An Experimental Shape Matching Approach for Protein Docking (Unknown language)

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Proteins play a vital role in biological processes, with their function being largely determined by their structure. It is important to know what a protein binds, where it binds, how it binds, and what is its final conformation. Several methodologies have been applied to solve this complex protein-protein docking problem, but the number of degrees of freedom renders this a very slow and computationally heavy challenge. To handle this problem, we propose a multi-level space partition approach to describe the three-dimensional shape of the protein. By combining two proteins in the same data structure we are able to easily detect the shape-complementary regions. Moreover, by directly integrating bio-energetic information, we can drive the algorithm by both parameters and provide a fast and efficient way to overcome some of the limitations of previous approaches.

Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

1
Shape Retrieval and 3D Gestural Interaction
Giachetti, Andrea / Caputo, Fabio Marco / Carcangiu, Alessandro / Scateni, Riccardo / Spano, Lucio Davide | 2016
5
Towards an Observer-oriented Theory of Shape Comparison
Frosini, Patrizio | 2016
9
3D Objects Exploration: Guidelines for Future Research
Biasotti, Silvia / Falcidieno, Bianca / Giorgi, Daniela / Spagnuolo, Michela | 2016
13
A Descriptor for Voxel Shapes Based on the Skeleton Cut Space
Feng, Cong / Jalba, Andrei C. / Telea, Alexandru C. | 2016
21
An Experimental Shape Matching Approach for Protein Docking
Fernandes, Francisco / Ferreira, Alfredo | 2016
27
An Edit Distance for Reeb Graphs
Bauer, Ulrich / Fabio, Barbara Di / Landi, Claudia | 2016
35
An Evaluation of Local Feature Encodings for Shape Retrieval
Tasse, Flora Ponjou / Kosinka, Jiri / Dodgson, Neil A. | 2016
41
Retrieval of Human Subjects from Depth Sensor Data
Giachetti, Andrea / Fornasa, Francesco / Parezzan, Federico / Saletti, Alessandro / Zambaldo, Leonardo / Zanini, Luisa / Achilles, Felix / Ichim, Alexandru-Eugen / Tombari, Federico / Navab, Nassir et al. | 2016
47
3D Sketch-Based 3D Shape Retrieval
Li, Bo / Lu, Yijuan / Duan, Fuqing / Dong, Shuilong / Fan, Yachun / Qian, Lu / Laga, Hamid / Li, Haisheng / Li, Yuxiang / Liu, Peng et al. | 2016
55
Matching of Deformable Shapes with Topological Noise
Lähner, Zorah / Rodolà, Emanuele / Bronstein, Michael M. / Cremers, Daniel / Burghard, Oliver / Cosmo, Luca / Dieckmann, Alexander / Klein, Reinhard / Sahillioğlu, Yusuf | 2016
61
Partial Matching of Deformable Shapes
Cosmo, Luca / Rodolà, Emanuele / Bronstein, Michael M. / Torsello, Andrea / Cremers, Daniel / Sahillioğlu, Yusuf | 2016
69
Shape Retrieval of Low-Cost RGB-D Captures
Pascoal, Pedro B. / Proença, Pedro / Gaspar, Filipe / Dias, Miguel Sales / Ferreira, Alfredo / Tatsuma, Atsushi / Aono, Masaki / Logoglu, K. Berker / Kalkan, Sinan / Temizel, Alptekin et al. | 2016
79
Partial Shape Queries for 3D Object Retrieval
Pratikakis, Ioannis / Savelonas, Michalis A. / Arnaoutoglou, Fotis / Ioannakis, George / Koutsoudis, Anestis / Theoharis, Theoharis / Tran, Minh-Triet / Nguyen, Vinh-Tiep / Pham, V.-K. / Nguyen, Hai-Dang et al. | 2016
89
Large-Scale 3D Shape Retrieval from ShapeNet Core55
Savva, Manolis / Yu, Fisher / Su, Hao / Aono, Masaki / Chen, Baoquan / Cohen-Or, Daniel / Deng, Weihong / Su, Hang / Bai, Song / Bai, Xiang et al. | 2016
99
3D Object Retrieval with Multimodal Views
Gao, Yue / Nie, Weizhi / Liu, Anan / Su, Yuting / Dai, Qionghai / An, Le / Chen, Fuhai / Cao, Liujuan / Dong, Shuilong / De, Yu et al. | 2016