Identifying similar surface patches on proteins using a spin-image surface representation

1. Identifying similar surface patches on proteins using a spin-image surface representation M. E. Bock Purdue University, USA G. M. Cortelazzo, C. Ferrari, C. Guerra University of Padova, Italy

2. Protein Surface Matching Problem: detect similar surface patches on two proteins Motivation: identify similar binding sites in proteins useful when the proteins are unrelated by sequence or overall fold.

3. Our approach • is purely geometric • is based on a protein surface representation as a set of two-dimensional (2D) images, called spin images • finds a collection of pairs of points on the two proteins such that the corresponding members of the pairs for one of the proteins form a surface patch for which the corresponding spin images are a "match".

4. Spin Images(Jonhson, Hebert, 1997) • A surface representation introduced in the area of computer vision that uses 2D images to describe 3-D oriented points • It allows to apply powerful techniques from 2-D template matching and pattern classification to the problem of 3-D surface recognition.

5. An oriented point basis

6. Spin Image ai bj The spin image of O is an array that accumulates the pairs (a, b) relative to the surface points

7. Examples of spin images

8. Why spin images? Spin-images are useful representations the following reasons. • invariant to rigid transformation • object-centered • simple to compute • scalable from local to global representation

9. Matching Algorithm Step 1. Obtain Connolly’s surface representation of two given proteins P and Q Step 2. Label Connolly’s points as blocked, shadowed, or clear. Step 2. Find individual point correspondences on the two proteins based on correlation value of their spin images. Restrict correspondences to points with the same label Step3. Group point correspondences into patches using geometric consistency criteria

10. Labeling surface points • A surface point P is unblocked if no other surface point with positive bvalue lies on the oriented line l through P parallel to the normal n and in the same orientation as n • A point that is not unblocked is blocked • The unblocked points that belong to the convex hull of the protein surface are labeled clearpoints all others are shadowed. This labeling is easily obtained from the spin images

11. Examples of blocked and shadowed points

12. Grouping Point Correspondences Geometric Consistency : 1. the distances between m1 and m2 and between s1 and s2 are within a given tolerance 2. the angles between the normals at m1 and m2and between the normals at s1 and s2are within a given tolerance.

13. Grouping strategy • A greedy approach that grows a patch of geometrically consistent points around a point that is a member of a pair of points on the two proteins whose corresponding spin images are highly correlated • The obtained patches are ranked according to the number of points they contain

14. Results The active site is found generally among the top solutions As an example, for proteins 1BCK and 1CYN (cyclophilin B and A, respectively), that bind to the same ligand (cyclosporin), the first solution corresponds to the active site

15. Active sites on 1BCK and 1CYN(from PDB)

16. Top solution of our approach

17. Future work • Apply a spin image representation for protein-protein interfaces and docking