-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathMolDynamics.html
91 lines (64 loc) · 4.26 KB
/
MolDynamics.html
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
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
<html>
<head>
<meta charset="utf-8">
<title>
</title>
<link rel="stylesheet" href="comp.css">
</head>
<body>
<h1><pre>
#################################
# MODELING COMPLEX INTERACTIONS #
#################################
</pre></h1>
<div class="topnav">
<a href="./Index.html">Home</a>
<a href="./Main.html">Previous Page</a>
</div>
<p></p>
<center>
<img src="GRAPHICS/GROMACS.png" alt="Gromacs, a FOSS molecular dynamics utility for protein and viral anaylsis"
style="float:center;width:s350px;height:140px;">
<p>
<caption style="color:ffb000;">
GROningen MAchine for Chemical Simulations <a href="https://www.gromacs.org/">(GROMACS)</a> is a popular molecular dynamics tool
</caption>
</p>
</center>
<h2><pre>
****************************
* ENTER MOLECULAR DYNAMICS *
****************************
</pre></h2>
<p>~ Molecular Dynamics (MD) is a computational method used to simulate a specific scenario involving a chemical of interest, often interactions between proteins and water molecules </p>
<p>~ In order to accomplish this, MD uses a reductionist approach: </p>
<ul style="color:ffb000;">
<li>Atoms, monomers, or even entire molecules are treated as point-like objects with uniform charge</li>
<li>These point-like charges are subject to <a href="https://www.physicsclassroom.com/Physics-Tutorial/Newton-s-Laws"> Newton's</a> Laws of Motion, which usually do not apply to individual atoms or subatomic particles</li>
<li> There are no external forces being acted upon the simulation</li>
</ul>
<p>~ This gives MD an advantage over Quantum Mechanics, mainly</p>
<ul style="color:ffb000;">
<li>Reduced computational complexity, allowing for larger simulations to be run optimally</li>
<li>A "good enough" approach when studying proteins at a larger scale, such as interactions in water or with other proteins</li>
</ul>
<p>~ However, this does not mean MD is without faults, as the quality of the simulation is ultimately depenant on the variables and parameters used in the simulation
</p>
<p>~ Below is a video produced by a collaborative team from RIKEN and Michigan State University detailing MD at a larger scale</p>
<center>
<iframe width="560" height="315" src="https://www.youtube.com/embed/5JcFgj2gHx8" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
</center>
<h2><pre>
*************************
* DISCOVERING NEW DRUGS *
*************************
</pre></h2>
<p>~ Drug discovery (DD) is an labor, resource, and time intensive venture, and means of screening potential drug candidates for animal studies and eventual clinical studies are increasingly reliant on computational methods </p>
<p>~ Current issues within the DD community include discovering new <a href="https://www.nature.com/articles/d41586-020-00018-3">antibiotics</a> as bacteria are increasingly able to resist currently existing antibiotics, cancer medications that reduce side effects, and the development of <a href="https://www.nature.com/articles/d41591-020-00027-9">vaccines</a> to prevent emerging diseases from spiraling out of control</p>
<p>~ DD takes a hybrid approach, combining Artificial Intelligence (AI), MD, and Quantum Mechanics to examine drug candidates </p>
<p>~ In order to accomplish this, DD methods have to probe the way proteins interact with ligands, the chemicals they process, cofactors, secondary chemicals that interact with proteins, and other proteins. When a ligand interacts with a protein's active site, the computational modeling process is known as <a href="https://en.wikipedia.org/wiki/Docking_(molecular)">docking</a></p>
<p>~ <a href="http://vina.scripps.edu/">AutoDock Vina</a>, and <a href="https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00411">Gnina</a> are utilities that use hardware accelerated AI, known as deep learning, to probe protein ligand interactions. Vina computationally generates the protein-ligand interactions, whereas Gnina scores these docking interactions using a partition grid, and what is known as a <a href="https://machinelearningmastery.com/convolutional-layers-for-deep-learning-neural-networks/">Convolutional Neural Network (CNN)</a> <p>
<span style="background-color: #ffb000;color: black;">(END)</span>
<p></p>
</body>
</html>