[HN Gopher] A Primer on Molecular Dynamics
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A Primer on Molecular Dynamics
Author : EvgeniyZh
Score : 76 points
Date : 2025-06-06 19:39 UTC (4 days ago)
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| roughly wrote:
| This is neat! I'm not fully through it yet, but just wanted to
| emphasize this:
|
| > And understanding molecular motion is key for everything in
| biology, everything in biology is vibrating molecules underneath
| the surface!
|
| Coming into bio as a programmer, this is the absolute sin qua non
| rule you need to internalize: there are no boundaries between
| systems, because everything is jiggling atoms. DNA encodes for
| genes, except the transcription process is heavily mediated by
| the physical environment and physical constraints of accessing
| the DNA; RNA transcribes to amino acid strings, except it's also
| a molecule, and so sometimes it folds into a structure and just
| does shit itself; proteins have a function, except sometimes they
| have many functions, because the "lock and key" metaphor isn't
| wrong, except when you've got a billion locks and your key's
| kinda floppy, it'll probably fit more than one. Nature plays with
| physical systems and will repurpose anything to do anything else
| - the informatics only take you so far, all the real action is
| vibrating molecules.
| holodro wrote:
| > Coming into bio as a programmer, this is the absolute sin qua
| non rule you need to internalize: there are no boundaries
| between systems, because everything is jiggling atoms.
|
| (Similar background as you.) Another sine qua non rule is that
| evolution created biology, it wasn't engineered like software
| and it doesn't decompose like software. Evolution creates
| hairballs that has don't respect traditional engineering
| boundaries and abstraction hierarchies.
|
| From that, along with probabilistic molecular jiggling, we get
| biological systems that are quite difficult to understand,
| predict, and control.
| kurthr wrote:
| It's a good start to realize that what underlies all the
| understanding of science are simplified predictive models, and
| usually only statistical models at that.
|
| What this means is that running an experiment in many fields is
| so difficult that replication is a real challenge. There are so
| MANY ways you can screw up, or you could just have a
| statistical fluke that screws you over. Just a tiny
| contamination or seemingly irrelevant missed step will cause a
| failure. That's why the idea of having journals composed of
| failed experiments just doesn't work. Unstated experimental
| process assumptions are legion. Sometimes an expert can look at
| the result and see what you've done wrong (like bad contacts in
| "Electron Band Structure In Germanium, My Ass") and often not
| even that. Sometimes there's something interesting in the
| failure, but 99% of the time it's just your pitch is so bad you
| can't hit the strike zone. Do better!
|
| The things that are easy to replicate (and usually they've been
| specifically designed that way like Starbucks' over roasted
| beans), have actually been reduced to engineering. They're not
| on the edge where scientists can get published. That way
| perverse incentive madness lies.
|
| Enjoy the controlability of inputs, the repeatability of bugs,
| the near perfection of compilers and memory allocation, the
| complete independence of variables while you can. Unless that
| is, you like Rowhammer and voltage glitch attacks.
| siver_john wrote:
| Amazing article on Molecular dynamics, in the infinite number of
| things they could add is a small segment on coarse graining.
| Though I'm biased (and have been thinking about writing one
| myself).
|
| Granted wished this had been around when I started my journey
| instead of having to delve into things like the Amber manual...
| (which I will grant is wonderful for its information but the
| organization isn't as convenient).
| abhishaike wrote:
| Author here, I wish I added a section on coarse graining as
| well :) hope you write a post about it!
| fentonc wrote:
| Fun article! I was one of the architects on Anton 2 and Anton
| 3 at DESRES.
| max_ wrote:
| Hi,
|
| Do you have any resources that you recommend on coarse
| graining?
|
| I am really interested in the topic.
| frgoe wrote:
| I am currently working on CG potentials. Can really
| recommend the basics from Gregory A. Voth.
| seamossfet wrote:
| Great write up, we're working on a drug discovery CAD tool and MD
| has been one of our focal points. Extremely challenging and fun
| problem to work on!
|
| What complicates things is the experimental data we get back from
| labs to validate MD behavior is extremely tricky to work with.
| Most of what we're working with is NMR data which shows
| flexibility in areas of the proteins, but even then we're left
| with these mathematical models to attempt to "make sense" of the
| flexibility and infer dynamics from that. Sometimes it feels like
| an art and a science trying to get meaningful insights for lab
| data like this.
|
| It's extremely difficult to experimentally verify any MD model
| since, as mentioned in the article, most of the data we're
| working with are static mugshots in the form of crystal
| structures.
| the__alchemist wrote:
| That's so cool! What's the software like, compared to say,
| PyMol? Is it like PyMol, integrated with docking? Are you using
| MD to position the drugs instead of trying different combos,
| like Vina does?
| forgotpwagain wrote:
| Very cool. There are also methods that allow you to extract
| some notion of motion from variability in CryoEM data, e.g.
| CryoDRGN-ET [1].
|
| I'm curious if you've worked with any of those models and how
| they relate to NMR data and MD simulations.
|
| [1] https://www.nature.com/articles/s41592-024-02340-4
| abhishaike wrote:
| +1 to this!
|
| I've also written a potentially helpful coverage piece on
| extracting conformations from cryo-EM data:
| https://www.owlposting.com/p/a-primer-on-ml-in-cryo-
| electron...
| colingauvin wrote:
| There are also techniques that combine both. In my experience
| (as an experimental structural biologist working in drug
| design), they frequently disagree.
| edwardbernays wrote:
| hello, I have an undergrad degree in computer science and I'm
| trying to reach myself informatics to get into this field. do
| you have any tips, or perhaps an internship available?
|
| if you can reach out at all, you can find me at [masterfully
| dot blundered] on the normal g-domain. I briefly skimmed your
| profile for contact info but could not find any.
| max_ wrote:
| There is brilliant video by the hedge fund manager DE Shaw about
| molecular dynamics simulation.
|
| Its very accessible and I found it very interesting --
| https://youtu.be/PGqCeSjNuTY?feature=shared
| GubbinEel wrote:
| MD is a great entry point for anyone interested in scientific
| computing. A naive simulation is super easy to implement but you
| quickly learn hard lessons regarding performance scaling. I wrote
| an MD engine as a demo project for learning the basics of CUDA C.
|
| For anyone with further interest in MD, two of the popular
| engines, Amber and Gromacs have excellent documentation for
| learning (1, 2). MDAnalysis is a popular analysis package. Their
| docs give a great rundown of what type of information you can
| glean from MD (3). If you're strictly interested in eye candy,
| there's a a fabulous blender plugin for visualizing MD
| simulations and protein structures (4). I also wrote a little
| Python program for setting up simulation systems you can do some
| fun stuff with it (5).
|
| (1) https://ambermd.org/Manuals.php
|
| (2) https://manual.gromacs.org/current/index.html
|
| (3) https://www.mdanalysis.org/pages/documentation/
|
| (4) https://bradyajohnston.github.io/MolecularNodes/
|
| (5) https://github.com/AppleIntusion/MMAEVe
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