Conformational splitting: A more powerful criterion for dead-end elimination

Pierce, Niles A.; Spriet, J.A.; Desmet, Jan; Mayo, Stephen L.

doi:10.1002/1096-987x(200008)21:11<999::aid-jcc9>3.0.co;2-a

Cited by 134 publications

(46 citation statements)

References 14 publications

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“…Unfortunately, protein design by searching for the GMEC using rotamers and a pairwise energy function on a rigid peptide backbone has recently been shown to be NP-hard (Pierce and Winfree, 2002). As a result, a number of heuristic (random sampling, neural network, genetic algorithm) GMEC-based approaches for protein design have been reported (Street and Mayo, 1999;Jin et al, 2003;Jaramillo et al, 2001;Hellinga and Richards, 1991;Marvin and Hellinga, 2001); however, the dominant algorithm for assisting in the GMEC search has been dead-end elimination (DEE) (Desmet et al, 1992;Lasters and Desmet, 1993;Pierce et al, 2000). Given a protein backbone, a set of allowable mutations, and a rotamer library, DEE employs a number of sophisticated conformation pruning techniques to prune conformations that are provably not part of the GMEC.…”

Section: Computational Protein Designmentioning

confidence: 99%

A Novel Ensemble-Based Scoring and Search Algorithm for Protein Redesign and Its Application to Modify the Substrate Specificity of the Gramicidin Synthetase A Phenylalanine Adenylation Enzyme

Lilien

Stevens

Anderson

et al. 2005

Journal of Computational Biology

102

176

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Realization of novel molecular function requires the ability to alter molecular complex formation. Enzymatic function can be altered by changing enzyme-substrate interactions via modification of an enzyme's active site. A redesigned enzyme may either perform a novel reaction on its native substrates or its native reaction on novel substrates. A number of computational approaches have been developed to address the combinatorial nature of the protein redesign problem. These approaches typically search for the global minimum energy conformation among an exponential number of protein conformations. We present a novel algorithm for protein redesign, which combines a statistical mechanics-derived ensemble-based approach to computing the binding constant with the speed and completeness of a branch-and-bound pruning algorithm. In addition, we developed an efficient deterministic approximation algorithm, capable of approximating our scoring function to arbitrary precision. In practice, the approximation algorithm decreases the execution time of the mutation search by a factor of ten. To test our method, we examined the Phe-specific adenylation domain of the nonribosomal peptide synthetase gramicidin synthetase A (GrsA-PheA). Ensemble scoring, using a rotameric approximation to the partition functions of the bound and unbound states for GrsA-PheA, is first used to predict binding of the wildtype protein and a previously described mutant (selective for leucine), and second, to switch the enzyme specificity toward leucine, using two novel active site sequences computationally predicted by searching through the space of possible active site mutations. The top scoring in silico mutants were created in the wetlab and dissociation/binding constants were determined by fluorescence quenching. These tested mutations exhibit the desired change in specificity from Phe to Leu. Our ensemble-based algorithm, which flexibly models both protein and ligand using rotamer-based partition functions, has application in enzyme redesign, the prediction of protein-ligand binding, and computer-aided drug design.

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Section: Computational Protein Designmentioning

confidence: 99%

A Novel Ensemble-Based Scoring and Search Algorithm for Protein Redesign and Its Application to Modify the Substrate Specificity of the Gramicidin Synthetase A Phenylalanine Adenylation Enzyme

Lilien

Stevens

Anderson

et al. 2005

Journal of Computational Biology

102

176

View full text Add to dashboard Cite

show abstract

“…Given the fixed backbone and the sequences, side-chains from the Richardson penultimate rotamer library 81 were placed using the dead end elimination (DEE) algorithm followed by an A * branch and bound search. 45,[82][83][84][85][86] The energy function used in conjunction with this consisted of the following terms:…”

Section: Repacking and Minimizationmentioning

confidence: 99%

Structure-based Prediction of bZIP Partnering Specificity

Grigoryan

Keating

2006

Journal of Molecular Biology

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“…In 1C and 2C, s np and s p were set to 0.026 kcal mol K1 Å K2 and 0.1 kcal mol K1 Å K2 , respectively (Table 1). An algorithm based on the dead-end elimination theorem 19,20 was used to obtain the minimum energy amino acid sequences and conformations.…”

mentioning

confidence: 99%