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

Elodie Laine (1) , Stephan Eismann (2) , Arne Elofsson (3) , Sergei Grudinin (4, 4)

1 LCQB - Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology
2 Stanford University
3 Stockholm University
4 DAO - Données, Apprentissage et Optimisation

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, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained 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; and (vi) finally truly end-to-end architectures, that is, 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 2 years and widely used in CASP14.

Domaines: Bio-informatique [q-bio.QM]; Apprentissage [cs.LG]

Elodie Laine, Stephan Eismann, Arne Elofsson, Sergei Grudinin. Protein sequence-to-structure learning: Is this the end(-to-end revolution)?. Proteins - Structure, Function and Bioinformatics, 2021, 89 (12), pp.1770-1786. ⟨10.1002/prot.26235⟩. ⟨hal-03342140⟩