• Download and Installation
• Data Flow and Design
• Creating a Simulated Target Map
• Preliminary Rigid-Body Registration
• Vector Quantization of the High-Resolution Structure with qpdb
• Vector Quantization of the Low-Resolution Map with qvol
• Flexible Fitting using Interpolation
  
• Visualization
• Part II: Skeleton-Based Docking

Follow first the simple registration and download steps .

In addition to the executables, the Situs_2.5_flex_tutorial/bin directory contains 4 data files:

• 0_actin_orig.pdb: Original actin (start) structure.
• 0_actin_target.pdb: Target structure for flexible docking.

• 0_rnap1.pdb: atomic structure of RNA polymerase in "closed" conformation.
• 0_rnap2.situs: simulated EM map of RNA polymerase in "open" conformation.

In the following, we will use the first 2 files to dock actin to various low-resolution maps. The user can compare all generated files to the files in the "solutions" directory. The other 2 files (brown) are used in part II of this tutorial.

Schematic diagram of flexing related routines. Major Situs components (blue) are classified by their functionality. The main data flow is indicated by brown arrows. The modeling of distance constraints for the motion capture skeleton is shown in dark blue. Visualization (orange) for the rendering of the data requires a molecular graphics viewer (we use here the VMD graphics program, Chimera and Sculptor also support Situs format). 

Standard EM formats are supported and are converted to cubic lattices in Situs format. This is done with the map2map utility. Subsequently, the data is inspected and, if necessary, prepared for the vector quantization using a variety of visualization and analysis tools. Atomic coordinates in PDB format can be transformed to low-resolution maps, if necessary, and vice versa. During vector quantization of the high-resolution structure, distances can be learnt that are sent to the vector quantizer of the low-resolution structure to enable skeleton-based fitting. After the vector quantization, the high-resolution structure is flexibly docked by the qplasty tool to the low-resolution density by the corresponding codebook vectors.

First we create a simulated EM map from a known atomic structure for later validation. We lower the resolution of the target structure to 15 Å with the pdb2vol kernel convolution utility. Enter at the shell prompt:

The program projects the atomic structure to the lattice, computes the Gaussian kernel, and carries out the real-space convolution, writing the resulting volumetric map to the file 1_actin_target.situs.

Before we start fitting the original actin structure to the target structure, it is mandatory to roughly align the atomic structure and the target map by rigid-body fitting.

Alternatively, an automated rigid-body fitting procedure can also be employed, e.g. using colores, colacor, qrange, or matchpoint as explained in other tutorials. It is a good idea to export a number of best-scoring rigid body fits, and to explore the alignment of these fits by eye, before selecting one for subsequent flexible fitting.

Now, we perform the vector quantization of the roughly fitted structure with the qpdb utility.

and select mass-weighting (enter 2), ignore the B-factor cutoff (enter 1). Next, enter the number of codebook vectors: 4 (one for each of actin's subdomains). Watch the program compute a number of datasets (default: 8) for statistical averaging. The file 2_actin_orig_dock_target.qpdb now contains the four new codebook vectors, their rms variability, and the effective radius of their Voronoi cells, in PDB format. Finally, the user is asked whether nearest-neighbor connectivities should be learnt, or whether the Voronoi cells should be saved. Here we enter 1 (don't save the connectivities or Voronoi cells).

For the vector quantization of the volumetric dataset with the qvol utility we use the previous qpdb codebook vectors as start positions. After entering

the user is prompted to enter the density cutoff value. Again, we enter 10. Subsequently, the program asks whether we wish to optimize the data. or analyse it only. We enter 1 for LBG optimization. Next, the program asks if the users wishes to use distance constraints, we enter 1 (No). Finally, the user is asked whether nearest-neighbor connectivities should be learnt. This functionality is important later, but not now, so we enter no. The file 3_actin_target.qvol now contains the "QVOL" codebook vectors.

The flexible docking is approximated, based on the sparsely sampled displacements from the above qvol and qpdb codebook vectors, by interpolation with qplasty. This is sufficient for carbon alpha level accuracy. We use here the default parameters for qplasty. For more information on the algorithm see Rusu et al., 2008.

To start the flexing, enter at the shell prompt:

The flexed structure has been written to file 4_flexed_to_target_pdb.

We are now prepared to improve the stereochemical quality of the flexing. It is possible to constrain the distances between the landmarks to reduce the effect of noise and experimental limitations on the codebook vector positions. This "skeleton" based approach, as described on the Vector Quantization page, is related to 3D motion capture technology used in the entertainment industry and in biomechanics. The application is demonstrated in the Flexible Docking Tutorial, Part II.