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Title: Improving the discrimination of near-native complexes for protein rigid docking by implementing interfacial water into protein interfaces
Authors: Su, Tran To Chinh
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Issue Date: 2014
Source: Su, T. T. C. (2014). Improving the discrimination of near-native complexes for protein rigid docking by implementing interfacial water into protein interfaces. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Protein-protein docking is an in silico method to predict the formation of protein complexes. Due to the limited computational resources, the protein docking approach has been originated and developed under the assumption of rigid docking, in which one of the two protein partners remains rigid during the protein associations. The rigid docking has successfully predicted structures of various protein complexes, but often failed if the proteins acquire conformational changes or are driven by influences of other factors (e.g. solvent) while interacting. Formulation of the initial rigid docking contains two main stages: (1) searching for all possible surface matches on a rotational and translational sampling space and (2) ranking those possible solutions to distinguish the correct predictions by locating them on the top high ranks. To obtain better results of the protein rigid docking, one can improve it by optimizing the solutions in the search space or by developing more effective ranking methodology to discriminate the correct predictions from the incorrect or false positive ones. However, while development and improvement are reported in the searching stage, it seems to-date that most initial docking algorithms find it difficult or even fail to locate successfully the correct predictions apart from the others in the ranking stage, especially for Antigen/Antibody complexes. To tackle this issue, a new energy-based scoring function is proposed in this research, namely IFACEwat, to re-rank the results of an initial rigid docking algorithm and therefore further improve the discrimination of the near-native structures from the other false positives. Unlike other re-ranking techniques, in which the solvent effect is ignored or implicitly presented, the IFACEwat implements interfacial water into the protein interfaces and explicitly reflects the water influences on the protein interactions. The IFACEwat was implemented based on the interface Atomic Contact Energy (IFACE) of the initial rigid docking algorithm ZDOCK3.0.2 and the derived energies of water-involved interactions at the protein interfaces. The IFACEwat therefore not only takes advantages of shape complementarity from the initial rigid docking algorithm for protein recognitions but also accounts for the water-mediated contacts during the protein associations. Evaluated for various types of protein complexes, the IFACEwat both increases the numbers of the near-native structures and improves their ranks (i.e. most of them ranked at the top-1) as compared to the original rigid docking. In fact, it achieves a success rate of 83.8% for Antigen/Antibody complexes, which is 10% better than ZDOCK3.0.2. As compared to another re-ranking technique ZRANK, the IFACEwat obtains success rates of 92.3% (8% better) and 90% (5% better) respectively for medium and difficult cases. When comparing with the latest published re-ranking method in F2Dock, it performs equivalently well or better for several Antigen/Antibody complexes. In conclusion, with the inclusion of interfacial water, the IFACEwat improves significantly the results of the initial rigid docking, especially for Antigen/Antibody complexes. The improvement is achieved by explicitly accounting the contribution of water during the protein interactions, which was ignored or not fully presented by the initial rigid docking and other re-ranking techniques. In addition, the IFACEwat maintains the sufficient computational efficiency of the initial docking algorithm, yet improves the ranks as well as the number of the near-native structures found.
DOI: 10.32657/10356/62097
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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