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Autodock Add Parameters For It To The Parameter Library First

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Autodock Add Parameters For It To The Parameter Library First

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\", line 1747, in __call__return apply(self.func, args)File "C:\Program Files (x86)\MGLTools-1.5.7\", line 4573, in TheFunctionprepareDPF(dpf_file, receptor, ligand, flex_res)File "C:\Program Files (x86)\MGLTools-1.5.7\", line 4022, in prepareDPFdm.write_dpf(dpf_filename, parameter_list, pop_seed)File "C:\Program Files (x86)\MGLTools-1.5.7\", line 3761, in write_dpfself.dpo.write42(dpf_filename, parm_list)File "C:\Program Files (x86)\MGLTools-1.5.7\lib\site-packages\AutoDockTools\", 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\", 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.

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. Th




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