4 research outputs found

    Discretization Orders for Protein Side Chains

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    International audienceProteins are important molecules that are widely studied in biology. Since their three-dimensional conformations can give clues about their function, an optimal methodology for the identification of such conformations has been researched for many years. Experiments of Nuclear Magnetic Resonance (NMR) are able to estimate distances between some pairs of atoms forming the protein, and the problem of identifying the possible conformations satisfying the available distance constraints is known in the scientific literature as the Molecular Distance Geometry Problem (MDGP). When some particular assumptions are satisfied, MDGP instances can be discretized, and solved by employing an ad-hoc algorithm, named the interval Branch & Prune (iBP). When dealing with molecules such as proteins, whose chemical structure is known, a priori information can be exploited for generating atomic orderings that allow for the discretization. In previous publications, we presented a handcrafted order for the protein backbones. In this work, we propose 20 new orders for the 20 side chains that can be present in proteins. Computational experiments on artificial and real instances from NMR show the usefulness of the proposed orders

    Finding Optimal Discretization Orders For Molecular Distance Geometry By Answer Set Programming

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    Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The Molecular Distance Geometry Problem (MDGP) is the problem of finding the possible conformations of a molecule by exploiting available information about distances between some atom pairs. When particular assumptions are satisfied, the MDGP can be discretized, so that the search domain of the problem becomes a tree. This tree can be explored by using an interval Branch & Prune (iBP) algorithm. In this context, the order given to the atoms of the molecules plays an important role. In fact, the discretization assumptions are strongly dependent on the atomic ordering, which can also impact the computational cost of the iBP algorithm. In this work, we propose a new partial discretization order for protein backbones. This new atomic order optimizes a set of objectives, that aim at improving the iBP performances. The optimization of the objectives is performed by Answer Set Programming (ASP), a declarative programming language that allows to express our problem by a set of logical constraints. The comparison with previously proposed orders for protein backbones shows that this new discretization order makes iBP perform more efficiently. © Springer International Publishing Switzerland 2016.610115CNPq, Conselho Nacional de Desenvolvimento Científico e TecnológicoConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E., The protein data bank (2000) Nucleic Acid Res, 28, pp. 235-242Brewka, G., Eiter, T., Truszczyński, M., Answer set programming at a glance (2011) Commun. ACM, 54 (12), pp. 92-103Cassioli, A., Bardiaux, B., Bouvier, G., Mucherino, A., Alves, R., Liberti, L., Nilges, M., Malliavin, T.E., An algorithm to enumerate all possible protein conformations verifying a set of distance restraints (2015) BMC Bioinform, , (, to appear)Cassioli, A., Gunluk, O., Lavor, C., Liberti, L., Discretization vertex orders in distance geometry (2015) Discrete Appl. Math, , (, to appear)Costa, V., Mucherino, A., Lavor, C., Cassioli, A., Carvalho, L.M., Maculan, N., Discretization orders for protein side chains (2014) J. Glob. Optim, 60 (2), pp. 333-349Crippen, G.M., Havel, T.F., (1988) DistanceGeometry and Molecular Conformation, , (Wiley, NewYork,)Eiter, T., Ianni, G., Krennwallner, T., Answer set programming: A primer (2009) Reason. Web, 5689, pp. 40-110Gebser, M., Kaufmann, B., Schaub, T., Conflict-driven answer set solving: From theory to practice (2012) Artif. Intell, 187, pp. 52-89Gelfond, M., Answer Sets (2007) Handbook of Knowledge Representation, , Chapter 7 (Elsevier, Amsterdam,)Gonçalves, D.S., Mucherino, A., Discretization orders and efficient computation of Cartesian coordinates for distance geometry (2014) Optim. Lett, 8 (7), pp. 2111-2125Gramacho, W., Gonçalves, D., Mucherino, A., Maculan, N., A new algorithm to finding discretizable orderings for distance geometry (2013) Proceedings of Distance Geometry and Applications (DGA13), pp. 149-152. , Manaus, Amazonas, BrazilHavel, T.F., Distance Geometry (1995) Encyclopedia of nuclear magnetic resonance, pp. 1701-1710. , ed. by D.M. Grant, R.K. Harris (Wiley, New York,)Lavor, C., Lee, J., Lee-St John, A., Liberti, L., Mucherino, A., Sviridenko, M., Discretization orders for distance geometry problems (2012) Optim. Lett, 6 (4), pp. 783-796Lavor, C., Liberti, L., Maculan, N., Mucherino, A., The discretizable molecular distance geometry problem (2012) Comput. Optim. Appl, 52, pp. 115-146Lavor, C., Liberti, L., Mucherino, A., The interval branch-and-prune algorithm for the discretizable molecular distance geometry problem with inexact distances (2013) J. Glob. Optim, 56 (3), pp. 855-871Lavor, C., Mucherino, A., Liberti, L., Maculan, N., On the computation of protein backbones by using artificial backbones of hydrogens (2011) J. Glob. Optim, 50 (2), pp. 329-344Liberti, L., Lavor, C., Maculan, N., A branch-and-prune algorithm for the molecular distance geometry problem (2008) Int. Trans. Oper. Res, 15, pp. 1-17Liberti, L., Lavor, C., Maculan, N., Mucherino, A., Euclidean distance geometry and applications (2014) SIAM Rev, 56 (1), pp. 3-69Malliavin, T.E., Mucherino, A., Nilges, M., Distance geometry in structural biology: New perspectives (2013) Distance Geometry: Theory, pp. 329-350. , Methods and Applications, ed. by A. Mucherino, C. Lavor, L. Liberti, N. Maculan (Springer, Berlin,)Mucherino, A., On the Identification of Discretization Orders for Distance Geometry with Intervals, Lecture Notes in Computer Science 8085 (2013) Proceedings of Geometric Science of Information (GSI13), pp. 231-238. , ed. by F. Nielsen, F. Barbaresco, Paris, FranceMucherino, A., APseudo de Bruijn GraphRepresentation for DiscretizationOrders for Distance Geometry, LectureNotes in Computer Science 9043, LectureNotes in Bioinformatics series (2015) Proceedings of the 3rd InternationalWork-Conference on Bioinformatics and Biomedical Engineering (IWBBIO15), pp. 514-523. , ed. by F. Ortuño, I. Rojas Granada, SpainMucherino, A., Lavor, C., Liberti, L., The discretizable distance geometry problem (2012) Optim. Lett, 6 (8), pp. 1671-1686Ramachandran, G.N., Ramakrishnan, C., Sasisekharan, V., Stereochemistry of polypeptide chain conformations (1963) J. Mol. 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    Euclidean Distance Geometry And Applications

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    Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consist of an incomplete set of distances and the output is a set of points in Euclidean space realizing those given distances. We survey the theory of Euclidean distance geometry and its most important applications, with special emphasis on molecular conformation problems. © 2014 Society for Industrial and Applied Mathematics.561369Alexandrov, A., (1950) Convex Polyhedra, Gosudarstv. Izdat. Tekhn.-Theor. Lit., , MoscowAlfakih, A., Khandani, A., Wolkowicz, H., Solving Euclidean distance matrix completion problems via semidefinite programming (1999) Comput. Optim. Appl., 12, pp. 13-30Alves, R., Cassioli, A., Mucherino, A., Lavor, C., Liberti, L., Adaptive branching in iBP with Clifford algebra (2013) Proceedings of the Workshop on Distance Geometry and Applications, pp. 65-69. , A. Andrioni, C. Lavor, L. Liberti, A. 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