- ข้อความ
- ประวัติศาสตร์
1.4 Molecular modelling and compute
1.4 Molecular modelling and computer simulations
of peptide-membrane interactions
Over the years, a number of theoretical and computer simulation approaches have
been developed to describe membrane behaviour and peptide-membrane interactions.
These approaches vary in the way the peptide-membrane system is modelled and
what type of information can be obtained from this model. For example, a lipid bilayer
can be modelled as essentially an infinite hydrophobic slab (with varying degree
of complexity) through some effective field function. This approach is usually adopted
in various mean-field models developed over the years. Quite often, to reduce the
computational load, solvent is not included in the model explicitly, and its presence is
accounted for through some effective interactions between the remaining components.
In its general form, a mean-field method indicates that the lipid bilayer and the surrounding
water are described by an empirical energy function. In Figure 1.5 there
is a schematic of the lipid bilayer and the water phase and one of the possible energy
functions f(z) used in the mean-field methods to define the different levels of
hydrophobicity in the system. When a peptide or protein is considered (represented
by a cylinder in the schematic), a hydrophobic term is included in the potential energy
function. This term shows the contribution of each residue of the peptide in the
peptide-membrane interactions.
An approach based on the mean-field theory is the self-consistent field (SCF) theory.
In SCF theory the van der Waals-type interactions are used for the different types of
particles as well as the configurational entropy of the lipid tails. Leermakers and coworkers
have applied SCF theory in a series of membrane and membrane-peptide
studies [57–59]. In a recent study, SCF theory was also used by Liang and Ma to study
the effects of inclusions in supported mixed lipid bilayers [60]. In [61] and [62], the
same authors combined SCF theory and density functional theory to investigate the
structural organization of membrane proteins in lipid bilayers as well as the effect of
nanosized hydrophobic inclusions in lipid bilayers. Mean-field theory was also used
by Lague and co-workers [63–65]. The authors employed a mean-field approach based
on results from fully detailed atomistic simulations, to develop a theory for defining
the structure of the lipid chains around a model membrane protein and to study the
lipid-mediated protein-protein interactions. Also, La Rocca et al. used mean-field
theory to determine the optimal orientation of a helical peptide in a lipid bilayer [66,
67].
Another implicit-solvent approach is the generalized Born/surface area (GB/SA) models
[68]. Im et al. in a series of studies have used the GB/SA approach to study membrane
peptides [69, 70]. Also, Ulmschneider and co-workers developed an implicitmembrane
representation and applied it in influenza M2 peptide and glycophorin A
dimer [71, 72].
One of the most important limitations of mean-field based approaches are the sim-
plifications that need to be made in order to develop feasible analytical theories. The
description of the lipid bilayer by a free energy functional cannot capture the complexity
that lies at the molecular level of the membrane. Moreover, the difficulty in linking
the parameters used in these models with physical properties constitutes an important
disadvantage.
In order to select an appropriate approach from a huge number of models and methods
developed over the last 40-50 years, it is important to formulate the long term goals of
this study. I would like to develop a capability to describe peptide-membrane interaction
processes in their entire complexity, from the dynamics of the self-assembly processes
to equilibrium properties of peptide-membrane systems. This objective imposes
several key restrictions on our choice of methods. It is evident, that peptide-membrane
self-assembly processes, such as formation of trans-membrane pores, requires signifi-
cant structural rearrangement of both peptides and the membrane. This process also
seems to be mediated (at least to some extent) by the solvent. Thus, our description
must be based on a reasonably detailed model of all the components of the system, i.e.
solvent, peptides, lipid bilayer. This restriction excludes the models based on membrane
as an effective hydrophobic medium and implicit solvent models. Next, I am
interested not only in the final equilibrium properties of the system, but in the actual
process of self-assembly. Therefore, it seems that most of the conventional Monte Carlo
approaches would not be appropriate here. On the other hand, Molecular Dynamics
seems to satisfy all the required conditions and, therefore, in the review of the recent
studies of peptide-membrane interactions I will mostly focus on this approach, with
occasional diversion into other methods.
There has been significant progress in the field of molecular dynamics simulations
of biomolecular systems since the first simulation of a protein in vacuum, reported
33 years ago [73] (see Table A, Appendix A). Some of the first studies of membranepeptide
interactions employing molecular dynamics simulations on a sub-nanosecond
timescale include the study of a model peptide designed to anchor to bilayer surfaces
[74], amphipathic α-helices [75] and the bee venom peptide melittin [76].
Recently, several atomistic molecular simulation studies attempted to address longscale
peptide-membrane phenomena in their full complexity. In one of these studies,
Leontiadou and co-workers captured toroidal pore formation in simulations of an-
timicrobial peptide magainin-H2 and a model phospholipid membrane [77]. Studies
of toroidal pore formation and its structural characteristics have been further extended
by Sengupta and co-workers [78]. In another example, Herce and Garcia applied
fully atomistic simulations to propose a complex multistage mechanism of HIV-1 TAT
peptide translocation across the membrane [79]. Formation of a transient pore was
observed, with the peptides diffusing on the surface of the pore to cross the membrane.
An alternative mechanism, based on micropinocytosis, has been suggested for
TAT translocation in fully atomistic studies by Yesylevskyy and co-workers. In micropinocytosis
a cluster of peptides wraps the membrane around itself to form a small
vesicle [80]. A similar mechanism of translocation was reported by the same group
for another cell-penetrating peptide, Penetratin. None of these simulations however
spanned timescale beyond several hundred of nanoseconds, and in many cases the
simulations were limited to tens of nanoseconds. Routine operation on longer time
scale still remains prohibitively expensive in atomistic simulations. This limitation imposed
by atomistic simulations led to the development of coarse-grained approaches
to study complex biomolecular phenomena.
Coarse-grained approaches are based on the idea of systematically reducing the level
of detail in the way the system is represented, and thus increasing the time/length
scale of the simulation. One way of doing this is by modelling the system as a group
of effective particles (‘beads’). Each of these beads represents an ensemble of atoms
whose atomistic degrees of freedom do not play an important role in the process under
consideration and are integrated out. This leads to several implications. First of all, it
results in the expected improvement in computational efficiency of the model due to
the reduced number of degrees of freedom (depending on the level of coarse-graining).
Furthermore, as has been noted in a number of studies, smoothing out of fine-grained
degrees of freedom in CG models reduces the effective friction between the molecules.
As a result, many complex processes such as biomolecular self-assembly occur on a
shorter effective time scale.
Several strategies to construct CG models have been offered over the years. For example,
the interactions between coarse-grained beads can be calibrated to reproduce the
forces between the corresponding groups of atoms in atomistic simulations [81]. Alternatively,
the coarse-grained model can be calibrated to reproduce certain physical
characteristics of the system of interest, such as density, phase transitions and structure
[82]. In Figure 1.6, some representative coarse-grained models for lipids are shown. he
article by Venturoli and co-workers is an excellent review of the current developments
and achievements in this field [83]. State-of-the-art in atomistic and CG simulation
studies of lipid membranes, including peptide-membrane interactions, has also been
recently reviewed by [84]. Another recent review on the advances in the area of multiscale
modelling is the one by Murtola et al. [85].
Figure 1.6: Coarse-grained models for lipids. (a) Atomistic representation. (b) Group
of ∼4-5 atoms is represented as a ’bead’ [82]. (c) Every lipid is represented as a Gay-Berne
particle [86].
Using coarse-grained models, it has been possible to investigate a number of processes
related to biomembrane physics, which have been difficult to study by MD simulation
methods on all-atom models. In the early 90’s, Smit and co-workers developed a CG
model of oil/water/surfactant system. Two types of particles are defined, labeled with
the letters o and w. In this model, oil molecules are represented by a single o particle,
water molecules by a single w particle and surfactant molecules are represented by
a chain of two w particles followed by five o particles, each bound to its neighbour
by a strong harmonic force. Simulations showed for the first time the spontaneous
formation of micelles [87, 88].
Some years later, Groot and Warren introduced the Dissipative Particle Dynamics
(DPD) technique into the field of biological systems [89]. In this technique, the forces
are grouped together to yield an effective friction and a fluctuating force between the
interac
of peptide-membrane interactions
Over the years, a number of theoretical and computer simulation approaches have
been developed to describe membrane behaviour and peptide-membrane interactions.
These approaches vary in the way the peptide-membrane system is modelled and
what type of information can be obtained from this model. For example, a lipid bilayer
can be modelled as essentially an infinite hydrophobic slab (with varying degree
of complexity) through some effective field function. This approach is usually adopted
in various mean-field models developed over the years. Quite often, to reduce the
computational load, solvent is not included in the model explicitly, and its presence is
accounted for through some effective interactions between the remaining components.
In its general form, a mean-field method indicates that the lipid bilayer and the surrounding
water are described by an empirical energy function. In Figure 1.5 there
is a schematic of the lipid bilayer and the water phase and one of the possible energy
functions f(z) used in the mean-field methods to define the different levels of
hydrophobicity in the system. When a peptide or protein is considered (represented
by a cylinder in the schematic), a hydrophobic term is included in the potential energy
function. This term shows the contribution of each residue of the peptide in the
peptide-membrane interactions.
An approach based on the mean-field theory is the self-consistent field (SCF) theory.
In SCF theory the van der Waals-type interactions are used for the different types of
particles as well as the configurational entropy of the lipid tails. Leermakers and coworkers
have applied SCF theory in a series of membrane and membrane-peptide
studies [57–59]. In a recent study, SCF theory was also used by Liang and Ma to study
the effects of inclusions in supported mixed lipid bilayers [60]. In [61] and [62], the
same authors combined SCF theory and density functional theory to investigate the
structural organization of membrane proteins in lipid bilayers as well as the effect of
nanosized hydrophobic inclusions in lipid bilayers. Mean-field theory was also used
by Lague and co-workers [63–65]. The authors employed a mean-field approach based
on results from fully detailed atomistic simulations, to develop a theory for defining
the structure of the lipid chains around a model membrane protein and to study the
lipid-mediated protein-protein interactions. Also, La Rocca et al. used mean-field
theory to determine the optimal orientation of a helical peptide in a lipid bilayer [66,
67].
Another implicit-solvent approach is the generalized Born/surface area (GB/SA) models
[68]. Im et al. in a series of studies have used the GB/SA approach to study membrane
peptides [69, 70]. Also, Ulmschneider and co-workers developed an implicitmembrane
representation and applied it in influenza M2 peptide and glycophorin A
dimer [71, 72].
One of the most important limitations of mean-field based approaches are the sim-
plifications that need to be made in order to develop feasible analytical theories. The
description of the lipid bilayer by a free energy functional cannot capture the complexity
that lies at the molecular level of the membrane. Moreover, the difficulty in linking
the parameters used in these models with physical properties constitutes an important
disadvantage.
In order to select an appropriate approach from a huge number of models and methods
developed over the last 40-50 years, it is important to formulate the long term goals of
this study. I would like to develop a capability to describe peptide-membrane interaction
processes in their entire complexity, from the dynamics of the self-assembly processes
to equilibrium properties of peptide-membrane systems. This objective imposes
several key restrictions on our choice of methods. It is evident, that peptide-membrane
self-assembly processes, such as formation of trans-membrane pores, requires signifi-
cant structural rearrangement of both peptides and the membrane. This process also
seems to be mediated (at least to some extent) by the solvent. Thus, our description
must be based on a reasonably detailed model of all the components of the system, i.e.
solvent, peptides, lipid bilayer. This restriction excludes the models based on membrane
as an effective hydrophobic medium and implicit solvent models. Next, I am
interested not only in the final equilibrium properties of the system, but in the actual
process of self-assembly. Therefore, it seems that most of the conventional Monte Carlo
approaches would not be appropriate here. On the other hand, Molecular Dynamics
seems to satisfy all the required conditions and, therefore, in the review of the recent
studies of peptide-membrane interactions I will mostly focus on this approach, with
occasional diversion into other methods.
There has been significant progress in the field of molecular dynamics simulations
of biomolecular systems since the first simulation of a protein in vacuum, reported
33 years ago [73] (see Table A, Appendix A). Some of the first studies of membranepeptide
interactions employing molecular dynamics simulations on a sub-nanosecond
timescale include the study of a model peptide designed to anchor to bilayer surfaces
[74], amphipathic α-helices [75] and the bee venom peptide melittin [76].
Recently, several atomistic molecular simulation studies attempted to address longscale
peptide-membrane phenomena in their full complexity. In one of these studies,
Leontiadou and co-workers captured toroidal pore formation in simulations of an-
timicrobial peptide magainin-H2 and a model phospholipid membrane [77]. Studies
of toroidal pore formation and its structural characteristics have been further extended
by Sengupta and co-workers [78]. In another example, Herce and Garcia applied
fully atomistic simulations to propose a complex multistage mechanism of HIV-1 TAT
peptide translocation across the membrane [79]. Formation of a transient pore was
observed, with the peptides diffusing on the surface of the pore to cross the membrane.
An alternative mechanism, based on micropinocytosis, has been suggested for
TAT translocation in fully atomistic studies by Yesylevskyy and co-workers. In micropinocytosis
a cluster of peptides wraps the membrane around itself to form a small
vesicle [80]. A similar mechanism of translocation was reported by the same group
for another cell-penetrating peptide, Penetratin. None of these simulations however
spanned timescale beyond several hundred of nanoseconds, and in many cases the
simulations were limited to tens of nanoseconds. Routine operation on longer time
scale still remains prohibitively expensive in atomistic simulations. This limitation imposed
by atomistic simulations led to the development of coarse-grained approaches
to study complex biomolecular phenomena.
Coarse-grained approaches are based on the idea of systematically reducing the level
of detail in the way the system is represented, and thus increasing the time/length
scale of the simulation. One way of doing this is by modelling the system as a group
of effective particles (‘beads’). Each of these beads represents an ensemble of atoms
whose atomistic degrees of freedom do not play an important role in the process under
consideration and are integrated out. This leads to several implications. First of all, it
results in the expected improvement in computational efficiency of the model due to
the reduced number of degrees of freedom (depending on the level of coarse-graining).
Furthermore, as has been noted in a number of studies, smoothing out of fine-grained
degrees of freedom in CG models reduces the effective friction between the molecules.
As a result, many complex processes such as biomolecular self-assembly occur on a
shorter effective time scale.
Several strategies to construct CG models have been offered over the years. For example,
the interactions between coarse-grained beads can be calibrated to reproduce the
forces between the corresponding groups of atoms in atomistic simulations [81]. Alternatively,
the coarse-grained model can be calibrated to reproduce certain physical
characteristics of the system of interest, such as density, phase transitions and structure
[82]. In Figure 1.6, some representative coarse-grained models for lipids are shown. he
article by Venturoli and co-workers is an excellent review of the current developments
and achievements in this field [83]. State-of-the-art in atomistic and CG simulation
studies of lipid membranes, including peptide-membrane interactions, has also been
recently reviewed by [84]. Another recent review on the advances in the area of multiscale
modelling is the one by Murtola et al. [85].
Figure 1.6: Coarse-grained models for lipids. (a) Atomistic representation. (b) Group
of ∼4-5 atoms is represented as a ’bead’ [82]. (c) Every lipid is represented as a Gay-Berne
particle [86].
Using coarse-grained models, it has been possible to investigate a number of processes
related to biomembrane physics, which have been difficult to study by MD simulation
methods on all-atom models. In the early 90’s, Smit and co-workers developed a CG
model of oil/water/surfactant system. Two types of particles are defined, labeled with
the letters o and w. In this model, oil molecules are represented by a single o particle,
water molecules by a single w particle and surfactant molecules are represented by
a chain of two w particles followed by five o particles, each bound to its neighbour
by a strong harmonic force. Simulations showed for the first time the spontaneous
formation of micelles [87, 88].
Some years later, Groot and Warren introduced the Dissipative Particle Dynamics
(DPD) technique into the field of biological systems [89]. In this technique, the forces
are grouped together to yield an effective friction and a fluctuating force between the
interac
0/5000
1.4 ระดับโมเลกุลแบบจำลองและเครื่องคอมพิวเตอร์จำลองโต้ตอบเมมเบรนเพปไทด์ปี หมายเลขของทฤษฎี และการจำลองคอมพิวเตอร์ มีวิธีถูกพัฒนาขึ้นเพื่ออธิบายการโต้ตอบที่พฤติกรรมและเพปไทด์เยื่อเมมเบรนวิธีเหล่านี้แตกต่างกันไปในทางระบบเมมเบรนเพปไทด์จะคือ แบบจำลอง และชนิดของข้อมูลที่ได้จากแบบจำลองนี้ ตัวอย่าง bilayer ไขมันสามารถ modelled เป็นหลักเป็นอนันต์ hydrophobic พื้นกับองศาที่แตกต่างกันของความซับซ้อน) ผ่านฟังก์ชันบางฟิลด์มีผลบังคับใช้ มักจะมีนำวิธีการนี้ในฟิลด์หมายความว่ารุ่นต่าง ๆ พัฒนาปี ค่อนข้างบ่อย การลดการคำนวณโหลด ตัวทำละลายไม่อยู่ในรูปแบบอย่างชัดเจน และสถานะของตนเป็นบัญชีสำหรับผ่านการโต้ตอบบางอย่างมีประสิทธิภาพระหว่างส่วนประกอบที่เหลือแบบทั่วไป วิธีการหมายถึงฟิลด์หมายถึง bilayer ไขมันและรายล้อมด้วยอธิบายน้ำ ด้วยฟังก์ชันการรวมพลังงาน ในรูป 1.5 มีคือมัน bilayer ไขมันระยะน้ำ และพลังงานไปได้อย่างใดอย่างหนึ่งฟังก์ชัน f(z) ใช้ในฟิลด์หมายความว่าวิธีการในการกำหนดระดับต่าง ๆ ของhydrophobicity ในระบบ เมื่อถือเพปไทด์หรือโปรตีน (แทนโดยการสูบในมัน), คำ hydrophobic อยู่ในพลังงานศักย์ฟังก์ชันการ ระยะนี้แสดงสัดส่วนของแต่ละสารตกค้างของเพปไทด์ในการเมมเบรนเพปไทด์โต้ตอบวิธีการตามทฤษฎีหมายถึงฟิลด์สอดคล้องกันด้วยตนเอง (SCF) ทฤษฎีได้ในทฤษฎี SCF van der Waals-ชนิด ใช้โต้ตอบสำหรับชนิดต่าง ๆ ของอนุภาคเป็น entropy configurational ของหางระดับไขมันในเลือด Leermakers และเพื่อนร่วมงานใช้ทฤษฎี SCF ในชุดเมมเบรนและเพปไทด์เมมเบรนศึกษา [57-59] ในการศึกษาล่าสุด ทฤษฎี SCF ยังใช้ โดยเหลียงและมาเรียนผลรวมในกระบวนการผสมสนับสนุน bilayers [60] ใน [61] และ [62], การผู้เขียนเดียวกันรวม SCF ทฤษฎีและทฤษฎีการทำงานความหนาแน่นการตรวจสอบการองค์กรโครงสร้างของเมมเบรนโปรตีนในกระบวน bilayers เป็นผลของตัวรองรวม hydrophobic ใน bilayers ไขมัน ทฤษฎีหมายถึงฟิลด์ถูกใช้Lague และเพื่อนร่วมงาน [63 – 65] ผู้เขียนทำงานวิธีการหมายถึงฟิลด์ที่อยู่ผลจากทั้งหมดรายละเอียดจำลอง atomistic พัฒนาทฤษฎีในการกำหนดโครงสร้างของโซ่ไขมันโปรตีนเมมเบรนเป็นรุ่น และ การเรียนการการโต้ตอบ mediated ไขมันโปรตีนโปรตีน ยัง La Rocca et al. ใช้ฟิลด์หมายความว่าทฤษฎีการกำหนดวางแนวสูงสุดของเพปไทด์ helical ใน bilayer ไขมัน [6667]วิธีนัยในตัวทำละลายอื่นเป็นรูปแบบตั้ง (GB/SA) บอร์น/ผิวเมจแบบทั่วไป[68] . al. et Im ในชุดของการศึกษาได้ใช้วิธี GB/SA ศึกษาเมมเบรนเปปไทด์ [69, 70] , Ulmschneider และเพื่อนร่วมงานพัฒนาเป็น implicitmembranerepresentation and applied it in influenza M2 peptide and glycophorin Adimer [71, 72].One of the most important limitations of mean-field based approaches are the sim-plifications that need to be made in order to develop feasible analytical theories. Thedescription of the lipid bilayer by a free energy functional cannot capture the complexitythat lies at the molecular level of the membrane. Moreover, the difficulty in linkingthe parameters used in these models with physical properties constitutes an importantdisadvantage.In order to select an appropriate approach from a huge number of models and methodsdeveloped over the last 40-50 years, it is important to formulate the long term goals ofthis study. I would like to develop a capability to describe peptide-membrane interactionprocesses in their entire complexity, from the dynamics of the self-assembly processesto equilibrium properties of peptide-membrane systems. This objective imposesseveral key restrictions on our choice of methods. It is evident, that peptide-membraneself-assembly processes, such as formation of trans-membrane pores, requires signifi-cant structural rearrangement of both peptides and the membrane. This process alsoseems to be mediated (at least to some extent) by the solvent. Thus, our descriptionmust be based on a reasonably detailed model of all the components of the system, i.e.solvent, peptides, lipid bilayer. This restriction excludes the models based on membraneas an effective hydrophobic medium and implicit solvent models. Next, I aminterested not only in the final equilibrium properties of the system, but in the actualprocess of self-assembly. Therefore, it seems that most of the conventional Monte Carloapproaches would not be appropriate here. On the other hand, Molecular Dynamicsseems to satisfy all the required conditions and, therefore, in the review of the recentstudies of peptide-membrane interactions I will mostly focus on this approach, withoccasional diversion into other methods.There has been significant progress in the field of molecular dynamics simulationsof biomolecular systems since the first simulation of a protein in vacuum, reported33 years ago [73] (see Table A, Appendix A). Some of the first studies of membranepeptideinteractions employing molecular dynamics simulations on a sub-nanosecondtimescale include the study of a model peptide designed to anchor to bilayer surfaces[74], amphipathic α-helices [75] and the bee venom peptide melittin [76].Recently, several atomistic molecular simulation studies attempted to address longscalepeptide-membrane phenomena in their full complexity. In one of these studies,Leontiadou and co-workers captured toroidal pore formation in simulations of an-timicrobial peptide magainin-H2 and a model phospholipid membrane [77]. Studiesof toroidal pore formation and its structural characteristics have been further extendedby Sengupta and co-workers [78]. In another example, Herce and Garcia appliedfully atomistic simulations to propose a complex multistage mechanism of HIV-1 TATpeptide translocation across the membrane [79]. Formation of a transient pore wasobserved, with the peptides diffusing on the surface of the pore to cross the membrane.An alternative mechanism, based on micropinocytosis, has been suggested forTAT translocation in fully atomistic studies by Yesylevskyy and co-workers. In micropinocytosisa cluster of peptides wraps the membrane around itself to form a smallvesicle [80]. A similar mechanism of translocation was reported by the same groupfor another cell-penetrating peptide, Penetratin. None of these simulations howeverspanned timescale beyond several hundred of nanoseconds, and in many cases thesimulations were limited to tens of nanoseconds. Routine operation on longer timescale still remains prohibitively expensive in atomistic simulations. This limitation imposedby atomistic simulations led to the development of coarse-grained approachesto study complex biomolecular phenomena.Coarse-grained approaches are based on the idea of systematically reducing the levelof detail in the way the system is represented, and thus increasing the time/lengthscale of the simulation. One way of doing this is by modelling the system as a groupof effective particles (‘beads’). Each of these beads represents an ensemble of atomswhose atomistic degrees of freedom do not play an important role in the process under
consideration and are integrated out. This leads to several implications. First of all, it
results in the expected improvement in computational efficiency of the model due to
the reduced number of degrees of freedom (depending on the level of coarse-graining).
Furthermore, as has been noted in a number of studies, smoothing out of fine-grained
degrees of freedom in CG models reduces the effective friction between the molecules.
As a result, many complex processes such as biomolecular self-assembly occur on a
shorter effective time scale.
Several strategies to construct CG models have been offered over the years. For example,
the interactions between coarse-grained beads can be calibrated to reproduce the
forces between the corresponding groups of atoms in atomistic simulations [81]. Alternatively,
the coarse-grained model can be calibrated to reproduce certain physical
characteristics of the system of interest, such as density, phase transitions and structure
[82]. In Figure 1.6, some representative coarse-grained models for lipids are shown. he
article by Venturoli and co-workers is an excellent review of the current developments
and achievements in this field [83]. State-of-the-art in atomistic and CG simulation
studies of lipid membranes, including peptide-membrane interactions, has also been
recently reviewed by [84]. Another recent review on the advances in the area of multiscale
modelling is the one by Murtola et al. [85].
Figure 1.6: Coarse-grained models for lipids. (a) Atomistic representation. (b) Group
of ∼4-5 atoms is represented as a ’bead’ [82]. (c) Every lipid is represented as a Gay-Berne
particle [86].
Using coarse-grained models, it has been possible to investigate a number of processes
related to biomembrane physics, which have been difficult to study by MD simulation
methods on all-atom models. In the early 90’s, Smit and co-workers developed a CG
model of oil/water/surfactant system. Two types of particles are defined, labeled with
the letters o and w. In this model, oil molecules are represented by a single o particle,
water molecules by a single w particle and surfactant molecules are represented by
a chain of two w particles followed by five o particles, each bound to its neighbour
by a strong harmonic force. Simulations showed for the first time the spontaneous
formation of micelles [87, 88].
Some years later, Groot and Warren introduced the Dissipative Particle Dynamics
(DPD) technique into the field of biological systems [89]. In this technique, the forces
are grouped together to yield an effective friction and a fluctuating force between the
interac
consideration and are integrated out. This leads to several implications. First of all, it
results in the expected improvement in computational efficiency of the model due to
the reduced number of degrees of freedom (depending on the level of coarse-graining).
Furthermore, as has been noted in a number of studies, smoothing out of fine-grained
degrees of freedom in CG models reduces the effective friction between the molecules.
As a result, many complex processes such as biomolecular self-assembly occur on a
shorter effective time scale.
Several strategies to construct CG models have been offered over the years. For example,
the interactions between coarse-grained beads can be calibrated to reproduce the
forces between the corresponding groups of atoms in atomistic simulations [81]. Alternatively,
the coarse-grained model can be calibrated to reproduce certain physical
characteristics of the system of interest, such as density, phase transitions and structure
[82]. In Figure 1.6, some representative coarse-grained models for lipids are shown. he
article by Venturoli and co-workers is an excellent review of the current developments
and achievements in this field [83]. State-of-the-art in atomistic and CG simulation
studies of lipid membranes, including peptide-membrane interactions, has also been
recently reviewed by [84]. Another recent review on the advances in the area of multiscale
modelling is the one by Murtola et al. [85].
Figure 1.6: Coarse-grained models for lipids. (a) Atomistic representation. (b) Group
of ∼4-5 atoms is represented as a ’bead’ [82]. (c) Every lipid is represented as a Gay-Berne
particle [86].
Using coarse-grained models, it has been possible to investigate a number of processes
related to biomembrane physics, which have been difficult to study by MD simulation
methods on all-atom models. In the early 90’s, Smit and co-workers developed a CG
model of oil/water/surfactant system. Two types of particles are defined, labeled with
the letters o and w. In this model, oil molecules are represented by a single o particle,
water molecules by a single w particle and surfactant molecules are represented by
a chain of two w particles followed by five o particles, each bound to its neighbour
by a strong harmonic force. Simulations showed for the first time the spontaneous
formation of micelles [87, 88].
Some years later, Groot and Warren introduced the Dissipative Particle Dynamics
(DPD) technique into the field of biological systems [89]. In this technique, the forces
are grouped together to yield an effective friction and a fluctuating force between the
interac
การแปล กรุณารอสักครู่..
1.4 โมเลกุลแบบจำลองและการจำลองด้วยคอมพิวเตอร์
ของเปปไทด์ของเมมเบรน กว่าปีที่จำนวนของทฤษฎีและวิธีการจำลองคอมพิวเตอร์ได้ถูกพัฒนาขึ้นเพื่ออธิบายพฤติกรรมและ
วิธีการปฏิสัมพันธ์เปปไทด์เยื่อเมมเบรน เหล่านี้แตกต่างกันไปในทางระบบเมมเบรนเปปไทด์เป็นช่างปั้นและ
สิ่งที่ประเภทของข้อมูลที่ได้จากแบบจำลองนี้ ตัวอย่างเช่น
ของเปปไทด์ของเมมเบรน กว่าปีที่จำนวนของทฤษฎีและวิธีการจำลองคอมพิวเตอร์ได้ถูกพัฒนาขึ้นเพื่ออธิบายพฤติกรรมและ
วิธีการปฏิสัมพันธ์เปปไทด์เยื่อเมมเบรน เหล่านี้แตกต่างกันไปในทางระบบเมมเบรนเปปไทด์เป็นช่างปั้นและ
สิ่งที่ประเภทของข้อมูลที่ได้จากแบบจำลองนี้ ตัวอย่างเช่น
การแปล กรุณารอสักครู่..
ภาษาอื่น ๆ
การสนับสนุนเครื่องมือแปลภาษา: กรีก, กันนาดา, กาลิเชียน, คลิงออน, คอร์สิกา, คาซัค, คาตาลัน, คินยารวันดา, คีร์กิซ, คุชราต, จอร์เจีย, จีน, จีนดั้งเดิม, ชวา, ชิเชวา, ซามัว, ซีบัวโน, ซุนดา, ซูลู, ญี่ปุ่น, ดัตช์, ตรวจหาภาษา, ตุรกี, ทมิฬ, ทาจิก, ทาทาร์, นอร์เวย์, บอสเนีย, บัลแกเรีย, บาสก์, ปัญจาป, ฝรั่งเศส, พาชตู, ฟริเชียน, ฟินแลนด์, ฟิลิปปินส์, ภาษาอินโดนีเซี, มองโกเลีย, มัลทีส, มาซีโดเนีย, มาราฐี, มาลากาซี, มาลายาลัม, มาเลย์, ม้ง, ยิดดิช, ยูเครน, รัสเซีย, ละติน, ลักเซมเบิร์ก, ลัตเวีย, ลาว, ลิทัวเนีย, สวาฮิลี, สวีเดน, สิงหล, สินธี, สเปน, สโลวัก, สโลวีเนีย, อังกฤษ, อัมฮาริก, อาร์เซอร์ไบจัน, อาร์เมเนีย, อาหรับ, อิกโบ, อิตาลี, อุยกูร์, อุสเบกิสถาน, อูรดู, ฮังการี, ฮัวซา, ฮาวาย, ฮินดี, ฮีบรู, เกลิกสกอต, เกาหลี, เขมร, เคิร์ด, เช็ก, เซอร์เบียน, เซโซโท, เดนมาร์ก, เตลูกู, เติร์กเมน, เนปาล, เบงกอล, เบลารุส, เปอร์เซีย, เมารี, เมียนมา (พม่า), เยอรมัน, เวลส์, เวียดนาม, เอสเปอแรนโต, เอสโทเนีย, เฮติครีโอล, แอฟริกา, แอลเบเนีย, โคซา, โครเอเชีย, โชนา, โซมาลี, โปรตุเกส, โปแลนด์, โยรูบา, โรมาเนีย, โอเดีย (โอริยา), ไทย, ไอซ์แลนด์, ไอร์แลนด์, การแปลภาษา.
- Like ice sheets, ice caps completely bur
- ตลาดน้ำ ในคลองและมีบ้านริมน้ำ
- ถังเก็บสารเคมีอยู่ใกล้ตู้ไฟฟ้า
- ไวไฟ
- meeting
- flagel
- ครบรอบ 4 ปี
- I have a bed. My bed is in my bedroom. I
- ฉันมีปัญหาเกี่ยวกับการเรียนและฉันไม่มีคว
- คุณไม่ควรจะเอาชื่อหรือรูปของคนอื่นมาใช้
- ฉันเรียนเกี่ยวกับการทำการ์ตูน
- โรงงานใกล้กับสนามบิน
- เพราะมันไม่ดีสำหรับคนที่นี่
- ถังคนแก่
- Examples
- ฉันรอฟังอยู่
- การมีหลายสาขา ฝ่ายต่างๆอาจเกิดข้อผิดพลาด
- ถังคนแก่
- ฉันกินตลอด
- What's the matter
- ถาดโฟม
- มารยาทไทย
- To review scientific publications on hea
- What did he/she/it/they do? or What happ
