Error: 1 Exception in Tk callbackFunction: (type: )Args: ()Traceback (innermost last):File "C:\Program Files (x86)\MGLTools-1.5.7\lib\site-packages\Pmw\Pmw_1_3\lib\PmwBase.py", line 1747, in __call__return apply(self.func, args)File "C:\Program Files (x86)\MGLTools-1.5.7\raccoon.py", line 4573, in TheFunctionprepareDPF(dpf_file, receptor, ligand, flex_res)File "C:\Program Files (x86)\MGLTools-1.5.7\raccoon.py", line 4022, in prepareDPFdm.write_dpf(dpf_filename, parameter_list, pop_seed)File "C:\Program Files (x86)\MGLTools-1.5.7\raccoon.py", line 3761, in write_dpfself.dpo.write42(dpf_filename, parm_list)File "C:\Program Files (x86)\MGLTools-1.5.7\lib\site-packages\AutoDockTools\DockingParameters.py", line 1556, in write42dpf_ptr.write( self.make_param_string('autodock_parameter_version'))File "C:\Program Files (x86)\MGLTools-1.5.7\lib\site-packages\AutoDockTools\DockingParameters.py", line 1236, in make_param_stringraise NotImplementedError, "type (%s) of parameter %s unsupported" % (vt.__name__, param): type (unicode) of parameter autodock_parameter_version unsupported
Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.
Autodock Add Parameters For It To The Parameter Library First
We have used Smina as a tool to develop Vinardo (Vina RaDii Optimized), a scoring function which shares component terms with the Vina scoring function: steric interactions, hydrophobic interactions, and non-directional H-bonds. Despite sharing component terms, Vinardo displays several differences with Vina; a modified steric interaction term, new atomic radii, and simplified interactions (using a lower number of parameters). Vinardo is implemented as an optional scoring function in Smina. To compare the docking abilities of Vinardo and Vina, we performed re-docking assays on four high quality datasets. To measure the scoring and ranking abilities of Vinardo, we repeated the scoring function analyses performed in CASF 2013 [11]. Finally, we tested virtual screening capabilities by docking a multitude of active and inactive compounds against different proteins available in the DUD database, and verifying Vinardo's capability to rank active compounds above inactive ones.
An empirical scoring function calculates the affinity, or fitness, of protein-ligand binding by summing up the contributions of a number of individual terms [1]. Each of these terms generally represent an important energetic factor in protein-ligand binding. There are several parameters involved in each of these functions which can be modified to improve the predictions. Lastly, each term is weighted (multiplied by a constant) before being summed up into the final predicted binding affinity. In the following paragraphs we give a brief description of the Vina scoring function in order to compare it to Vinardo. For a more detailed description of the Vina scoring function, the reader is referred to the original paper by Trott and Olson [3].
The procedure followed to select the best docking function was as follows: A large set of trial scoring functions was generated by systematically exploring the vast combinatorial possibilities of individual energetic terms, parameters present in those terms, atomic radii, and weights applied to each term. The initial, final and step size values used in the exploration of the parameter space is shown in S1 Table.
A widespread observation in the field of protein-ligand prediction is that scoring functions are typically either apt at ranking crystallized ligands (scoring capability), or apt at predicting the best position and orientation of the ligand (docking capability) [11]. In this work our interest was to develop a scoring function based on the highly successful Vina scoring function, that improves its docking capabilities. To this end, a possible strategy is to select a protein-ligand dataset, define a scoring function, evaluate its capabilities in re-docking experiments and, based on this result, elaborate a new scoring function or refine its parameters. With a dataset in the order of hundreds of complexes, this methodology is not feasible due to the huge combinatorial space of possible terms and parameters involved. A common strategy (which was used in the development of both Vina and Dk_scoring) has been to train the scoring function by regression of experimental and predicted binding affinities of a selected dataset. In this way, only the calculation of the binding energy of the crystal protein-ligand structures in the dataset is performed, without the need of the more time consuming re-docking procedure. Vina was trained on the PDBBIND 2007 dataset, and Dk_scoring with the CSAR-NRC HiQ 2010 and CSAR 2012 datasets [3,9]. Regression ensures good scoring capability but not necessarily good docking capability; therefore we decided to explore the relationship between linear regression training and docking capability. The selected dataset was the PDBBIND 2013 dataset (195 structures) which is a curated protein-ligand dataset which includes experimental binding data. Due to time constraints, for this exploratory phase of the development, we reduced the size of the PDBBIND Core 2013 dataset, only retaining ligands with 7 or less rotatable bonds, which makes up a total of 122 structures. We prepared 72 scoring formulas by perturbing the Vina scoring function with small variations in the number of terms, parameters, weights, and atom radii. For each scoring formulation we calculated the correlation coefficient between experimental and calculated binding affinity (scoring ability), and compared these coefficients against re-docking ability. Re-docking ability was measured as the percentage of ligands for which the RMSD of the best scoring pose was within 2 Å of the crystallized ligand structure for each ligand present in the dataset. As shown in Fig 1A, scoring ability is a very poor predictor of the docking ability of a scoring function.
Having established that low average RMSD values after minimization correlate with good docking ability, we performed a systematic parameter search on the full PDBBIND 2013 dataset, as described in Methods. Succinctly, this consisted of searching for combinations of interaction terms, weights and parameters, which resulted in functions with low average RMSD values. In the final step of the development, an actual docking evaluation is performed on a pool of these functions, and we selected the scoring function with the best docking performance.
In the exploration of the parameter space we found a very degenerated landscape. For example it is possible to find different sets of weights wi in Eq 3 that produce practically the same final results. This means that there is no need to perform a fine grid search and go beyond two or three decimal places in defining the different parameters. This relieves some of the computational burden of searching the parameter space.
When running Oracle Database in automatic PGA memory management mode, sizing of work areas for all sessions is automatic, and the *_AREA_SIZE parameters are ignored by all sessions running in this mode. Oracle Database automatically derives the total amount of PGA memory available to active work areas from the PGA_AGGREGATE_TARGET initialization parameter. The amount of PGA memory is set to the value of PGA_AGGREGATE_TARGET minus the amount of PGA memory allocated to other components of the system (such as PGA memory allocated by sessions). Oracle Database then assigns the resulting PGA memory to individual active work areas based on their specific memory requirements.
Oracle Database attempts to adhere to the PGA_AGGREGATE_TARGET value set by the DBA by dynamically controlling the amount of PGA memory allotted to work areas. To accomplish this, Oracle Database first tries to maximize the number of optimal work areas for all memory-intensive SQL operations. The rest of the work areas are executed in one-pass mode, unless the PGA memory limit set by the DBA (using the PGA_AGGREGATE_TARGET parameter) is so low that multi-pass execution is required to reduce memory consumption to honor the PGA target limit.
1. Randomization of orientations and rigid-body minimization (it0) In this initial stage, the interacting partners are treated as rigid bodies, meaning that all geometrical parameters such as bonds lengths, bond angles, and dihedral angles are frozen. The partners are separated in space and rotated randomly about their centres of mass. This is followed by a rigid body energy minimization step, where the partners are allowed to rotate and translate to optimize the interaction.The role of AIRs in this stage is of particular importance. Since they are included in the energy function being minimized, the resulting complexes will be biased towards them. For example, defining a very strict set of AIRs leads to a very narrow sampling of the conformational space, meaning that the generated poses will be very similar. Conversely, very sparse restraints (e.g. the entire surface of a partner) will result in very different solutions, displaying greater variability in the region of binding. 2ff7e9595c
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