Based on the example, see below how to

1. How to submit the job and interpret the status of processing

In order to submit the job you need to provide:

Mark the option “Do not show my job on the queue page” if you don’t want the job to be visible to anyone else on the queue page (QUEUE). The example of submitting the job named “example_1SUR” by providing the PDB code: “1SUR” is displayed below.

After clicking the Submit button, the following info will appear, which contain the unique link to your job:

If you have chosen “Do not show my job on the queue page” it is important to save the link to your job, otherwise your job will be accessible from the queue page: ( QUEUE).
Under the unique link to your job you’ll find the job status updates and finally the job results.
The job status information will start from the “in queue status”:

followed by the “running status” with the progress bar and “Estimate finish time” provided:
The jobs ends with the done status:

Additionally, a movie is automatically generated by creating frames from pictures of the predicted models in different rotation states. The movie can be viewed on-line (click play button) or downloaded (right-click on the movie screen and the option: “download the movie” should appear - the movie file will be in OGV or MP4 format, depending on a web browser).

2. How to access and interpret the results data

2.1. Models tab

Under the job link, the results are accessible from the Models menu tab (marked below in a red circle). Under the Models tab, results are divided into several sub-tabs (marked below in green circles):

To see description of the displayed pictures, or the data to download (accessible under the blue buttons), drag the cursor over a picture, or a button.

2.1.1. Multimodel subtab

The Multimodel subtab presents the following data:

2.1.2. Model 1, Model 2, …, Model X subtabs

The Model 1, Model 2 and each subsequent Model subtabs presents the following data:

2.2. Details tab

Under the Details tab, results are divided into several sub-tabs:

2.2.1. "Clustering data" subtab

Clustering of protein models is the task of separating a set of protein models (here a protein dynamics trajectory) into groups (called clusters). The clustering is done in such a way that models are more similar in the same group to each other (here in the sense of RMSD measure), than those in other groups (clusters). CABS-flex utilizes classical K-means clustering method.
After clustering is done, each cluster representative is chosen (always the model which average dissimilarity to all models in a cluster is minimal). Predicted protein models, presented in the Models tab, are each cluster representatives (the clusters and the corresponding models are marked by the same numbers, e.g. Model 1 represents Cluster 1).
The clusters are numbered/ranked according to cluster density values, from the most dense (numbered as a first) to the least dense one.

The subtab Clustering Data contains a table with the following clusters data:

See the example table below:

In the example above, the Cluster 1 contains 357 models (selected out of entire trajectory which contains 2000 models), whose average RMSD between all pairs of models in the Cluster is 1.2 Angstroms, and the Cluster density is 300 (note that the average cluster RMSD value given in the table is rounded to one decimal place, however in the calculation of the cluster density value the exact number is used).
Since the Cluster 1 is the most dense and most numerous, the Model 1 can be considered as the representative of the most dominant conformation in the entire fluctuation ensemble, followed by the Model 2 (representative of the second most dominant structure), and so on.

2.2.2. "Cα RMSD and GDT_TS to the input structure" subtab

The table contains RMSD and GDT_TS values (calculated on the Cα atoms) between the predicted models and the input structure. Note that GDT_TS metric is intended as a more accurate measurement than the more common RMSD.
Read more about the root-mean-square deviation (RMSD) measure
Read more about the global distance test (GDT, also written as GDT_TS to represent "total score") measure.

2.2.3. "Cα RMSD between predicted models" subtab

The table contains RMSD values (calculated on the Cα atoms) between the predicted models.
Read more about the root-mean-square deviation (RMSD) measure.

2.2.4. "Cα GDT_TS between predicted models" subtab

The table contains GDT_TS values (calculated on the Cα atoms) between the predicted models.
Read more about the global distance test (GDT, also written as GDT_TS to represent "total score") measure.

2.3. Superimposition by the Theseus application

The Theseus simultaneously superimposes multiple protein structures and finds the optimal solution to the superposition problem using the method of maximum likelihood. By downweighting variable regions of the superposition and by correcting for correlations among atoms, the maximum likelihood superpositioning method produces much more accurate results than conventional methods using least-squares criteria. Read more [ ref 2], Theseus website.

3. Filling missing residues in PDB files

CABS-flex requires input PDB files with continuous (without breaks) protein chain. PDB files with gaps in structure have to be first prepared by filling up the missing fragment. Below is the list of example software and on-line servers that enable filling in the gaps in incomplete 3D models:

4. References

  1. Jamroz M., Orozco M., Kolinski A., Kmiecik S. 2013, Consistent View of Protein Fluctuations from All-Atom Molecular Dynamics and Coarse-Grained Dynamics with Knowledge-Based Force-Field, J. Chem. Theory Comput., 9 (1), 119–125 doi: 10.1021/ct300854w
  2. Theobald, D. L., Wuttke D. S. (2006). THESEUS: Maximum Likelihood Superpositioning and Analysis of Macromolecular Structures. Bioinformatics 22 (17): 2171–2. doi:10.1093/bioinformatics/btl332.

© Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw 2013