Protein sequence-to-structure learning: Is this the end(-to-end revolution)?

05/16/2021
by   Elodie Laine, et al.
22

The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, i.e. learning on representations such as graphs, 3D Voronoi tessellations, and point clouds; (ii) pre-trained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; (vi) and finally truly end-to-end architectures, i.e. differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last two years and widely used in CASP14.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 5

page 9

04/09/2021

Protein sequence design with deep generative models

Protein engineering seeks to identify protein sequences with optimized p...
03/11/2022

Protein Representation Learning by Geometric Structure Pretraining

Learning effective protein representations is critical in a variety of t...
06/10/2021

Adaptive machine learning for protein engineering

Machine-learning models that learn from data to predict how protein sequ...
01/23/2022

OntoProtein: Protein Pretraining With Gene Ontology Embedding

Self-supervised protein language models have proved their effectiveness ...
11/27/2020

Protein model quality assessment using rotation-equivariant, hierarchical neural networks

Proteins are miniature machines whose function depends on their three-di...
02/08/2022

ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning

Enzyme Commission (EC) numbers, which associate a protein sequence with ...
08/10/2021

A Brief Review of Machine Learning Techniques for Protein Phosphorylation Sites Prediction

Reversible Post-Translational Modifications (PTMs) have vital roles in e...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.