Protein Design is NP-hard
Biologists working in the area of computational protein design have never doubted the
seriousness of the algorithmic challenges that face them in attempting in silico sequence
selection. It turns out that in the language of the computer science community, this discrete
optimization problem is NP-hard. The purpose of this paper is to explain the context of this
observation, to provide a simple illustrative proof and to discuss the implications for future
progress on algorithms for computational protein design.
seriousness of the algorithmic challenges that face them in attempting in silico sequence
selection. It turns out that in the language of the computer science community, this discrete
optimization problem is NP-hard. The purpose of this paper is to explain the context of this
observation, to provide a simple illustrative proof and to discuss the implications for future
progress on algorithms for computational protein design.
Abstract
Biologists working in the area of computational protein design have never doubted the seriousness of the algorithmic challenges that face them in attempting in silico sequence selection. It turns out that in the language of the computer science community, this discrete optimization problem is NP-hard. The purpose of this paper is to explain the context of this observation, to provide a simple illustrative proof and to discuss the implications for future progress on algorithms for computational protein design.
Oxford University PressShowing the best result for this search. See all results