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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
Fast and accurate algorithms for protein sidechain packing
, 2006
"... This article studies the protein sidechain packing problem using the treedecomposition of a protein structure. To obtain fast and accurate protein sidechain packing, protein structures are modeled using a geometric neighborhood graph, which can be easily decomposed into smaller blocks. Therefor ..."
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This article studies the protein sidechain packing problem using the treedecomposition of a protein structure. To obtain fast and accurate protein sidechain packing, protein structures are modeled using a geometric neighborhood graph, which can be easily decomposed into smaller blocks. Therefore, the sidechain assignment of the whole protein can be assembled from the assignment of the small blocks. Although we will show that the sidechain packing problem is still NPhard, we can achieve a treedecompositionbased globally optimal algorithm with time complexity of O(Nn tw+1 rot) and several polynomialtime approximation schemes (PTAS), where N is the number of residues contained in the protein, nrot the average number of rotamers for each residue, and tw = O(N 2/3 log N) the treewidth of the protein structure graph. Experimental results indicate that after Goldstein deadend elimination is conducted, nrot is very small and tw is equal to 3 or 4 most of the time. Based on the globally optimal algorithm, we developed a protein sidechain assignment program TreePack, which runs up to 90 times faster than SCWRL 3.0, a widelyused sidechain packing program, on some large test proteins in the SCWRL benchmark database and an average of five times faster on all the test proteins in this database. There are also some realworld
Automated bond order assignment as an optimization problem. Bioinformatics
, 2011
"... Motivation: Numerous applications in Computational Biology process molecular structures and hence strongly rely not only on correct atomic coordinates, but also on correct bond order information. For proteins and nucleic acids, bond orders can be easily deduced but this does not hold for other types ..."
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Motivation: Numerous applications in Computational Biology process molecular structures and hence strongly rely not only on correct atomic coordinates, but also on correct bond order information. For proteins and nucleic acids, bond orders can be easily deduced but this does not hold for other types of molecules like ligands. For ligands bond order information is not always provided in molecular databases and thus a variety of approaches tackling this problem have been developed. In this work we extend an ansatz proposed by Wang et al. (2006) that assigns connectivity based penalty scores and tries to heuristically approximate its optimum. In this work we present three efficient and exact solvers for the problem replacing the heuristic approximation scheme of the original approach: an A*, an ILP, and an FPT approach. Results: We implemented and evaluated the original implementation, our A*, ILP, and FPT formulation on the MMFF94 validation suite and the KEGG Drug database. We show the benefit of computing exact solutions of the penalty minimization problem and the additional gain when computing all optimal (or even suboptimal) solutions. We close with a detailed comparison of our methods. Availability: The A * and ILP solution are integrated into the opensource C++ LGPL library BALL (Kohlbacher and Lenhof, 2000; Hildebrandt et al., 2010) and the molecular visualization and modelling tool BALLView (Moll et al., 2006) and can be downloaded from our homepage www.ballproject.org. The FPT implementation can be downloaded from
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U.S. census unit population exposures to ambient air pollutants
Computational Approaches to Problems in Protein Structure and Function
, 2005
"... We present computational approaches to solve several problems arising in protein structure and function. In the first part of this thesis, we develop a new method for finding the lowest energy positions of side chains when given the backbone of a protein, a widely studied problem that has applicatio ..."
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We present computational approaches to solve several problems arising in protein structure and function. In the first part of this thesis, we develop a new method for finding the lowest energy positions of side chains when given the backbone of a protein, a widely studied problem that has applications in homology modeling and protein design. We present an integer linear programming formulation of sidechain positioning and relax it to give a polynomialtime linear programming heuristic that allows us to tackle large problems. We test the integer and linear programming approach on native and homologous backbones, where we show that optimal solutions can usually be found using linear programming, and in protein redesign, where we find that instances often cannot be solved using linear programming directly, but where optimal solutions for large instances can be found using the more expensive integer programming procedure. We also present an alternative formulation of the sidechain positioning problem as a semidefinite program, which provides a tighter relaxation than the linear program. We introduce two novel rounding schemes to convert
doi:10.1093/bioinformatics/btl178BIOINFORMATICS ORIGINAL PAPER Structural bioinformatics
"... PROXIMO—a new docking algorithm to model protein complexes using data from radical probe mass spectrometry (RPMS) ..."
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PROXIMO—a new docking algorithm to model protein complexes using data from radical probe mass spectrometry (RPMS)
continuum
"... Vol. 23 ECCB 2006, pages e99–e103 doi:10.1093/bioinformatics/btl312BIOINFORMATICS Electrostatic potentials of proteins in water: a structured ..."
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Vol. 23 ECCB 2006, pages e99–e103 doi:10.1093/bioinformatics/btl312BIOINFORMATICS Electrostatic potentials of proteins in water: a structured
Balancing the Interactions of Ions, Water, and DNA in the Drude Polarizable Force Field
"... ABSTRACT: Recently we presented a firstgeneration allatom Drude polarizable force field for DNA based on the classical Drude oscillator model, focusing on optimization of key dihedral angles followed by extensive validation of the force field parameters. Presently, we describe the procedure for ba ..."
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ABSTRACT: Recently we presented a firstgeneration allatom Drude polarizable force field for DNA based on the classical Drude oscillator model, focusing on optimization of key dihedral angles followed by extensive validation of the force field parameters. Presently, we describe the procedure for balancing the electrostatic interactions between ions, water, and DNA as required for development of the Drude force field for DNA. The proper balance of these interactions is shown to impact DNA stability and subtler conformational properties, including the conformational equilibrium between the BI and BII states, and the A and B forms of DNA. The parametrization efforts were simultaneously guided by gasphase quantum mechanics (QM) data on small model compounds and condensedphase experimental data on the hydration and osmotic properties of biologically relevant ions and their solutions, as well as theoretical predictions for ionic distribution around DNA oligomer. In addition, finetuning of the internal base parameters was performed to obtain the final DNA model. Notably, the Drude model is shown to more accurately reproduce counterion condensation theory predictions of DNA charge neutralization by the condensed ions as compared to the CHARMM36 additive DNA force field, indicating an improved physical description of the forces dictating the ionic solvation of