.
An independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance.
An automatic protein structure prediction server. (PS)2 uses an effective consensus strategy both in template selection, which combines PSI-BLAST and IMPALA. The method uses a new substitution matrix, S2A2, that combines both sequence and secondary structure information for the detection of homologous proteins with remote similarity and the target-template alignment. The final three-dimensional structure is built using the modeling package MODELLER.
Allows prediction of protein structure. FRAGFOLD can generate compact structures with significant similarity to the experimentally determined structures even for proteins with entirely novel folds. Methods such as FRAGFOLD attempt to narrow the search of conformational space by preselecting structural fragments from a library of known protein structures. The original software was used in the CASP2 experiment in 1996.
Determinates side-chain conformations. SCWRL uses a backbone-dependent rotamer library, a simple energy function based on the library rotamer frequencies and a purely repulsive steric energy term, and a graph decomposition to solve the combinatorial packing problem. It calculates all the required energies and performs combinatorial optimization after which for each residue one of its rotamers will be marked as optimal.
Models side-chain conformation. OPUS-Rota uses simulated annealing by heat bath Monte Carlo as a sampling method, which is able to rapidly identify near-native conformations when combined with neighbor list techniques and efficient energy updates. It was benchmarked with 65 high-resolution X-ray structures used in the literature. The tool outperforms other related methods in terms of combined speed and accuracy.
Provides access to a variety of public and in-house bioinformatics tools. The MPI Bioinformatics Toolkit integrates a selected set of most useful methods for the analysis of protein sequences and structures. It offers more of 50 interconnected tools, so that the results of one tool can be forwarded to other tools. It also includes a useful platform for teaching bioinformatic enquiry to students in the life sciences.