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mrigankpawagi
requested changes
Oct 1, 2023
Update README.md
mrigankpawagi
requested changes
Oct 2, 2023
README.md
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| @@ -54,9 +54,9 @@ O-IL8-15 Biological Probe, in Structural Genomics Consortium: [thesgc.org/biolog | |||
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| ## AlphaFold2 | |||
| AlphaFold2 is a deep learning system that predicts protein structures from amino acid sequences. We used the open-source distribution of AlphaFold2, [ColabFold](https://github.com/sokrypton/ColabFold) to predict the structure of the antibody. In particular, we used the [AlphaFold2_mmseqs2](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb) notebook. This notebook differs from full AlphaFold2 and AlphaFold2 Colab in that it uses MMseqs2 (Many-against-Many sequence searching) in place of homology detection and MSA pairing. | |||
| AlphaFold2 is a deep learning system that predicts protein structures from amino acid sequences. We used the open-source distribution of AlphaFold2, [ColabFold](https://github.com/sokrypton/ColabFold) to predict the structure of the antibody. In particular, we used the [AlphaFold2_mmseqs2](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb) notebook. This notebook differs from full AlphaFold2 and AlphaFold2 Colab in that it uses MMseqs2 (Many-against-Many sequence searching) in place of homology detection and MSA pairing. MMseqs2 can predict complex structures with great accuracy, as compared to AlphaFold2. | |||
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this addition seems a little inaccurate. Can you mention a source in the comments?
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Yes, I reviewed the references, and this sentence has a flaw. It should be about speed and complexity, rather than accuracy, and it should be MSA pairing rather than AlphaFold2.
https://www.nature.com/articles/s41592-022-01488-1
https://computationalbiomed.hms.harvard.edu/tools-technologies-old/alphafold-colabfold/
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Okay, cool! Why not also cite the source ?
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the second one is not a citation, so shall I add that or not?
| @@ -171,7 +171,7 @@ This immediately places the I-TASSER model in a much more reliable position than | |||
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| ### Root Mean Square Deviation (RMSD) | |||
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| We now see the RMSD of the AlphaFold2 and I-TASSER models with respect to each other. This is done after superimposing the models on the same reference structure, through the Kabsch algorithm. | |||
| We now see the RMSD of the AlphaFold2 and I-TASSER models with respect to each other. RMSD determines the similarity between the two models. It provides the average deviation of corresponding atoms in the two models. This is done after superimposing the models on the same reference structure, through the Kabsch algorithm. Kabsch algorithm is a method of calculating the optimal translation and rotation which produces least value of RMSD. Lesser the value of RMSD, more similar are the two models. | |||
mrigankpawagi
requested changes
Oct 4, 2023
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Cool, looks good. I will hold this here for a while till our Wiki content is finalized (I will redirect this to that). Will modify the READMEs here after that. Thanks! |
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