Computational Advances in Structural Biology

Abstract:

The potential of deep learning has been recognized in structural bioinformatics for already some time and became indisputable after the CASP13 (Critical Assessment of Structure Prediction) community-wide experiment in 2018. In CASP14, held in 2020, deep learning has boosted the field to unexpected levels reaching near-experimental accuracy. Its results demonstrate dramatic improvement in computing the three-dimensional structure of proteins from the amino acid sequence, with many models rivaling experimental structures. This success comes from advances transferred from several machine-learning areas, including computer vision and natural language processing. Novel emerging approaches include, among others, geometric learning, i.e., learning on non-regular representations such as graphs, 3D Voronoi tessellations, and point clouds; equivariant architectures preserving the symmetry of 3D space; and truly end-to-end architectures, i.e., single differentiable models starting from a sequence and returning a 3D structure.