SDPsite: prediction of protein active and specific recognition sites from sequence and structure

Home & Submit job
Algorithm & 
Format requirements
Contact us
Gallery of examples

SDPsite is a tool for identification of protein active and other functional sites, based on spatial clustering of SDPs (specificity-determining positions, described here) with CPs (conserved positions).

Format requirements

The algorithm contains three parts:

  1. Prediction of SDPs
  2. Prediction of CPs
  3. Mapping predictied positions onto structure and construction of the best cluster

Each of these parts can be run separately.

Prediction of SDPs
SDPs for pre-defined specificity groups

The algorithm for prediction of SDPs is described in Kalinina et al. (2004) Prot Sci 13: 443-456. The input data of the algorithm are a multiple protein alignment divided into specificity groups. Proteins of the same specificity group have same specificity to the ligand (or DNA, or another interacting protein), and proteins of different specificity groups may have different specificity. Let's consider each position of the alignment independently. To evaluate if the considered position is SDP, calculate its mutual information, a measure of how well the distribution of amino acids in the positions is associated with grouping bu specificity:

mutual information

f(alpha, i) is the ratio of the number of occurrences of residue alpha in group i at position p to the length of the whole alignment column;
f(alpha) is the frequency of residue alpha in the whole alignment column;
f(i) is the fraction of proteins belonging to group i.

We introduce a number of corrections to account for properties of real biological data (discussed in Kalinina et al. (2004) Prot Sci 13: 443-456). Then, using random shuffling of the column, we calculate the mean mean I_p^exp and the variance sigma(I_p^exp) of expected column mutual information, and then a z-score of the position:


A high value of Z-scores indicates a position, where the amino acid distribution is much closer associated with grouping by specificity than for an average position of the alignment, and thus, which is likely to be an SDP. Given a series of Z-scores corresponding to every position of the multiple alignment, one needs to evaluate the significance of the Z-scores in order to tell whether the observed Z-score is sufficiently high to indicate a SDP. SDPpred uses an automated procedure for setting the thresholds based on the computation of the Bernoulli estimator. The observed Z-scores are oredered by decrease: Z_1, Z_2, .... The threshold is defined as:


q = 1 - p.

Thus, we conclude that k* highest-scoring position are non-randomly distributed and designate them SDPs. Probability

statistical significance

is called statistical significance of the set of k* positions.

Automated indetification of specificity groups

SDPsite does not request user-defined specificity groups. Instead, an automated procedure for identification of specificity groups is implemented. The user is requested to provide an unrooted tree, than SDPsite performs analysis similar to Evolutionary Trace.

The root of the tree is assumed to be placed in the middle of the longest path between leaves. Than a series of groupings obtained from dissection of the tree at different distance from the root is considered (see fig. below).

dissected tree

Groups containing less than three sequences were not considered. Than for each grouping we find SDPs as described above and calculate the statistical significance P* for the obtained set of SDPs. The calculated Z-scores are corrected:

Z := Z / log(average group size)

This correction is needed, because z-score logarithmically increases with the increase of the group size. Grouping that generates set of SDPs with the least P* is called best, or correct, grouping.

Prediction of CPs

There are different ways to measure conservation score of a position. SDPsite implements one proposed be Sander and Schneider (Sander and Schneider (1991) Proteins 9: 56-68):

conservation score

N is the number of sequences in the alignment
d(s_i, s_j) is distance between sequences si and sj, equal to 1 - %identity / 100
s_k(p) is amino acid in sequence k in position p
M(alpha, beta) is amino acid substitution matrix (BLOSUM62)

For each Cp, its statistical significance is calculated. First, we calculate the background distribution, which is distribution of conservation scores of columns made up of one random position from each sequence. For each Cp, 104 random comservation scores Cprand are calculated, and then z-score is computed as:


As alignment of two random sequences has a non-zero weight, the obtained z-scores has to be centered:

centered z-score

Then we select the correct number of CPs using the Bernoulli estimator procedure described above.

Construction of the best cluster

To construct the best cluster, SDPsite requires a 3D structure of one of the proteins of the considered alignment. On the 3D structure, it locates predicted SDPs and CPs and cluster them using layered of tightness set function algorithm (Mirkin and Muchnik (2002) Appl Math Lett 15(2): 147-151). The layered clusters are constructed as follows. The graph vertices correspond to the set of SDPs and CPs on the 3D structure. Initial cluster H0 includes all graoh vertices. Then, for each vertex i, ins weight is calculated:

vertex weight

where j indexes all the other vertices from H0 and omega_ij is calculated as follows:


dij is the Euclid distance between closest atoms of amino acids i and j
R=5A is average distance between centers of contacting atoms
D=15A is a technical parameter reflecting influence distance of atom
lambda_i=1, if vertex i is a CP, and 1 otherwise. So weight of CPs is generally lower than weight of SDPs, so that the algorithm will not choose a group of conserved hydrophobic residues as the best cluster.

Then, find set of vertices F_0 belongs to H_0, for which mu achieves its minimum and equals mu_0^min. Construct cluster H_1 = H_0 \ F_0. This procedure is repeated until the empty set is achieved. Thus a series of clusters is constructed: series. The cluster, for which max, is chosen as the most significant, or best, cluster.

Format requirements

The alignment is submitted in the standard GDE format, e.g.:

The tree is submitted in the Newick format: