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Autodock 4.2 free download for windows - |
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Federal government websites often end in. The site is secure. Virtual computational screening is an increasingly important tool for drug discovery.
AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program.
In this paper, we report on a multi-level parallelization of AutoDock 4. Performance of mpAD4 was examined on two multiprocessor computers. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level MPI and node-level OpenMP parallelization to best fit both workloads and computational resources.
Virtual screening, the use of computers to predict the binding of libraries of small molecules to known target structures, is an increasingly important component of the drug discovery process [ 1 , 2 ].
Although high-throughput biochemical screening is still the predominant technique for lead compound discovery, the success of in silico screening in identifying drug leads has led to the growing use of virtual screening as a complement to traditional empirical methods [ 3 , 4 ].
Of those five packages the most widely cited is AutoDock, which has been successfully used in a number of virtual screens and in the development of the HIV integrase inhibitor raltegravir [ 5 - 7 ].
This work is focused on AutoDock's most recent major version, AutoDock 4. In its current iteration, AutoDock 4. To perform a ligand-receptor docking experiment, the software accepts as inputs ligand and macromolecule coordinates, and then utilizes the LGA to generate ligand positions and minimize binding energies using precalculated pairwise potential grid maps [ 10 ]. While AD4 has been widely used for virtual screening, one limitation to its usefulness is its docking speed [ 11 , 12 ].
A potential way to increase AD4 performance is to parallelize aspects of its execution. Trends in processor architecture multicore and multithreaded , and the increasing importance of highly parallel hardware such as graphics cards in scientific computation, underscore the importance of optimizing applications for parallel workloads.
AD4 is a serial application not originally designed for computational clusters or to take advantage of parallel processing. In general, these programs either encapsulate AutoDock in code wrappers or supply scripts that automate aspects of the preparation, distribution, execution and load balancing of AutoDock on clusters.
DOVIS 2. Though log file writing can not easily be avoided, reuse of grid maps is a possibility as the majority of grid maps will be the same in each docking. One potential solution, if sufficient RAM is available, is to keep the grid maps in memory. In addition to optimizing AutoDock's execution on clusters, several previous efforts parallelized individual dockings. In a standard docking, the most time intensive task is the repeated execution of AutoDock's LGA, which is run tens or hundreds of times with identical structure files, grid maps and parameters.
This standards-based parallelization scheme is significant in that it results in a highly portable parallel implementation of AD4 with user customizability in the balance between system-level and node-level parallel execution.
AutoDock 4. To facilitate system-level parallelization, the mpAD4 main function was rewritten as a function call from the MPI driver. In this context, mpAD4 is executed within a master-slave scheme in which node-0 is the master node and all other nodes are slave nodes. AD4 requires the precalculation of one electrostatic map, one desolvation map, and individual atomic affinity grid maps for each AD4 atom type found in the ligand s.
The default AD4 behavior is to load all grid maps required for a specific docking into memory from the file system and to release that memory at the end of the docking. Thus, when the next docking begins many of the same grid maps are reloaded. Therefore, a parameter has been added to the mpAD4 executable to control grid maps persistence from one docking to another on the slave nodes. With mpAD4, as the main function begins execution on the slave the default behavior is to load all grid maps required to dock the first ligand into compute node memory.
Any remaining atomic affinity maps are loaded for subsequent dockings at the node only when required by a ligand with a previously unused atom type. To accomplish this, the scope of the multi-dimensional array holding the grid map data changed from local in the main function to global, allowing the grid map data to persist from docking to docking on a slave node. In addition, the code that manages and references this grid map array was modified to initially load only atomic affinity maps required for the first docking, and then subsequently load appropriate atomic affinity maps when required.
Workflow of mpAutoDock. With grid map reuse enabled, the precomputed grid maps, receptor and docking parameter files are loaded at the node level and reused for each additional docking. An individual docking on each node can be parallelized by running multiple instances of Lamarkian Genetic Algorithm in parallel using OpenMP threads.
The majority of an AD4 docking is spent within the search and scoring routines, making them appealing targets for parallelization. The LGA was chosen for parallelization as it was previously demonstrated to outperform either the SA or GA alone, and the LS is useful primarily for minimizing already docked structures [ 10 , 18 ].
To parallelize the LGA with OpenMP, modifications to the input seed value generation and docking output handling code were required. Thus, when OpenMP threads were created simultaneously with an unmodified RNG, each thread would receive an identical seed value. The other code change required was related to how log information about each iteration of the LGA is written to the docking log.
When the code was multithreaded, log information appeared scrambled as different threads simultaneously wrote LGA outputs. In addition to the parallelization code, performance profiling has been added to mpAD4. Profiling can be turned on or off at compile time with a compiler directive.
When profiling is enabled, a. Profiling outputs may be of interest to users of mpAD4 for characterizing performance bottlenecks on their system and for future developers of mpAD4. When not otherwise indicated, benchmarks were run with profiling enabled.
In SMP mode, each compute node executes a single task with a maximum of four threads, with node resources including memory and network bandwidth shared by all processes. Polar hydrogen atoms were added to the ligand and receptor. Indinavir libraries were then created with 4, copies 4 k indinavir , 8, copies 8 k indinavir , and 32, copies 32 k indinavir.
To generate grid maps, grid box centers were defined as the center of the bound indinavir 1HPV , extending 60 grid points 0. All other parameter values were default for AutoDock 4. Grid map reuse reduced single-threaded execution time by approximately Statistics for total duration in seconds of.
For both SMP and VN mode, deviations from ideal occur on larger systems as a virtual screen comes to the end and fewer ligands remain to be docked than capacity of the system, resulting in portions of the system remaining idle while the remaining active jobs finish.
Multithreaded execution helps to alleviate this inefficiency in two ways: 1 when using multithreading there are fewer MPI nodes in the system and a virtual screen proceeds closer to completion before nodes become idle, and 2 individual dockings are executed more quickly when multithreaded, reducing time spent with idle nodes. For very large screening libraries, node utilization efficiency differences at the end of screening are unlikely to contribute to significant difference overall docking time.
D Relative execution times for the 4, copy indinavir library were calculated for SMP and VN modes at each system size. In addition to multithreading, node utilization can be improved by pre-ordering ligands to be docked by complexity descending number of torsional angles. In cases where the availably of CPUs greatly exceeds the number of molecules to be screened, multithreading is particularly useful for increasing the usefully employable system size.
For larger systems, combining OpenMP multithreading with MPI allows for more efficient utilization of system resources at the end of screens. For smaller screens, multithreading has a clear advantages over serial execution when the number of available cores exceeds the number of ligand-receptor complexes to be docked. Overhead increased with thread number; with an apparent speedup of 1. We anticipate the specific OpenMP overhead will vary with both system characteristics and compiler options.
Though here we have only demonstrated multithreading up to 32 cores, the code is currently implemented to allow up to simultaneous threads, which we expect will allow further improvements total in docking speed. The speedup using OpenMP threads was calculated for 1 to 32 threads on a 3. For each docked complex, pairwise RMSDs were calculated for the overall lowest energy ligand and lowest energy member of largest ligand cluster.
When the lowest energy ligand was not also a member of the largest ligand cluster, the lesser pairwise RMSD value was used. The mean RMSD values in all three comparisons were less than 1. We therefore consider the outputs to be substantially similar. Statistics for the three comparisons were calculated. Similarly, we would predict that the OpenMP multithreading will generate performance gains on any modern multicore microprocessor, though overhead and absolute scalability may vary with compilers, compiler options and microprocessor architecture.
We have parallelized AutoDock 4. APN participated in the design of this work, performed validation and benchmarking of the parallel code, and wrote this manuscript. PKC parallelized the AutoDock code, and assisted in drafting this manuscript. JPK participated in the design of this work, and in revising this manuscript for publication.
DJK assisted in the analysis of the data, and in revising this manuscript for publication. CPS conceived this study, participated in the design of this work, coordinated its execution, and helped to revise this manuscript for publication. All authors read and approved the final manuscript. We acknowledge the Minnesota Supercomputing Institute for providing technical support and computational resources for this study. We are grateful to Michael Pique for thoughtful discussions and reviewing this manuscript.
Olson, The Scripps Research Institute. J Cheminform. Published online Apr Author information Article notes Copyright and License information Disclaimer. Corresponding author. Andrew P Norgan: ude. Received Jan 18; Accepted Apr Abstract Background Virtual computational screening is an increasingly important tool for drug discovery. Background Virtual screening, the use of computers to predict the binding of libraries of small molecules to known target structures, is an increasingly important component of the drug discovery process [ 1 , 2 ].
Implementation AutoDock 4. Open in a separate window. Figure 1. OpenMP Parallelization The majority of an AD4 docking is spent within the search and scoring routines, making them appealing targets for parallelization. Performance Profiling In addition to the parallelization code, performance profiling has been added to mpAD4.
Autodock 4.2 free download for windows.Molecular Docking Software
Small-molecules bind to proteins within surface cavities. The prediction of these interactions is done through docking simulations. Structure-based virtual screening molecular docking has been used to discover new ligands based on target structures. Docking methods are widely applied and accepted nowadays in drug design. Two approaches are particularly popular within the molecular docking community. One approach uses a matching technique that describes the protein and the ligand as complementary surfaces.
The second approach simulates the actual docking process in which the ligand-protein pairwise interaction energies are calculated.
Both approaches have significant advantages as well as some limitations. Table 1 Molecular docking software Pedro H. Torres, et al. Various docking software is under developed that will increase the biological accuracy in modeling docking processes. Predicting interactions between proteins and ligands using computer-aided methods or artificial intelligence AI models has attracted great interest in recent years.
Deep learning paired with drug docking and molecular dynamics simulations identify small molecules to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Figure 1 Overview of Molecular Docking. To perform a successful docking screen, the first requirement is a structure of the protein of interest.
Usually the structure has been determined using a biophysical technique such as x-ray crystallography, NMR spectroscopy or cryo electron microscopy cryo-EM , but can also derive from homology modeling construction.
This protein structure and a database of potential ligands serve as inputs to a docking program. The success of a docking program depends on two components: the search algorithm and the scoring function. Here, we introduce several molecular docking software with brief tutorials.
Taking the binding of natural ibuprofen ligand molecules to the COX-1 receptor as a point, the obtained docking conformation is compared with the natural conformation of the ligand in the crystal structure obtained by X-ray diffraction.
Prepare the […]. Read More. The attached program is written in C and Fortran The workflow of DOCK6 is shown in the figure. The first is to prepare according to the geometric coordinates of the receptor and the […].
It is designed for high-throughput virtual screening HTVS and combined model prediction research. The complete rDock software package requires less than 50 MB of hard disk […]. ZDOCK will search all translation and rotation spaces of the two proteins, and then score each possible pose. The scoring function is an energy-based scoring function, which calculates potential energy, spatial complementarity, and electric field […]. Step 1: Prepare PDB files for small and large molecules Preparation of small molecule ligands: There are many methods.
You can use chemdraw to draw the small molecule structure and save it in mol format. Then open the mol format file with chem3D and save it as a pdb file. Preparation of macromolecular receptors: You […]. In the early development of antibody drugs, the identification of antibody-antigen binding epitopes is the basic prerequisite for revealing the pharmacodynamic mechanism of antibody drugs.
It is of great significance for the subsequent humanization of antibody drugs and antibody affinity maturation, and can accelerate the process of antibody drug development. However, it is still difficult […]. AutoDockTools is a visualization program for AutoDock docking. The […]. The main functions of DS currently include: protein characterization including protein-protein interactions , homology modeling, molecular mechanics calculations and molecular dynamics simulations, structure-based drug design tools including ligand-protein interactions, novel drug design and molecular docking , small molecule-based drug design tools including quantitative conformational relationships, […].
Molecular Docking Software. Prepare the […] Read More. The first is to prepare according to the geometric coordinates of the receptor and the […] Read More. The scoring function is an energy-based scoring function, which calculates potential energy, spatial complementarity, and electric field […] Read More.
Preparation of macromolecular receptors: You […] Read More. SnugDock Tutorials In the early development of antibody drugs, the identification of antibody-antigen binding epitopes is the basic prerequisite for revealing the pharmacodynamic mechanism of antibody drugs. However, it is still difficult […] Read More. The […] Read More. The main functions of DS currently include: protein characterization including protein-protein interactions , homology modeling, molecular mechanics calculations and molecular dynamics simulations, structure-based drug design tools including ligand-protein interactions, novel drug design and molecular docking , small molecule-based drug design tools including quantitative conformational relationships, […] Read More.
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