Concentration Dependent Self-Assembly of TrK ... - ACS Publications

Subscriber access provided by UNIVERSITY OF SOUTH CAROLINA LIBRARIES

Article

Concentration Dependent Self-Assembly of TrKNGF Receptor Derived Tripeptide: New Insights From Experiment and Computer Simulations Parikshit Moitra, Yashonath Subramanian, and Santanu Bhattacharya J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.6b10511 • Publication Date (Web): 02 Dec 2016 Downloaded from http://pubs.acs.org on December 6, 2016

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

The Journal of Physical Chemistry B is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

The Journal of Physical Chemistry

Concentration Dependent Self-assembly of TrKNGF Receptor Derived Tripeptide: New Insights from Experiment and Computer Simulations Parikshit Moitra,1,† Yashonath Subramanian2,3 & Santanu Bhattacharya1,4,* 1

Department of Organic Chemistry, Indian Institute of Science, Bangalore 560012. 2Solid State

and Structural Chemistry Unit, Indian Institute of Science, Bangalore 560012. 3Condensed Matter Theory Unit, Jawaharlal Nehru Center for Advanced Scientific Research, Bangalore 560064. 4Director’s Research Unit, Indian Association for the Cultivation of Science, Jadavpur, Kolkata 700032, India. KEYWORDS. Lys-Phe-Gly tripeptide; Concentration-dependent self-assembly; Martini coarsegraining simulation; nanovesicles; nanotube; rectangular block.

ABSTRACT. Early research has shown that many neurodegenerative diseases are associated with the absence of a short and natural tripeptide sequence, Lys-Phe-Gly (KFG). Herein we report both experiments and extensive MD simulations of this tripeptide to understand the selfassembly and morphology as a function of its concentration. Morphologies of the aggregates formed by the tripeptide at low concentration (vesicles), and at high concentration (nanotubes) are studied by several independent 3 µs long Martini coarse-graining MD simulation runs.

ACS Paragon Plus Environment

1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 35

Further, prediction from MD at still higher concentrations about the formation of rectangular blocks, reported for the first time, has been verified through laboratory experiments. Thus the computational studies performed are in agreement with the experimental findings observed in our laboratory and a complete control over the formation of various nanostructures is achieved simply by changing the concentration of a short and naturally conserved tripeptide.

INTRODUCTION. Aggregation of peptides has been recognized to be a key mechanism in many diseases including threatening neurological disorders like Alzheimer’s, Parkinson and prionrelated diseases.1-4 Scientists have recognized the understanding of aggregation pattern of some of these peptides to be the fundamental step in order to design rational therapeutics against such diseases.5-11 Although a direct correlation between fibril-like aggregates and diseases have not yet been established beyond doubt,2 a study of the aggregation in peptides and proteins seems important in its own right. In this context, researchers have been attracted to the short peptide sequences because of the ease with which one can modulate their properties and their promise towards biomedical applications.12-18 Several groups have investigated the self-assembly of peptides with the help of different computational methods in the past few decades.19-34 A detailed theoretical study on the selfassembly of amphiphilic molecules was performed by Selinger et al.19 Later a coarse-grained model was proposed to investigate the self-assembly of β-sheet forming peptides.20 Molecular dynamics studies were performed to probe the self-assembly of amyloid forming peptides.21,22 It was shown that inter-aromatic interactions were not necessary for the formation of amyloid fibers and even aliphatic peptides could exhibit similar self-assembly.4,23 A virtual screening was


2

Page 3 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


then conducted to predict the nanostructures formed from the self-assembly of the dipeptides.24 Schatz and coworkers reported both atomistic and coarse-graining molecular dynamics (CGMD) simulation of the peptide amphiphiles.25,26 Interestingly the phenylalanine oligopeptides self-assembled to form planar nanostructures with high β-sheet content.15 Very recently, a detailed theoretical study has been performed with a large number of tripeptide sequences to explore their self-aggregation propensity to design and discover new hydrogels.12 Peng et al. showed that the absence of a short tripeptide sequence (Lys–Phe–Gly, KFG), situated in the juxtamembrane region of tyrosine kinase nerve growth factor (TrK NGF) receptors, resulted in serious neurological disorder.35 It was demonstrated that the absence of KFG sequence in the receptor chain greatly damages the activation of signaling cascades, which induces sometic hypertrophy and impaired neurite outgrowth. Recently we reported that this natural tripeptide, KFG, self-assembled in water to show a reversible and concentration dependent transformation of nanostructures from nanovesicles to nanotubes.36 At lower concentrations (1.43 mM), the tripeptide aggregated to form nanovesicles which showed random coil like CD signature and on concentrating this solution to 14 mM, they formed nanotubes with β-sheet like polypeptide arrangements. These biocompatible, stimuli sensitive nanovesicles were then successfully used to deliver anticancer drug, doxorubicin, both to the drug sensitive and drug resistant cancer cell lines. However, the arrangement and orientation of the hydrophilic and hydrophobic amino acids of the tripeptide to generate the nanovesicles and nanotubes has not been understood. Therefore a closer understanding of the self-organization pattern of this tripeptide, KFG, would be the primary step for rational drug delivery. Herein, we report extensive MD simulations of the CG model of this tripeptide to understand its aggregation properties. We have performed a combination of 2 µs NPT and 1 µs NVT run at five different


3


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 35

tripeptide concentrations. These show that the vesicles are formed only at lower concentrations, whereas the nanotubes are formed at relatively higher concentrations. MD simulations at still higher concentrations led to the formation of rectangular blocks, which is subsequently confirmed from SEM study. Several independent 3 µs long CG-MD runs have been carried out to confirm the reproducibility of the results. A comprehensive understanding of the arrangement and orientation of the hydrophilic and hydrophobic CG amino acids’ beads has also been obtained. Thus the computational studies performed herein are in agreement with the experimental findings.

METHODS. COMPUTATIONAL SECTION. COARSE-GRAINED MOLECULAR DYNAMICS SIMULATION. Herein we use coarse-grain (CG) MARTINI V2.1 force field37,38 to model the tripeptide, KFG. This force field uses four-toone mapping where four atoms and associated hydrogens are represented by one CG bead and three-to-one mapping for aromatic substituents. Hence one Lys-Phe-Gly (KFG) molecule is represented by eight beads where Lys is represented by three, Phe by four and Gly by one CG bead. The N-terminal Lys bead has two positive charges and the C-terminal Gly bead has one negative charge. Since the total charge of each KFG molecule is +1, we have added CG chloride and CG sodium ions in requisite amount to electrically neutralize the system having 1000 molecules of the tripeptide, KFG, and attain the salt concentration of 0.1 mol/L. CG waters, where four water molecules correspond to one bead with no charge, are also added to the system to achieve the concentration of interest. We have also replaced 0.1 mol fractions of the CG


4

Page 5 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


waters with the “antifreezing” model in order to avoid the unwanted freezing effect that sometimes occur with MARTINI model.37 Overall it allows a 4-fold reduction in the number of particles and speeds up the system by many folds as compared to the all-atom simulations. All of the molecular dynamics simulation runs have been carried out with NAMD software.39 The systems are coupled to external temperature and pressure baths of 310 K and 1 atm respectively using the Langevin coupling methods.40 We have utilized electrostatic and van der Waals (vdW) interactions in their shifted forms with a cutoff value of 1.2 nm. The neighbor list has been updated every 10 steps and the coordinates are saved every 5 ns for the trajectory monitoring where no coordinates are constrained during the simulation. All the periodic boxes are energy minimized using steepest descent integrator at 0 K, heated to 773 K and cooled to 310 K. System is then equilibrated at 310 K for 2 ns and simulated for a total of 3 µs at 310 K with a large time step of 25fs.41,42 ANALYSIS OF OUTPUTS FROM THE MD SIMULATION. The final trajectories at each concentration have been analyzed using our in-hose-developed codes and NAMD facilities. The various conformations at different time points are monitored to follow the assembly process and characterized the prevailing structures of the tripeptide assemblies. All of the snapshots are taken from VMD software.43

EXPERIMENTAL SECTION. FOURIER-TRANSFORM INFRARED (FT-IR) SPECTROSCOPY. The Infrared (IR) spectra were recorded using a Perkin Elmer Spectrum BX FT-IR spectrometer. The tripeptide (KFG) suspensions in water were prepared at different concentrations and drop-coated over calcium


5


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 35

fluoride cell. It is air-dried, freeze-dried and then IR spectra were recorded of the generated thin films. SCANNING ELECTRON MICROSCOPY (SEM). The different samples of the tripeptide suspensions were made by serial dilution from a concentrated sample. The morphology of the tripeptide, KFG, at various concentrations has been investigated by scanning electron microscopy. An aliquot of the suspension (20 µL) is loaded onto the brass stubs and the samples are kept at room temperature for 6 h to air dry. This is followed by freeze drying for another 6 h. Finally the samples are used for the SEM study on a Quanta 200 SEM operated at 5 KV after sputtering 20 nm of gold over the sample surfaces for proper imaging without charging effect. X-RAY DIFFRACTION (XRD). The aqueous suspension of the tripeptide at 57 mM concentration was placed on a pre-cleaned glass plate which, upon air-drying, afforded a thin film on the glass plate. X-ray diffraction (XRD) of the film was performed using the reflection method with a Phillips X-ray diffractometer. The X-ray beam was generated with a Cu anode and the Cu Kα1 beam of wavelength 1.5418 Å was used for the experiments. Scans were performed for 2θ values up to 45°.

RESULTS AND DISCUSSION. Previously we reported the concentration-dependent reversible transition of nanostructures from nanovesicles to nanotubes for a biologically conserved tripeptide sequence, Lys-Phe-Gly (KFG) in water.36 It was shown that the tripeptides form nanovesicles at lower concentration (1.43 mM) and nanotubes at higher concentration (14 mM). However, the arrangement and orientation of the hydrophilic and hydrophobic amino acids of the tripeptide to generate the nanovesicles and nanotubes is not understood. Hence, in order to


6

Page 7 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


understand the structural organization of the tripeptides to form these nanostructures, we have carried out detailed MD simulations at several concentrations with large number of water and ions. These multiscale simulations extended from few angstroms to several hundred angstroms and time scale covered included a range from few femtoseconds to microseconds and involved close to tenth of millions of atoms. Now, simulating the formation of nanostructures arising from the self-assembly of KFG with all-atom models is computationally too demanding due to the large system size as well as long simulation times. Hence CG-MD has been widely employed to understand and study the self-assembly of peptides.37,44-47 Here coarse-grain (CG) MARTINI V2.1 force field37,38 has been used to model the tripeptide, KFG (Figure S1) and then the simulation cell is prepared for a system with one thousand KFG molecules with appropriate number of CG water beads to attain the concentration of interest. The system is further neutralized and Martini CG-MD simulation run has been carried out at five different concentrations with NAMD software.39 Table 1 summarizes the parameters of various runs made. Multiple numbers of 3 µs long CG-MD runs at each concentration were performed to confirm the reproducibility and reliability of the results found from the simulation. The MD runs were started with thousand molecules of the tripeptide, KFG, and observed the formation of a closed three dimensional structure with no open ends at the three lowest KFG concentrations of 20, 38 and 83 mM (Table 1). Multiples of these structures, namely vesicles, were observed at the peptide concentration of 20 and 38 mM whereas a single vesicle was found at the peptide concentration of 83 mM after 3 µs of simulation run.


7


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 35

Table 1. Summary of the Martini coarse-graining simulation with thousand coarse grained KFG molecules at five different peptide concentrations.

Number of water molecules

Peptide concentration (mM)

Number of MD runs performed (each of 3 µs)

28,29,032

20

2

14,52,596

38

2

6,65,900

83

5

1,62,760

340

10

64,448

858

5

INVESTIGATION OF SELF-ASSEMBLY AT LOWER KFG CONCENTRATION OF 83 mM. In Figure 1, the detailed trajectory for the formation of a vesicle from a random configuration of KFG molecules at 83 mM concentration is demonstrated (Movie S1). The initial configuration (see Figure 1(a)) is composed of 1000 number of coarse-grained KFG molecules ordered on a lattice within a simulation cell of dimensions (30.51 × 29.73 × 25.75) nm3. The peptide units which carry a charge of +1 at physiological pH, were solvated with CG waters to achieve the defined concentration and ions were introduced to neutralize the system resulting in a salt concentration of 0.1 mol/L. The energy of the system was then minimized at 0 K (not shown in Figure 1). But the initial setup did not have a random configuration, hence the system was heated


8

Page 9 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


to a high temperature to ensure complete randomization and uniform distribution of the peptide units (see Figure 1(b)). The system was then equilibrated at 310 K for 2 ns and finally ran for 2 µs in NPT and 1 µs in NVT ensemble at 310 K. The aggregation propensity for the tripeptide, KFG, is found to be good as the peptides started to self-organize rather soon (10 ns) during the simulation run and generated small sized vesicles by the completion of 50-100 ns of MD run in the NPT ensemble. These vesicles fused among themselves with time and by 200 ns, several medium-sized vesicles of diameter between 5.11-10.23 nm were found in the simulation box. Longer runs led to further growth of these vesicles to a diameter of about 7.22-10.8 nm by 350 ns. After the completion of 1 µs of NPT run, different vesicles gradually coalesced into one larger amorphous spherical aggregate. This larger vesicle had a diameter about 14-16 nm. No significant change in the structure of these assembled peptides was observed on extending the MD run for one more µs in NPT ensemble and 1 µs in NVT ensemble, establishing the stability of the vesicle. In Figure 1, the detailed trajectory for the formation of a vesicle from a random configuration of KFG at 83 mM concentration is demonstrated (Movie S1).


9


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 35

Figure 1 | VMD snapshots of the simulation cell having the KFG concentration of 83 mM. The diagrams depict (a) the configuration of KFG molecules at start, (b) the randomized configuration of KFG molecules after the heating step and change in morphology of the peptides with the progress in simulation: (c) 300 ns, (d) 400 ns, (e) 450 ns, (f) 550 ns, (g) 650 ns, (h) 1 µs, (i) 1.5 µs, (j) 2 µs and (k) 3 µs respectively. The volume of the simulation cell during the initial 2 µs NPT and then 1 µs NVT is shown at the right bottom of the periodic box relative to the volume of the simulation box as shown in (k), i.e. after final NVT run. Waters are not shown inside the simulation cell for clarity.

It is known that there is a significant loss of atomic detail during CG simulation runs and detailed supramolecular structure is generally not available.24 Nevertheless it is still possible to


10

Page 11 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


obtain a clear idea about the polarity, non-bonded interaction potential, arrangement and orientation of the hydrophilic and hydrophobic amino acids within the self-assembled organization of the peptides. Therefore CG-MD simulations do provide insight about the driving force for the aggregation and subsequently for the self-assembly of the tripeptide units. We have first examined the arrangement of the KFG units in the vesicular morphology and computed the various radial distribution functions (rdfs) from the center of the vesicle. The calculations show that the center of the vesicle is hollow with density much below the bulk density [g(r)=1.0]. The density of the vesicle for various beads decreased sharply beyond 7-8 nm, which defines the outer radius of the vesicle. Hence the volume of the vesicle was calculated from its inner (~0.63 nm) and outer radius (~7.25 nm) and was found to be around 1215.24 nm3. Again, the hydrophobic beads dominated this mid region in-between the outer and central core of the vesicle, in comparison to the central and outer region of the vesicle which were dominated by the hydrophilic beads (Figure 2(i)). These results suggest that the vesicles are indeed capable of encapsulating cargos like the anticancer drug, DOX, inside its inner core due to the hydrophilic character.36 Further, the nanovesicle itself can remain intact and sustain its morphology in water because of their hydrophilic outer surface. The orientation of the hydrophobic CG beads is expressed in terms of the vector from Cα carbon to C of the phenyl ring of phenylalanine (defined as the unit vector from BAS/P5C to SI1/SC4F) of KFG to the radial vector of the vesicle was computed by defining a dot product of these two unit vectors (Figure S2). It is seen that the angle assumes values between 85-105 degrees suggesting an almost perpendicular orientation of the hydrophobic residues to the radial vector (Figure 2(ii)).


11


10

all peptide beads all hydrophilic beads all hydrophobic beads

g (r)

8 6 4 2

(ii) orientation (radian)

(i)

2.5 2.0 1.5 1.0 0.5 0.0

0 0

2

4

6

8

10

12

0

2

4

radius (nm) 7

8

10

12

10

12

(iv)

6

2.0

all peptide beads all hydrophilic beads all hydrophobic beads

5 4

g (r)

6

radius (nm)

orientation (radian)

(iii)

3 2 1

1.5

1.0

0.5

0 0.0 0

2

4

6

8

10

0

12

2

4

(v)

6

8

radius (nm)

radius (nm) 3.0

(vi) g (x) g (y) g (z)

2.5 2.0

3.0 2.5 2.0

g (x/y/z)

g (x/y/z)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 35

1.5 1.0

a b c d e f

1.5 1.0 0.5

0.5

0.0

0.0 -20

-15

-10

-5

0

5

10

15

20

-15

-10

-5

x/y/z (nm)

0

5

10

15

x/y/z (nm)

Figure 2. Radial density distribution function of all peptide beads, hydrophilic and hydrophobic CG beads along the radius of (i) vesicle, (iii) nanotube. The orientation of the hydrophobic CG residues is shown relative to the radial vector of the vesicle in (ii) and to the radial vector of the long axis of nanotube in (iv). (v) Dependence of the ratio of local density to bulk density for the rectangular block along the X- and Y-directions. (vi) Dependence of the ratio of local density to bulk density for the hydrophilic (a, b, c) and hydrophobic (d, e, f) beads along the X-, Y- and Zdirections respectively.


12

Page 13 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


INVESTIGATIONS AT A MEDIUM CONCENTRATION OF 340 mM. Then simulations were then carried out in a cell having dimensions of (20.51 × 19.73 × 15.75) nm3 at KFG concentration of 340 mM. Ten independent MD simulation runs were made to predict the reliability and reproducibility of the results and in each case we observed the formation of tubular nanostructures at the end of the simulation runs. Simulations were initially carried out in the NPT ensemble so that the system density can adjust to the desired value. Subsequently, once desired volume had been attained, the ensemble was changed to NVT to speed up the calculation. Initially no nanotube formation was evidenced even after the NPT simulation run of 500-750 ns, except the formation of large sheet like aggregates upon fusion of the smaller vesicles. However, on continuing the NPT runs beyond 1 µs or even longer, we observed the formation of well-defined tubular morphology. The stability of the nanotube was further monitored by extending the NPT run for one more µs and NVT run of 1 µs (Figure 3 and Movie S2).


13


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 35

Figure 3 | VMD snapshots of the simulation cell having the KFG concentration of 340 mM.

The diagrams depict (a) the configuration of KFG molecules at start, (b) the randomized configuration of KFG molecules after the heating step and change in morphology of the peptides with progress in simulation: (c) 500 ns, (d) 550 ns, (e) 1 µs, (f) 1.5 µs and (g) 2 µs. The final configuration of the tube after complete 3 µs of simulation run is shown view down the (h) Zaxis, (i) Y-axis and (j) X-axis. The volume of the simulation cell during the total simulation run (2 µs NPT + 1 µs NVT) in comparison to the volume of the simulation box shown in (h) (after the completion of 1 µs NVT run) is indicated at the right bottom of the simulation cell. Waters are omitted from the simulation cell to maintain clarity.


14

Page 15 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


We have reduced the time for the convergence of the simulated system to a desired morphology by methods which are discussed in Appendix 1. The nanotube spanned from one end of the simulation cell to another and was essentially infinite in length because of the periodic boundary conditions. The diameter and length of the nanotube were approximately 10.21 nm and ~16.8 nm respectively, while the center of the tube maintained a void space of diameter ~1.5 nm. The radial distribution function showed a void space in the center of the nanotube and the volume of the tube was calculated from the inner (~0.49 nm), outer (~5.52 nm) and length (~16.8 nm) of the tube as 1335.35 nm3 (Figure 1(iii)). It is to be noted that the outermost region of the tube was dominated primarily by the hydrophilic CG beads. The orientation of the hydrophobic CG beads is expressed in terms of the vector from Cα carbon to C of the phenyl ring of phenylalanine (defined as the unit vector from BAS/P5C to SI1/SC4F) of KFG to the radial vector perpendicular to the long axis of the tube was computed by defining a dot product of these two unit vectors (Figure S2). It is confirmed that the hydrophobic groups or the intra-peptide aromatic rings are oriented perpendicular to the radial vector, suggesting an almost parallel arrangement of them to the long axis of the tube (Figure 1(iv)). The results obtained at both of these concentrations are in agreement with experimental findings.36

INVESTIGATIONS AT HIGHER CONCENTRATION OF 858 mM. Simulations were further carried out at still higher concentrations. The concentration was increased from 340 mM to 858 mM with a view to monitor the role of concentration on the rate of formation of tubular morphology, if any. It was anticipated that the nanotubes might form faster, in less simulation


15


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 35

time. But the CG-MD simulations at 858 mM concentration (periodic box of (16.52 × 15.74 × 11.75) nm3 dimension) led to the formation of aggregates with shapes of rectangular blocks at the end of 3 µs of the combined NPT and NVT runs. The length and width of the rectangular block were seen to be extended across the X and Z directions and spanned along the whole XZplane. The height of the rectangular blocks along the Y axis was limited to ~6.99 nm. The calculated volume of this rectangular block was found to be around 1356.77 nm3 (Figure 4 and Movie S4). The density variation along X-, Y- and Z-directions are shown in Figure 2(v). The density along Y- is significantly higher than other two directions. The different densities along different directions suggest increasing anisotropy with concentration. Another interesting observation is the presence of the hydrophilic residues towards the two extremes along only the Y-direction and not X- or Z-directions (Figure 2(vi)). A spherical vesicle is completely isotropic in all directions while the nanotube is partially isotropic and the present structure is strongly anisotropic along one direction. The self-assembly occurs because the peptide-peptide interactions provide a considerably more favorable free energy as compared to the peptide molecules dispersed in isolation in solvent. The change in geometry or morphology of the selfassembly arises due to the availability of a larger number of peptides at higher concentration leads to larger and denser structures with better packing. Hence the nanotube provides a better packing than the nanovesicles and rectangular block structure provides a better packing (and lower energy) than that of a nanotube.


16

Page 17 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 4 | VMD snapshots of the distribution of peptides in the simulation cell at a KFG concentration of 858 mM. The diagrams depict (a) the configuration of KFG molecules at start,

(b) the randomized configuration of KFG molecules after the heating step and changes in morphology of the peptides with the progress in simulation as a function of time: (c) 400 ns, (d) 500 ns, (e) 750 ns, (f) 1 µs, (g) 1.5 µs and (h) 2 µs. The final configuration of the layered solid after complete 3 µs of simulation run is shown view down the (i) Z-axis, (j) Y-axis and (k) Xaxis. The multiplication factor to obtain the volume of the simulation cell at various times during the simulation run of 3 µs (2 µs NPT + 1 µs NVT) is shown at the right bottom of the periodic box in comparison to the final volume of the simulation cell (see (i)). Waters are not shown to keep clarity.


17


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 35

A detailed understanding about the self-assembly of KFG molecules can thus be achieved from the cartoon representation of vesicle, nanotube and rectangular block (Figure 5) which is based from the results obtained from Figure 1. It has been observed that the representative four KFG molecules self-organize due to the interplay of electrostatic, π-stacking, van der Waals and hydrogen bonding interactions (Figure 5a) to generate these types of nanostructures.

Figure 5. Cartoon representation of (a) four KFG molecules, (b) cross-section of a vesicle, (c)

cross-section of nanotube and (d) rectangular block. The red, black, green and blue spheres represent the hydrophilic and hydrophobic CG beads, CG ions and CG waters respectively. One Lys-Phe-Gly, KFG molecule is represented by half of one red and half of one black bead. The


18

Page 19 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


half of the red and black bead represents the hydrophilic (Lys, K and Gly, G) and hydrophobic (Phe, F) coarse-grain beads respectively.

COMPARISON WITH THE EXPERIMENTAL FINDINGS. These interesting theoretical observations, from MD simulation, are then corroborated with further experiments at higher concentrations. To our pleasant surprise we found indeed the formation of similar type of nanostructures. SEM study of the aqueous suspension of tripeptides at a concentration of 1.43, 14 and 57 mM evidenced the formation of nanovesicles, nanotube and rectangular block type structures (Figure 6). More interestingly, we found the simultaneous existence of tubes and rectangular blocks, where the tubes projected outward from the blocks, from the SEM images at an intermediate peptide concentration of 29 mM (Figure 6d). SEM image however cannot give indication whether the phase is crystalline or disordered. In order to address the crystallinity of the rectangular phase, we provide herein the XRD pattern of the rectangular phase obtained from experiment (Figure S3). Note that there are broad diffuse peaks at low theta values suggesting the experimental phase is not totally crystalline, but there is a long range order. It is interesting to note that our results are also reproducible on a larger scale. We have now carried out simulations with 2000 KFG units at three different tripeptide concentrations and some of these results are presented. The tube formation occurs in the case of medium tripeptide concentration and the results are in agreement with the aforementioned studies on 1000 KFG units (Figure S4). Thus the present study shows a strong correspondence between the results from the computer simulations and experimental observations, although the concentrations used in the former (i.e. in the theoretical studies) are generally higher than those employed in the experimental concentrations (Figure 6e-6g). We believe that this could be due to slower convergence and


19


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 35

limited time scales accessible in the computer simulations. As we can access only a few microseconds at the most during a MD simulation and stable structures are needed to form within this timescale, one may need to use higher solution concentration during the simulation. Presumably the peptide-peptide interactions also might play a critical role in the formation of nanostructures and with the availability of faster computers it might be possible to see a better convergence between simulations and experiments. This is in line with the earlier studies where experimentation and computation working together involving structural analyses of certain peptide-assembled nanostructures have been reported.12,25,48 Overall, it is seen that the tripeptide (KFG) self-assembled to form a rich variety of nanostructures simply by changing its concentration.

Figure 6. SEM images of suspensions of KFG in water showing the formation of (a)

nanovesicles, (b) nanotube and (c) rectangular blocks at a concentration of 1.43, 14 and 57 mM


20

Page 21 of 35

respectively. (d) The outward projection of the tube from the blocks has been observed at 29 mM tripeptide concentration. The one-to-one correspondence between theory and experiment is also at its bottom (e-g).

Further we have investigated the nanostructures through FT-IR spectroscopy to bring out the physical mechanisms behind this kind of self-assemblies at a molecular level. In this respect, we have recorded concentration dependent changes in the FT-IR spectra of the tripeptide (Figure 7(i)). The amide I band originates primarily from the C=O stretching vibration and is directly related to the backbone conformation of the polypeptides. A distinct difference has been found in this region with the change in concentration of the tripeptides. At lower concentration (1.43 mM), the tripeptide showed a band at around 1640 cm-1 indicating the random coil like organization, whereas at higher concentrations, the band shifted towards 1675 cm-1 showing the anti-parallel β-sheet type arrangement.49-51 It is further evident that the β-sheet content has

(i)

(ii)

99 96

-1

1640 cm

93 -1

1675 cm

90 87

1.43 mM 14 mM 29 mM 57 mM

84 81 78 4000

3500

3000

2500

2000 -1

Wavenumber (cm )

1500

1000

Total Potential Energy per CG Water (KJ/mol)

greatly increased in rectangular block type organizations than in the case of nanotube.

% Transmittance

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


-6 -7 Vesicles

-8 Tube

-9 -10 Rectangular Block

-11 0

200

400 600 Concentration (mM)

800

1000

Figure 7. (i) Fourier transform infrared (FTIR) spectra of the tripeptide (KFG) solutions in water

at different concentrations and (ii) shows the variation in total potential energy per CG water


21


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 35

bead with concentration as obtained from the MD simulation for vesicle (black, red and green color square), nanotube (deep blue color square) and rectangular block (cyan color square).

Finally the thermodynamic arguments for the formation of different nanostructures with variation in concentration of the tripeptide are also provided. First note that the entropy decreases with the formation of the ordered nanostructure in comparison to the randomly distributed initial configuration in each case. The free energy change therefore must be driven not by the T∆S term but by the enthalpy ∆H term. The enthalpy must compensate for the decrease in entropy. In Fig. 7(ii), the potential energy (PE) per CG water as a function of peptide concentration is shown. It is seen that PE decreases with increase in concentration suggesting the important role of the enthalpy term. The nanotube has 10% lower enthalpy than the nanovesicles, whereas the rectangular block structure has about 15% lower enthalpy as compared to nanotube. We note that Guo et al. reported the formation of bilayer by FF dipeptide at high concentrations only theoretically.52 These authors found this type of structure formation to be rather rare and only favorable at rather high theoretical concentrations of 496 mM. It was also found that the long peptide units like KLVFFAL formed bilayer membranes and the peptide with sequence AAAAAAK formed only flat bilayers.53,54 Thus to the best of our knowledge, the present study is one of the few reports in literature where such kind of rectangular block structures have been formed from the self-assembly of short tripeptide, KFG, units, both theoretically and experimentally.

CONCLUSIONS. We have performed experiments and Martini CG-MD simulation runs (both in NPT and NVT ensemble) to demonstrate the concentration dependent self-assembly of the


22

Page 23 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


natural tripeptide, Lys-Phe-Gly (KFG). The simulation runs were carried out at five different concentrations, where vesicles could be seen only at low concentration and nanotubes at intermediate concentration. Again the prediction from the MD simulation about the formation of rectangular block like organizations at higher concentrations was subsequently verified by SEM experiments. Both structures of the nano-morphologies as well as thermodynamic arguments are provided which give insight into the underlying reasons for their formation. Thus a control over the formation of various nanostructures is achieved simply by changing the concentration of the short tripeptide sequence, Lys-Phe-Gly (KFG). Also the reliability and reproducibility of the simulation results were verified by several independent 3 µs long MD runs at each concentration. Insight into packing of the vesicles and tube has been obtained through the calculation of radial distribution functions for various CG beads and orientation of the hydrophobic CG beads to the radial vector. Collectively, our findings offer useful theoretical insights which would be helpful for designing a versatile variety of peptide nanostructures with unique properties that may be useful in drug delivery. In future, we plan to further reduce the computer simulation time for rapid and immediate determination of probable nanostructures by modifying the simulation strategies. Also we are making an attempt to monitor the effect of changes in pH on the selfassembled nanostructures.

APPENDIX 1. Here we discuss ways by which the convergence to the desired morphology can be accelerated. We have tried to reduce this computer simulation time by varying the conditions during the simulation. In the first trial run, the MD simulations were carried out for 3 µs in only NVT


23


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 35

ensemble instead of initial 2 µs of NPT and 1 µs of NVT run. Although the formation of tubular morphology was monitored after 1 µs of NVT run and the tube remained stable for a further 2 µs of NVT simulation run, certain changes were observed during intermediate times in the case of formation of tube (Figure S5 and Movie S3). As we started the NVT simulation run from a regular arrangement of peptides, there was no formation of vesicles or large sheet like aggregates at intermediate times but the tube was formed directly from the initial arrangement. In contrast we found that for the simulation run consisting of both NPT and NVT ensembles, the tube formation was preceded by formation of vesicles. This was followed by formation of large sheet like aggregates before the formation of the tubular morphology. However, both the purely NVT run as well as the NPT followed by NVT runs take similar computer time. In order to reduce the time taken for the convergence from a random starting configuration to the tubular structure (which is likely to be a thermodynamically stable state), we have explored the method of annealing. Here the simulation runs were made by heating the system to high temperature followed by step-wise cooling of the same which led to the formation of nanotube at shorter run times. It is to be noted that the heating and stepwise cooling runs are made in the NPT ensemble and hence the volume of the simulation cell has increased significantly when the temperature was raised to 773 K. This allows the system to access a much larger amount of phase space.26 However, the volume reduced drastically as the system was cooled stepwise by lowering the temperature by 50 K at each step till 323K and then to 310 K. A short MD run of 2 ns was performed at each temperature and this computer experiment shows that the formation of nanostructures could be significantly speeded up by annealing the system. The tubular morphology was observed to form within the simulation run of 500-750 ns as compared to at least a µs or more by normal MD runs (Figure S6).


24

Page 25 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ASSOCIATED CONTENT Supporting Information.

The supporting figures and movie legends are provided in the supporting information. AUTHOR INFORMATION Corresponding Author

*Prof. (Dr.) Santanu Bhattacharya; Email: [email protected] Present Address

†Director’s Research Unit, Indian Association for the Cultivation of Science, Jadavpur, Kolkata 700032, India. Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

ACKNOWLEDGMENT The authors wish to acknowledge the support received from Council of Scientific & Industrial Research (CSIR) through “Advanced Drug Delivery” project and Department of Science and Technology, New Delhi through Nano-mission. This work was also supported by the J C Bose fellowship of the Department of Science and Technology to SB.


25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 35

REFERENCES 1. Chiti, F.; Dobson, C. M. Protein misfolding, functional amyloid, and human disease. Annu. Rev. Biochem. 2006, 75, 333-366. 2. Lansbury, P. T.; Lashuel, H. A. A century old debate on protein aggregation and neurodegeneration enters the clinic. Nature 2006, 443, 774-779. 3. Perutz, M. F. Amyloid fibrils. Mutations make enzyme polymerize. Nature 1997, 385, 773775. 4. Hauser, C. A.; Deng, R.; Mishra, A.; Loo, Y.; Khoe, U.; Zhuang, F.; Cheong, D. W.; Accardo, A.; Sullivan, M. B.; Riekel, C.; Ying, J. Y.; Hauser, U. A. Natural tri- to hexapeptides selfassemble in water to amyloid β-type fiber aggregates by unexpected α-helical intermediate structures. Proc. Natl. Acad. Sci. USA 2011, 108, 1361-1366. 5. Wei, G.; Song, W.; Derreumaux, P.; Mousseau, N. Self-assembly of amyloid-forming peptides by molecular dynamics simulations. Front. Biosci. 2008, 13, 5681-5692. 6. Misra, S. K.; Moitra, P.; Kondaiah, P.; Bhattacharya, S. Co-liposomes having anisamide tagged lipid and cholesteryl tryptophan trigger enhanced gene transfection in sigma receptor positive cells. Colloid Surface B 2016, 142, 130-140. 7. Misra, S. K.; Naz, S.; Kondaiah, P.; Bhattacharya, S. A cationic cholesterol based nanocarrier for the delivery of p53-EGFP-C3 plasmid to cancer cells. Biomaterials 2014, 35, 1334–1346. 8. Misra, S. K.; Kondaiah, P.; Bhattacharya, S.; Rao, C. N. R. Graphene as a Nanocarrier for Tamoxifen Induces Apoptosis in Transformed Cancer Cell Lines of Different Origins. Small 2012, 8, 131-143.


26

Page 27 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


9. Bejagam, K. K.; Balasubramanian, S. Supramolecular Polymerization: A Coarse Grained Molecular Dynamics Study. J. Phys. Chem. B 2015, 119, 5738-5746. 10. Sengupta, N.; Maekawa, H.; Zhuang, W.; Toniolo, C.; Mukamel, S.; Tobias, D. J.; Ge, N.-H. Sensitivity of 2D IR spectra to peptide helicity: a concerted experimental and simulation study of an octapeptide. J. Phys. Chem. B 2009, 113, 12037-12049. 11. Tzanov, A. T.; Cuendet, M. A.; Tuckerman, M. E. How accurately do current force fields predict experimental peptide conformations? An adiabatic free energy dynamics study. J. Phys. Chem. B 2014, 118, 6539-6552. 12. Frederix, P. W. J. M.; Scott, G. G.; Abul-Haija, Y. M.; Kalafatovic, D.; Pappas, C. G.; Javid, N.; Hunt, N. T.; Ulijn, R. V.; Tuttle, T. Exploring the sequence space for (tri-) peptide selfassembly to design and discover new hydrogels. Nat. Chem. 2015, 7, 30-37. 13. Adams, D. J. Dipeptide and tripeptide conjugates as low-molecular-weight hydrogelators. Macromol. Biosci. 2011, 11, 160-73. 14. Bertolani, A.; Pirrie, L.; Stefan, L.; Houbenov, N.; Haataja, J. S.; Catalano, L.; Terraneo, G.; Giancane, G.; Valli, L.; Milani, R.; Ikkala, O.; Resnati, G.; Metrangolo, P. Supramolecular amplification of amyloid self-assembly by iodination. Nat. Commun. 2015, 6, 7574. 15. Marchesan, S.; Styan, K. E.; Easton, C. D.; Waddington, L.; Vargiu, A. V. Higher and lower supramolecular orders for the design of self-assembled heterochiral tripeptide hydrogel biomaterials. J. Mater. Chem. B 2015, 3, 8123-8132.


27


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 35

16. Vargiu, A. V.; Iglesias, D.; Styan, K. E.; Waddington, L. J.; Easton, C. D.; Marchesan, S. Design of a hydrophobic tripeptide that self-assembles into amphiphilic superstructures forming a hydrogel biomaterial. Chem. Commun. 2016, 52, 5912-5915. 17. Martin, A. D.; Robinson, A. B.; Thordarson, P. Biocompatible small peptide superhydrogelators bearing carbazole functionalities. J. Mater. Chem. B 2015, 3, 2277-2280. 18. Martin, A. D.; Wojciechowski, J. P.; Bhadbhade, M. M.; Thordarson, P. A Capped Dipeptide Which Simultaneously Exhibits Gelation and Crystallization Behavior. Langmuir 2016, 32, 2245–2250. 19. Selinger, J. V.; Spector, M. S.; Schnur, J. M. Theory of Self-Assembled Tubules and Helical Ribbons. J. Phys. Chem. B 2001, 105, 7157–7169. 20. Bellesia, G.; Shea, J. E. Self-assembly of beta-sheet forming peptides into chiral fibrillar aggregates. J. Chem. Phys. 2007, 126, 245104. 21. Saric, A.; Chebaro, Y. C.; Knowles, T. P. J.; Frenkel, D. Crucial role of nonspecific interactions in amyloid nucleation. Proc. Natl. Acad. Sci. USA 2014, 111, 17869–17874. 22. Sorensen, J.; Periole, X.; Skeby, K. K.; Marrink, S.-J.; Schiott, B. Protofibrillar Assembly Toward the Formation of Amyloid Fibrils. J. Phys. Chem. Lett. 2011, 2, 2385–2390. 23. Lakshmanan, A.; Cheong, D. W.; Accardo, A.; Di Fabrizio, E.; Riekel, C.; Hauser, C. A. Aliphatic peptides show similar self-assembly to amyloid core sequences, challenging the importance of aromatic interactions in amyloidosis. Proc. Natl. Acad. Sci. USA 2013, 110, 519524.


28

Page 29 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


24. Frederix, P. W. J. M.; Ulijn, R. V.; Hunt, N. T.; Tuttle, T. Virtual Screening for Dipeptide Aggregation: Toward Predictive Tools for Peptide Self-Assembly. J. Phys. Chem. Lett. 2011, 2, 2380-2384. 25. Lee, O. S.; Stupp, S. I.; Schatz, G. C. Atomistic Molecular Dynamics Simulations of Peptide Amphiphile Self-Assembly into Cylindrical Nanofibers. J. Am. Chem. Soc. 2011, 133, 3677– 3683. 26. Lee, O. S.; Cho, V.; Schatz, G. C. Modeling the Self-Assembly of Peptide Amphiphiles into Fibers Using Coarse-Grained Molecular Dynamics. Nano. Lett. 2012, 12, 4907-4913. 27. Johnson, J. C.; Korley, L. T. J.; Tsige, M. Coarse-Grained Modeling of Peptidic/PDMS Triblock Morphology. J. Phys. Chem. B 2014, 118, 13718-13728. 28. Hyeon, C.; Thirumalai, D. Capturing the essence of folding and functions of biomolecules using coarse-grained models. Nat. Commun. 2011, 2, 487. 29. Gkeka, P.; Sarkisov, L. Spontaneous Formation of a Barrel-Stave Pore in a Coarse-Grained Model of the Synthetic LS3 Peptide and a DPPC Lipid Bilayer. J. Phys. Chem. B 2009, 113, 6-8. 30. Wang, Y.; Voth, G. A. Molecular Dynamics Simulations of Polyglutamine Aggregation Using Solvent-Free Multiscale Coarse-Grained Models. J. Phys. Chem. B 2010, 114, 8735–8743. 31. Sun, F.; Xu, L.; Chen, P.; Wei, P.; Qu, J.; Chen, J.; Luo, S.-Z. Insights into the Packing Switching of the EphA2 Transmembrane Domain by Molecular Dynamic Simulations. J. Phys. Chem. B 2015, 119, 7816-7824. 32. Dahlberg, M. Polymorphic Phase Behavior of Cardiolipin Derivatives Studied by CoarseGrained Molecular Dynamics. J. Phys. Chem. B 2007, 111, 7194-7200.


29


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 35

33. Terakawa, T.; Takada, S. p53 dynamics upon response element recognition explored by molecular simulations. Scientific Reports 2015, 5, 17107. 34. Khalfa, A.; Tarek, M. On the Antibacterial Action of Cyclic Peptides: Insights from CoarseGrained MD Simulations. J. Phys. Chem. B 2010, 114, 2676–2684. 35. Peng, X.; Greene, L. A.; Kaplan, D. R.; Stephens, R. M. Deletion of a conserved juxtamembrane sequence in Trk abolishes NGF-promoted neuritogenesis. Neuron 1995, 15, 395406. 36. Moitra, P.; Kumar, K.; Kondaiah, P.; Bhattacharya, S. Efficacious Anticancer Drug Delivery Mediated by a pH-Sensitive Self-Assembly of a Conserved Tripeptide Derived from Tyrosine Kinase NGF Receptor. Angew. Chem. Int. Ed. 2014, 53, 1113-1117. 37. Marrink, S. J.; Risselada, H. J.; Yefimov, S.; Tieleman, D. P.; Vries, A. H. The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations. J. Phys. Chem. B 2007, 111, 7812-7824. 38. Monticelli, L.; Kandasamy, S. K.; Periole, X.; Larson, R. G.; Tieleman, D. P.; Marrink, S.-J. The MARTINI Coarse-Grained Force Field: Extension to Proteins. J. Chem. Theory and Comput. 2008, 4, 819-834. 39. Phillips, J. C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R. D.; Kale, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781-1802. 40. Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. Molecular-Dynamics with Coupling to an External Bath. J. Chem. Phys. 1984, 81, 3684-3690.


30

Page 31 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


41. Winger, M.; Trzesniak, D.; Baron, R.; van Gunsteren, W. F. On Using a Too Large Integration Time Step in Molecular Dynamics Simulations of Coarse-Grained Molecular Models. Phys. Chem. Chem. Phys. 2009, 11, 1934-1941. 42. Marrink, S. J.; Periole, X.; Tieleman, D. P.; Vries, A. H. Comment on “On using a too large integration time step in molecular dynamics simulations of coarse-grained molecular models” by M. Winger, D. Trzesniak, R. Baron and W. F. van Gunsteren, Phys. Chem. Chem. Phys., 2009, 11, 1934. Phys. Chem. Chem. Phys. 2010, 12, 2254-2256. 43. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual Molecular Dynamics. J. Mol. Graph. 1996, 14, 33-38.

44. Krone, M. G.; Hua, L.; Soto, P.; Zhou, R.; Berne, B. J.; Shea, J.-E. Role of Water in Mediating the Assembly of Alzheimer Amyloid-β Aβ16-22 Protofilaments. J. Am. Chem. Soc. 2008, 130, 11066-11072.

45. Khurana, E.; DeVane, R. H.; Kohlmeyer, A.; Klein, M. L. Probing Peptide Nanotube SelfAssembly at a Liquid−Liquid Interface with Coarse-Grained Molecular Dynamics. Nano Lett. 2008, 8, 3626–3630.

46. Guo, C.; Luo, Y.; Zhou, R.; Wei, G. Triphenylalanine peptides self-assemble into nanospheres and nanorods that are different from the nanovesicles and nanotubes formed by diphenylalanine peptides. Nanoscale 2014, 6, 2800-2811. 47. Slyngborg, M.; Fojan, P. A computational study of the self-assembly of the RFFFR peptide. Phys. Chem. Chem. Phys. 2015, 17, 30023-30036.


31


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 35

48. Gobbo, C.; Beurroies, I.; de Ridder, D.; Eelkema, R.; Marrink, S. J.; Feyter, S. D.; van Esch, J. J.; de Vries, A. H. MARTINI Model for Physisorption of Organic Molecules on Graphite. J. Phys. Chem. C 2013, 117, 15623-15631. 49. Ganim, Z.; Chung, H. S.; Smith, A. W.; DeFlores, L. P.; Jones, K. C.; Tokmakoff, A. Amide I two-dimensional infrared spectroscopy of proteins. Acc. Chem. Res. 2008, 41, 432-441. 50. Byler, D. M.; Susi, H. Examination of the secondary structure of proteins by deconvolved FTIR spectra. Biopolymers 1986, 25, 469-487. 51. Susi, H.; Byler, D. M. Resolution-enhanced fourier transform infrared spectroscopy of enzymes. Methods Enzymol. 1986, 130, 290-311. 52. Guo, C.; Luo, Y.; Zhou, R.; Wei, G. Probing the Self-Assembly Mechanism of Diphenylalanine-Based Peptide Nanovesicles and Nanotubes. ACS Nano 2012, 6, 3907-3918. 53. Childers, W. S.; Mehta, A. K.; Ni, R.; Taylor, J. V.; Lynn, D. G. Peptides Organized as Bilayer Membranes. Angew. Chem. Int. Ed. 2010, 49, 1-5. 54. Hamley, I. W. Peptide Nanotubes. Angew. Chem. Int. Ed. 2014, 53, 6866-6881.


32

Page 33 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Insert Table of Contents Graphic and Synopsis Here

New insights both from experiment and computer simulation are shown herein for the concentration dependent self-assembly of TrK-NGF receptor derived tripeptide, Lys-Phe-Gly (KFG).


33

The Journal of Physical Chemistry Page 34 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13


hePage Journal 35 of of 35 Physical Chemistr

1 2 3 ACS 4 Paragon Plus Environment 5 6

Concentration Dependent Self-Assembly of TrK ... - ACS Publications

Recommend Documents