An Extension of 3D Zernike Moments for Shape
Description and Retrieval of Maps Defined in
Rectangular Solids

Sit, Atilla; Mitchell, Julie C.; Phillips, George N.; Wright, Stephen J.

doi:10.2478/mlbmb-2013-0004

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…The description can be as detailed as desired according to the selected limit on the order of the expansion used17. In particular we use here a method based on the 3D Zernike descriptor formalism18 tailored to compare antibody binding sites.…”

mentioning

confidence: 99%

Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen

Rienzo

Milanetti

Lepore

et al. 2017

Sci Rep

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We describe here a superposition free method for comparing the surfaces of antibody binding sites based on the Zernike moments and show that they can be used to quickly compare and cluster sets of antibodies. The clusters provide information about the nature of the bound antigen that, when combined with a method for predicting the number of direct antibody antigen contacts, allows the discrimination between protein and non-protein binding antibodies with an accuracy of 76%. This is of relevance in several aspects of antibody science, for example to select the framework to be used for a combinatorial antibody library.

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mentioning

confidence: 99%

Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen

Rienzo

Milanetti

Lepore

et al. 2017

Sci Rep

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“…We compared the classification performance of the proposed method with two well known methods, that the descriptor of 3D Zernike moments (Sit et al, 2013) and descriptor of moments invariants 3D (Suk and Flusser, 2011). For each one of the chosen shape categories, we have calculated the average Recall-Precision graph by using all shapes of the test database by a query object Fig.…”

Section: Results and Performancementioning

confidence: 99%

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

Elkhal¹,

Lakehal²,

Satori³

2014

Journal of Computer Science

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In this study, we propose a new method for indexing and retrieval of 3D models in large databases based on binary images extracted from the 3D object called "level cut" LC. These cuts are obtained by the intersection of the set of the plans with the 3D object. A set of equidistant parallel plans generates by the intersection with the 3D object a set of cuts that used to indexing the 3D model. We are based on these cuts to describe the 3D object by using the vectors descriptors based on these cuts. To validate our descriptor we extract a test database from the NTU base. The robustness of our descriptor is well demonstrated by the comparison with the two will known descriptors, the 3D Zernike descriptor and the 3D invariant moment's descriptor. The topological problem of the external surfaces that representing the 3D object has been confronted, that shows the superiority of the cut method, because it is keeping the external surfaces details of the 3D object during the cutting step.Keywords: Characteristics Level Cut, Vector Descriptor, 3D Shape Indexing and Retrieval, X-means Algorithm, Similarity Measuring INTROCUCTIONRecently, the technological development of 3D modeling tools is forever exploited. Many 3D models are now downloadable for free thanks to the internet, as well as the large databases of 3D models have become available in the market. The retrieval of these models in databases demands sophisticated methods for a rapid and effective response.Among the indexation approaches of 3D models, we find the 2D/3D approach (Haris et al., 2014;Jain and Singh, 2013;Li and Johan, 2013;Petre et al., 2010) which is based on a set of images (the characteristic views, images of depths, the slices...). The importance of this approach is extract for each image a descriptor vector for construct a set of vectors descriptors associated with the object to index. The similarity measure between two 3D objects then returns to calculate the distance between the two sets of descriptors representing them. The extraction of information of the 3D object in a robust and efficient way Recently, the technological development of 3D has become a real challenge. The proposed method permits to deal with this problem. Given a 3D object, the method starts by a normalization step of the processed object using the CPCA technique. Then, we extract a set of cuts based on our cutting method. This set of cuts (binary images) will be indexed by a 2D image descriptor to construct the descriptor vector associated to the 3D model, using the Hausdorff distance to calculate the distance between the vectors descriptors which allows us to measure the similarity between the 3D objects.The proposed descriptor shows the robustness in terms of response to the query provided by the user and the comparative study between the proposed descriptor and two other descriptors well known of the 3D/3D approach, the 3D Zernike moments and 3D invariant moments shows considerable results.

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“…A more complete description of the Zernike formalism can be found here [68] , [69] . The calculation of the Zernike descriptors is made using the python code described in Ref.…”

Section: Methodsmentioning

confidence: 99%

A novel strategy for molecular interfaces optimization: The case of Ferritin-Transferrin receptor interaction

Rienzo

Milanetti

Testi

et al. 2020

Computational and Structural Biotechnology Journal

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Protein-protein interactions regulate almost all cellular functions and rely on a fine tune of surface amino acids properties involved on both molecular partners. The disruption of a molecular association can be caused even by a single residue mutation, often leading to a pathological modification of a biochemical pathway. Therefore the evaluation of the effects of amino acid substitutions on binding, and the ad hoc design of protein-protein interfaces, is one of the biggest challenges in computational biology. Here, we present a novel strategy for computational mutation and optimization of protein-protein interfaces. Modeling the interaction surface properties using the Zernike polynomials, we describe the shape and electrostatics of binding sites with an ordered set of descriptors, making possible the evaluation of complementarity between interacting surfaces. With a Monte Carlo approach, we obtain protein mutants with controlled molecular complementarities. Applying this strategy to the relevant case of the interaction between Ferritin and Transferrin Receptor, we obtain a set of Ferritin mutants with increased or decreased complementarity. The extensive molecular dynamics validation of the method results confirms its efficacy, showing that this strategy represents a very promising approach in designing correct molecular interfaces.

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An Extension of 3D Zernike Moments for Shape Description and Retrieval of Maps Defined in Rectangular Solids

Cited by 10 publications

References 4 publications

Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen

Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

A novel strategy for molecular interfaces optimization: The case of Ferritin-Transferrin receptor interaction

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