13| Quellen: Q1: https://t.co/f6TsxM4xkZ Q2: https://t.co/b905GDgtFw Q3: https://t.co/u3C9HexokU Q4: https://t.co/NoERDjhwGK
Highly accurate protein structure prediction for the human proteome.
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Jul 2021 – Sep 2025Where it's discussed
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News99 articles
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X / Twitter18 posts
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AI synopsis
What people are saying
Discussions emphasize the groundbreaking nature of using AlphaFold for the human proteome, with comments like 'very much looking forward' and phrases like 'inspiring paper' signaling strong interest. Several posts critique and analyze the methodology, such as those referencing pLDDT scores as predictors of disordered regions, demonstrating active engagement and recognition of the research's broad impact on structural bioinformatics.
Most discussions express strong support, highlighting the significance and promising aspects of the publication.
Participants show high interest, mentioning specific metrics, potential applications, and enthusiasm for the resource.
The responses feature detailed analyses, referencing additional studies, and actively discussing implications, indicating deep engagement.
There is a consensus that this work can significantly advance biological research, drug discovery, and understanding of protein structures.
Abstract in brief
- The study uses the advanced AI tool AlphaFold to predict the 3D structures of almost all human proteins, greatly increasing the amount of available structural data from only 17% coverage to about 58% of residues with high confidence.
- The researchers developed new metrics to evaluate these predictions, identifying well-structured regions, multi-domain proteins, and disordered areas, making the data more useful for biological research and drug development.
- They provide case studies to show how these high-quality models can help generate new biological hypotheses, and they are sharing their data openly to support widespread scientific use.]
Top posts
Most liked on X and Bluesky@_BalintMeszaros @DaveyLab @DeepMind @emblebi Cool. I assume you have also seen the analysis of pLDDT as a disorder predictor in the new DM/EBI paper (Fig. 4b). https://t.co/wQkXXo5ECQ
Finally managed to read the #AlphaFold paper by @DeepMind with @emblebi in @Nature. Structural predictions for a large part of the human proteome. Very much looking forward to exploring how this vast data resource can help to guide our research. (1/3) https://t.co/QOYQlPxvnC
@fabferrage @GntlmnScientist Indeed, low AF confidence scores (pLDDT) is an excellent predictor of intrinsic disorder as shown here for example: https://t.co/wQkXXo5ECQ (Fig. 4b) https://t.co/jlsT72tzDW (Fig. 1d)
@larsjuhljensen DM/EBI themselves showed that pLDDT works well as a disorder predictor (Fig. 4b https://t.co/wQkXXo5ECQ) and this preprint (https://t.co/3iefY5rmfS) uses it to study protein-peptide interactions. A few other threads w similar analysis out there.
@agrossfield @CrollTristan @boehninglab @UCSFChimeraX @ditlevbrodersen The DM/EBI team showed that the AF2 confidence scores work well as disorder predictors (Fig. 4B of https://t.co/wQkXXo5ECQ) and @_BalintMeszaros has followed up with additional analyses (https://t.co/tXUX3Nujo0), suggesting that many low confidence regions are in fact disordered.
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@GntlmnScientist @ProteinMagnus A low AF pLDDT score is a good predictor of intrinsic disorder; see eg: https://t.co/wQkXXo5ECQ & https://t.co/jlsT72tzDW and disordered regions with high pLDDT scores might be “conditionally folded”: https://t.co/nPoxYy4RHE
13| Quellen: Q1: https://t.co/f6TsxM4xkZ Q2: https://t.co/b905GDgtFw Q3: https://t.co/u3C9HexokU Q4: https://t.co/NoERDjhwGK
RT @LindorffLarsen: @fabferrage @GntlmnScientist Indeed, low AF confidence scores (pLDDT) is an excellent predictor of intrinsic disorder a…
@fabferrage @GntlmnScientist Indeed, low AF confidence scores (pLDDT) is an excellent predictor of intrinsic disorder as shown here for example: https://t.co/wQkXXo5ECQ (Fig. 4b) https://t.co/jlsT72tzDW (Fig. 1d)
RT @LindorffLarsen: @agrossfield @CrollTristan @boehninglab @UCSFChimeraX @ditlevbrodersen The DM/EBI team showed that the AF2 confidence s…
@agrossfield @CrollTristan @boehninglab @UCSFChimeraX @ditlevbrodersen The DM/EBI team showed that the AF2 confidence scores work well as disorder predictors (Fig. 4B of https://t.co/wQkXXo5ECQ) and @_BalintMeszaros has followed up with additional analyses (https://t.co/tXUX3Nujo0), suggesting that many low confidence regions are in fact disordered.
@larsjuhljensen DM/EBI themselves showed that pLDDT works well as a disorder predictor (Fig. 4b https://t.co/wQkXXo5ECQ) and this preprint (https://t.co/3iefY5rmfS) uses it to study protein-peptide interactions. A few other threads w similar analysis out there.
Finally managed to read the #AlphaFold paper by @DeepMind with @emblebi in @Nature. Structural predictions for a large part of the human proteome. Very much looking forward to exploring how this vast data resource can help to guide our research. (1/3) https://t.co/QOYQlPxvnC
https://t.co/2Xo33M7CUT “… several metrics … identifying … regions likely to be disordered.” An intrinsically disordered region has no unique structure. They get structured regarding their partners. Thanks @DeepMind for the release of #AlphaFold predicted structures.
メモ: features.pklのaatypeは[残基数, 21]の2Dアレイ、One-hot representation of the input amino acid sequence (20 amino acids + unknown).順番はアミノ酸3文字表記ののアルファベット順のよう https://t.co/8dU8bxdIyl https://t.co/L9gUkoZlC7
@_BalintMeszaros @DaveyLab @DeepMind @emblebi Cool. I assume you have also seen the analysis of pLDDT as a disorder predictor in the new DM/EBI paper (Fig. 4b). https://t.co/wQkXXo5ECQ
Highly accurate protein structure prediction for the human proteome https://t.co/hdMyIuglgP
https://t.co/etdS3Y1vJY
Slightly different but what an inspiring paper! https://t.co/IZxiqwLqME
RT @LindorffLarsen:
RT @LindorffLarsen:
Check out the paper here! https://t.co/nz6l8Z0Zkf
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