Efficient Algorithms to Explore Conformation Spaces of Flexible Protein Loops

Dhanik, Ankur; Yao, Peggy; Marz, Nathan; Propper, Ryan; Kou, Charles; Liu, Guanfeng; Bedem, Henry van den; Latombe, Jean‐Claude

doi:10.1007/978-3-540-74126-8_25

Cited by 5 publications

(7 citation statements)

References 22 publications

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“…Another class of methods sample the conformational space efficiently by perturbing the molecular structure at certain degrees of freedom and using geometric and biophysical constraints to guide the search. The search is done using various techniques such as normal mode analysis [24,27], elastic network [7,17], robotic motion planning [2,4,5] and more. These methods are usually faster than MD but do not result in physical pathways due to randomness in the search and the approximations being used.…”

Section: Introductionmentioning

confidence: 99%

Multi-Resolution Rigidity-Based Sampling of Protein Conformational Paths

Luo

Haspel

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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We present a geometry-based, sampling based method to explore conformational pathways in medium and large proteins which undergo large-scale conformational transitions. In a past work we developed a coarse-grained geometry-based method that was able to trace large-scale conformational motions in proteins using residues between secondary structure elements as hinges, and a simple yet effective energy function. In this work we apply a rigidity-analysis tool to determine the rigid and flexible regions in protein structures, since hinges may not always lie on loops between secondary structure elements. This method allows for better accuracy in determining the rotational degrees of freedom of the proteins. We conducted a multi-resolution search scheme, as both C-α and backbone representations are used for sampling the protein conformational paths. Characteristic conformations detected by clustering the paths are converted to full atom protein structures and minimized to detect interesting intermediate conformations that may correspond to transition states or other events. Our algorithm was able to run efficiently on a proteins of various sizes and the results agree with experimentally determined intermediate protein structures.

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Section: Introductionmentioning

confidence: 99%

Multi-Resolution Rigidity-Based Sampling of Protein Conformational Paths

Luo

Haspel

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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“…For complex 1OAZ, neither U/B/B-Greedy-10 nor U/B/B-HClust-10 leads to a high number of predictions below 5Å I-rmsd and below −15 energy units: 5 with one loop, and 13 with 5 different loops respectively. But considering predictions with I-rmsd in interval (5,7] and energy below −15 units, U/B/B-Greedy-10 leads to 195 predictions with the same loop while U/B/B-HClust-10 leads to one such prediction. Tie between Greedy and HClust.…”

Section: Resultsmentioning

confidence: 99%

“…Conformer generations methods. A number of algorithms exist to generate atomic loop geometries [21], [22], [7], [23], [24]. We selected Direx [23] and Loopy [21], which respectively generate dense and sparse (exploring more space) ensembles of conformers.…”

Section: Data Sets and Conformer Generation Methodsmentioning

confidence: 99%

“…First and foremost, the exhaustive exploration of the conformational space of large systems or of systems with large amplitude deformations is not possible. To keep calculations tractable, methods undertaking this task favor geometric calculations, and defer energy calculations to later stages [6], [7]. Second, when conformers are used to model a region of a protein, the energy associated to each conformer varies with its environment.…”

Section: On the Importance Of Diverse Conformational Ensemblesmentioning

confidence: 99%

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On the Characterization and Selection of Diverse Conformational Ensembles with Applications to Flexible Docking

Loriot

Sachdeva

Bastard

et al. 2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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To address challenging flexible docking problems, a number of docking algorithms pregenerate large collections of candidate conformers. To remove the redundancy from such ensembles, a central problem in this context is to report a selection of conformers maximizing some geometric diversity criterion. We make three contributions to this problem. First, we resort to geometric optimization so as to report selections maximizing the molecular volume or molecular surface area (MSA) of the selection. Greedy strategies are developed, together with approximation bounds. Second, to assess the efficacy of our algorithms, we investigate two conformer ensembles corresponding to a flexible loop of four protein complexes. By focusing on the MSA of the selection, we show that our strategy matches the MSA of standard selection methods, but resorting to a number of conformers between one and two orders of magnitude smaller. This observation is qualitatively explained using the Betti numbers of the union of balls of the selection. Finally, we replace the conformer selection problem in the context of multiple-copy flexible docking. On the aforementioned systems, we show that using the loops selected by our strategy can improve the result of the docking process.

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“…We fix bond lengths and dihedral angles ω around peptide bonds to their canonical values. This leads us to treat the fragment as a kinematic linkage [8] whose degrees of freedoms are the dihedral φ and ψ angles around N-C α and C α -C bonds, the bond angles in the main chain, and the χ angles in the side-chains. We divide the fragment into a front and a back half, each with p = n 2 residues.…”

Section: Conformation Samplingmentioning

confidence: 99%

Modeling Structural Heterogeneity in Proteins from X-Ray Data

Dhanik

Bedem

Deacon

et al. 2009

Springer Tracts in Advanced Robotics

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In a crystallographic experiment, a protein is precipitated to obtain a crystalline sample (crystal) containing many copies of the molecule. An electron density map (edm) is calculated from diffraction images obtained from focusing X-rays through the sample at different angles. This involves iterative phase determination and density calculation. The protein conformation is modeled by placing the atoms in 3-D space to best match the electron density. In practice, the copies of a protein in a crystal are not exactly in the same conformation. Consequently the obtained edm, which corresponds to the cumulative distribution of atomic positions over all conformations, is blurred. Existing modeling methods compute an "average" protein conformation by maximizing its fit with the edm and explain structural heterogeneity in the crystal with a harmonic distribution of the position of each atom. However, proteins undergo coordinated conformational variations leading to substantial correlated changes in atomic positions. These variations are biologically important. This paper presents a sample-select approach to model structural heterogeneity by computing an ensemble of conformations (along with occupancies) that, collectively, provide a near-optimal explanation of the edm. The focus is on deformable protein fragments, mainly loops and side-chains. Tests were successfully conducted on simulated and experimental edms.

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Efficient Algorithms to Explore Conformation Spaces of Flexible Protein Loops

Abstract: Several applications in biology e.g., incorporation of protein exibility in ligand docking algorithms, interpretation of fuzzy X-ray crystallographic data, and homology modeling require computing the internal parameters of a exible fragment

Cited by 5 publications

References 22 publications

Multi-Resolution Rigidity-Based Sampling of Protein Conformational Paths

Multi-Resolution Rigidity-Based Sampling of Protein Conformational Paths

On the Characterization and Selection of Diverse Conformational Ensembles with Applications to Flexible Docking

Modeling Structural Heterogeneity in Proteins from X-Ray Data

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