Kabsch Algorithm

The Kabsch algorithm, named after Wolfgang Kabsch, is a method for calculating the optimal rotation matrix that minimizes the RMSD ( root mean squared deviation) between two paired sets of points. It is useful in graphics, cheminformatics to compare molecular structures, and also bioinformatics for comparing protein structures (in particular, see root-mean-square deviation (bioinformatics)).

The algorithm only computes the rotation matrix, but it also requires the computation of a translation vector. When both the translation and rotation are actually performed, the algorithm is sometimes called partial Procrustes superimposition (see also orthogonal Procrustes problem).

Description

The algorithm for the rotation of into starts with two sets of paired points, and . Each set of points can be represented as an matrix. The first row is the coordinates of the first point, the second row is the coordinates of the second point, the th row is the coordinates of the th point. Check the matrix below

:$\begin x_1 & y_1 & z_1 \\ x_2 & y_2 & z_2 \\ \vdots & \vdots & \vdots \\ x_N & y_N & z_N \end$

The algorithm works in three steps: a translation, the computation of a covariance matrix, and the computation of the optimal rotation matrix.

Translation

Both sets of coordinates must be translated first, so that their centroid coincides with the origin of the coordinate system. This is done by subtracting from the point coordinates of the respective centroid.

Computation of the covariance matrix

The second step consists of calculating a matrix . In matrix notation,

:$H = P^\mathsfQ \,$

or, using summation notation,

:$H_ = \sum_^N P_ Q_,$

which is a cross-covariance matrix when and are seen as data matrices.

Computation of the optimal rotation matrix

It is possible to calculate the optimal rotation based on the matrix formula

:$R = \left(H^\mathsf H\right)^\frac12 H^$

but implementing a numerical solution to this formula becomes complicated when all special cases are accounted for (for example, the case of not having an inverse).

If singular value decomposition (SVD) routines are available, the optimal rotation, , can be calculated using the following simple algorithm.

First, calculate the SVD of the covariance matrix .

:$H = U \Sigma V^\mathsf$

Next, decide whether we need to correct our rotation matrix to ensure a right-handed coordinate system

:$d = \mathrm\left(\det\left(V U^\mathsf\right)\right)$

Finally, calculate our optimal rotation matrix, , as

:$R = V \begin 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & d \end U^\mathsf$

The optimal rotation matrix can also be expressed in terms of quaternions. This alternative description has been used in the development of a rigorous method for removing rigid-body motions from molecular dynamics trajectories of flexible molecules. In 2002 a generalization for the application to probability distributions (continuous or not) was also proposed.

Generalizations

The algorithm was described for points in a three-dimensional space. The generalization to dimensions is immediate.

External links

This SVD algorithm is described in more detail at http://cnx.org/content/m11608/latest/

A Matlab function is available at http://www.mathworks.com/matlabcentral/fileexchange/25746-kabsch-algorithm

C++ implementation
(and unit test) using Eigen

A Python script is available at https://github.com/charnley/rmsd. Another implementation can be found i
SciPy

A free PyMol plugin easily implementing Kabsch i

(This previously linked to CEalig

but this uses the Cross-entropy method , CE Algorithm. ) VMD uses the Kabsch algorithm for its alignment.

The FoldX modeling toolsuite incorporates the Kabsch algorithm to measure RMSD between Wild Type and Mutated protein structures.

See also

* Wahba's Problem
* Orthogonal Procrustes problem

References

*
** With a correction in
*
* {{cite journal, last=Umeyama, first=Shinji, date=1991, title=Least-Squares Estimation of Transformation Parameters Between Two Point Patterns, journal=IEEE Trans. Pattern Anal. Mach. Intell., volume=13, issue=4, page=376–380, doi=10.1109/34.88573

Bioinformatics algorithms