Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes.

Journal: Annual review of biophysics
Published Date:

Abstract

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

Authors

  • Shoshana J Wodak
    VIB-VUB Center for Structural Biology, Vrije Universiteit Brussel, Brussels, Belgium; email: Shoshana.wodak@gmail.com.
  • Sandor Vajda
    Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA; email: vajda@bu.edu.
  • Marc F Lensink
    Univ. Lille, CNRS, UMR 8576-UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France; email: marc.lensink@univ-lille.fr.
  • Dima Kozakov
    Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA; email: midas@laufercenter.org.
  • Paul A Bates
    Biomolecular Modelling Laboratory, The Francis Crick Institute, London, NW1 1AT, UK.