Individual entries of the SMTL can be inspected using the web interface. The sequence features are linked to a 3D structure viewer and can be interactively explored. Ligands can be marked as synthetic, natural or part of crystallisation buffer. This information is used by the modelling pipeline to determine whether a ligand is considered for inclusion into the final model. The biological assembly biounit describes the oligomeric state, or quaternary assembly, which is thought of as the biologically most relevant form of the molecule.
SMTL entries are organised if more than one assembly is available by likely quaternary structure assemblies which are created according to the author and software-annotated oligomeric states listed in the PDB deposition. If not all chains of the asymmetric unit are included by any biounit of a PDB entry, the asymmetric unit is included as a template. The amino acid sequence of the target protein can be submitted either as plain text, or in FASTA format.
In this case, the identifier is immediately validated and replaced with the corresponding sequence. The "Add Hetero Target" button is provided to input multiple target sequences representing different subunits of a hetero-oligomer.
If a hetero-oligomer is requested, we only look for biounits of templates that contain connected chains with all desired subunits. Oligomeric templates are accepted, and it is also possible to build heteromers by adding multiple target sequences to the input. To start a modelling job with your own template:.
For more information about PDB file format please see this link. An example of DeepView Project and its application in modelling of Oligomeric proteins can be found here. The degree of difficulty in identifying a suitable template for a target sequence can range from "trivial" for well-characterised protein families to "impossible" for proteins with an unknown fold. The combined usage of these two approaches guarantees good alignments at high and low sequence identity levels.
For the latter we build a profile for the target sequence as outlined in Steinegger et al. The found templates are listed together with relevant structural information that can be readily used to rank the templates and select the best one according to user-defined criteria.
When the template search is complete, templates and alignments are first filtered to remove redundancy. A set of maximally 50 top-ranked templates is then chosen from the full list of templates according to a simple score which combines sequence coverage and sequence similarity.
The top-ranked templates and alignments are further analysed and sorted according to the expected quality of the resulting models, as estimated by GMQE and, if the target model is predicted to be an oligomer, QSQE. The Template Results page serves both as an overview of available templates as well as an interactive template selection tool. The top part of the screen contains a summary of the top-ranking templates identified by the template search methods.
The identified templates and the default template ranking correspond to the ones used in the Automated Mode. Please note that in the Automated Mode , additional templates, apart from the top-ranked one, may be chosen for modelling if they represent alternative conformational states or cover different regions of the target protein. Four types of views can be available based on the data input : i a Templates summary table, listing all templates in tabular form and providing an overview of relevant attributes of each template, ii the Quaternary Structure , iii an interactive chart showing the templates in relation to each other in Sequence Similarity space, and iv the sequence Alignment of Selected Templates.
Templates can be selected in any of these views for the subsequent modelling step. Selected templates are automatically shown in the 3D viewer. If multiple templates are selected, their structural superposition is shown, allowing instant visualisation of structural differences between them.
The complete list of all identified templates can be accessed at the bottom of the Template Results page. In the Templates, a summary table, template annotations, and target—template alignments can be retrieved by clicking on the arrows at the left end of the table rows to expand the box with the description of the individual templates. For each template, the following information is provided: the SMTL ID, the title of the structure, the target sequence coverage, GMQE , QSQE , the sequence identity to the target, the experimental method used to obtain the structure and the resolution, if applicable , the oligomeric state, the ligands if any , the sequence similarity to the target, and the template search method used.
For each template, the oligomeric state of the model is predicted. If the predicted oligomeric state of the model differs from the one of the template biounit or not all chains from the biounit are included, a warning symbol is shown exclamation mark in a triangle. Whenever possible, the user can choose the oligomeric state manually by expanding the template view under the point "Target Prediction".
Several methods are currently used to determine the structure of a protein. In homology modelling, it is generally preferrable to use structures determined by X-ray crystallography with high resolution as templates. We generally discourage the use of averaged NMR structures. In individual cases, taking into account the ensemble of structures determined by NMR spectroscopy, might provide useful insights. Special care is required when using structures determined by electron microscopy as they range from low resolution "blobology" to structures at atomic resolution.
The sequence similarity of the alignment is calculated as the sum of the substitution scores divided by the number of aligned residue pairs. Gaps are not taken into account. The Quaternary Structure view provides information on the quaternary structure analysis.
Templates are clustered and displayed in a decision tree according to their oligomeric state, stoichiometry, topology and interface similarity. On the level of the oligomeric state, the templates are grouped in either monomeric, homomeric or heteromeric clusters.
Stoichiometry considers only the number of chains in the structure while on the topology level the templates are grouped according to the interactions between the subunits. The interface similarity quantifies the similarity between interfaces as a function of shared interfacial contacts between the chains and thus allows to distinguish between different quaternary structures and binding modes.
Each leaf of the tree corresponds to a template labelled with the PDB code and a bar indicating sequence identity to the target and coverage. Protein—protein interaction PPI Fingerprint curves inform about the conservation of template interfaces. Residues participating in interfaces are subject to different evolutionary constraints than residues at the protein surface, e.
A value of interface conservation y-axis below 0 indicates that interface residues are less prone to mutate when compared to surface residues. An estimate of conservation is typically derived from a multiple sequence alignment MSA of homologous proteins.
The alignment is sliced using different sequence identity cut-offs x-axis to filter the MSA of the target protein e. In this way it can be observed how the various template interfaces "adapt" to the target protein family.
Considering the full set of homologues, the alternative quaternary structure can have similar interface conservation, making the selection of template harder. Considering closer homologues, the PPI fingerprints of the various templates will diverge, allowing an easier selection, as better-adapted interfaces will reach lower values of interface conservation. In the Sequence Similarity chart each template is shown as a circle.
The distances between the templates in the plot is proportional to the sequence identity between them. Thus, similar sequences cluster together. In the Alignment of Selected Templates view the alignments of the selected templates to the target are visualised.
DeepView project files can be accessed from the drop-down menu, using the ' More ' button. This allows the user to visualise different alignments in the structural context of the template, helping to correct misplaced insertions and deletions, and manually adjust misaligned regions. Next, the rule or rules are given in curly braces; only one rule has to be met for the colour to be applied. The minimum percentage is given first, followed by the residue or residues which must meet or exceed this percentage within the column.
Introduction 2. Input Form 3. Input Format Requirements 4. Input Data Processing 5. Programmatic Access 6. Introduction Estimating the quality of protein structure models is a vital step in protein structure prediction. The user has the possibility to choose between the following three scoring functions: QMEANDisCo is a composite scoring function which is able to derive both global i.
Input Form Look at some example runs Structural input, either browse your file system or drag and drop. Input Data Processing Local qualities are visible as color gradients in the model viewer. Close Delete Project. A single model method combining statistical potentials and agreement terms in a linear manner. A single model method combining statistical potentials and agreement terms with a distance constraints DisCo score.
DisCo evaluates consistencies of pairwise CA-CA distances from a model with constraints extracted from homologous structures. All scores are combined using a neural network trained to predict per-residue lDDT scores. QMEANBrane is a combination of statistical potentials targeted at local quality estimation of membrane protein models in their naturally occurring oligomeric state: after identifying the transmembrane region using an implicit solvation model, specifically trained statistical potentials get applied on the different regions of a protein model.
This sequence is used for secondary structure and solvent accessibility predictions. If not provided, the sequence gets directly extracted from the model. See the help page for further input information. The plot relates the obtained global QMEAN4 value to scores calculated from a set of high-resolution X-ray structures. They all provide scores in range [0,1] with one being good.
It is trained to predict global lDDT score in range [0,1]. The value displayed here is transformed into a Z-score to relate it with what one would expect from high resolution X-ray structures.
Bioinformatics for the terrified An introduction to the science of bioinformatics. Open Tree arrow-right-1 Course overview Search within this course What is bioinformatics? Open Tree arrow-right-1 Who is bioinformatics for? The role of public databases Open Tree arrow-right-1 Data sharing collaborations What makes a good bioinformatics database?
Open Tree arrow-right-1 Primary and secondary databases Describing data consistently Minimum information standards Controlled vocabularies Open Tree arrow-right-1 Non-hierarchical list Taxonomy Thesaurus Using ontologies to provide controlled vocabularies Gene Ontology Tips on managing and sharing data Where do I submit my data?
0コメント