In this paper, we have proved the existence of projective moment invariants of images using finite combinations of weighted moments, with relative projective differential invariants as weight functions. We have given some instances constructed in that way, and analyzed possible issues could affect the performance. Some procedures are taken to estimate partial derivatives of discrete images, and a new method is designed to normalize the number of pixels for discrete images to minimize the changes before and after the projective transformation. We have carried out experiments using popular image databases and real images to test the performance. And the results show that the invariants proposed in this paper have better stability and discriminability than other previously used moment invariants in image retrieval and classification. Users can directly extract invariant features of images for a given planar object from different viewpoints without knowing the parameters of the 2D projective transformations. Therefore, the projective moment invariant could be potentially useful for planar object recognition, image description and classification.

MOTIVATION: Most proteins consist of multiple domains, independent structural and evolutionary units that are often reshuffled in genomic rearrangements to form new protein architectures. Template-based modeling methods can often detect homologous templates for individual domains, but templates that could be used to model the entire query protein are often not available. RESULTS: We have developed a fast docking algorithm ab initio domain assembly (AIDA) for assembling multi-domain protein structures, guided by the ab initio folding potential. This approach can be extended to discontinuous domains (i.e. domains with 'inserted' domains). When tested on experimentally solved structures of multi-domain proteins, the relative domain positions were accurately found among top 5000 models in 86% of cases. AIDA server can use domain assignments provided by the user or predict them from the provided sequence. The latter approach is particularly useful for automated protein structure prediction servers. The blind test consisting of 95 CASP10 targets shows that domain boundaries could be successfully determined for 97% of targets. AVAILABILITY AND IMPLEMENTATION: The AIDA package as well as the benchmark sets used here are available for download at http://ffas.burnham.org/AIDA/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Homology detection enables grouping proteins into families and prediction of their structure and function. The range of application of homology-based predictions can be significantly extended by using sequence profiles and incorporation of local structural features. However, incorporation of the latter terms varies a lot between existing methods, and together with many examples of distant relations not recognized even by the best methods, suggests that further improvements are still possible. RESULTS: Here we describe recent improvements to the fold and function assignment system (FFAS) method, including adding optimized structural features (experimental or predicted), 'symmetrical' Z-score calculation and re-ranking the templates with a neural network. The alignment accuracy in the new FFAS-3D is now 11% higher than the original and comparable with the most accurate template-based structure prediction algorithms. At the same time, FFAS-3D has high success rate at the Structural Classification of Proteins (SCOP) family, superfamily and fold levels. Importantly, FFAS-3D results are not highly correlated with other programs suggesting that it may significantly improve meta-predictions. FFAS-3D does not require 3D structures of the templates, as using predicted features instead of structure-derived does not lead to the decrease of accuracy. Because of that, FFAS-3D can be used for databases other than Protein Data Bank (PDB) such as Protein families database or Clusters of orthologous groups thus extending its applications to functional annotations of genomes and protein families. AVAILABILITY AND IMPLEMENTATION: FFAS-3D is available at http://ffas.godziklab.org.

We convert watertight polygonal models to volumetric models by using voxelization methods. Then we use invariant feature descriptors such as volume moment invariants and shape histogram for shape retrieval. The experiment result reveals that 3D models represented by voxels are highly robust to noise and perturbations.

3D face scans often contain too much noise and other irregularities in them. Geometric processing techniques such as smoothing and hole filling are employed to preprocess this kind of data. Each face model is then segmented to get a series of partial surfaces and curves. We use surface moment invariants and curve moment invariants as shape descriptors for the shape retrieval of them.

In this paper, volume models are obtained from closed surface models by an accurate voxelization method which can handle the hidden cavities. This kind of 3D binary images is then converted to gray-level images by a fast Euclidean distance transform (EDT). Moment invariants (Mis) which are invariant shape descriptors under similarity transformations, are then computed based on the gray images. Applications in shape analysis area such as principal axis determination, skeleton and medial axis extraction, and shape retrieval can be carried out base on EDT and Mis.

Structure alignment could help to find shape similarities between proteins and guide structure classification and fold recognition. Common substructure detection and extraction are especially important, for which could guide the biologist to discover binding site or active site. We represent each segment of alpha-carbon backbone by using dihedral angles and curve moment invariants. Then, local and global structure alignment could be performed by iterative closest point algorithm. Maximum common substructures between a pair of proteins or within a protein could be found. Active sites also could be detected by the proposed algorithm.