# Normal Mode Algebra¶

This part shows how to use some handy features of `Mode`

objects.

## ANM Calculations¶

We will compare modes from two ANMs for the same protein, but everything applies to comparison of ANMs and PCAs (as long as they contain same number of atoms).

Let’s get started by getting ANM models for two related protein structures:

```
In [1]: from prody import *
In [2]: str1 = parsePDB('1p38')
In [3]: str2 = parsePDB('1r39')
```

**Find and align matching chains**

```
In [4]: matches = matchChains(str1, str2)
In [5]: match = matches[0]
In [6]: ch1 = match[0]
In [7]: ch2 = match[1]
```

Minimize RMSD by superposing `ch2`

onto `ch1`

:

```
In [8]: ch2, t = superpose(ch2, ch1) # t is transformation, already applied to ch2
In [9]: calcRMSD(ch1, ch2)
Out[9]: 0.8984016339868075
```

**Get ANM models for each chain**

```
In [10]: anm1, ch1 = calcANM(ch1)
In [11]: anm2, ch2 = calcANM(ch2)
In [12]: anm1[0]
Out[12]: <Mode: 1 from ANM 1p38>
```

Let’s rename these `ANM`

instances, so that they print short:

```
In [13]: anm1.setTitle('1p38_anm')
In [14]: anm2.setTitle('1r39_anm')
```

This is how they print now:

```
In [15]: anm1[0]
Out[15]: <Mode: 1 from ANM 1p38_anm>
In [16]: anm2[0]
Out[16]: <Mode: 1 from ANM 1r39_anm>
```

## Calculate overlap¶

We need Numpy in this part:

```
In [17]: from numpy import *
```

Multiplication of two `Mode`

instances returns dot product
of their eigenvectors. This dot product is the overlap or cosine correlation
between modes.

Let’s calculate overlap for slowest modes:

```
In [18]: overlap = anm1[0] * anm2[0]
In [19]: overlap
Out[19]: -0.9840211954504442
```

This shows that the overlap between these two modes is 0.98, which is not
surprising since ANM modes come from structures of the *same* protein.

To compare multiple modes, convert a list of modes to a `numpy.array()`

:

```
In [20]: array(list(anm1[:3])) * array(list(anm2[:3]))
Out[20]:
array([-0.9840211954504442, -0.9815834854497086, -0.9913578118318772],
dtype=object)
```

This shows that slowest three modes are almost identical.

We could also generate a matrix of overlaps using `numpy.outer()`

:

```
In [21]: outer_product = outer(array(list(anm1[:3])), array(list(anm2[:3])))
In [22]: outer_product
Out[22]:
array([[-0.9840211954504442, -0.14494461667592362,
-0.0021711558324553353],
[0.14836678827918132, -0.9815834854497086, 0.08077361095287733],
[0.01043287216284389, -0.08407811447302836, -0.9913578118318772]],
dtype=object)
```

This could also be printed in a pretty table format using
`printOverlapTable()`

:

```
In [23]: printOverlapTable(anm1[:3], anm2[:3])
Overlap Table
ANM 1r39_anm
#1 #2 #3
ANM 1p38_anm #1 -0.98 -0.14 0.00
ANM 1p38_anm #2 +0.15 -0.98 +0.08
ANM 1p38_anm #3 +0.01 -0.08 -0.99
```

**Scaling**

`Mode`

instances can be scaled, but after this operation they will
become `Vector`

instances:

```
In [24]: anm1[0] * 10
Out[24]: <Vector: (Mode 1 from ANM 1p38_anm)*10>
```

## Linear combination¶

It is also possible to linearly combine normal modes:

```
In [25]: anm1[0] * 3 + anm1[1] + anm1[2] * 2
Out[25]: <Vector: (((Mode 1 from ANM 1p38_anm)*3) + (Mode 2 from ANM 1p38_anm)) + ((Mode 3 from ANM 1p38_anm)*2)>
```

Or, we could use eigenvalues for linear combination:

```
In [26]: lincomb = anm1[0] * anm1[0].getEigval() + anm1[1] * anm1[1].getEigval()
```

It is the name of the `Vector`

instance that keeps track of operations.

```
In [27]: lincomb.getTitle()
Out[27]: '((Mode 1 from ANM 1p38_anm)*0.148971269751) + ((Mode 2 from ANM 1p38_anm)*0.24904210757)'
```

## Approximate a deformation vector¶

Let’s get the deformation vector between *ch1* and *ch2*:

```
In [28]: defvec = calcDeformVector(ch1, ch2)
In [29]: abs(defvec)
Out[29]: 16.68706972787035
```

Let’s see how deformation projects onto ANM modes:

```
In [30]: array(list(anm1[:3])) * defvec
Out[30]:
array([-5.608605947838763, 2.153933659593523, -3.137016091986499],
dtype=object)
```

We can use these numbers to combine ANM modes:

```
In [31]: approximate_defvec = sum((array(list(anm1[:3])) * defvec) *
....: array(list(anm1[:3])))
....:
In [32]: approximate_defvec
Out[32]: <Vector: ((-5.60860594784*(Mode 1 from ANM 1p38_anm)) + (2.15393365959*(Mode 2 from ANM 1p38_anm))) + (-3.13701609199*(Mode 3 from ANM 1p38_anm))>
```

Let’s deform 1r39 chain along this approximate deformation vector and see how RMSD changes:

```
In [33]: ch2.setCoords(ch2.getCoords() - approximate_defvec.getArrayNx3())
In [34]: calcRMSD(ch1, ch2)
Out[34]: 0.8209600870337731
```

RMSD decreases from 0.89 A to 0.82 A.