Toward the solution of the protein‐structure prediction problem

Abstract

Protein structure prediction aims to determine the spatial location of every atom in protein molecules from the amino acid sequence by computational modeling. Depending on whether homologous structures are found in the Protein Data Bank (PDB), protein structure prediction have been historically categorized into template‐based modeling (TBM) and template‐free modeling (FM, or ab initio folding). In this talk, we first review recent progress in computer‐based protein structure prediction and show that the problem can be solved in principle (but not yet realistically) by TBM in case that fold‐recognition algorithms could identify the best structural templates from the PDB. Next, we discuss protein structure prediction results in the most recent community‐wide blind CASP experiments, showing that new approaches combining ab initio folding and deep neural‐network contact and distance predictions, which are built on residue coevolution data from multiple sequence alignments, can result in consistent and successful folding of proteins with sequence longer than 200–300 amino acids with a root‐mean‐square deviation below 3–4 Angstroms. This progress essentially breaks through the 50‐years‐old modeling border between TBM and FM and makes the success of high‐resolution structure prediction no longer relying on the PDB library. It also demonstrates the promise to solve the problem of protein structure prediction in a foreseeable future, by integrating deep machine learning techniques and the rapid advancement of genome sequencing databases, with the aid of advanced structure assembly simulation algorithms.Support or Funding InformationThis work is supported in part by the National Institute of General Medical Sciences (GM083107, GM116960), the National Institute of Allergy and Infectious Diseases (AI134678), and the National Science Foundation (DBI1564756, IIS1901191).