CN108664729B - GROMACS cloud computing flow control method - Google Patents
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Abstract
The invention provides a GROMACS cloud computing process control method, which comprises the following steps: step (1): obtaining res or cif files of a plurality of crystal structures with front energy obtained by clustering ranking from an ArangoDB database, and then obtaining a structure file of GROMACS; step (2): acquiring the optimal force field parameters prm and rtf obtained by force field development research from an ArangoDB database, and then calling yoda library functions to automatically convert the optimal force field parameters into a force field parameter file of GROMACS; and (3): calling yoda library function to automatically generate a corresponding simulation parameter file of GROMACS according to the simulation types corresponding to different stages; and (4): calling a mixc library function, and submitting GROMACS calculation and analysis tasks to a Majorana task scheduling platform; adding corresponding monitoring tasks to the same batch of tasks corresponding to each structure, and acquiring the execution state of the tasks in real time; and (6) after all tasks are executed, acquiring a corresponding analysis result from the ArangoDB database, calling a matplotlib drawing library, and directly drawing a corresponding curve in jupyter.
Description
Technical Field
The invention belongs to the field of high-flux GROMACS scientific computing, and relates to a GROMACS cloud computing flow control method.
Background
Molecular Dynamics (MD) calculation is widely applied to various fields of material science, and GROMACS is the first choice of simulation calculation as open-source and efficient MD calculation software. At present, almost all supercomputer centers and cloud platforms are installed with GROMACS software of various versions, and have corresponding job management systems to complete functions of task submission, modification, deletion and the like.
The current GROMACS cloud computing process mainly has the following defects:
1. scheduling across platforms is not possible: almost all the current GROMACS cloud computing platforms need to log in and write task scripts manually and then submit the task scripts to the respective platforms for computing.
2. It is not possible to calculate continuously: generally, the subsequent tasks of the GROMACS all need to use the final structure of the previous task, so that the continuous tasks all need to wait for the previous task to complete the calculation before being submitted.
3. The storage form is single: all the calculation and analysis results of GROMACS are stored in the platform, which is not beneficial to real-time viewing and monitoring of the simulation state.
4. Lack of user interface: all current GROMACS cloud computing and analyzing processes are based on a script form, so that the user experience is poor, and task state monitoring, result graphical display and the like are not facilitated.
Disclosure of Invention
In order to solve the technical problem, the invention provides a GROMACS cloud computing flow control method, which comprises the following steps:
step (1): obtaining res or cif files of a plurality of crystal structures with front energy obtained by clustering ranking from an ArangoDB database, and then calling yoda library function to carry out symmetry operation, format conversion and cell expansion to obtain a structural file of GROMACS;
step (2): acquiring the optimal force field parameters prm and rtf obtained by force field development research from an ArangoDB database, and then calling yoda library functions to automatically convert the optimal force field parameters into a force field parameter file of GROMACS;
and (3): calling yoda library function to automatically generate a corresponding simulation parameter file of GROMACS according to the simulation types corresponding to different stages;
and (4): calling a mixc library function, and submitting GROMACS calculation and analysis tasks to a Majorana task scheduling platform;
adding corresponding monitoring tasks to the same batch of tasks corresponding to each structure, and acquiring the execution state of the tasks in real time;
(6) and after all tasks are executed, acquiring a corresponding analysis result from the ArangoDB database, calling a matplotlib drawing library, and directly drawing a corresponding curve in jupyter.
In the prior art, the 3 types of files necessary for GROMACS simulation include: structure (. gro), force field parameters (. top and. itp), and simulation parameters (. mdp). The invention is used for calculating the temperature stability (free energy calculation) of the crystal in the crystal form prediction process. The structure is derived from the clustering ranking results (. res and. cif) in the crystal form prediction process, the force field parameters are derived from the force field development calculation results, and the simulation parameters refer to the existing literature reports and the related test and optimization results (based on different simulation types, corresponding parameter files are automatically generated). Therefore, the method seamlessly links the force field development, the clustering ranking and the free energy calculation in the crystal form prediction process.
By adopting the technical scheme, the invention can solve the cross-platform and software complicated steps of platform login, task script compiling, analysis script compiling, task monitoring, analysis data drawing and the like, and integrates all user operations into Jupyter, thereby greatly improving the efficiency of calculation and analysis.
Preferably, the task parameter is at least one of a task type, a mirror name, a memory and a core number.
Preferably, the task type is MD or REMD.
Preferably, in the step (5), the operation state includes: at least one of end normal, fail, prepare, queue, and out of memory.
Correspondingly, the invention also provides a copy exchange molecular dynamics flow method based on the GROMACS cloud computing, which comprises the following steps:
the method comprises the following steps: selecting N crystal structures with lower energy from the clustering ranking calculation result, and obtaining corresponding GROMACS structures after format conversion and supercell expansion; wherein, the N crystal structures with lower energy refer to 5 to 10 structures with energy ranking in front
Step two: selecting optimal force field parameters, and performing isothermal and isobaric relaxation on each structure for 1 ns; the optimal force field parameters are known to those skilled in the art to best describe the structure-activity relationship of the molecules and have good crystallographic performance.
Step (ii) of
: obtaining a relaxed structure, and performing temperature scanning of conventional molecular dynamics simulation under an NPT ensemble at a given series of temperatures;
step (ii) of
: carrying out REMD simulation on the final structure of each group of temperature scanning under NPT ensemble; by extracting the change of common physicochemical properties along with time, a panorama of the evolution of a certain property along with time at all temperatures is directly drawn in Jupyter;
calculating the curve of the volume, the internal energy and the free energy changing along with the temperature according to the result of the REMD simulation; while calculating the free energy using MBAR, simultaneously calculating the correlation time and the overlap matrix between adjacent temperatures; these data can be mapped directly in Jupyter for real-time viewing and convergence checking.
Preferably, in the fifth step, the simulated calculation process may monitor whether convergence is caused by a graph of changes in temperature, pressure, volume, various energies, RMSD, centroid shift, etc. with time. The various energies are meant to include at least one of potential energy, kinetic energy, position limiting interaction energy, electrostatic interaction energy, van der waals interaction energy, and total energy.
REMD simulation is mainly used to enhance sampling of the system in configuration space, so as to traverse all points on the potential energy surface to the maximum extent to obtain accurate free energy data. But is complicated in task execution and resource scheduling due to the large amount of computing resources it requires. Therefore, a complete set of calculation flow is designed for REMD, REMD tasks are conveniently and rapidly submitted, and results are automatically analyzed.
The invention further adopts the technical scheme, and has the advantages that after the initial structure, the force field parameters and the simulation parameters (including simulation step length, time length, temperature range and the like) of each step are specified, the calculation process only needs to be submitted once, and all subsequent calculation and data analysis tasks can be automatically completed. Users can view and modify the execution state of the task at any time in Jupitter and can also view the existing calculation and analysis results in a graphical mode in real time.
The invention brings the following effects:
1. the cross-platform scheduling of the GROMACS tasks with high flux and high parallelism, the automatic continuous calculation and the continuous calculation of the GROMACS tasks and the automatic analysis of results are realized.
2. The method realizes the automatic creation of the GROMACS structure file, the force field parameter file and the simulation parameter file, and the automatic analysis and integration of the common physicochemical properties.
3. Force field development, clustering ranking calculation and free energy calculation in the crystal form prediction process are seamlessly connected; the design stores the balance structure, the analysis result and the GROMACS track separately, so that the calculation result can be conveniently and quickly obtained; the speed of result display and convergence analysis is greatly improved.
4. All the GROMACS task creating, submitting and managing, physicochemical property monitoring, result analyzing, drawing and other steps are integrated into Jupitter, and visual operation of the GROMACS cloud computing process is achieved.
Drawings
Fig. 1 is a flowchart of a complete GROMACS cloud computing according to the present invention.
Fig. 2 is a flow chart of the copy exchange molecular dynamics of GROMACS cloud computing-based system of the present invention.
FIG. 3 is a graph of the time-dependent changes in temperature, pressure, volume, various energies, RMSD, centroid shift, etc. during the calculation of the MD simulation of the present invention to monitor convergence; relating to the step (III).
Fig. 4 is a graph of the change of volume, internal energy, temperature and the like with time at different temperatures according to the present invention, wherein the curve represents the gradual temperature decrease from top to bottom (i.e. the temperature from top to bottom is from 350K to 10K), and the involved process is the process.
FIG. 5 shows the change of volume, internal energy and free energy, etc. with temperature, according to the present invention, involving the process: fifthly.
Fig. 6 is a convergence analysis of the free energy calculation of the present invention, and the correlation time of adjacent copies varies with the number of temperature scans (the correlation time is within 10, indicating that the convergence is better), involving the following process: fifthly.
Fig. 7 is a convergence analysis of the free energy calculation of the present invention, which relates to the process of energy overlap matrix for different scanning temperature numbers (the upper and lower diagonal grids have a large color difference from the overall background, indicating that the overlap between adjacent copies is sufficient): fifthly.
Detailed Description
Preferred embodiments of the present invention will be described in further detail below with reference to the accompanying drawings:
example 1
The complete GROMACS cloud computing process, as shown in fig. 1, includes the following steps:
step (1): obtaining res or cif files of a plurality of crystal structures with front energy obtained by clustering ranking from an ArangoDB database, and then calling yoda library function to carry out symmetry operation, format conversion and cell expansion to obtain a structural file of GROMACS;
step (2): acquiring the optimal force field parameters prm and rtf obtained by force field development research from an ArangoDB database, and then calling yoda library functions to automatically convert the optimal force field parameters into a force field parameter file of GROMACS;
and (3): calling yoda library function to automatically generate a corresponding simulation parameter file of GROMACS according to the simulation types corresponding to different stages; the parameters can be replaced by the input parameters in json format provided by the user, so that the controllable adjustment of the parameters is realized;
and (4): and calling a mixc library function, and submitting GROMACS calculation and analysis tasks to a Majorana task scheduling platform.
These tasks include: conventional MD calculation, transcript exchange molecular dynamics (REMD), trajectory recalculation, simulation time extension, breakpoint recalculation, multi-dimensional data polymorphic Bernt acceptance rate calculation (MBAR) and the like.
The task parameters adopt task types, mirror names, memories, core numbers and the like.
Majorana submits tasks to a specified Cloud computing platform (Amazon Cloud, Tencent Cloud, etc.) according to the task parameters. For example, whether cross-node parallelism is needed or not is selected according to the task type (MD or REMD) and the number of cores, and the cloud platform to be submitted is specified according to the selected mirror name. The Majorana can acquire and record the execution state of the task in real time, and a user can check the execution state of the task by calling an obiwan library function.
According to actual needs, parameters and states of a specified task can be modified through the obiwan library function and the handle of the task. For example: the list _ job can view the submitted task information; dump _ job can get the standard error/output of the task; the run can be recalculated (when the task is interrupted accidentally, breakpoint recalculation can be automatically realized); the dump _ file can acquire input and output files of GROMACS; kill may delete tasks, etc. The commands can automatically trigger the Majorana to send corresponding instructions to the cloud platform, so that the aim of remote management tasks is fulfilled.
(5) In order to realize the automatic continuous calculation of the tasks, a monitoring task (joiner) is added to the same batch of tasks corresponding to each structure, and the monitoring task acquires the execution states of the tasks in real time.
The operation state comprises the following steps: end of normal (DONE), FAILED (FAILED), IN preparation (IN _ PREP), IN QUEUE (IN _ QUEUE), out of memory, etc. (fail). Once all tasks have been successfully performed (DONE), a data analysis program (based on numpy, pandas and scipy libraries) is invoked to automatically calculate the time-dependent changes of common physicochemical properties (including various energies, volumes, temperatures, pressures, RMSD, centroid shifts, etc.), free energies, correlation times, and overlap matrices, etc., and save the task performance information, parameters, final structures, and analysis results into the arango db database. Meanwhile, track files (trr and xtc) with large data size, csv files with various properties evolving along with time, convergence data (npy files) and the like are uploaded to S3 (Simple Storage Service) (used for monitoring changes of various parameters, debug, convergence analysis and the like in real time). Since S3 can realize high-throughput transmission of files, it is convenient to download and analyze intermediate data at any time. And finally, automatically submitting the next calculation task as required. At this point, the state of the joiner task becomes DONE and a new joiner task is regenerated.
(6) After all tasks are executed, acquiring a corresponding analysis result from the ArangoDB database, calling a matplotlib drawing library, and directly drawing a corresponding curve in jupyter to visually check the calculation result.
Example 2
A copy exchange molecular dynamics process based on GROMACS cloud computing, as shown in fig. 2 to 7, includes the following specific steps:
the method comprises the following steps: selecting N crystal structures with lower energy from the clustering ranking calculation results (energy landscape), and obtaining corresponding GROMACS structures after format conversion and supercell expansion.
Step two: and selecting optimal force field parameters, and performing isothermal and isobaric (NPT) relaxation on each structure for 1 ns.
Step (ii) of
: the relaxed structure was taken and subjected to a conventional molecular dynamics simulation (temperature scan) at a given series of temperatures (N =68, 10-350K) for an NPT ensemble of 5 ns.
Step (ii) of
: the final structure for each set of temperature scans was subjected to REMD simulation at an NPT ensemble of 5 ns. By extracting the change of the common physicochemical property along with time, a panorama of the evolution of a certain property along with time at all temperatures can be directly drawn in Jupyter.
According to the result of REMD simulation, the curve of volume, internal energy and free energy changing with temperature can be calculated. When calculating the free energy using MBAR, we also calculate the correlation time and overlap matrix between adjacent temperatures at the same time. These data can be mapped directly in Jupyter for real-time viewing and convergence checking.
In addition, whether the MD simulation calculation process is convergent or not can be monitored through a time-dependent change diagram of temperature, pressure, volume, various energies, RMSD, centroid offset and the like, and the related processes comprise (i), (iii) and (iv).
Currently, after an initial structure, force field parameters and simulation parameters (including simulation step length, duration, temperature range and the like) of each step are specified, the calculation process only needs to be submitted once, and all subsequent calculation and data analysis tasks can be automatically completed. Users can view and modify the execution state of the task at any time in Jupitter and can also view the existing calculation and analysis results in a graphical mode in real time.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (6)
1. A GROMACS cloud computing flow control method is characterized by comprising the following steps:
step (1): selecting N res or cif files with lower energy crystal structures from the clustering ranking calculation result obtained from an ArangoDB database, and then calling yoda library functions to perform symmetry operation, format conversion and cell expansion to obtain a GROMACS structure file;
step (2): acquiring the optimal force field parameters obtained by the force field development research from an ArangoDB database, and then calling yoda library functions to automatically convert the optimal force field parameters into a force field parameter file of GROMACS;
and (3): calling yoda library function to automatically generate a corresponding simulation parameter file of GROMACS according to the simulation types corresponding to different stages;
and (4): calling a mixc library function, and submitting GROMACS calculation and analysis tasks to a Majorana task scheduling platform;
and (5): adding corresponding monitoring tasks to the same batch of tasks corresponding to each structure, and acquiring the execution states of the same batch of tasks in real time;
and (6): and after all tasks are executed, acquiring a corresponding analysis result from the ArangoDB database, calling a matplotlib drawing library, and directly drawing a corresponding curve in jupyter.
2. The method of claim 1, wherein the task parameters employ at least one of a task type, a mirror name, memory, and a number of cores.
3. The method of claim 2, wherein the task type employs molecular dynamics or transcript exchange molecular dynamics.
4. The method of claim 1, wherein in step (5), the execution state comprises: at least one of end normal, fail, prepare, queue, and out of memory.
5. A replica exchange molecular dynamics flow method based on GROMACS cloud computing as claimed in claim 1, comprising the steps of:
the method comprises the following steps: selecting N crystal structures with lower energy from the clustering ranking calculation result, and obtaining corresponding GROMACS structures after format conversion and supercell expansion;
step two: selecting optimal force field parameters, and performing isothermal and isobaric relaxation on each structure for 1 ns;
step (ii) of
: obtaining a relaxed structure, and performing temperature scanning of conventional molecular dynamics simulation under an isothermal and isobaric ensemble at a given series of temperatures;
step (ii) of
: carrying out copy exchange molecular dynamics simulation on the final structure scanned by each group of temperature under an isothermal and isobaric ensemble; by extracting the change of common physicochemical properties along with time, a panorama of the evolution of a certain property along with time at all temperatures is directly drawn in Jupyter; the common physicochemical properties are at least one of various energies, volumes, temperatures, pressures, replica exchange molecular dynamics, and centroid shifts; the various energies are at least one of potential energy, kinetic energy, total energy, electrostatic interaction energy, van der waals interaction energy, and crystal free energy;
step five: calculating curves of the volume, the internal energy and the free energy changing along with the temperature according to the result of the replica exchange molecular dynamics simulation; calculating a correlation time and an overlap matrix between adjacent temperatures at the same time when calculating free energy using the multi-state bennett acceptance rate of the multi-dimensional data; these data can be mapped directly in Jupyter for real-time viewing and convergence checking.
6. The method of claim 5, wherein in the fifth step, the calculation process of the replica exchange molecular dynamics simulation monitors whether convergence is achieved by a graph of the change of temperature, pressure, volume, various energies, coordinate root mean square deviation, centroid shift with time.
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| US11609807B2 (en) | 2018-12-21 | 2023-03-21 | Shenzhen Jingtai Technology Co., Ltd. | Computing task management and analysis system for molecular force field parameter building and operation method thereof |
| CN109637592B (en) * | 2018-12-21 | 2022-04-12 | 深圳晶泰科技有限公司 | Calculation task management analysis system for molecular force field parameter generation and operation method thereof |
| CN109885390B (en) * | 2019-02-21 | 2023-06-23 | 深圳晶泰科技有限公司 | Molecular docking cloud computing flow control method |
| WO2020168507A1 (en) * | 2019-02-21 | 2020-08-27 | 深圳晶泰科技有限公司 | Molecular docking cloud computing process control method |
| CN111341391B (en) * | 2020-02-25 | 2023-12-01 | 深圳晶泰科技有限公司 | Free energy perturbation calculation scheduling method for heterogeneous cluster environment |
| CN112487636B (en) * | 2020-11-26 | 2023-12-26 | 北京迈高材云科技有限公司 | Molecular dynamics calculation method and system based on cloud calculation technology |
| CN114187971B (en) * | 2021-12-10 | 2023-03-28 | 上海智药科技有限公司 | Molecular free energy calculation and stability analysis method, device, equipment and storage medium |
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