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Rapid protein sidechain packing via tree decomposition
 Research in Computational Molecular Biology, Lecture Notes in Computer Science
, 2005
"... Abstract. This paper proposes a novel tree decomposition based sidechain assignment algorithm, which can obtain the globally optimal solution of the sidechain packing problem very efficiently. Theoretically, the computational complexity of this algorithm is O((N +M)n tw+1 rot) where N is the numbe ..."
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Abstract. This paper proposes a novel tree decomposition based sidechain assignment algorithm, which can obtain the globally optimal solution of the sidechain packing problem very efficiently. Theoretically, the computational complexity of this algorithm is O((N +M)n tw+1 rot) where N is the number of residues in the protein, M the number of interacting residue pairs, nrot the average number of rotamers for each residue and tw( = O(N 2 3 log N)) the tree width of the residue interaction graph. Based on this algorithm, we have developed a sidechain prediction program SCATD (Side Chain Assignment via Tree Decomposition). Experimental results show that after the Goldstein DEE is conducted, nrot is around 3.5, tw is only 3 or 4 for most of the test proteins in the SCWRL benchmark and less than 10 for all the test proteins. SCATD runs up to 90 times faster than SCWRL 3.0 on some large proteins in the SCWRL benchmark and achieves an average of five times faster speed on all the test proteins. If only the postDEE stage is taken into consideration, then our treedecomposition based energy minimization algorithm is more than 200 times faster than that in SCWRL 3.0 on some large proteins. SCATD is freely available for academic research upon request. 1
A treedecomposition approach to protein structure prediction
 In Proc. 4th International IEEE Computer Society Computational Systems Bioinformatics Conference (CSB 2005
, 2005
"... This paper proposes a tree decomposition of protein structures, which can be used to efficiently solve two key subproblems of protein structure prediction: protein threading for backbone prediction and protein sidechain prediction. To develop a unified treedecomposition based approach to these two ..."
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This paper proposes a tree decomposition of protein structures, which can be used to efficiently solve two key subproblems of protein structure prediction: protein threading for backbone prediction and protein sidechain prediction. To develop a unified treedecomposition based approach to these two subproblems, we model them as a geometric neighborhood graph labeling problem. Theoretically, we can have a lowdegree polynomial time algorithm to decompose a geometric neighborhood graph G = (V, E) into components with size O(V  2 3 log V ). The computational complexity of the treedecomposition based graph labeling algorithms is O(V  ∆ tw+1) where ∆ is the average number of possible labels for each vertex and tw( = O(V  2 3 log V )) the tree width of G. Empirically, tw is very small and the treedecomposition method can solve these two problems very efficiently. This paper also compares the computational efficiency of the treedecomposition approach with the linear programming approach to these two problems and identifies the condition under which the treedecomposition approach is more efficient than the linear programming approach. Experimental result indicates that the treedecomposition approach is more efficient most of the time. 1
RotamerPair Energy Calculations Using a Trie Data Structure
"... Abstract. Protein design software places amino acid side chains by precomputing rotamerpair energies and optimizing rotamer placement. If the software optimizes by rapid stochastic techniques, then the precomputation phase dominates run time. We present a new algorithm for rapid rotamerpair energy ..."
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Abstract. Protein design software places amino acid side chains by precomputing rotamerpair energies and optimizing rotamer placement. If the software optimizes by rapid stochastic techniques, then the precomputation phase dominates run time. We present a new algorithm for rapid rotamerpair energy computation that uses a trie data structure. The trie structure avoids redundant energy computations, and lends itself to timesaving pruning techniques based on a simple geometric criteria. With our new algorithm, we compute rotamerpair energies nearly 4 times faster than the previous approach. 1
Substrate Recognition by the Phenylalanine Adenylating Domain of Gramicidin Synthetase, and Redesign of Nonribosomal Peptide Synthetases by Modulation of Substrate Specificity
"... Nonribosomal peptide synthetases (NRPS) are a family of enzymes that assemble a variety of pharmacologically interesting polypeptides from canonical and noncanonical amino acids. The identity and connectivity of the monomers in the final product are directly determined by the order of domains in t ..."
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Nonribosomal peptide synthetases (NRPS) are a family of enzymes that assemble a variety of pharmacologically interesting polypeptides from canonical and noncanonical amino acids. The identity and connectivity of the monomers in the final product are directly determined by the order of domains in the enzyme that are specific for the recognition and incorporation of a particular amino acid. Here we use K*, an ensemblebased, statistical mechanics–derived approximation to the binding constant, to predict mutations to the phenylalanine adenylation domain (PheA) of gramcidin synthetase (GrsA) that will improve binding of a miscognate amino acid, either leucine or tyrosine. PheA mutants predicted by K * to have improved binding of leucince or tyrosine have been made and demonstrate preferred binding of the targeted amino acid over phenylalanine. The catalytic specificity (k cat/K M) of mutants was also evaluated by steadystate kinetics, and improvement for targeted substrate was observed, though not enough to become the catalytically preferred substrate. To better