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The proof that Polymer Structure Prediction is NP-hard suggests that exploiting the functional form of the potential energy is not enough to make the problem efficiently solvable

Computational Complexity, Protein Structure Prediction, and the Levinthal Paradox

(1994)

被引用666|浏览120
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摘要

The task of determining the globally optimal (minimum-energy) conformation of a proteingiven its potential-energy function is widely believed to require an amount of computer time thatis exponential in the number of soft degrees of freedom in the protein. Conventional reasoning asto the exponential time complexity of this problem is fall...更多

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简介
  • Introduction to NP

    Completeness Theory

    Before the advent of computational complexity theory, computer scientists were in the quandary of not being able to describe or characterize accurately the difficulty of several important computational problems.
  • One well-known example was the Traveling Salesman Problem (TSP), described in Figure 14-2.
  • The central question was this: Were these problems in some way inherently difficult, or was it merely that nobody had been clever enough to discover efficient algorithms for them?.
  • The maximum distance M that the salesman is allowed to travel.
  • Question: Can the salesman visit each city once and return to the original city from which he departed without traveling further than the maximum distance, M?
重点内容
  • Introduction to NP

    Completeness Theory

    Before the advent of computational complexity theory, computer scientists were in the quandary of not being able to describe or characterize accurately the difficulty of several important computational problems
  • The results that we review here are related directly to questions about structure prediction (4) and indirectly to the consideration of folding rates (3), but they have little to do with the existence of unique native structures (1) and the pathway(s) by which a protein folds (2). (This is not to say that the four questions are unrelated to each other
  • The proof that Polymer Structure Prediction is NP-hard suggests that exploiting the functional form of the potential energy is not enough to make the problem efficiently solvable
  • The arguments that we have proposed, those based on the NP-hardness of Polymer Structure Prediction, are about the computational complexity of exploiting that information to find the configuration of globally minimal energy-in other words, the difficulty of deciding which nativelike propensities to satisfy, given that they cannot all be satisfied at once
  • We examine one model from which Zwanzig et al concluded that "Levinthal's paradox becomes irrelevant to protein folding when some ofthe interactions between amino acids are taken into account" (Zwanzig et al, 1992)
  • 27 Most known approximation algorithms guarantee solutions with error bounds multiplicatively related to the optimal cost, e.g., "at most 50% worse than optimal." Because absolute energies have little meaning, a useful guarantee for molecularstructure prediction would be additive, e.g., "at most 1 kcallmol worse than optimal." There is no obvious reason that additive guarantees should be more difficult to obtain than multiplicative ones, since an optimization problem does not change if its cost function f is replaced by log f
结果
  • 27 Most known approximation algorithms guarantee solutions with error bounds multiplicatively related to the optimal cost, e.g., "at most 50% worse than optimal." Because absolute energies have little meaning, a useful guarantee for molecularstructure prediction would be additive, e.g., "at most 1 kcallmol worse than optimal." There is no obvious reason that additive guarantees should be more difficult to obtain than multiplicative ones, since an optimization problem does not change if its cost function f is replaced by log f.
结论
  • The result that PSP is NP-hard goes a step further than previous arguments as to the intractability of protein-structure prediction.
  • Useful algorithms for NP-hard problems do exist
  • These include exponential-time searches (Section 4.3) and other heuristic algorithms (Section 4.4) that are "fast enough" and "correct enough" to be practical; approximation algorithms (Section 4.5); and algorithms for restricted forms of the problem (Section 4.6).
  • The latter two types of algorithm might be eliminated from consideration by future theoretical results (Section 6)
基金
  • This research was supported in part by grants from the National Science Foundation and the National Institutes of Health
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