visualCMAT: New task

Version 1.0

Attention!

This message should not be visible. If you can see it please contact Support.

Dear User. We appreciate your interest in the visualCMAT. This service will be closed for maintenance starting from ... and until ... Please plan your research activities involving this service accordingly. You can contact Support with urgent requests.

Choose input mode

Submit a new task

Claim your results by TaskID (access the results and progress log of a previously submitted task)

Submit a multiple protein alignment and a representative protein structure as a new task, or access a previous submission by TaskID

Claim your results by TaskID

TaskID:

Enter TaskID (a 14-symbol code XXXXXXXXXXXXXXX) which was assiged to a previously submitted task and press "Claim". You will be redirected to the progress log page.

Submit a new task

Show tips on how to use visualCMAT

The Input: You have to submit (1) a multiple alignment of proteins in the fasta format and (2) a representative protein structure in the PDB format which should correspond to one of the proteins in the multiple alignment. Choose the representative protein based on your particular task and primary interest. It can be the target protein selected for the further experimental design, the most studied member of the superfamily, or a protein which you are the most familiar with. The multiple alignment of your protein families can be automatically prepared using the Mustguseal server. See the notes on preparing the input data for more details.

The visualCMAT annotation includes binding sites prediction. If your protein has multiple chains (e.g., A and B) and you multiple alignment represents only a single chain (e.g., A) you should submit the full-size complete protein structure for adequate prediction of pockets and cavities.

What is the Structural filtration of correlated pairs? The purpose of this filter is to classify the predicted co-evolving pairs into networks of physically interacting correlated residues and long-range correlations. A pair of correlated residues i and j is classified as interacting if the distance between them is within the cut-off (direct physical interaction), or if the distance between them and the same ligand (e.g., a substrate or a structural water molecule) is within the cut-off (mediated interaction). The default cut-off is 5 angstroms to include interactions between hydrophobic residues.

The CMAT options: The Mutual Information based CMAT algorithm [Jeong & Kim, 2012] is implemented to predict the correlated mutations/co-evolving residues by the visualCMAT server. For the description of the CMAT parameters please refer to the CMAT documentation. Generally speaking, the CMAT algorithm usually does not require task-specific fine tuning of the parameters and the default values can be implemented in the majority of cases.

How to select the MI-based score to create the visualCMAT output? The visualCMAT annotations created with either of the two statistics are equivalent (the default is MI_c / Z_c). The CMAT algorithm, which is implemented by the visualCMAT, calculates two independent scoring functions to estimate the residue correlation - the MI_c / Z_c and MI_p / Z_p statistics. Both statistics are used to select the most significant correlations by the CMAT algorithm (see the Minimum Z_p and Minimum Z_c filtering parameters in the CMAT options), but only one of them is used to compile the visualCMAT output.

The Output: In brief, the primary visualCMAT output is a content-rich file in the PyMol PSE format which contains the structure of the representative protein annotated according to the bioinformatic, statistical and structural analyzes of the predicted correlated mutations. The visualCMAT annotation can be downloaded to your computer for a local use or studied on-line using the built-in interactive analysis tools. See the visualCMAT page for a detailed description of the output.

Example: Bioinformatic analysis of proteins from the Porins superfamily is discussed here. Use the Demo mode.

Useful links:

The visualCMAT paper: Suplatov D. et al. (2018) J Bioinform Comput Biol. doi: 10.1142/S021972001840005X;
The visualCMAT main page
Notes on preparing the input data
The visualCMAT parameters
Detailed explanation of the visualCMAT output
The visualCMAT example
Citing the visualCMAT server

Hidden input mode selector If you see this message and an input field above, please report the bug to administrator

The multiple protein alignment

Upload the multiple protein alignment file

Choose a file…

Upload a file with a multiple protein alignment in fasta format. File size must not exceed 100 MB. File extention must be "fasta" or "fasta_aln". E.g.: file.fasta_aln

No alignment? Press to run the Mustguseal web-server with a pre-selected set of parameters and automatically construct a multiple alignment of your protein families for further analysis by the visualCMAT.

Remote link to file

Remote links to the Mustguseal server only are accepted

The representative protein structure

Upload the protein structure file

Choose a file…

Upload a file with protein structure in the PDB format. File size must not exceed 10 MB. File extention must be "pdb". E.g.: filename.pdb.

Remote link to file

Remote links to the Mustguseal server only are accepted

Select a protein chain Enter a case sensitive chain ID (e.g., 'A' to select chain A) or 'first' to select the first chain in the uploaded file

Set the ID of the representative protein in the multiple alignment Enter a positive integer (e.g., '10' to select the 10-th protein in the alignment to match the protein structure, or 'auto' to select the best match automatically)

Structural filtration of correlated pairs

Set the cut-off distance between heavy atoms of two residues Enter a floating-point number (e.g., '5' to set the cut-off to 5 angstroms)

Security and sharing

Service code: Administrative use only

Allow access to task files by IP address:

No Yes

Allow access to task files by password:

No Yes

Enter a string of letters and numbers at least 6 symbols long Enter the same password again

CMAT options

Maximum number of correlated amino-acid pairs to show Enter an integer between 1 and 9999 (e.g., 500)

Sequence weight method

Henikoff`s position-based weights

No sequence weight

Select the CMAT sequence weight method

Pseudocount method

Profile-based pseudocount

Simple pseudocount

No pseudocount

Select the CMAT pseudocount method

Constant for simple pseudocount Enter a floating-point number (e.g., 0.05)

Parameter A for profile-based pseudocount Enter a floating-point number (e.g., 0.5)

Parameter B for profile-based pseudocount Enter a floating-point number (e.g., 1.0)

Joint probability adjustment

Profile-based adjusment

No adjustment

Select the CMAT joint probability adjustment method

Filtering options: Minimum mutual information Enter a floating-point number (e.g., 0)

Filtering options: Minimum Zp (Z-score of MIp) Enter a floating-point number (e.g., 3.5)

Filtering options: Minimum Zc (Z-score of MIc) Enter a floating-point number (e.g., 3.5)

Filtering options: Minimum effective number of sequences Enter a floating-point (e.g., 0)

Filtering options: Minimum distance between residue positions Enter an integer number (e.g., 4)

Select the MI-based score to create the visualCMAT output

The MI_c / Z_c statistics

The MI_p / Z_p statistics

Select the Mutial Information-based score measure of residue correlation