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DeepMind Increases The Accuracy Of AlphaFold, With A New Release

DeepMind Increases The Accuracy Of AlphaFold, With A New Release

Alphabet-owned research firm DeepMind has introduced AlphaFold-Multimer, a model that can predict the structure of multi-chain protein complexes. The new model significantly increases the accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy

A majority of well-structured single protein chains could be easily predicted using the previous AlphaFold model, but the prediction of multi-chain protein complexes remained a challenge in many cases, which the AlphaFold-Multimer addresses readily. 

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AlphaFold-Multimer analyzes multiple chains during both training and inference, with native support for multi-chain featurization and symmetry handling. Multiple changes to the previous AlphaFold system were made to adapt it to training on protein complex. The AlphaFold-Multimer introduces a new way of selecting subsets of residues for training and makes various small adjustments to the structure losses and the model architecture.

AlphaFold-Multimer was tested on a benchmark dataset of 17 heterodimer proteins without templates, where it achieved at least medium accuracy on 14 targets and high accuracy on 6 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system. 

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The new model shows that the confidence metrics provided by the model correlate well with the true accuracy, something that is vital for the useability of a structure prediction model.

Deepmind says that this method will enable biologists to further accelerate the recent progress in structural bioinformatics and act as a stepping stone towards executing on more complex folds, such as RNA & DNA molecules.


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