A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures

Proc Natl Acad Sci U S A. 2020 Jan 14;117(2):1059-1068. doi: 10.1073/pnas.1908723117. Epub 2019 Dec 31.


Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.

Keywords: data-driven protein design; protein design; protein structure; structure search; structure-based analysis.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Amino Acid Motifs*
  • Amino Acid Substitution
  • Computational Biology / methods*
  • Computer-Aided Design
  • Databases, Protein
  • Models, Molecular
  • Protein Engineering / methods*
  • Protein Structure, Tertiary*
  • Proteins / chemistry*


  • Proteins