'The entire protein universe': AI predicts shape of nearly every known protein.

  title={'The entire protein universe': AI predicts shape of nearly every known protein.},
  author={Ewen Callaway},

What is hidden in the darkness? Characterization of AlphaFold structural space

A “shape-mer” approach, a structural fragmentation method analogous to sequence k-mers, is used to describe these structures and look for novelties - both in terms of proteins with rare or novel structural composition and possible functional annotation of under-studied proteins.

Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative “Superbugs”: Six Protein Clusters Amenable for Distinct Scientific Applications

  • Carlos SiciliaAndrés Corral-LugoPawel SmialowskiMichael J. McConnellAntonio J. Martín-Galiano
  • Biology
    ACS Omega
  • 2022

Art in an age of artificial intelligence

Artificial intelligence (AI) will affect almost every aspect of our lives and replace many of our jobs. On one view, machines are well suited to take over automated tasks and humans would remain

AlphaFold predictions: great hypotheses but no match for experiment

Assessment of the accuracy of AlphaFold predictions to density maps obtained from automated redeterminations of recent crystal structures and to the corresponding deposited models finds that some AlphaFolds match experimental maps closely, but most differ on a global scale through distortion and domain orientation and on a local scale in backbone and side-chain conformation.

OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

OpenFold, a fast, memory-efficient, and trainable implementation of AlphaFold2, and OpenProtein-Set, the largest public database of protein multiple sequence alignments, are reported, which demonstrate the power and utility of OpenFold.

End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification.

A series of models that provide end-to-end predictions of nanodynamical properties of proteins, focused on high-throughput normal mode predictions directly from the amino acid sequence are reported, offering atomistically based mechanistic predictions of key protein mechanical features.

Accelerating crystal structure determination with iterative AlphaFold prediction

Experimental structure determination can be accelerated with AI-based structure prediction methods such as AlphaFold. Here we present an automatic procedure requiring only sequence information and

Cross-linking mass spectrometry discovers, evaluates, and validates the experimental and predicted structural proteome

The power of combining cross-linking mass spectrometry (XL-MS) with artificial intelligence-based structure prediction to discover and experimentally substantiate models for protein and protein complex structures at proteome scale is demonstrated.

An unbroken network of interactions connecting flagellin domains is required for motility in viscous environments

This work shows that in the Pseudomonas aeruginosa POA1 strain, a bacterium that forms a ridged filament on account of the arrangement of the two outer domains of its flagellin protein, motility is categorically dependent on these flageLLin outer domains.