Fast protein tertiary structure retrieval based on global surface shape similarity

Abstract

Characterization and identification of similar tertiary structure of proteins provides rich information for investigating function and evolution. The importance of structure similarity searches is increasing as structure databases continue to expand, partly due to the structural genomics projects. A crucial drawback of conventional protein structure comparison methods, which compare structures by their main-chain orientation or the spatial arrangement of secondary structure, is that a database search is too slow to be done in real-time. Here we introduce a global surface shape representation by three-dimensional (3D) Zernike descriptors, which represent a protein structure compactly as a series expansion of 3D functions. With this simplified representation, the search speed against a few thousand structures takes less than a minute. To investigate the agreement between surface representation defined by 3D Zernike descriptor and conventional main-chain based representation, a benchmark was performed against a protein classification generated by the combinatorial extension algorithm. Despite the different representation, 3D Zernike descriptor retrieved proteins of the same conformation defined by combinatorial extension in 89.6% of the cases within the top five closest structures. The real-time protein structure search by 3D Zernike descriptor will open up new possibility of large-scale global and local protein surface shape comparison.

An Exhaustive Shape-Based Approach for Proteins' Secondary, Tertiary and Quaternary Structures Indexing, Retrieval and Docking

“…Furthermore, the 3D Zernike polynomials are more adapted to closed shapes like macromolecules than to an open regions or patches. Finally, (Sael et al, 2008) study the impact of the metric when comparing the rotation invariants. They compare the Euclidian distance, the Manhattan distance and the correlation coefficient, which all seem to provide comparable results in this particular case.…”

Fast screening of protein surfaces using geometric invariant fingerprints

“…As a result, previous studies of local structural comparisons have often been limited to predefined protein-ligand binding pockets (16-18). Using geometric hashing and a hierarchical scoring approach, complete local surface screening has been performed on a nonredundant PDB database containing 4,375 structures (11).Recently, new approaches (19,20) have emerged that can compare protein surfaces without explicit alignment. Borrowed from the computer vision field, the key idea behind these new approaches is the usage of geometric invariant descriptors, or fingerprints.…”

“…Recently, new approaches (19,20) have emerged that can compare protein surfaces without explicit alignment. Borrowed from the computer vision field, the key idea behind these new approaches is the usage of geometric invariant descriptors, or fingerprints.…”

“…Using fingerprints, the comparison of 3D objects can be achieved at high speed and without the need to explicitly translate and rotate objects into alignment. Although promising applications of this approach have been demonstrated (19,20), the fingerprint-based method often suffers from lack of accuracy, which limits its application in large-scale screening for protein surface similarity. Therefore, the application of fingerprint-based methods has only been applied to small-scale datasets of protein pockets (19) or the evaluation of the overall shapes of proteins (20).…”

Preparation of Translated, Scaled, and Rotated ATS Drugs 3D Molecular Structure for the Validation of 3D Moment Invariants-based Molecular Descriptors

“…Second, due to its rotation and translation invariance properties, molecular structures need not be aligned for comparison. Lastly, the resolution of the description of molecular structures can be easily and naturally adjusted by changing the order of shape descriptors [43,44].…”