CN114611314A - Simulation method for regulating tyrosine dipeptide self-assembly structure - Google Patents
Simulation method for regulating tyrosine dipeptide self-assembly structure Download PDFInfo
- Publication number
- CN114611314A CN114611314A CN202210277805.5A CN202210277805A CN114611314A CN 114611314 A CN114611314 A CN 114611314A CN 202210277805 A CN202210277805 A CN 202210277805A CN 114611314 A CN114611314 A CN 114611314A
- Authority
- CN
- China
- Prior art keywords
- self
- simulation method
- assembly
- simulation
- dipeptide
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 108010016626 Dipeptides Proteins 0.000 title claims abstract description 84
- 238000001338 self-assembly Methods 0.000 title claims abstract description 75
- 238000004088 simulation Methods 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims abstract description 64
- OUYCCCASQSFEME-UHFFFAOYSA-N tyrosine Natural products OC(=O)C(N)CC1=CC=C(O)C=C1 OUYCCCASQSFEME-UHFFFAOYSA-N 0.000 title claims abstract description 57
- OUYCCCASQSFEME-QMMMGPOBSA-N L-tyrosine Chemical compound OC(=O)[C@@H](N)CC1=CC=C(O)C=C1 OUYCCCASQSFEME-QMMMGPOBSA-N 0.000 title claims abstract description 56
- 230000001105 regulatory effect Effects 0.000 title claims abstract description 27
- 239000002086 nanomaterial Substances 0.000 claims abstract description 18
- 238000000329 molecular dynamics simulation Methods 0.000 claims abstract description 13
- 230000001052 transient effect Effects 0.000 claims abstract description 8
- 230000001276 controlling effect Effects 0.000 claims abstract description 6
- 238000005457 optimization Methods 0.000 claims abstract description 6
- 238000002360 preparation method Methods 0.000 claims abstract description 5
- 238000012545 processing Methods 0.000 claims abstract description 4
- 230000003993 interaction Effects 0.000 claims description 13
- 239000002904 solvent Substances 0.000 claims description 9
- 108090000765 processed proteins & peptides Proteins 0.000 claims description 8
- 238000011160 research Methods 0.000 claims description 7
- 230000009881 electrostatic interaction Effects 0.000 claims description 6
- 238000012617 force field calculation Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 239000002245 particle Substances 0.000 claims description 5
- 230000000737 periodic effect Effects 0.000 claims description 5
- 238000002945 steepest descent method Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 3
- 229910052739 hydrogen Inorganic materials 0.000 claims description 3
- 239000001257 hydrogen Substances 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 239000007864 aqueous solution Substances 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 239000002077 nanosphere Substances 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 238000005411 Van der Waals force Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000011365 complex material Substances 0.000 description 1
- 239000007943 implant Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000004377 microelectronic Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005232 molecular self-assembly Methods 0.000 description 1
- 239000002071 nanotube Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000007614 solvation Methods 0.000 description 1
- 150000003668 tyrosines Chemical class 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/14—Details relating to CAD techniques related to nanotechnology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Peptides Or Proteins (AREA)
Abstract
The invention provides a simulation method for regulating and controlling a tyrosine dipeptide self-assembly structure, which comprises the following steps; s1, drawing a coordinate file of a single tyrosine dipeptide molecule self-assembly structure and carrying out structure optimization; step S2, taking the molecular structure of step S1 as a basic unit, configuring self-assembly parameters of different environments, and obtaining a system capable of setting dynamic balance parameters; step S3, setting dynamic balance parameters by taking the system obtained in the step S2 as an object, and performing energy minimization and NVT (non-volatile transient) and NPT (non-volatile transient) relaxation; s4, performing molecular dynamics simulation on the relaxation structure obtained in the S3; s5, summarizing the integrated self-assembly structure, analyzing and processing corresponding data to obtain parameter conditions and nano-structure suitable for tyrosine dipeptide self-assembly; the invention can determine the most suitable density and temperature of tyrosine dipeptide self-assembly, and obtain the corresponding zero-dimensional or one-dimensional structure, and has important guiding significance for the experimental preparation of the tyrosine dipeptide nano-structure.
Description
Technical Field
The invention relates to the technical field of simulation, in particular to a simulation method for regulating and controlling a tyrosine dipeptide self-assembly structure.
Background
Most nanomaterials are inorganic and incompatible with biological systems. Therefore, there is an urgent need to develop an environmentally friendly organic semiconductor material for use in the fields of sensors, microelectronic devices, and bio-implants. Peptide molecules are bridges connecting biological and non-biological boundaries, and due to the spatial ordering of the atoms in the peptide molecules, this structural feature ensures that the peptide molecules are able to self-assemble into various ordered structures, such as filaments, layers, and nanotubes, such as tubes, plates, spheres, filaments, rods, spokes, and various ordered patterns, and the like, the type of structure formed depending on many factors, including PH of the solution, light, temperature, ionic strength, solvent selection, and the like. These nanostructures, in turn, can serve as a basis for the fabrication of more complex materials with different properties.
Self-assembled structures of many peptide molecules and their derivatives have been widely used in nanotechnology during the last decades. At present, the research aiming at the self-assembly of tyrosine dipeptide molecules is less, the self-assembly structure of the tyrosine dipeptide molecules is difficult to predict in related experiments, the zero-dimensional spherical nano structure based on the molecular self-assembly is not reported, and no clear mechanism can explain the self-assembly reason, so that the related experiments and the application lack theoretical basis. The zero-dimensional spherical nano structure or the one-dimensional columnar nano structure is prepared by regulating and controlling the self-assembly environmental parameters, and has important guiding significance in the fields of functional nano materials, biomedicine and the like.
Disclosure of Invention
The invention provides a simulation method for regulating a tyrosine dipeptide self-assembly structure, which can explore the influence of density and temperature on self-assembly through regulating and controlling self-assembly environmental parameters, determine the density and temperature most suitable for tyrosine dipeptide self-assembly by analyzing the structural evolution of a dipeptide self-assembly process under different environmental parameters, and obtain a corresponding zero-dimensional or one-dimensional structure, and has important guiding significance for experimental preparation of a tyrosine dipeptide nano-structure.
The invention adopts the following technical scheme.
A simulation method for regulating and controlling a tyrosine dipeptide self-assembly structure comprises the following steps;
s1, drawing a coordinate file of a single tyrosine dipeptide molecule self-assembly structure and carrying out structure optimization;
step S2, configuring self-assembly parameters of different environments by taking the molecular structure of the step S1 as a basic unit to obtain a system capable of setting dynamic balance parameters;
step S3, setting dynamic balance parameters by taking the system obtained in the step S2 as an object, and performing energy minimization and NVT (non-volatile transient) and NPT (non-volatile transient) relaxation;
s4, performing molecular dynamics simulation on the relaxation structure obtained in the S3;
and S5, summarizing the integrated self-assembly structure, and analyzing and processing corresponding data to obtain the parameter condition and the nano structure suitable for the self-assembly of the tyrosine dipeptide.
In step S1, the coordinate file of the tyrosine dipeptide molecule self-assembly structure is a pdb model file constructed by gauss view software, and the model file is structurally optimized by a common platform providing force field calculation to obtain an optimized pdb file and a force field topology file.
The public platform providing force field calculation is an ATB website, and the website is https:// ATB.
In step S2, 5X 5nm was constructed by means of the Gromacs software3The cubic simulation box of (a), by adding different chain identifiers, to distinguish individual peptide chains; setting parameter conditions of different densities and different temperatures, and performing related simulation research by using the parameter conditions as an initial box, wherein the density setting range is 980.36-1008.38 g/L, and the temperature setting range is 298-403K.
In step S3, the energy minimization is performed by a steepest descent method, the NVT and NPT relaxations are performed by a velocity relaxation method and a Parriello-Rahman method, and the temperature and pressure of the kinetic equilibrium parameters are maintained at 298K and 1bar, respectively.
The simulation method in the step S4 is specifically to perform 40ns molecular dynamics simulation, collect dynamics trajectories and information every 100ps, and visualize trajectory information by using a VMD program; simulating the reclaimed water molecules by adopting an SPC model, and simulating the dipeptide molecules by adopting a combined atomic force field; in the simulation, an LINCS algorithm is adopted to restrict the molecular bond length, periodic boundary conditions are adopted in the three-dimensional direction, and the simulation step length is set to be 2 fs; in addition, the long-range electrostatic interaction was processed using the Particle Mesh Ewald method with the cutoff radius for non-bond interactions set at 1.4 nm.
In step S5, the solvent accessible surface area of the dipeptide molecule is calculated by Gromacs software for the simulation results, and when the curve is stabilized at a certain value, the self-assembly is completed.
The simulation method further includes step S6, which specifically includes: analyzing the motion track of the dipeptide molecule in VMD software according to the track files of gro and xtc, finishing the structure evolution appearance of the dipeptide molecule, calculating the corresponding non-covalent interaction and hydrogen bond, and researching the change of the microstructure and the interaction in the self-assembly process.
The simulation method is used for simulating and simulating a method for preparing corresponding zero-dimensional spherical aggregates or one-dimensional columnar aggregates to verify the preparation method based on the condition of the self-assembly parameters of the tyrosine dipeptide simulated and regulated by molecular dynamics.
The zero-dimensional spherical aggregate or the one-dimensional columnar aggregate is of a nano structure.
Aiming at the problem that the self-assembly structure is difficult to predict from a microscopic level in the prior art, the invention provides a simulation method for regulating the self-assembly structure of tyrosine dipeptide, which researches the influence of density and temperature on self-assembly through regulating the self-assembly environmental parameters, calculates the influence of non-covalent interaction on self-assembly through analyzing the structural evolution of the dipeptide self-assembly process under different environmental parameters, determines the most suitable density of the self-assembly of tyrosine dipeptide to be 985.45g/L and 993.89g/L and the temperature to be 313-.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can obtain the structural evolution of the atomic layer, and discloses that the arrangement of proper environmental parameters such as density, temperature and the like can obtain the tyrosine dipeptide self-assembly structures of different types such as spherical or columnar structures; in other words, the densities of 985.45g/L and 993.89g/L and the ambient temperature of 313-343K can be used as the regulation parameters of the self-assembly structure of the tyrosine dipeptide.
2. The invention provides a research method and a system for analyzing self-assembly of peptide molecules, which can be widely applied to the field of nanotechnology.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a schematic diagram of the structure of a tyrosine dipeptide molecule according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of an initial model at different densities in one embodiment of the invention;
FIG. 3 is a schematic diagram of a stable nanostructure resulting from self-assembly of a tyrosine dipeptide molecule according to one embodiment of the present invention;
FIG. 4 is a schematic representation of the convolution radius of a tyrosine dipeptide molecule according to one embodiment of the present invention;
FIG. 5 is a graphical representation of calculated electrostatic interactions and van der Waals forces for a tyrosine dipeptide molecule in accordance with one embodiment of the present invention;
FIG. 6 is a schematic diagram of a molecular dynamics simulation process according to an embodiment of the present invention;
FIG. 7 is a schematic representation of the solvent accessible surface area of the system at various temperatures in one embodiment of the present invention;
FIG. 8 is a schematic diagram of the nanostructure of tyrosine dipeptide self-assembly at different temperatures in one embodiment of the present invention.
Detailed Description
As shown in the figure, the simulation method for regulating the self-assembly structure of the tyrosine dipeptide comprises the following steps;
s1, drawing a coordinate file of a single tyrosine dipeptide molecule self-assembly structure and carrying out structure optimization;
step S2, configuring self-assembly parameters of different environments by taking the molecular structure of the step S1 as a basic unit to obtain a system capable of setting dynamic balance parameters;
step S3, setting dynamic balance parameters by taking the system obtained in the step S2 as an object, and performing energy minimization and NVT (non-volatile transient) and NPT (non-volatile transient) relaxation;
s4, performing molecular dynamics simulation on the relaxation structure obtained in the S3;
and S5, summarizing the integrated self-assembly structure, and analyzing and processing corresponding data to obtain the parameter condition and the nano structure suitable for the self-assembly of the tyrosine dipeptide.
In step S1, the coordinate file of the tyrosine dipeptide molecule self-assembly structure is a pdb model file constructed by gauss view software, and the model file is structurally optimized by a common platform providing force field calculation to obtain an optimized pdb file and a force field topology file.
The public platform providing force field calculation is an ATB website, and the website is https:// ATB.
In step S2, 5X 5nm was constructed by means of the Gromacs software3The cubic simulation box of (a), by adding different chain identifiers, to distinguish individual peptide chains; setting parameter conditions of different densities and different temperatures, and performing related simulation research by using the parameter conditions as an initial box, wherein the density setting range is 980.36-1008.38 g/L, and the temperature setting range is 298-403K.
In step S3, the energy minimization is performed by a steepest descent method, the NVT and NPT relaxations are performed by a velocity relaxation method and a Parriello-Rahman method, and the temperature and pressure of the kinetic equilibrium parameters are maintained at 298K and 1bar, respectively.
The simulation method in the step S4 is specifically to perform 40ns molecular dynamics simulation, collect dynamics trajectories and information every 100ps, and visualize trajectory information by using a VMD program; simulating the reclaimed water molecules by adopting an SPC model, and simulating the dipeptide molecules by adopting a combined atomic force field; in the simulation, an LINCS algorithm is adopted to restrict the molecular bond length, periodic boundary conditions are adopted in the three-dimensional direction, and the simulation step length is set to be 2 fs; in addition, the Particle Mesh Ewald method was used to handle long range electrostatic interactions, with the cutoff radius for non-bond interactions set at 1.4 nm.
In step S5, the solvent accessible surface area of the dipeptide molecule is calculated by Gromacs software for the simulation results, and when the curve is stabilized at a certain value, the self-assembly is completed.
The simulation method further includes step S6, which specifically includes: analyzing the motion track of the dipeptide molecule in VMD software according to the track files of gro and xtc, finishing the structure evolution appearance of the dipeptide molecule, calculating the corresponding non-covalent interaction and hydrogen bond, and researching the change of the microstructure and the interaction in the self-assembly process.
The simulation method is used for simulating and simulating a method for preparing corresponding zero-dimensional spherical aggregates or one-dimensional columnar aggregates to verify the preparation method based on the condition of the self-assembly parameters of the tyrosine dipeptide simulated and regulated by molecular dynamics.
The zero-dimensional spherical aggregate or the one-dimensional columnar aggregate is of a nano structure.
Example 1:
referring to fig. 7, the present invention provides a simulation method for regulating a tyrosine dipeptide self-assembly structure, comprising the following steps:
in this embodiment, a simulation method for regulating the self-assembly structure of tyrosine dipeptide is provided, which comprises the following steps:
(1) and drawing a tyrosine dipeptide molecular coordinate file by means of GaussView software, storing the tyrosine dipeptide molecular coordinate file in a pdb file format, uploading the pdb file format to an ATB (automatic configuration library) website for structural optimization, and obtaining a new pdb file and a force field topology file.
(2) Construction of 5X 5nm with the aid of Gromacs3The tyrosine dipeptide with the molecular number of 15-55 is randomly distributed in the cubic simulation box, water is added around the molecules of the tyrosine dipeptide for solvation to obtain dipeptide aqueous solution with the density of 980.36-1008.38 g/L, and the dipeptide aqueous solution is used as an initial box for carrying out related simulation research.
(3) Energy minimization was performed using the steepest descent method followed by coupling of solute and solvent into external temperature and pressure baths using NVT, NPT relaxation, respectively. The temperature and pressure were maintained at 298K and 1bar, respectively, using the velocity recaling method and the Parriello-Rahman method.
(4) The system is subjected to molecular dynamics simulation of 40ns respectively, dynamics tracks and information are collected every 100ps, and track information is visualized by adopting a VMD program. The simulated reclaimed water molecules adopt an SPC model, and the dipeptide molecules adopt a combined atomic force field. In the simulation, an LINCS algorithm is adopted to restrict the molecular bond length, periodic boundary conditions are adopted in the three-dimensional direction, and the simulation step length is set to be 2 fs. In addition, the Particle Mesh Ewald method was used to handle long range electrostatic interactions, with the cutoff radius for non-bond interactions set at 1.4 nm.
(5) And analyzing the simulation result, calculating the solvent accessible surface area of the dipeptide molecule through Gromacs, and enabling the curve to suddenly drop in a short time and randomly stabilize at a certain value, so that the completion of self-assembly is indicated.
(6) The structure evolution morphology of the dipeptide molecules is finished in VMD software, as shown in figure 3, the dipeptide molecules can form a one-dimensional nano-column or zero-dimensional nano-sphere structure under different densities. Calculation of the respective non-covalent interactions and the radius of gyration of the dipeptide molecule, as shown in fig. 5 and 6, demonstrates that the density influences the self-assembly behavior of the dipeptide molecule in aqueous solution. The density can be used to regulate and control self-assembly to obtain the nano structure required by experiment or application.
Example 2:
in this embodiment, a simulation method for regulating the self-assembly structure of tyrosine dipeptide is provided, which comprises the following steps:
(1) and drawing a tyrosine dipeptide molecular coordinate file by means of GaussView software, storing the tyrosine dipeptide molecular coordinate file in a pdb file format, uploading the pdb file format to an ATB (automatic configuration library) website for structural optimization, and obtaining a new pdb file and a force field topology file.
(2) Construction of 5X 5nm with the aid of Gromacs3The cubic simulation cassette of (2) was used to construct an aqueous dipeptide solution with a density of 1002.33g/L, and this was used as an initial cassette for conducting a relevant simulation study.
(3) Energy minimization was performed using the steepest descent method followed by coupling of solute and solvent into external temperature and pressure baths using NVT, NPT relaxation, respectively. By adopting a velocity recaling method and a Parriello-Rahman method, the pressure is maintained at 1bar, the temperature is within the range of 298-403k, 15k is used as increment, 8 experimental points are arranged, and the influence of the temperature on the self-assembly structure is researched.
(4) The system is subjected to molecular dynamics simulation of 40ns respectively, dynamics tracks and information are collected every 100ps, and track information is visualized by adopting a VMD program. The simulated reclaimed water molecules adopt SPC models, and the dipeptide molecules adopt combined atomic force fields. In the simulation, an LINCS algorithm is adopted to restrict the molecular bond length, periodic boundary conditions are adopted in the three-dimensional direction, and the simulation step length is set to be 2 fs. In addition, the Particle Mesh Ewald method was used to handle long range electrostatic interactions, with the cutoff radius for non-bond interactions set at 1.4 nm.
(5) And (3) analyzing a simulation result, calculating the solvent accessible surface area of the dipeptide molecule through Gromacs, wherein the curve is suddenly dropped in a short time and randomly stabilized at a certain value, so that the self-assembly is completed, and as shown in FIG. 7, the tyrosine dipeptide can be completed at 298-358K.
(6) The structure evolution morphology of dipeptide molecules is finished in VMD software, the dipeptide molecules can form a one-dimensional nano-column or zero-dimensional nano-sphere structure at different temperatures, as shown in figure 8, and 313-343K is analyzed by combining figure 7 and is a temperature condition suitable for tyrosine dipeptide self-assembly.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (10)
1. A simulation method for regulating and controlling a tyrosine dipeptide self-assembly structure is characterized by comprising the following steps: comprises the following steps;
s1, drawing a coordinate file of a single tyrosine dipeptide molecule self-assembly structure and carrying out structure optimization;
step S2, configuring self-assembly parameters of different environments by taking the molecular structure of the step S1 as a basic unit to obtain a system capable of setting dynamic balance parameters;
step S3, setting dynamic balance parameters by taking the system obtained in the step S2 as an object, and performing energy minimization and NVT (non-volatile transient) and NPT (non-volatile transient) relaxation;
s4, performing molecular dynamics simulation on the relaxation structure obtained in the S3;
and S5, summarizing the integrated self-assembly structure, and analyzing and processing corresponding data to obtain the parameter condition and the nano structure suitable for the self-assembly of the tyrosine dipeptide.
2. The simulation method for regulating the self-assembly structure of tyrosine dipeptide according to claim 1, wherein the simulation method comprises the following steps: in step S1, the coordinate file of the tyrosine dipeptide molecule self-assembly structure is a pdb model file constructed by gauss view software, and the model file is structurally optimized by a common platform providing force field calculation to obtain an optimized pdb file and a force field topology file.
3. The simulation method for regulating the self-assembly structure of tyrosine dipeptide according to claim 2, wherein the simulation method comprises the following steps: the public platform providing force field calculation is an ATB website, and the website is https:// ATB.
4. The simulation method for regulating the self-assembly structure of tyrosine dipeptide according to claim 1, wherein the simulation method comprises the following steps: in step S2, 5X 5nm was constructed by means of the Gromacs software3The cubic simulation box of (a), by adding different chain identifiers, to distinguish individual peptide chains; setting parameter conditions of different densities and different temperatures, and taking the parameter conditions as an initial box to perform related simulation research, wherein the density setting range is 980.36-1008.38 g/L, and the temperature setting range is 298-403K.
5. The simulation method for regulating the self-assembly structure of tyrosine dipeptide according to claim 1, wherein the simulation method comprises the following steps: in step S3, the energy minimization is performed by a steepest descent method, the NVT and NPT relaxations are performed by a velocity relaxation method and a Parriello-Rahman method, and the temperature and pressure of the kinetic equilibrium parameters are maintained at 298K and 1bar, respectively.
6. The simulation method for regulating the self-assembly structure of tyrosine dipeptide according to claim 1, wherein the simulation method comprises the following steps: the simulation method in the step S4 is specifically to perform 40ns molecular dynamics simulation, collect dynamics trajectories and information every 100ps, and visualize trajectory information by using a VMD program; simulating the reclaimed water molecules by adopting an SPC model, and simulating the dipeptide molecules by adopting a combined atomic force field; in the simulation, an LINCS algorithm is adopted to constrain the molecular bond length, periodic boundary conditions are adopted in the three-dimensional direction, and the simulation step length is set to be 2 fs; in addition, the Particle Mesh Ewald method was used to handle long range electrostatic interactions, with the cutoff radius for non-bond interactions set at 1.4 nm.
7. The simulation method for regulating the self-assembly structure of tyrosine dipeptide according to claim 1, wherein the simulation method comprises the following steps: in step S5, the solvent accessible surface area of the dipeptide molecule is calculated by Gromacs software for the simulation results, and when the curve is stabilized at a certain value, the self-assembly is completed.
8. The simulation method for regulating the self-assembly structure of tyrosine dipeptide according to claim 1, wherein the simulation method comprises the following steps: the simulation method further includes step S6, which specifically includes: analyzing the motion track of the dipeptide molecule in VMD software according to the track files of gro and xtc, finishing the structure evolution appearance of the dipeptide molecule, calculating the corresponding non-covalent interaction and hydrogen bond, and researching the change of the microstructure and the interaction in the self-assembly process.
9. The simulation method for regulating the self-assembly structure of tyrosine dipeptide according to claim 1, wherein the simulation method comprises the following steps: the simulation method is used for simulating and simulating a method for preparing corresponding zero-dimensional spherical aggregates or one-dimensional columnar aggregates to verify the preparation method based on the condition of the self-assembly parameters of the tyrosine dipeptide simulated and regulated by molecular dynamics.
10. The simulation method for regulating the self-assembly structure of tyrosine dipeptide according to claim 9, wherein the simulation method comprises the following steps: the zero-dimensional spherical aggregate or the one-dimensional columnar aggregate is of a nano structure.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210277805.5A CN114611314A (en) | 2022-03-21 | 2022-03-21 | Simulation method for regulating tyrosine dipeptide self-assembly structure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210277805.5A CN114611314A (en) | 2022-03-21 | 2022-03-21 | Simulation method for regulating tyrosine dipeptide self-assembly structure |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114611314A true CN114611314A (en) | 2022-06-10 |
Family
ID=81864744
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210277805.5A Pending CN114611314A (en) | 2022-03-21 | 2022-03-21 | Simulation method for regulating tyrosine dipeptide self-assembly structure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114611314A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101122933A (en) * | 2007-09-10 | 2008-02-13 | 山东大学 | Method for calculating force between protein and DNA by computer simulation |
| WO2012094655A2 (en) * | 2011-01-07 | 2012-07-12 | Indiana University Research And Technology Corporation | Deductive multiscale simulation using order parameters |
| CN107330235A (en) * | 2017-05-08 | 2017-11-07 | 华南理工大学 | A kind of molecule power science study method of polypeptide chain self assembling process |
| CN107488209A (en) * | 2016-08-03 | 2017-12-19 | 四川大学 | Gelator and its hydrogel prepare and application |
| CN110767273A (en) * | 2019-10-30 | 2020-02-07 | 华南理工大学 | Simulation method for self-assembly behavior of rigid block copolymer solution |
-
2022
- 2022-03-21 CN CN202210277805.5A patent/CN114611314A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101122933A (en) * | 2007-09-10 | 2008-02-13 | 山东大学 | Method for calculating force between protein and DNA by computer simulation |
| WO2012094655A2 (en) * | 2011-01-07 | 2012-07-12 | Indiana University Research And Technology Corporation | Deductive multiscale simulation using order parameters |
| CN107488209A (en) * | 2016-08-03 | 2017-12-19 | 四川大学 | Gelator and its hydrogel prepare and application |
| CN107330235A (en) * | 2017-05-08 | 2017-11-07 | 华南理工大学 | A kind of molecule power science study method of polypeptide chain self assembling process |
| CN110767273A (en) * | 2019-10-30 | 2020-02-07 | 华南理工大学 | Simulation method for self-assembly behavior of rigid block copolymer solution |
Non-Patent Citations (1)
| Title |
|---|
| 郭凯;张恒;孙继超;苑世领;刘成卜;: "Fmoc-FF二肽分子自组装过程的分子动力学研究", 高等学校化学学报, no. 11, 10 November 2015 (2015-11-10), pages 2171 - 2178 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lah et al. | Synthesis and modelling of the mechanical properties of Ag, Au and Cu nanowires | |
| Ku et al. | Self-assembly of magnetic nanoparticles in evaporating solution | |
| Lee et al. | Revealing the Effects of the Non-solvent on the Ligand Shell of Nanoparticles and Their Crystallization | |
| Li et al. | Atomic scale imaging of nucleation and growth trajectories of an interfacial bismuth nanodroplet | |
| Valencia et al. | Mechanical properties obtained by indentation of hollow pd nanoparticles | |
| Lavrskyi et al. | Quasiparticle approach to diffusional atomic scale self-assembly of complex structures: from disorder to complex crystals and double-helix polymers | |
| Maeda et al. | Rational design of highly photoresponsive surface-confined self-assembly of diarylethenes: reversible three-state photoswitching at the liquid/solid interface | |
| Bragança et al. | Detection and stabilization of a previously unknown two-dimensional (Pseudo) polymorph using lateral nanoconfinement | |
| Dai et al. | Side-to-side cold welding for controllable nanogap formation from “dumbbell” ultrathin gold nanorods | |
| CN114611314A (en) | Simulation method for regulating tyrosine dipeptide self-assembly structure | |
| Bogdanov et al. | Molecular dynamics simulation of the formation of bimetallic core-shell nanostructures with binary Ni–Al nanoparticle quenching | |
| El-Tawargy et al. | Multiple structural transitions in Langmuir monolayers of charged soft-shell nanoparticles | |
| Zhao et al. | Self-Assembly of Asymmetric Diblock Copolymers under the Spherical Confinement | |
| Dong et al. | In situ liquid cell transmission electron microscopy investigation on the dissolution-regrowth mechanism dominating the shape evolution of silver nanoplates | |
| Myasnichenko et al. | Simulated annealing method for metal nanoparticle structures optimization | |
| Morozova et al. | Surface Activity of Soft Polymer Colloids | |
| Wang et al. | Controlled growth and phase transition of silver nanowires with dense lengthwise twins and stacking faults | |
| Huang et al. | Study of THF hydrate crystallization based on in situ observation with atomic force microscopy | |
| Willis et al. | Discrete free-boundary reaction-diffusion model of diatom pore occlusions | |
| Sutrakar et al. | Universal stability and temperature dependent phase transformation in group VIIIB–IB transition metal FCC nanowires | |
| Jáger et al. | Nanoparticle formation by spinodal decomposition in ion implanted samples | |
| Park et al. | Role of nanoparticle selectivity in the symmetry breaking of cylindrically confined block copolymers | |
| Aghdam et al. | Modeling interaction in nanowire growth process toward improved yield | |
| Kletenik-Edelman et al. | Coarse-grained lattice models for drying-mediated self-assembly of nanoparticles | |
| Lai et al. | Numerical method for calculating nanocrystals’ edge energies from experimentally observed shape evolution |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |