Abstract
Computational Protein Design aims at rationally designing amino-acid sequences that fold into a given three-dimensional structure and that will bestow the designed protein with desirable properties/functions. Usual criteria for design include stability of the designed protein and affinity between it and a ligand of interest. However, estimating the affinity between two molecules requires to compute the partition function, a #P-complete problem.
Because of its extreme computational cost, bio-physicists have designed the K * algorithm, which combines Best-First A * search with dominance analysis to provide an estimate of the partition function with deterministic guarantees of quality. In this paper, we show that it is possible to speed up search and keep reasonable memory requirement using a Cost Function Network approach combining Depth First Search with arc consistency based lower bounds. We describe our algorithm and compare our first results to the CPD-dedicated software Osprey 2.0.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Fersht, A.: Structure and mechanism in protein science: a guide to enzyme catalysis and protein folding. W.H. Freeman and Co., New York (1999)
Peisajovich, S.G., Tawfik, D.S.: Protein engineers turned evolutionists. Nature Methods 4(12), 991–994 (2007)
Pabo, C.: Molecular technology. Designing proteins and peptides. Nature 301(5897), 200 (1983)
Miklos, A.E., Kluwe, C., Der, B.S., Pai, S., Sircar, A., Hughes, R.A., Berrondo, M., Xu, J., Codrea, V., Buckley, P.E., et al.: Structure-based design of supercharged, highly thermoresistant antibodies. Chemistry & Biology 19(4), 449–455 (2012)
Siegel, J.B., Zanghellini, A., Lovick, H.M., Kiss, G., Lambert, A.R., St Clair, J.L., Gallaher, J.L., Hilvert, D., Gelb, M.H., Stoddard, B.L., Houk, K.N., Michael, F.E., Baker, D.: Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction. Science 329(5989), 309–313 (2010)
Georgiev, I., Lilien, R.H., Donald, B.R.: The minimized dead-end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles. Journal of Computational Chemistry 29(10), 1527–1542 (2008)
Rossi, F., van Beek, P., Walsh, T. (eds.): Handbook of Constraint Programming. Elsevier (2006)
Cooper, M., de Givry, S., Sanchez, M., Schiex, T., Zytnicki, M., Werner, T.: Soft arc consistency revisited. Artificial Intelligence 174, 449–478 (2010)
Larrosa, J.: Boosting search with variable elimination. In: Dechter, R. (ed.) CP 2000. LNCS, vol. 1894, pp. 291–305. Springer, Heidelberg (2000)
Gainza, P., Roberts, K.E., Georgiev, I., Lilien, R.H., Keedy, D.A., Chen, C.Y., Reza, F., Anderson, A.C., Richardson, D.C., Richardson, J.S., et al.: Osprey: Protein design with ensembles, flexibility, and provable algorithms. Methods Enzymol. (2012)
Desmet, J., De Maeyer, M., Hazes, B., Lasters, I.: The dead-end elimination theorem and its use in protein side-chain positioning. Nature 356(6369), 539–542 (1992)
Pierce, N.A., Winfree, E.: Protein design is NP-hard. Protein Engineering 15(10), 779–782 (2002)
Rubinstein, R.Y., Ridder, A., Vaisman, R.: Fast sequential Monte Carlo methods for counting and optimization. John Wiley & Sons (2013)
Allouche, D., André, I., Barbe, S., Davies, J., de Givry, S., Katsirelos, G., O’Sullivan, B., Prestwich, S., Schiex, T., Traoré, S.: Computational protein design as an optimization problem. Artificial Intelligence 212, 59–79 (2014)
Rendl, F., Rinaldi, G., Wiegele, A.: Solving Max-Cut to optimality by intersecting semidefinite and polyhedral relaxations. Math. Programming 121(2), 307 (2010)
Traoré, S., Allouche, D., André, I., de Givry, S., Katsirelos, G., Schiex, T., Barbe, S.: A new framework for computational protein design through cost function network optimization. Bioinformatics 29(17), 2129–2136 (2013)
Case, D., Babin, V., Berryman, J., Betz, R., Cai, Q., Cerutti, D., Cheatham Iii, T., Darden, T., Duke, R., Gohlke, H., et al.: Amber 14 (2014)
Lovell, S.C., Word, J.M., Richardson, J.S., Richardson, D.C.: The penultimate rotamer library. Proteins 40(3), 389–408 (2000)
Larrosa, J., de Givry, S., Heras, F., Zytnicki, M.: Existential arc consistency: getting closer to full arc consistency in weighted CSPs. In: Proc. of the 19th IJCAI, Edinburgh, Scotland, pp. 84–89 (August 2005)
Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: ECAI, vol. 16, p. 146 (2004)
Lecoutre, C., Saïs, L., Tabary, S., Vidal, V.: Reasoning from last conflict(s) in constraint programming. Artificial Intelligence 173, 1592–1614 (2009)
Shapovalov, M.V., Dunbrack, R.L.: A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure 19(6), 844–858 (2011)
Subramaniam, S., Senes, A.: Backbone dependency further improves side chain prediction efficiency in the energy-based conformer library (bebl). Proteins: Structure, Function, and Bioinformatics 82(11), 3177–3187 (2014)
Sang, T., Bacchus, F., Beame, P., Kautz, H., Pitassi, T.: Combining component caching and clause learning for effective model counting. In: Proc. of the 7th Int. Conf. on Theory and Applications of Satisfiability Testing (SAT 2004) (2004)
Choi, A., Kisa, D., Darwiche, A.: Compiling probabilistic graphical models using sentential decision diagrams. In: van der Gaag, L.C. (ed.) ECSQARU 2013. LNCS, vol. 7958, pp. 121–132. Springer, Heidelberg (2013)
Larrosa, J., Heras, F.: Resolution in max-sat and its relation to local consistency in weighted csps. In: IJCAI, pp. 193–198 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Viricel, C., Simoncini, D., Allouche, D., de Givry, S., Barbe, S., Schiex, T. (2015). Approximate Counting with Deterministic Guarantees for Affinity Computation. In: Le Thi, H., Pham Dinh, T., Nguyen, N. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. Advances in Intelligent Systems and Computing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-319-18167-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-18167-7_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18166-0
Online ISBN: 978-3-319-18167-7
eBook Packages: EngineeringEngineering (R0)