16,605 research outputs found
Improved protein structure prediction using potentials from deep learning
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.Comment: 20 pages, 8 figures, 5 table
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
The inapplicability of amino acid covariation methods to small protein
families has limited their use for structural annotation of whole genomes.
Recently, deep learning has shown promise in allowing accurate residue-residue
contact prediction even for shallow sequence alignments. Here we introduce
DMPfold, which uses deep learning to predict inter-atomic distance bounds, the
main chain hydrogen bond network, and torsion angles, which it uses to build
models in an iterative fashion. DMPfold produces more accurate models than two
popular methods for a test set of CASP12 domains, and works just as well for
transmembrane proteins. Applied to all Pfam domains without known structures,
confident models for 25% of these so-called dark families were produced in
under a week on a small 200 core cluster. DMPfold provides models for 16% of
human proteome UniProt entries without structures, generates accurate models
with fewer than 100 sequences in some cases, and is freely available.Comment: JGG and SMK contributed equally to the wor
Distance-based Protein Folding Powered by Deep Learning
Contact-assisted protein folding has made very good progress, but two
challenges remain. One is accurate contact prediction for proteins lack of many
sequence homologs and the other is that time-consuming folding simulation is
often needed to predict good 3D models from predicted contacts. We show that
protein distance matrix can be predicted well by deep learning and then
directly used to construct 3D models without folding simulation at all. Using
distance geometry to construct 3D models from our predicted distance matrices,
we successfully folded 21 of the 37 CASP12 hard targets with a median family
size of 58 effective sequence homologs within 4 hours on a Linux computer of 20
CPUs. In contrast, contacts predicted by direct coupling analysis (DCA) cannot
fold any of them in the absence of folding simulation and the best CASP12 group
folded 11 of them by integrating predicted contacts into complex,
fragment-based folding simulation. The rigorous experimental validation on 15
CASP13 targets show that among the 3 hardest targets of new fold our
distance-based folding servers successfully folded 2 large ones with <150
sequence homologs while the other servers failed on all three, and that our ab
initio folding server also predicted the best, high-quality 3D model for a
large homology modeling target. Further experimental validation in CAMEO shows
that our ab initio folding server predicted correct fold for a membrane protein
of new fold with 200 residues and 229 sequence homologs while all the other
servers failed. These results imply that deep learning offers an efficient and
accurate solution for ab initio folding on a personal computer
Machine learning-guided directed evolution for protein engineering
Machine learning (ML)-guided directed evolution is a new paradigm for
biological design that enables optimization of complex functions. ML methods
use data to predict how sequence maps to function without requiring a detailed
model of the underlying physics or biological pathways. To demonstrate
ML-guided directed evolution, we introduce the steps required to build ML
sequence-function models and use them to guide engineering, making
recommendations at each stage. This review covers basic concepts relevant to
using ML for protein engineering as well as the current literature and
applications of this new engineering paradigm. ML methods accelerate directed
evolution by learning from information contained in all measured variants and
using that information to select sequences that are likely to be improved. We
then provide two case studies that demonstrate the ML-guided directed evolution
process. We also look to future opportunities where ML will enable discovery of
new protein functions and uncover the relationship between protein sequence and
function.Comment: Made significant revisions to focus on aspects most relevant to
applying machine learning to speed up directed evolutio
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