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• W2887788718 abstract "Despite decades of development, protein-protein docking remains a largely unsolved problem. The main difficulties are the immense space spanned by the translational and rotational degrees of freedom and the prediction of the conformational changes of proteins upon binding. FFT is generally the preferred method to exhaustively explore the translation-rotation space at a fine grid resolution, albeit with the trade-off of approximating force fields with correlation functions. This work presents a direct search alternative that samples the states in Cartesian space at the same resolution and computational cost as standard FFT methods. Operating in real space allows the use of standard force field functional forms used in typical non-FFT methods as well as the implementation of strategies for focused exploration of conformational flexibility. Currently, a few misplaced side chains can cause docking programs to fail. This work specifically addresses the problem of side chain rearrangements upon complex formation. Based on the observation that most side chains retain their unbound conformation upon binding, each rigidly docked pose is initially scored ignoring up to a limited number of side chain overlaps which are resolved in subsequent repacking and minimization steps. On test systems where side chains are altered and backbones held in their bound state, this implementation provides significantly better native pose recovery and higher quality (lower RMSD) predictions when compared with five of the most popular docking programs. The method is implemented in the software program ProPOSE (Protein Pose Optimization by Systematic Enumeration)." @default.
• W2887788718 created "2018-08-22" @default.
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• W2887788718 date "2018-08-14" @default.
• W2887788718 modified "2023-10-16" @default.
• W2887788718 title "ProPOSE: Direct Exhaustive Protein–Protein Docking with Side Chain Flexibility" @default.
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• W2887788718 doi "https://doi.org/10.1021/acs.jctc.8b00225" @default.
• W2887788718 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30107730" @default.
• W2887788718 hasPublicationYear "2018" @default.
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