LUTE (Local Unpruned Tuple Expansion): Accurate Continuously Flexible Protein Design with General Energy Functions and Rigid Rotamer-Like Efficiency.
Most protein design algorithms search over discrete conformations and an energy function that is residue-pairwise, that is, a sum of terms that depend on the sequence and conformation of at most two residues. Although modeling of continuous flexibility and of non-residue-pairwise energies significantly increases the accuracy of protein design, previous methods to model these phenomena add a significant asymptotic cost to design calculations. We now remove this cost by modeling continuous flexibility and non-residue-pairwise energies in a form suitable for direct input to highly efficient, discrete combinatorial optimization algorithms such as DEE/A* or branch-width minimization. Our novel algorithm performs a local unpruned tuple expansion (LUTE), which can efficiently represent both continuous flexibility and general, possibly nonpairwise energy functions to an arbitrary level of accuracy using a discrete energy matrix. We show using 47 design calculation test cases that LUTE provides a dramatic speedup in both single-state and multistate continuously flexible designs.
1 Department of Computer Science, Levine Science Research Center, Duke University , Durham, North Carolina.
Mark A
MA
Hallen
1 Department of Computer Science, Levine Science Research Center, Duke University , Durham, North Carolina.
Jonathan D
JD
Jou
1 Department of Computer Science, Levine Science Research Center, Duke University , Durham, North Carolina.|2 Department of Chemistry, Duke University , Durham, North Carolina.|3 Department of Biochemistry, Duke University Medical Center , Durham, North Carolina.
Bruce R
BR
Donald
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D015195:Drug Design; D008958:Models, Molecular; D011487:Protein Conformation; D015202:Protein Engineering / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D012984:Software; D013816:Thermodynamics
D000596:Amino Acids; D011506:Proteins
algorithms; combinatorial optimization; drug design; machine learning; protein structure
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