QMEAN

QMEAN Server - Quick Help

Contents


1. Introduction
2. Input Form
3. Input Format Requirements
4. Result Pages
5. Input Data Processing
6. Programmatic Access
7. References

1. Introduction


Estimating the quality of protein structure models is a vital step in protein structure prediction. Often one ends up in having a set of alternative models (e.g. from different modeling servers or based on alternative template structures and alignments) from which the best candidate shall be selected. Or a singe model has been built from which the absolute quality needs to be predicted in order to have an idea about its suitability for subsequent experiments. The QMEAN server provides access to three scoring functions for the quality estimation of protein structure models which allow to rank a set of models and to identify potentially unreliable region within these. Both single models and set of models submitted as tar.gz-archives can be analysed. The user has the possibility to choose between the following three scoring functions:

NOTE: QMEANBrane and QMEANDisCo are only available for local quality estimates. Independent of the choosen scoring function the global quality score will be computed with QMEAN.



2. Input Form


1: Look at some example runs

2: Structural input, either browse your file system or drag and drop. You can find more info on input format here.

3: Optionally add the reference sequence of your model(s). You can find more info on input format here.

4: Choose you method

5: Optional Input: name of your project

6: Optional Input: If you provide an email address, you'll get the link to your results as soon as they're ready

7: Fire the job...



3. Input Format Requirements

Structural Data

Either a model in PDB format or tar.gz-archives with multiple models in PDB format sharing the same reference sequence (SEQRES) can be uploaded.

SEQRES

The SEQRES input is used to generate sequence profiles for secondary structure and solvent accessibility predictions. The observed sequence in the model must be a subsequence of the SEQRES. If not provided, the SEQRES gets directly extracted from the model itself. If the model is incomplete, this can lead to inaccurate profiles affecting the aforementioned predictions. In case of single chain models or homo-oligomers, the SEQRES can be provided as plain string or in FASTA format. In case of hetero-oligomers, the SEQRES can be provided in FASTA format, where the sequence names in the SEQRES input must match the chain names in the model input.



4. Result Pages


QMEAN Result Page


1: Your project title

2: Download archive containing all project results in parseable files

3: Structure(s) you uploaded

4: Yes, It's QMEAN

5: Whether you provided a reference sequence

6: Your email if provided

7: Title for per model result

8: Download archive containing all project results for that particular model in parseable files

9: Clickable thumbnail to activate/deactivate model in viewer

10: Relates score of particular model to what we would expect from high resolution X-ray structures of similar size, transforming it to a Z-score. This Z-score is also available for all single terms giving raise to the QMEAN4 score.

11: QMEAN4 score of particular model. It is calculated as a linear combination from 4 statistical potential terms and transformed to a Z-score relating it to high resolution X-ray structures of similar size. The QMEAN6 score including the scores assessing the match of a model profile based predictions is available in the downloadable archives

12: Sequence of model coloured by local QMEAN score. You can hover over the score to display the local QMEAN score and the according residue in the viewer. The residues are numbered according the preprocessing described here.

13: Displays all models with activated thumbnail


QMEANDisCo Result Page


1: Your project title

2: Download archive containing all project results in parseable files

3: Structure(s) you uploaded

4: Yes, It's QMEANDisCo

5: Whether you provided a reference sequence

6: Your email if provided

7: Title for per model result

8: Download archive containing all project results for that particular model in parseable files

9: Clickable thumbnail to activate/deactivate model in viewer

10: Compares local QMEAN scores with local DisCo scores and the resulting QMEANDisCo scores. Depending on the situation of found homologues, DisCo is not necessarlity defined everywhere.

11: QMEAN4 score of particular model that does not get affected by DisCo. It is calculated as a linear combination from 4 statistical potential terms and transformed to a Z-score relating it to high resolution X-ray structures of similar size. The QMEAN6 score including the scores assessing the match of a model profile based predictions is available in the downloadable archives

12: Sequence of model coloured by local QMEANDisCo score. You can hover over the score to display the local QMEANDisCo score and the according residue in the viewer. The residues are numbered according the preprocessing described here.

13: Displays all models with activated thumbnail


QMEANBrane Result Page


1: Your project title

2: Download archive containing all project results in parseable files

3: Structure(s) you uploaded

4: Yes, It's QMEANBrane

5: Whether you provided a reference sequence

6: Your email if provided

7: Title for per model result

8: Download archive containing all project results for that particular model in parseable files

9: Clickable thumbnail to activate/deactivate model in viewer

10: In the process of calculating the QMEANBrane scores, the membrane gets detected using a pseudo energy function representing an implicit solvation model. This plot relates the resulting energy with what we would expect from a typical membrane protein. You might even get a warning if the algorithm thinks that we're not dealing with a membrane protein model.

11: Sequence of model coloured by local QMEANBrane score. You can hover over the score to display the local QMEANBrane score and the according residue in the viewer. The residues are numbered according the preprocessing described here.

12: Displays all models with activated thumbnail



5. Input Data Processing


Local qualities are visible as color gradients in the model viewer. They additionally get mapped onto the structures available in the downloadable archives as bfactors. The server provides you with two alternative structures in the archives that undergo certain processing steps.

<model_name>_raw.pdb

This is your input structure with gentle processing. Hydrogens are stripped away, modified residues are stripped to represent their base residue (e.g. Phospho-Tyrosine to Tyrosine) and unknown residues are removed.

<model_name>_processed.pdb

This is the model being displayed on the results page. Additionally to the aforementioned processing steps we renumber the residues so they match the SEQRES, assign chain names if they're missing and potentially apply a transformation to display in the viewer.



6. Programmatic Access


One can access QMEAN-SERVER programatically with provided API. In order to use QMEAN submission API you have to make a POST request to the https://swissmodel.expasy.org/qmean/submit/ with following parameters:

The server returns a JSON file with details of the submitted project and and the link to the results page eg. using Python:
>>> import json
>>> import requests
>>> url = "https://swissmodel.expasy.org/qmean/submit/"
>>> # To upload from URL, put the URL as the structure
>>> response = requests.post(url=url, data={"structure": "https://files.rcsb.org/download/1CRN.pdb", "email": "your@email.com"})
>>>
>>> # When using Python requests - to upload from file, put the file in files
>>> response = requests.post(url=url, data={"email": "your@email.com"}, files={"structure": open('my_structure.pdb','r')})
>>>
>>> print json.dumps(response.json(), indent=4, sort_keys=True)
{
    "download_url": "https://swissmodel.expasy.org/qmean/qH7VxZ.json",
    "error": "",
    "meta": {
        "created": "2016-08-31 10:26:13.128564",
        "email": "your@email.com",
        "project_name": "Project",
        "results_page": "https://swissmodel.expasy.org/qmean/project/qH7VxZ/",
        "seqres_uploaded": false
    },
    "method": "QMEANDisCo",
    "models": [
        {
            "modelid": "model_001",
            "name": "1CRN.pdb",
            "seqres": [
                {
                    "atomseq": "TTCCPSIVARSNFNVCRLPGTPEAICATYTGCIIIPGATCPGDYAN",
                    "chain_name": "A",
                    "name": "seq_chain_0",
                    "sequence": "TTCCPSIVARSNFNVCRLPGTPEAICATYTGCIIIPGATCPGDYAN"
                }
            ]
        }
    ],
    "options": {
        "qmeanbrane": false,
        "qmeandisco": true
    },
    "project_type": "default",
    "sequences": []
}
    

The link to the results is locaded in json["meta"]["results_page"]. One can also access more detailed results via the link provided in json["download_url"]. This URL returns another JSON with the details of the submission and with the calculated results.

>>> print json.dumps(requests.get(response.json()["download_url"]).json(), indent=4, sort_keys=True)
{
    "input_data": {
        "meta": {
            "created": "2016-08-31T10:26:13.128",
            "email": "your@email.com",
            "project_name": "Project",
            "seqres_uploaded": false
        },
        "method": "QMEANDisCo",
        "models": [
            {
                "error_status": null,
                "global_scores": {
                    "acc_agreement": {
                        "norm": "0.717391304347826",
                        "zscore": "0.05268376272696417"
                    },
                    "all_atom": {
                        "norm": "-0.0339911322820547",
                        "zscore": "-0.13629601167404343"
                    },
                    "cbeta": {
                        "norm": "-0.01683348974304112",
                        "zscore": "-0.5113357610693555"
                    },
                    "qmean4": {
                        "norm": "0.7926328948738024",
                        "zscore": "0.04068390776086432"
                    },
                    "qmean6": {
                        "norm": "0.7371336447291417",
                        "zscore": "-0.36494081991050903"
                    },
                    "solvation": {
                        "norm": "-0.7047250189858935",
                        "zscore": "-0.2029050929145821"
                    },
                    "ss_agreement": {
                        "norm": "0.2899284981515097",
                        "zscore": "-1.1455771987070753"
                    },
                    "torsion": {
                        "norm": "-0.3343335838899726",
                        "zscore": "0.2997166227220592"
                    }
                },
                "model_pdb": "https://swissmodel.expasy.org/qmean/project/qH7VxZ/model_001.pdb",
                "modelid": "model_001",
                "name": "1CRN.pdb",
                "seqres": [
                    {
                        "atomseq": "TTCCPSIVARSNFNVCRLPGTPEAICATYTGCIIIPGATCPGDYAN",
                        "chain_name": "A",
                        "name": "seq_chain_0",
                        "sequence": "TTCCPSIVARSNFNVCRLPGTPEAICATYTGCIIIPGATCPGDYAN"
                    }
                ]
            }
        ],
        "options": {
            "qmeanbrane": false,
            "qmeandisco": true
        },
        "project_type": "default",
        "sequences": []
    },
    "status": "COMPLETED"
}

    

For each model you can download processed PDB file with the link provided ("model_pdb").

7. References


You can find the references here.



A single model method combining statistical potentials and agreement terms in a linear manner
Adding distance constraint score to QMEAN to improve local quality predictions. Evaluated are consistencies of pairwise CA-CA distances from a model with constraints extracted from homologous structures
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
Reference sequence (SEQRES) of submitted 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.
Local quality is either estimated using the raw QMEAN scoring function or one of the two specialized functions QMEANBrane and QMEANDisCo. They all provide scores in range [0,1] with one being good.
The value of the QMEAN4 is not affected by DisCo.