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Version 1.0
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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
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Claim your results by TaskID
Submit a new task
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 MIc / Zc). The CMAT algorithm, which is implemented by the visualCMAT, calculates two independent scoring functions to estimate the residue correlation - the MIc / Zc and MIp / Zp statistics. Both statistics are used to select the most significant correlations by the CMAT algorithm (see the Minimum Zp and Minimum Zc 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 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 MIc / Zc). The CMAT algorithm, which is implemented by the visualCMAT, calculates two independent scoring functions to estimate the residue correlation - the MIc / Zc and MIp / Zp statistics. Both statistics are used to select the most significant correlations by the CMAT algorithm (see the Minimum Zp and Minimum Zc 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