DeepMind’s AlphaFold2 Predicts Protein Structures with Atomic-Level Accuracy
The prediction of protein structures from amino acid sequence
information alone, known as the “protein folding problem,” has been an
important open research question for more than 50 years. In the fall of
2020, DeepMind’s neural network model AlphaFold took a huge leap forward
in solving this problem, outperforming some 100 other teams in the
Critical Assessment of Structure Prediction (CASP) challenge, regarded
as the gold-standard accuracy assessment for protein structure
prediction. The success of the novel approach is considered a milestone
in protein structure prediction.
This week, the DeepMind paper Highly Accurate Protein Structure Prediction with AlphaFold was published in the prestigious scientific journal Nature.
The paper introduces AlphaFold2, a completely redesigned and
open-sourced model that can predict protein structures with atomic-level
accuracy.
Although machine learning researchers have long sought to develop
computational methods for predicting 3-D protein structures from protein
sequences, there had been limited progress along this path, chiefly due
to the computational intractability of molecular simulation, the
context-dependence of protein stability, and the difficulty of producing
sufficiently accurate models for protein physics.
In this work,
the DeepMind team introduces the first computational approach capable of
predicting protein structures to near experimental accuracy. The
proposed AlphaFold2 model achieved “outstanding” results in the recent CASP14 assessment.
AlphaFold2’s achievements are based on neural network architectures
that jointly embed multiple sequence alignments (MSAs) and pairwise
features. The AlphaFold network can directly predict the 3-D coordinates
of all heavy atoms for a given protein using the primary amino acid
sequence and aligned sequences of homologues as inputs. The network
consists of two large modules: Evoformer and a Structure Prediction
Module.
Evoformer views protein structure prediction as a graph
inference problem, representing the data as a graph in which the nodes
represent as amino-acid pairs and the edges as the proximity of those
pairs to one another in the protein. By applying deep learning
techniques, Evoformer gradually refines a forecast for what the backbone
of the protein should look like, then passes the prediction results to
the Structure Prediction Module.
The Structure Prediction Module
performs a series of geometric transformations to further refine the
protein’s shape for greater accuracy. This module’s abstract 3D protein
images appear as twisted, ribbonlike curlicues that branch off from the
main protein backbone.
As described at the CASP14 conference, AlphaFold2’s methodological
advances include: 1) Starting from multiple sequence alignments (MSAs)
rather than from more processed features such as inverse covariance
matrices derived from MSAs, 2) Replacement of 2D convolution with an
attention mechanism that better represents interactions between residues
distant along the sequence, 3) Use of a two-track network architecture
in which information at the 1D sequence level and the 2D distance map
level is iteratively transformed and passed back and forth, 4) Use of an
SE(3)-equivariant transformer network to directly refine atomic
coordinates (rather than 2D distance maps as in previous approaches)
generated from the two-track network, and 5) End-to-end learning in
which all network parameters are optimized by backpropagation from the
final generated 3D coordinates through all network layers back to the
input sequence.
AlphaFold has now clearly demonstrated its
effectiveness in this important and rapidly evolving research field, and
DeepMind believes the model and associated computational approaches
that apply its techniques for other biophysical problems could soon
become essential tools in cutting-edge biology research.
The AlphaFold2 code is available on the project Github. The paper Highly Accurate Protein Structure Prediction with AlphaFold is on Nature.
Author: Hecate He | Editor: Michael Sarazen, Chain Zhang
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