#af2 — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #af2, aggregated by home.social.
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After this exhaustive first pass inspecting each residue, I try to clear the biggest problems flagged by #ISOLDE's validation tools (there are always some). Clashes are rare because atoms that are too close strongly repel each other under the MD force field. Ramachandran and rotamer outliers are rare in #AF2 models, but can arise when the simulation distorts the model. These problems often point to regions where the map is ambiguous, and where the model needs more attention.
12/19 -
During this stage I often get help from two additional sources of information:
1. The model from #model_angelo sometimes helps moving the #AF2 model to the correct location, in these cases where a segment of the AF2 model doesn't match the map. Very often these discrepancies between map and prediction point to regions of the protein involved in conformational change, so I make a note of the chain ID and residue range so I know to take a close look later and compare to related structures.
10/19 -
During this stage I often get help from two additional sources of information:
1. The model from #model_angelo sometimes helps moving the #AF2 model to the correct location, in these cases where a segment of the AF2 model doesn't match the map. Very often these discrepancies between map and prediction point to regions of the protein involved in conformational change, so I make a note of the chain ID and residue range so I know to take a close look later and compare to related structures.
10/19 -
At this point, I use #ISOLDE to do manual fitting. If the map has a low resolution, I apply secondary structure restraints to the model. I start with a global simulation to let the model settle, which I let run until the dots in the interactive Ramachandran plot have reached an equilibrium. With #AF2 models, there shouldn't be Ramachandran outliers in the first place, but this is a good overview of how many residues are moving. I want them to settle in the nearest density before I continue.
8/19 -
Using #ChimeraX, I dock these #AF2 models into the map. I have seen a case with two different protein subunits of very similar sequences, which makes assigning each AF2 model to the density pretty difficult. It is however very easy to align (with the `matchmaker` command in ChimeraX) the AF2 models to the model produced by #model_angelo. This will unambiguously place similar AF2 models to where model_angelo detected their sequences.
5/19 -
Using #ChimeraX, I dock these #AF2 models into the map. I have seen a case with two different protein subunits of very similar sequences, which makes assigning each AF2 model to the density pretty difficult. It is however very easy to align (with the `matchmaker` command in ChimeraX) the AF2 models to the model produced by #model_angelo. This will unambiguously place similar AF2 models to where model_angelo detected their sequences.
5/19 -
Using #ChimeraX, I dock these #AF2 models into the map. I have seen a case with two different protein subunits of very similar sequences, which makes assigning each AF2 model to the density pretty difficult. It is however very easy to align (with the `matchmaker` command in ChimeraX) the AF2 models to the model produced by #model_angelo. This will unambiguously place similar AF2 models to where model_angelo detected their sequences.
5/19 -
Using #ChimeraX, I dock these #AF2 models into the map. I have seen a case with two different protein subunits of very similar sequences, which makes assigning each AF2 model to the density pretty difficult. It is however very easy to align (with the `matchmaker` command in ChimeraX) the AF2 models to the model produced by #model_angelo. This will unambiguously place similar AF2 models to where model_angelo detected their sequences.
5/19 -
Using #ChimeraX, I dock these #AF2 models into the map. I have seen a case with two different protein subunits of very similar sequences, which makes assigning each AF2 model to the density pretty difficult. It is however very easy to align (with the `matchmaker` command in ChimeraX) the AF2 models to the model produced by #model_angelo. This will unambiguously place similar AF2 models to where model_angelo detected their sequences.
5/19 -
Having identified the proteins, I fetch their #AlphaFold2 predictions from AlphaFold-DB, or compute them if not in the DB. #AF2 models have excellent geometry and complete sequence correctly numbered, so they are excellent starting models. I rarely use #PDB entries as starting models anymore. Rare exceptions: a PDB entry I deposited myself, or one containing a post-translational modification or non-natural amino acid I need (never present in #AF2 models, only the 20 standards amino acids).
4/19 -
#SaprotHub: Making #protein #modeling accessible to all biologists:
-create & train own #DeepLearning models without the need for advanced #MachineLearning & coding expertise
https://doi.org/10.1101/2024.05.24.595648
#DIYbio #bioinformatics #CompChem #AI #ML #KI #LLM #AF2 #ESMfold #ColabFold #sciece #research -
#SaprotHub: Making #protein #modeling accessible to all biologists:
-create & train own #DeepLearning models without the need for advanced #MachineLearning & coding expertise
https://doi.org/10.1101/2024.05.24.595648
#DIYbio #bioinformatics #CompChem #AI #ML #KI #LLM #AF2 #ESMfold #ColabFold #sciece #research -
#SaprotHub: Making #protein #modeling accessible to all biologists:
-create & train own #DeepLearning models without the need for advanced #MachineLearning & coding expertise
https://doi.org/10.1101/2024.05.24.595648
#DIYbio #bioinformatics #CompChem #AI #ML #KI #LLM #AF2 #ESMfold #ColabFold #sciece #research -
#SaprotHub: Making #protein #modeling accessible to all biologists:
-create & train own #DeepLearning models without the need for advanced #MachineLearning & coding expertise
https://doi.org/10.1101/2024.05.24.595648
#DIYbio #bioinformatics #CompChem #AI #ML #KI #LLM #AF2 #ESMfold #ColabFold #sciece #research -
#SaprotHub: Making #protein #modeling accessible to all biologists:
-create & train own #DeepLearning models without the need for advanced #MachineLearning & coding expertise
https://doi.org/10.1101/2024.05.24.595648
#DIYbio #bioinformatics #CompChem #AI #ML #KI #LLM #AF2 #ESMfold #ColabFold #sciece #research -
What a week... #OpenFold is out and supposedly faster and more memory efficient then #AF2. This reaches the #AF3 token limit on a local GPU 👍 (only with proteins though...) #StructuralBiology @strucbio https://www.nature.com/articles/s41592-024-02272-z?utm_source=twitter&utm_medium=social&utm_campaign=nmeth
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@xtaldave @strucbio I always had the feeling #AF2 was published because of #RosettaFold.... But surely nobody knows for sure 😉
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#USAF - C32A flight #AF2 (#98-0002) landed at Los Angeles / Tom Bradley International Airport at 8/16/2023, 1:42:30 AM
https://radarplane.com/?hex=ADFEB8 -
The beauty of #alphafold #AF2 is that the #algorithm knows when it's wrong but I am not convinced that everyone is well aware of this.
Always remember to look at the confidence scores (PAE (multiple domains or chains relative to each other.), pLDDT (local confidence per residue) and PTM (interface scores) before interpreting the results.
