The fuzzy-end elimination theorem: correctly implementing the side chain placement algorithm based on the dead-end elimination theorem

Lasters, Ignace; Desmet, Jan

doi:10.1093/protein/6.7.717

Cited by 72 publications

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“…It may be possible to simplify the combinatorial nature of the side chain problem and reduce it to pairwise (36,37,38) or to self-consistency (19) methods. However, such approaches cannot produce an accurate or close approximation to the ensemble of structures, the ''best population'' that may be crucial for the physical and biological characteristics of a protein.…”

Section: Discussionmentioning

confidence: 99%

“…It is based on the identification of rotamers that are absolutely incompatible with the global minimum energy conformation, eliminating rotamers that cannot contribute to local energy minima of a certain or higher order. Conformations composing such rotamers can be qualified as dead ending (30,36,37). If enough rotamers can be eliminated by recursive applications, the global minimum can be found (38,39).…”

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A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

Glick

Rayan

Goldblum

2002

Proc. Natl. Acad. Sci. U.S.A.

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The problem of global optimization is pivotal in a variety of scientific fields. Here, we present a robust stochastic search method that is able to find the global minimum for a given cost function, as well as, in most cases, any number of best solutions for very large combinatorial ''explosive'' systems. The algorithm iteratively eliminates variable values that contribute consistently to the highest end of a cost function's spectrum of values for the full system. Values that have not been eliminated are retained for a full, exhaustive search, allowing the creation of an ordered population of best solutions, which includes the global minimum. We demonstrate the ability of the algorithm to explore the conformational space of side chains in eight proteins, with 54 to 263 residues, to reproduce a population of their low energy conformations. The 1,000 lowest energy solutions are identical in the stochastic (with two different seed numbers) and full, exhaustive searches for six of eight proteins. The others retain the lowest 141 and 213 (of 1,000) conformations, depending on the seed number, and the maximal difference between stochastic and exhaustive is only about 0.15 Kcal͞mol. The energy gap between the lowest and highest of the 1,000 low-energy conformers in eight proteins is between 0.55 and 3.64 Kcal͞mol. This algorithm offers real opportunities for solving problems of high complexity in structural biology and in other fields of science and technology.M any problems in life sciences and in other fields of science and technology are of high complexity, thus requiring sophisticated methods of searching and scoring to achieve the ability to study and to simulate them by means of a computer simulation. An excellent search method coupled with a highly reliable scoring method should allow comparisons to some natural phenomena. In this article, we have taken the approach of comparing best populations found by a stochastic search method to a full, exhaustive search, as the crucial test of this method. However, comparisons to experimental results also are included. The problem chosen to exemplify this method is the positions of side chains in proteins, which is essential for both theoretical and experimental purposes. On the theoretical side, it is a subproblem in de novo protein structure prediction. It is essential for structure-based drug design (1), for inverse folding and threading algorithms (2), for predicting the effect of mutations on structure (3), for ab initio predictions of tertiary structure (4), for homology-based modeling (5), and others. From the x-ray crystallographer's point of view, it could speed the placement of side chains using the electron density maps of the main chain before refinement calculations. The main limitation is the large amount of possible conformations that each side chain may adopt (6). An exhaustive search of all possible conformations is beyond the scope of state of the art computers.Current strategies for side chain addition to a given backbone differ in three categories. The ...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

Glick

Rayan

Goldblum

2002

Proc. Natl. Acad. Sci. U.S.A.

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“…A number of studies have been reported, which exploit rotamer libraries for homology modeling, 46,47 protein design, 48,49 and ligand docking. 25,26,28 The space of receptor conformations arising from all possible rotameric combinations is often too large to sample exhaustively, and, thus, optimization and clustering techniques have been invoked, including Monte Carlo approaches, 48,50 simulated annealing, 51 dead-end-elimination 52 and its refinements, 53,54 and branch-and-bound schemes. 49 In protein structure prediction or design, the main aim is often to identify the single lowest energy conformation.…”

Section: Introductionmentioning

confidence: 99%

Receptor Flexibility in de Novo Ligand Design and Docking

Alberts¹,

Todorov²,

Dean³

2005

J. Med. Chem.

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One of the major problems in computational drug design is incorporation of the intrinsic flexibility of protein binding sites. This is particularly crucial in ligand binding events, when induced fit can lead to protein structure rearrangements. As a consequence of the huge conformational space available to protein structures, receptor flexibility is rarely considered in ligand design procedures. In this work, we present an algorithm for integrating protein binding-site flexibility into de novo ligand design and docking processes. The approach allows dynamic rearrangement of amino acid side chains during the docking and design simulations. The impact of protein conformational flexibility is investigated in the docking of highly active inhibitors in the binding sites of acetylcholinesterase and human collagenase (matrix metalloproteinase-1) and in the design of ligands in the S1′ pocket of MMP-1. The results of corresponding simulations for both rigid and flexible binding sites are compared in order to gauge the influence of receptor flexibility in drug discovery protocols.

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“…Fuzzy-end elimination 4 and improved forms of the elimination criteria 5 extend the utility of the theorem to more difficult problems. De Maeyer et al 6 have demonstrated that the calculation speed can be increased by simultaneously implementing an energy threshold and a more detailed rotamer library.…”

Section: Introductionmentioning

confidence: 99%

“…Lasters and Desmet 4 have described how the calculation can be continued by finding pairs of rotamers, called doubles, that cannot coexist in the GMEC. The variation is Ž .…”

Section: Introductionmentioning

confidence: 99%

Radical performance enhancements for combinatorial optimization algorithms based on the dead-end elimination theorem

Gordon

Mayo

1998

J. Comput. Chem.

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The fuzzy-end elimination theorem: correctly implementing the side chain placement algorithm based on the dead-end elimination theorem

Cited by 72 publications

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A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

Receptor Flexibility in de Novo Ligand Design and Docking

Radical performance enhancements for combinatorial optimization algorithms based on the dead-end elimination theorem

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