GDT - Global Distance Test¶
Implements the GDT score, i.e. identifies the largest number of positions that can be superposed within a given distance threshold. The final GDT score is then the returned number divided by the total number of reference positioons. The algorithm is similar to what is described for the LGA tool but simpler. Therefore, the fractions reported by OpenStructure tend to be systematically lower. For benchmarking we computed the full GDT_TS, i.e. average GDT for distance thresholds [1, 2, 4, 8], on all CASP15 TS models. 96.5% of differences to the LGA results from the predictioncenter are within 2 GDT points and 99.2% are within 3 GDT points. The max difference is 7.39 GDT points.
The algorithm expects two position lists of same length and applies a sliding window with specified length to define a subset of position pairs as starting point for iterative superposition. Each iterative superposition applies the following steps:
Compute minimal RMSD superposition on subset of position pairs
Apply superposition on all model positions
Compute pairwise distances of all model positions and reference positions
Define new subset of position pairs: pairs within distance threshold
Stop if subset doesn’t change anymore
The subset in any of the iterations which is largest is stored.
This is done for each sliding window position and the largest subset ever observed is reported. To avoid long runtimes for large problem sizes, the sliding window is not applied on each possible position but is capped. If the number of positions is larger than this threshold, the sliding window is only applied on N equidistant locations.
- GDT(mdl_pos, ref_pos, window_size, max_windows, distance_thresh)¶
Returns number of positions that can be superposed within distance_thresh and the respective transformation matrix.
- Parameters:
mdl_pos (
ost.geom.Vec3List) – Positions representing the model, typically alpha-carbon positionsref_pos (
ost.geom.Vec3List) – Positions representing the reference, typically alpha-carbon positionswindow_size (
int) – Size of the sliding window that is used to serve as starting point for iterative superposition. The described benchmark was done with a value of 7.max_windows (
int) – Cap for number of starting points. The described benchmark was done with a value of 1000.distance_thresh (
float) – Distance threshold for GDT algorithm
- Returns:
tuplewith first element being the number of superposable positions (int) and the second element the transformation matrix (ost.geom.Mat4)