1. MotivationThe TRAPP webserver provides a workflow for simulation, analysis, and visualization of protein cavity dynamics and for detection of transient sub-pockets using protein motion trajectories or ensembles of protein structures obtained either from experiment or from simulations. The TRAPP webserver includes an interface for several methods for generating conformational changes of a binding site and to analyze them.
TRAPP is not designed for identification of all of a protein's binding sites. The binding site regions to be analysed must be defined by the user.
2. OverviewThe TRAPP workflow consists of the following tools:
- TRAPP-structure: Simulation of protein conformational changes (from side-chain rotation to distortion of secondary structure or domain motion). It is a python wrap around several programs for the generation of protein conformational changes (tCONCOORD; L-RIP; RIPlig; NAMD);
- TRAPP-analysis: Analysis of protein conformational changes. It helps to identify the most flexible parts of a binding site from protein trajectories or structures generated by TRAPP-structure or provided by the user
- TRAPP-pocket: Analysis, and visualization of protein cavity dynamics and detection of transient pockets/subpockets from protein motion trajectories or ensembles of protein structures obtained either from TRAPP-structure or provided by the user.
TRAPP-pocket identifies conserved and transient regions of a binding pocket and variations of the binding site composition, size, surface and some other characteristics such as amino acid sequence. It enables the user to trace the opening or closing of particular pocket regions or sub-pockets along each trajectory.
- Webserver visualization uses JSmol, a protein structure viewer that does not require Java plugins in your browser. However, if there are more than 100 snapshots in a trajectory to be used for pocket simulations - all snapshots will be taken into account in pocket property computations, but not all snapshots will be shown in Jsmol visualization ( the stride is calculated as: int(total_number_of_snapshots)/100 +1 )
3. Frequently Asked Questions
4. Details of the algorithm, input parameters, and results:
- A single protein structure (reference structure)
- The position of the binding site to be analysed, which that can be defined either by a ligand or by the coordinates of the binding site center.
- Finding transient sub-pockets in a MD trajectory (Aldose reductase, AR);
- Finding transient sub-pockets in crystal structures (p38 MAP kinase)
- Simulation of the binding pocket flexibility in HSP90 using RIPLIG and/or L-RIP
- Differentiating binding pocket features using a multiple sequence alignment (Dihydrofolate reductase, DHFR)
See also Perturbation Approaches for Exploring Protein Binding Site Flexibility to Predict Transient Binding Pockets
Daria B. Kokh, Paul Czodrowski, Friedrich Rippmann, Rebecca C. Wade
Journal of Chemical Theory and Computation, 2016, 12:4100–4113
See also TRAPP: a Tool for Analysis of Transient Binding Pockets in Proteins
Daria B. Kokh, Stefan Richter, Stefan Henrich, Paul Czodrowski, Friedrich Rippmann, and Rebecca C Wade
Journal of Chemical Information and Modeling, 2013, 53:1235-1252
- Sequence conservation score calculation
- Description of contact residues
4.1 Input data requiredTo run the complete TRAPP workflow you need to provide:
The required format of the input structures and trajectories can be viewed here:
4.2 Input parameters
4.3 Examples of using the TRAPP server:
4.4 Analysis of simulation results
4.5 Description of methods employed
4.6 Some comments on the computational time
5. Current Version
6. Testing Information
Trapp was tested with the following versions of these browsers and operating systems.
|Browser/OS||Windows 10||Linux (Ubuntu 16.04)||Mac OS X (10.11.6)|