Project CG_model_LLPS

In table are reported the atomic types with relative mass, charge and sigma. As a first attempt we have chosen the following parameters.

Atomtype	mass	charge	sigma
A	100 Da	0.0	1 $\sigma$
Bc	100 Da	0.0	3 $\sigma$
Bs	100 Da	0.0	1 $\sigma$

In table are reported the molecules present in our model and the relative sequence.

Molecule	Sequence
poiyA	A- $\cdots$ - A (n $\cdot$ A)
B	Bs - Bc

In table are reported the bond interactions of molecule in our model.

molecule	atoms	interaciton type	params
polyA	A - A	Harmonic	$k_A$ = 1000 $kJ/mol$ $\cdot$ $\frac{1}{nm^{2}}$; $r_{0A}$ = $\sigma_A$
B	Bs-Bc	Harmonic	$ K_B$ = 1000 $kJ/mol$ $\cdot$ $\frac{1}{nm^{2}} $; $r_{0B}$ = $\frac{\sigma_{Bs}}{2}$ + $\frac{\sigma_{Bc}}{2}$

In table are reported all possible non-bonded interaction and the type of function used to described it.

atoms	type
A -A	Lennard - Jones Troncated
A - Bs	Wingreen
A - Bc	Lennard - Jones Troncated
Bs - Bs	Lennard - Jones Troncated
Bs - Bc	Lennard - Jones Troncated
Bc - Bc	Ashbaugh - Hatch

Here the explicit analitical expressions of the interactions.

1- Lennard - Jones Troncated

$$ V(r) = 4 \epsilon \cdot \left[(\frac{\sigma}{r})^{12} - (\frac{\sigma}{r})^6 \right] $$

if $r < \sigma$. And

$$ V(r) = 0 $$

if $r \ge \sigma$.

2 - Wingreen (*)

$$ V(r) = -\frac{1}{2}V_0 (1 + \cos{\frac{\pi r}{\sigma}}) $$

if $r < \sigma$. $V_0$ = 14 $K_bT$ from litterature. And

$$ V(r) = 0 $$

if $r \ge \sigma$.

3 - Ashbaugh - Hatch

$$ V(r) = \Phi_{LJ} + (1 -\lambda) \epsilon $$

if $r < 2^{\frac{1}{6}}\sigma$}. And

$$ V(r) = \lambda \Phi_{LJ} $$

if $r \ge 2^{\frac{1}{6}} \sigma$

Using Gromacs to run CG model simulations, we cannot express interactions through analytic functions but we have to use numerical values with tables. We decided to provide 3 tables; each referring to a specific $ \sigma $ value.

atoms	type (index)	$\sigma$	table name
A -A	1	1 $\sigma$	table_ABsite_ABsite.xvg
A - Bs	2	1 $\sigma$
Bs - Bs	1	1 $\sigma$
A - Bc	1	2 $\sigma$	table_ABsite_Bcore.xvg
Bs - Bc	1	2 $\sigma$
Bc - Bc	3	3 $\sigma$	table_Bcore_Bcore.xvg

---

Here is possible to see how the tables are designed.

table_ABsite_ABsite.xvg with $\sigma'$ = 1 $\sigma$

table_ABsite_ABsite.xvg
r	f	-f	g	-g'	h	-h'
0.02	0.00	0.00	$\vdots$	$\vdots$	$\vdots$	$\vdots$
0.02	0.00	0.00	$\mathrm{LJ_T^{\sigma}}$	$\mathrm{- \nabla {LJ_T^{\sigma}}}$	$\mathrm{ W^{\sigma} }$	$\mathrm{\nabla W^{\sigma}}$
$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$
$\mathrm{r_{cutoff} + 1}$	$ 0.00 $	$ 0.00 $	$\vdots$	$\vdots$	$\vdots$	$\vdots$

with $\mathrm{LJ_T^{\sigma}}$ Lennard-Jonnes trucated ($\sigma$);

$$ \mathrm{LJ_T^{\sigma} = 4 \epsilon \cdot \left[\left(\frac{\sigma}{r}\right)^{12} - \left(\frac{\sigma}{r}\right)^6 \right] \cdot (r<\sigma)} $$

$$ \mathrm{ - \nabla LJ_T^{\sigma} = 4 \epsilon \cdot \left(-12 \cdot \frac{\sigma^{12}}{r^{13}} - 6 \frac{\sigma^6}{r^7}\right) \cdot (r<\sigma)} $$

with $\mathrm{W^{\sigma}}$ Wingreen ($\sigma$);

$$ \mathrm{W^{\sigma} = - \frac{1}{2} \cdot V_0 \cdot \left(1 + \cos{\frac{\pi r}{\sigma}}\right) \cdot (r<\sigma)}$$

$$ \mathrm{- \nabla W^{\sigma} = - \frac{1}{2} \cdot V_0 \cdot \sin \left({\frac{\pi r}{\sigma}}\right) \cdot \frac{\pi}{\sigma} \cdot (r<\sigma) } $$

---

table_ABsite_Bcore.xvg with $\sigma'$ = 2 $\sigma$

table_ABsite_Bcore.xvg
r	f	-f	g	-g'	h	-h'
0.02	0.00	0.00	$\vdots$	$\vdots$	$0.00$	$0.00$
0.02	0.00	0.00	$\mathrm{LJ_T^{2 \sigma}}$	$\mathrm{- \nabla {LJ_T^{2 \sigma}}}$	$0.00$	$0.00$
$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$
$\mathrm{r_{cutoff}+1}$	$ 0.00 $	$ 0.00 $	$\vdots$	$\vdots$	$0.00$	$0.00$

with $\mathrm{LJ_T^{2 \sigma}}$ Lennard-Jonnes trucated ($2 \sigma$);

$$ \mathrm{LJ_T^{2\sigma} = 4 \epsilon \cdot \left[\left(\frac{2\sigma}{r}\right)^{12} - \left(\frac{2\sigma}{r}\right)^6 \right] \cdot (r<2\sigma)} $$

$$ \mathrm{ - \nabla LJ_T^{2\sigma} = 4 \epsilon \cdot \left(-12 \cdot \frac{(2\sigma)^{12}}{r^{13}} - 6 \frac{(2\sigma)^6}{r^7}\right) \cdot (r<2\sigma)} $$

---

table_Bcore_Bcore.xvg with $\sigma'$ = 3 $\sigma$

table_Bcore_Bcore.xvg
r	f	-f	g	-g'	h	-h'
0.02	0.00	0.00	$\vdots$	$\vdots$	$\vdots$	$\vdots$
0.02	0.00	0.00	$\mathrm{LJ_{l1}^{3\sigma}}$	$\mathrm{- \nabla {LJ_{l1}^{3\sigma}}}$	$\mathrm{ LJ_{l2}^{3\sigma} }$	$\mathrm{\nabla LJ_{l2}^{3\sigma}}$
$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$
$\mathrm{r_{cutoff} + 1}$	$ 0.00 $	$ 0.00 $	$\vdots$	$\vdots$	$\vdots$	$\vdots$

with $\mathrm{LJ_{like 1}^{3 \sigma}}$ Lennard-Jonnes like_1 ($3 \sigma$) [Ashbaugh - Hatch ];

$$ \mathrm{LJ_{like 1}^{3\sigma} = \left[ 4 \epsilon \cdot \left[\left(\frac{3\sigma}{r}\right)^{12} - \left(\frac{3\sigma}{r}\right)^6 \right] + \epsilon \right] \cdot (r< 2^\frac{1}{6} \cdot 3\sigma)} $$

$$ \mathrm{ - \nabla LJ_{like 1}^{3\sigma} = 4 \epsilon \cdot \left(-12 \cdot \frac{(3\sigma)^{12}}{r^{13}} - 6 \frac{(3\sigma)^6}{r^7}\right) \cdot (r< 2^\frac{1}{6} \cdot 3\sigma)} $$

with $\mathrm{LJ_{like 2}^{3 \sigma}}$ Lennard-Jonnes like_2 ($3 \sigma$);

$$ \mathrm{LJ_{like 2}^{3\sigma} = \lambda \cdot \left[ - \epsilon \cdot (r< 2^\frac{1}{6} \cdot 3\sigma) + 4 \epsilon \cdot \left[\left(\frac{3\sigma}{r}\right)^{12} - \left(\frac{3\sigma}{r}\right)^6 \right] \cdot (r \ge 2^\frac{1}{6} \cdot 3\sigma) \right] } $$

$$ \mathrm{ - \nabla LJ_{like 2}^{3\sigma} = \lambda \cdot 4 \epsilon \cdot \left(-12 \cdot \frac{(3\sigma)^{12}}{r^{13}} - 6 \frac{(3\sigma)^6}{r^7}\right) \cdot (r \ge 2^\frac{1}{6} \cdot 3\sigma)}$$

---

The following cell shows the expressions of the interactions in Python.

# functions section

def LJ_repulsive(r,sigma,epsilon):
    V_r = 4 * epsilon * ((sigma/r)**12 - (sigma/r)**6) * (r<sigma)
    return V_r

def force_LJ_repulsive(r,sigma,epsilon):
    F_r = 4 * epsilon * ( (12 * (sigma**12) / r**13)  - ( 6 * (sigma**6) / r**7 ) ) * (r<sigma)
    return F_r

def Wingreen(r,V_0,r_0):
    V_r = -0.5 * V_0 * (1 + np.cos( math.pi * r / r_0)) * (r < r_0)
    return V_r

def force_Wingreen(r,V_0,r_0):
    F_r = -0.5 * V_0 *  np.sin( math.pi * r / r_0) *  math.pi / r_0 * (r < r_0)
    return F_r

def LJ_like(r,sigma,epsilon,lambd):
    V_r = (4 * epsilon * ((sigma/r)**12 - (sigma/r)**6) +  (1-lambd)*epsilon) * (r<= 2**(1/6)*sigma) + lambd * 4 * epsilon * ((sigma/r)**12 - (sigma/r)**6) *  (r> 2**(1/6)*sigma)                                                                            
    return V_r

def force_LJ_like(r,sigma,epsilon,lambd):
    F_r = 4 * epsilon * ( (12 * (sigma**12) / r**13)  - ( 6 * (sigma**6) / r**7 ) ) * (r<= 2**(1/6)*sigma) + 4 * lambd * epsilon * ( (12 * (sigma**12) / r**13)  - ( 6 * (sigma**6) / r**7 ) ) * (x > 2**(1/6)*sigma)     
    return F_r

#--------------------------------------------------------------------
# for simplicity split 2 terms. One depending on lambda.

def LJ_like_4_table_1(r,sigma,epsilon):
    V_r = (( 4 * epsilon * ((sigma/r)**12 - (sigma/r)**6)) + epsilon ) * (r<= 2**(1/6)*sigma)                                                                                                                
    return V_r

def force_LJ_like_4_table_1(r,sigma,epsilon):
    F_r = 4 * epsilon * ( (12 * (sigma**12) / r**13)  - ( 6 * (sigma**6) / r**7 ) ) * (r<= 2**(1/6)*sigma)  
    return F_r

    
def LJ_like_4_table_2(r,sigma,epsilon):
    V_r = -epsilon * (r<= 2**(1/6)*sigma)  + 4 * epsilon * ((sigma/r)**12 - (sigma/r)**6) *  (r> 2**(1/6)*sigma)                                                                        
    return V_r


def force_LJ_like_4_table_2(r,sigma,epsilon):
    F_r = 4 * epsilon * ( (12 * (sigma**12) / r**13)  - ( 6 * (sigma**6) / r**7 ) ) * (r> 2**(1/6)*sigma)
    return F_r

In the following cell are plotted the analytical funcions describing the interactions in the CG model.

# check analytical functions

fig, ax = plt.subplots(3,2, figsize=(20, 20), sharex=True)

# x points for gromacs
x = np.arange(0,10,0.02)

ax[0,0].set_xlim(0,3)
ax[0,0].set_ylim(-1,3*10)
ax[0,0].plot(x,LJ_repulsive(x, sigma=1, epsilon=2))
ax[0,0].set_title('repulsive LJ (V)')
ax[0,0].set_xlabel('$ \sigma$ ')
ax[0,0].set_ylabel(' V ')

ax[0,1].plot(x,force_LJ_repulsive(x ,sigma=1,epsilon=2), color='r')
ax[0,1].set_ylim(-1,3*10)
ax[0,1].set_title('repulsive LJ (F)')
ax[0,1].set_xlabel('$ \sigma$ ')
ax[0,1].set_ylabel(' F ')

ax[1,0].set_xlim(0,3)
ax[1,0].plot(x,Wingreen(x, V_0=1, r_0=1))
ax[1,0].set_title('Wingreen (V)')
ax[1,0].set_xlabel('$ \sigma$ ')
ax[1,0].set_ylabel(' V ')

ax[1,1].plot(x,force_Wingreen(x, V_0=1, r_0=1), color='r')
ax[1,1].set_title('Wingreen (F)')
ax[1,1].set_xlabel('$ \sigma$ ')
ax[1,1].set_ylabel(' F ')

ax[2,0].set_xlim(0,3)
ax[2,0].plot(x,LJ_like(x, sigma=1, epsilon=2, lambd=-0.5), '--' , color='C0', label='lambda = -0.5')
ax[2,0].plot(x,LJ_like(x, sigma=1,epsilon=2, lambd=1),'.-.' , color='C0', label='lambda = 1.0')
ax[2,0].plot(x,LJ_like(x, sigma=1, epsilon=2, lambd=0.1), '-', color='C0',label='lambda = 0.1')
ax[2,0].set_ylim(-5,5)
ax[2,0].set_title(' LJ like (V)')
ax[2,0].set_xlabel('$ \sigma$ ')
ax[2,0].set_ylabel(' V ')
ax[2,0].legend()

ax[2,1].plot(x,force_LJ_like(x, sigma=1, epsilon=2, lambd=-0.5), '--' , color='r', label='lambda = -0.5')
ax[2,1].plot(x,force_LJ_like(x,sigma=1,epsilon=2, lambd=1),'.-.' , color='r', label='lambda = 1.0')
ax[2,1].plot(x,force_LJ_like(x,sigma=1,epsilon=2, lambd=0.1), '-', color='r',label='lambda = 0.1')
ax[2,1].set_ylim(-5,5)
ax[2,1].set_title(' LJ like (F)')
ax[2,1].set_xlabel('$ \sigma$ ')
ax[2,1].set_ylabel(' F ')
ax[2,1].legend();

#plt.plot(x,force_LJ_repulsive(x,1,1))

For convenience we set to 0 all parametres ($\epsilon$, $V_0$ and $\lambda$) present in tables. In this way is possible to change all params directly on topol.top file, without changing the tables. Here is reported the summary table:

[nonbond_parmas]
atoms	C	A	$\sigma$
A A	$\epsilon$	0	$1 \sigma$
A Bs	0	$V_0$
Bs Bs	$\epsilon$	0
A  Bc	$\epsilon$	0	$2 \sigma$
Bs Bc	$\epsilon$	0
Bc Bc	$\epsilon$	$\lambda \epsilon$	$3 \sigma$

note that $V(r) = C \cdot g(r) + A \cdot h(r)$.