ASTRO-FOLD 2.0: An enhanced framework for protein structure prediction

A. Subramani, Y. Wei, C. A. Floudas

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles-based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented.

Original languageEnglish
Pages (from-to)1619-1637
Number of pages19
JournalAICHE Journal
Issue number5
StatePublished - May 2012


  • First-principles
  • Global optimization
  • Protein structure prediction

ASJC Scopus subject areas

  • Biotechnology
  • Environmental Engineering
  • Chemical Engineering (all)


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