MOTIVATION: Software systems predicting automatically whether and how two proteins may interact are highly desirable, both for understanding biological processes and for the rational design of new proteins. As a part of a future complete solution to this problem, a bundle of programs is presented designed (i) to estimate initial docking positions for a given pair of docking candidates, (ii) to adjust them, and (iii) to filter them, thus preparing more detailed computations of free energies. RESULTS: The system is evaluated on a test set of 51 co-crystallized complexes aiming at redocking the subunits. It works completely automatically and the evaluation is performed using one single set of parameters for all complexes in the test set. The number of solutions is fixed to 50 positions with a median CPU time of 26 min. For 30 complexes, these contain a near-correct solution with root mean square deviation ( RMSD ) </=5.0 A, which is ranked first in five cases. For all complexes, the best solution is scored on rank 16 as the worst case, and has a median RMSD of 4.3 A. Alternatively to this initial estimation of docking positions, a global sampling of rotations was tested. Whereas this yields top-ranked solutions with RMSD </=3.0 A for all 51 complexes, the median CPU time increases to 11 h. This shows that this blind sampling is not feasible for most applications. AVAILABILITY: The system and its components are available on request from the authors. CONTACT: [email protected] or [email protected]

Advanced sequencing techniques allow rapid deduction of individual amino acid sequences of highly related proteins. Due to their quasi-species nature, viral genomes (e.g. HIV-1) represent one of the most common sources of related proteins. Another example of related proteins are immunoglobulins. Local differences in amino acid conservation are useful indicators of potential domain structures and immunological or functional epitopes prior to structural analysis of proteins. Although variability indices can be calculated by several methods, delineation of boundaries between sequence stretches with similar variability indices is left to the user. We use algorithmic scale-space filtering for delineation of conserved and variable sequence stretches within a protein which is performed on an algorithmic basis avoiding arbitrary assignments. Out method correctly identified variable regions for the human immunoglobulin lambda-chain V-regions (subgroup I). Prediction of the variable regions of the HIV-1 gp120 env protein was in agreement with empirical derived definitions. These examples indicate that our method is useful for the regional assignment of protein variability solely on the basis of amino acid sequences.

We present an algorithm to identify potential functional elements like protein binding sites in DNA sequences, solely from nucleotide sequence data. Prerequisites are a set of at least seven not closely related sequences with a common biological function which is correlated to one or more unknown sequence elements present in most but not necessarily all of the sequences. The algorithm is based on a search for n-tuples which occur at least in a minimum percentage of the sequences with no or one mismatch, which may be at any position of the tuple. In contrast to functional tuples, random tuples show no preferred pattern of mismatch locations within the tuple nor is the conservation extended beyond the tuple. Both features of functional tuples are used to eliminate random tuples. Selection is carried out by maximization of the information content first for the n-tuple, then for a region containing the tuple and finally for the complete binding site. Further matches are found in an additional selection step, using the ConsInd method previously described. The algorithm is capable of identifying and delimiting elements (e.g. protein binding sites) represented by single short cores (e.g. TATA box) in sets of unaligned sequences of about 500 nucleotides using no information other than the nucleotide sequences. Furthermore, we show its ability to identify multiple elements in a set of complete LTR sequences (more than 600 nucleotides per sequence).

Docking of two or more proteins to protein complexes is based on the complementarity of shape and chemical attributes of the involved molecular surfaces. A geometry-based segmentation technique is proposed, generating characteristically shaped regions of molecular surfaces, used to predict possible docking sites of proteins. Thus, an unrestricted search in 6D configuration space is avoided. The segmentation algorithm is based on the convex hull of the molecular surface. Its implementation has been shown to produce stable segmentations of the 3D surfaces, well approximating the true contact sides of the involved proteins. Making as few as possible assumptions on the underlying surfaces this technique might be useful to other applications as well.

A preprocessing algorithm is presented, integrating the computation of biomolecular surfaces, the calculation of geometrical and chemical features on each surface point, and a complex segmentation into characteristic surface regions. Calculated regions cover the whole computed protein surface and are a feature of shape, roughness and hydrophobicity on the appropriate surface area. Such regions can be paired in complementary parts, used to predict possible docking sites of proteins. The segmentation algorithm of docking sites proceeds in two steps: first, surface points are classified with a vector quantizer. Second, connected areas of surface points are computed by a complex region growing technique. The detected regions were compared to the real docking sites calculated from complexes with known 3D structures with good results of approximation.