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Besides the ML-based approaches mentioned above, we also utilized several classical methods for comparison, including the docking scores from Surflex-Dock (or AutoDock Vina or Glide SP), the Vina scores extracted from the NNscore features, empirical SF X-Score  and more robust Prime-MM/GBSA . For X-Score, the FixPDB and FixMol2 utilities were first utilized to prepare the protein and ligand files, respectively, and then the average score of the three individual SFs available in X-Score was employed for rescoring the binding poses. Prime-MM/GBSA was executed with the prime_mmgbsa utility implemented in Schrödinger. The rescoring was conducted with the variable-dielectric generalized Born (VSGB) solvation model and OPLS2005 force field.
The top1 and top3 success rates of the models trained on PDBbind-ReDocked-Refined and tested on PDBbind-CrossDocked-Core-s, based on A all poses, B re-docked poses, and C cross-docked poses. The error bars represent the standard deviation of the random sampling of 1000 redundant copies with replacements, and the dotted line indicates the ceiling of the success rate
The top1 and top3 success rates of the models trained on PDBbind-ReDocked-Refined and tested on the A cross-docked and D re-docked poses in PDBbind-CrossDocked-Core-s, the B cross-docked and E re-docked poses in PDBbind-CrossDocked-Core-g, and the C cross-docked and F re-docked pose in PDBbind-CrossDocked-Core-v. The error bars represent the standard deviation of the random sampling of 1000 redundant copies with replacements, and the dotted line indicates the ceiling of the success rate
Taken together, it seems that those ML-models trained on the re-docked poses can be well generalized to the re-docked or cross-docked poses generated by the same docking program. For the pose space defined by other docking programs, their performance is limited, especially for the predictions of cross-docked poses. Hence, a feasible strategy is to enlarge the training set, either through the augmentation and the diversification of the pose space for a certain complex or through the involvement of more complexes in the training set.
To address the issue left in the previous section, we try to enlarge our training set by introducing the cross-docked poses into the training set, thus creating the PDBbind-CrossDocked-Refined set. At first, we also tried to include the native pose of each cross-docked complex (cross-native pose), which was generated through the alignment of two crystal structures regardless of the possible steric conflicts, in the training set just as we have conducted for the re-docked poses. Although they exert little effects on other test sets (e.g., PDBbind-ReDocked-Core), the performance on CASF-docking is awfully poor, as shown as Fig. 7. Except ECIF_XGB, the other models can gain prominent improvements when removing those native poses from the training set, suggesting that these models have learnt incorrect information from the cross-native poses. We guess that two reasons may majorly account for the higher sensitivity of this dataset to these incorrect cross-native poses. Firstly, the poses in CASF-docking were manually preprocessed and clustered, so that they are uniformly distributed in each RMSD window; secondly, CASF-docking owns more poses for a certain complex than the other test sets and even the training set (at most 100 vs at most 20). As for the minor influence on the ECIF features, we guess that this type of pure atomic pairwise counts-based features may be insusceptible to the possible conflicts between the protein and ligand because it only relies on the counts within the predefined distance, while the NNscore (containing several interaction-pairwise counts) and Vina (some physics-based energy terms) features are obviously not the case. Anyway, we will not include these cross-native poses in the following experiments, and we also do not recommend this type of poses to be involved if the researchers would like to carry out a similar study in the future.
The impacts of the inclusion of the cross-native poses in training set on the top1 and top3 success rates of the models trained on PDBbind-CrossDocked-Refined and tested on CASF-docking. The error bars represent the standard deviation of the random sampling of 1000 redundant copies with replacements
The impacts of the contents of the training set on the top1 success rates of the models trained on PDBbind-CrossDocked-Refined and tested on the A cross-docked and D re-docked poses in PDBbind-CrossDocked-Core-s, the B cross-docked and E re-docked poses in PDBbind-CrossDocked-Core-g, and the C cross-docked and F re-docked poses in PDBbind-CrossDocked-Core-v. The error bars represent the standard deviation of the random sampling of 1000 redundant copies with replacements
The top1 success rates of the models trained on PDBbind-CrossDocked-Refined and tested on the cross-docked poses in A in PDBbind-CrossDocked-Core-s, B PDBbind-CrossDocked-Core-g, and C PDBbind-CrossDocked-Core-v using the ensemble strategy. The error bars represent the standard deviation of the random sampling of 1000 redundant copies with replacements
The datasets, feature files, some representative scripts utilized in this study and an example to use the trained models to predict the binding poses of the in-house ligands can be available at _pose_prediction with the MIT License and with the Creative Commons Attribution 4.0 International license. The original source codes of NNscore, ECIF and E3FP can be available at , and , respectively. The source code of X-score can be available at ~dock/manuals/xscore_1.2_manual/.
Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes in our website ( ).
When it is unknown how a ligand binds within the pocket, orthogonal experimental data can guide the modeling such as iterating between docking and modeling60,61. In the case of MRGPRX262 and GPR6863, for instance, the authors predicted multiple binding poses of a known active ligand and used mutagenesis and binding assays to test these predictions. A binding mode and receptor structure were identified that was consistent with the mutagenesis data and used for subsequent preparation in the prospective screen. Despite many difficulties, homology models have been successful in identifying novel ligands from prospective docking campaigns41,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76; though it is also true that, given the choice, most investigators will prefer to use a well-determined experimental structure.
The arguments are the path where the docking is located (./), the name of the extract_all.sort.uniq.txt file, the number of poses to get (10000, i.e., set to larger than the number of compounds docked for retrospective calculations since we want to get all), the name of the output mol2 file (poses.mol2), and the name of the input mol2 files (test.mol2.gz), containing 3D coordinates of predicted poses from the docking calculation (located in the controls0000 directory).
1000 Poses in Fashion why, when we imagine a fashion model, do we immediately think of a stylized drawing featuring unnatural, to say the least, poses? And why do we see those poses in fashion magazines? Would it capture the imagination of someone to see endless models simply standing still, with straight arms, and no kind of expression on their face, all the time? 2b1af7f3a8