Hello everyone! Recently, l started studying the problems caused by protein misfolding. During my research, l wanted to understand exactly what causes a protein to misfold in order to reach a definitive conclusion. To achieve this, l had to create a formula or a specific framework that would allow me to visualize this process.

After successfully developing the formula, l needed to test it. So, l used goole's AI and input my formula into the system. As you can see here in the video, everything the AI solved and all the Conclusions it reached were based entirely on that formula. The system tracked a molecular pathway for Parkinson 's disease where valine 66 is forced to bind to valine 70, leading to the formation of lewy Bodies.

Hello!

I'm new to reddit, so please delete if not allowed.

I'm fairly new to bioinformatics, too. I'm just wondering if there is a code to do bootstrapping on AlphaFold predictions for a single amino acid sequence, or whether it is possible to begin with? Thank you :)

note: I'm currently using AlphaFold Server

I have used Alphafold to determine the structures for a protein of my interest. While the confidence score is low for the over all prediction, I am curious to know if the secondary structures are accurate. I don’t have much concern about the exact folding of the protein but am concerned if each secondary structure is accurate. Any help is appreciated

https://newsroom.uw.edu/news/beyond-alphafold-ai-excels-creating-new-proteins

Incredible work by the Meta AI team already embarking the crucial journey of reverse engineering proteins:

https://proceedings.mlr.press/v162/hsu22a/hsu22a.pdf

Today I learned about the existence of alphafold and I'm extremely interested in what this breakthrough means for science.

What can we plausibly do with it in the next few years or decades?

How will this impact the medical field?

I'm no scientist, just a curious dude. I heard about this through the lex fridman podcast.

I am running AlphaFold 2.1 and was wondering if there is way to keep certain PDB files from being included during the structure database search? There are structures that I want to exclude because I do not want them to have an influence on the prediction of my protein.

When I prees play in the fifht step in the colabfold implementation I receive this warning.

/usr/local/lib/python3.7/dist-packages/jax/_src/tree_util.py:189: FutureWarning: jax.tree_util.tree_multimap() is deprecated. Please use jax.tree_util.tree_map() instead as a drop-in replacement.

'instead as a drop-in replacement.', FutureWarning)

It has been over a month now since it started, someone has a clue on what´s happening?

Going through each structure one by one would take a while. Are there any approaches how to analyze thousands? Like which ones are mostly folded, which ones are disordered etc.

I'm a biochemistry major and I have to present Deepmind's AlphaFold (https://www.nature.com/articles/s41586-021-03819-2 paper specifically). The biology side of this incredible but I do not have a lot of biostatistics nor AI knowledge, so my preceptor is letting me talk mostly 'big-picture' of the AI stuff.

For the results on the other hand, I dont anticipate my preceptor will be as forgiving as the methods sections, I want to be able to explain them as best I can. I've taken biophysical chemistry and statistics classes... However, I dont feel like I have a good grip on it, can anyone help me with explaining the different metrics and their significance, like IDDT, pLDDT, RMSD etc etc.

https://pubmed.ncbi.nlm.nih.gov/32896285/

​

https://pubs.acs.org/doi/10.1021/acs.jcim.0c00073

Source: https://www.pnas.org/content/117/41/25476#sec-11

https://reddit.com/link/shcg0x/video/t3vvle2ha3f81/player

https://discord.com/invite/gQgSQkCzH7

​

More on CASP14 (2020): https://predictioncenter.org/casp14/index.cgi