S. Sun, Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Science 2 (1993), 762-785.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....from our algorithm only when relatively few DOFs are changed at each step. The same is true for pure MDS in torsion angle space [24, 55] Some optimization approaches could also benefit from our algorithm. For instance, a popular one uses genetic algorithms with crossover and mutation operators [47, 56, 59]. The crossover operator generates a new conformation by combining two halves, each extracted from a previously created conformation. Most mutation operators also reuse long fragments from one or several previous conformations. For both types of operators, our algorithm would allow partial sums of ....

S. Sun. Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Protein Science, 2(5):762--785, 1993.

....unknown due to the intrinsic complexity of the problem. In folding simulations, several computational approaches have been applied to this exponentialtime problem, including energy minimization [39, 49] molecular dynamics simulation [37] Monte Carlo methods [14, 33] and genetic algorithms [11, 48]. Among these, molecular dynamics is most closely related to our approach. Much work had been carried out in this area [16, 20, 24, 37] which tries to simulate the true dynamics of the folding process using the classical Newton s equations of motion. The forces applied are usually approximations ....

S. Sun. Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Protein Sci., 2:762-785, 1993.

....functions. 2. Protein Model and Global Search Strategy Practical search strategies for the protein folding problem currently require a simplified, yet sufficiently realistic, molecular model with an associated potential energy function representing the dominant forces involved in protein folding [9]. In our present model, each residue in the primary sequence of a protein is characterized by its backbone components NH C a H CO and one of 20 possible amino acid sidechains attached to the central C a atom. The three dimensional structure of the chain is determined by internal molecular ....

S. Sun, Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Science 2 (1993), 762-785.

....of degrees of freedom, in a simple graph (see Figure 1.7) that shows the distribution of local minima. 1.3 SUMMARY OF RECENT RESULTS 1.3. 1 Current Search Methods are Much Too Slow Conformational searching is often done by Monte Carlo Simulated Annealing ( 22, 25, 27, 29] Genetic Algorithms ([35, 37]) Molecular Dynamics ( 2, 24] and by transformation methods, for example based on the diffusion equation ( 16, 17, 18, 33] SA, GA, and MD are much too slow, arguably by 3 10 orders 4 of magnitude. What is the basis for this estimate First, search methods need to provide some assurance they ....

Sun, S. (1993), Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Science 2:762785. REFERENCES 15

....the folding pathways. In folding simulations for protein structure prediction, several computational approaches have been applied to this exponential time problem, including energy minimization [28, 40] molecular dynamics simulation [26] Monte Carlo methods [10, 22] and genetic algorithms [8, 39]. Among these, molecular dynamics is most closely related to our approach. Much work has been carried out in this area [11, 12, 15, 26] which tries to simulate the true dynamics of the folding process using the classical Newton s equations of motion. The forces applied are usually approximations ....

S. Sun. Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Protein Sci., 2:762-785, 1993.

....the folding pathway. In folding simulations for protein structure prediction, several computational approaches have been applied to this exponential time problem, including energy minimization [20, 31] molecular dynamics simulation [18] Monte Carlo methods [7, 15] and genetic algorithms [5, 30]. Among these, molecular dynamics is most closely related to our approach. Much work has been carried out in this area [8, 9, 11, 18] which tries to simulate the true dynamics of the folding process using the classical Newton s equations of motion. The forces applied are usually approximations ....

S. Sun. Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Protein Sci., 2:762-785, 1993.

....Chemistry, 40, 69] Journal of Molecular Biology, 42, 109] Journal of Physical Chemistry, 60] Journal of Supercomputing, 28] Journal of Theoretical Biology, 110] Mach. Learn. Netherlands) 15] Nature Structural Biology, 47] Protein Engineering, 51, 64, 85] Protein Science, [41, 112, 118] The Journal of Physical Chemistry, 34] THEOCHEM, 48] Trends in Biotechnology, 74] total 35 articles in 26 journals 4.3 Theses The following two lists contain theses, first PhD theses and then Master s etc. theses, arranged in alphabetical order by the name of the school. 4.3.1 PhD ....

....Schneider, Gisbert, 53] Schulze Kremer, Steffen, 54, 59, 61, 113, 114, 115, 116, 117] Semertzidis, Michael T. 55, 118] Shapiro, Bruce A. 28] Sheridan, Robert P. 30] Sherman, Christopher J. 75] Soderlund, C. A. 19] Stender, Joachim, 119] Suhai, Sandor, 45] Sun, Shaojian, [112] Tanaka, Hidetoshi, 10] Tarroux, Philippe, 38] Tiedemann, Ulrich, 54, 59, 116] Totoki, Yasushi, 21, 22] Toya, Tomoyuki, 21, 22] Treasurywala, Adi M. 48, 92, 94] Tropsha, Alexander, 79] Tuffrey, P. 120, 121] Uberbacher, E. C. 43] Unger, Ron, 104, 105, 106, 107, 108, 109] ....

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Shaojian Sun. Reduced representation model of protein structure prediction: Statistical potential and genetic algorithms. Protein Science, 2(5):762--785, May 1993. ga:SSun93a.

.... protein structure have been explored (see [39] for a review) In folding simulations, several computational approaches have been applied to this exponential time problem, including energy minimization [30, 41] molecular dynamics simulation [28] Monte Carlo methods [9, 23] and genetic algorithms [6, 40]. Among these, molecular dynamics is most closely related to our approach. Much work had been carried out in this area [10, 12, 16, 28] which tries to simulate the true dynamics of the folding process using the classical Newton s equations of motion. The forces applied are usually approximations ....

S. Sun. Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Protein Sci., 2:762-785, 1993.

.... of Mechanical Engineers, Part D, Journal of Automobile Engineering) 812] Proceedings of the Institution of Mechanical Engineers, Part D, Journal of Automobile Engineering) 811] Proceedings of the National Academy of Sciences of the United States of America, 1045] Protein Science, [893, 924] Res. Eng. Des. USA) 195] Rev. Int. Syst. France) 606, 1058] Sci. Comput. Autom. USA) 554] Science, 308] Scientific American, 880] Sebutsu Kogaku Kaishi Journal of the Society for Fermentation and Bioengineering, 932] Sens. Actuators A. Phys. Switzerland) 146] SIGBIO ....

....Sternad, Dagmar, 906, 907] Stockton, D. J. 832] Stoffa, Paul L. 656] Stromboni, Jean Paul, 965] Stuber, Kurt, 513] Su, Suchen, 966] Subacius, Vytautas, 94, 95] Subramanian, S. 175] Sugimoto, Hiroyuki, 720, 721, 967] Suginohara, N. 898] Sun, Chuen Tsai, 150] Sun, Shaojian, [893] Surry, P. D. 968, 969] Suykens, Johan, 1009] Suzuki, Joe, 512] Suzuki, Keiji, 542, 544] Suzuki, Tetsuo, 884] Sved, G. 970] Sverdlik, William, 867] Swarup, K. S. 497] Szalas, A. 716] Szarkowicz, Donald S. 971] Tackett, Walter Alden, 976, 977] Tadel, M. 225] ....

[Article contains additional citation context not shown here]

Shaojian Sun. Reduced representation model of protein structure prediction: Statistical potential and genetic algorithms. Protein Science, 2(5):762--785, May 1993. ga:SSun93a.

.... of the National Academy of Sciences of the United States of America, 629, 913, 923, 961, 1025, 1044] Proceedings of the National Science Council, Republic of China, Part A: Physical Science and Engineering, 144] Protein Engineering, 932, 949, 978, 1023, 1028, 1077] Protein Science, [916, 953, 997, 1015, 1034, 1100, 1106] Proteins: Structure, Function, and Genetics, 912, 936, 954, 967, 987, 1001, 1011, 1019, 1020, 1029, 1033, 1177, 1053, 1060] Publ. Fac. Electr. Eng. Ser. Eng. Phys. Serbia) 866] Quantitative Structure Activity Relationships, 930, 942, 274, 314, 1141] Quim. Nova, 376] Railw. Gaz. Int. ....

....S. E. 599, 600] Stevens Jr, S. E. 605] St ockelmann, Elmar, 881] Sto a, Paul L. 831, 902, 193, 194] Stork, Christof, 566] Strepp, F. 387] Stupp, Samuel I. 88] Succi, S. 165] Sugamoto, A. 752] Sugimoto, D. 313] Sugimoto, Nobuo, 674] Suhai, Sandor, 924, 48] Sun, Shaojian, [969, 978, 1100] Sun, Xun, 857] Sun, Zhirong, 1027, 1071] Sundaram, Anantha, 368, 370] Sundaram, A. 260] Sundararajan, V. 575, 1000] Sunol, A. K. 303] S u , W. 222] Sutherland, John, 1066] Sutherling, W. W. 474] Sutton, Patrick, 127, 814] Suyama, Masanori Arita Akira, 603] Suzuki, Atsuyuki, ....

[Article contains additional citation context not shown here]

Shaojian Sun. Reduced representation model of protein structure prediction: Statistical potential and genetic algorithms. Protein Science, 2(5):762-785, May 1993. ga:SSun93a.

....however, it is incorrect to presume that the pseudoatom problem is automatically NP hard. Stochastic algorithms When used as function optimizers, randomized methods such as Monte Carlo searches (Metropolis et al. 1953) and genetic algorithms (LeGrand and Merz, 1993; Dandekar and Argos, 1992; Sun, 1993) are very general techniques for trying to overcome local minimum problems. Because of their probabilistic nature, they carry no guarantees of correctness or efficiency. Thus, a stochastic algorithm for PSP automatically satisfies the conclusions that follow from PSP s NP hardness, regardless of ....

Sun, S. (1993). Reduced representation model of protein structure prediction: Statistical potential and genetic algorithms. Protein Science, 2(5):762--785.

....et al. 28] The fact that the potential is directly derived from geometric data implies that it automatically takes account of solvation and entropy corrections; on the other hand, one only gets a mean potential of low resolution. Reconstructions using mean potentials are reported by Sun [25] for apamin (18 residues) and mellitin (26 residues) using genetic algorithms, by Sun [26] for mellitin, APPI (36 residues) and crambin (46 residues) using simulated annealing, by Gunn et al. 4] for myoglobin (153 residues) using a combination of simulated annealing and genetic algorithms, and by ....

....technology, it is desirable to have a smooth (i.e. twice continuously differentiable with respect to x) potential. This allows for robust local optimization (e.g. 3,20] and can be combined with global search techniques such as simulated annealing (e.g. 13,26] genetic algorithms (e.g. [8,25]) smoothing methods (e.g. 17,14] or branch and bound techniques (e.g. 16] to approach the global minimizer for sequences s with unknown native geometry. Unfortunately, the approach of determining empirical potentials from equilibrium data is intrinsically limited, even if we assume ....

S. Sun, Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Sci. 2 (1993), 762-785.

....takes account of solvation and entropy corrections; on the other hand, one only gets a mean potential of less resolution. Reconstructions using such mean potentials are reported by Sun [313] for mellitin (26 residues) APPI (36 residues) and crambin (46 residues) using simulated annealing, by Sun [312] for mellitin and apamin (18 residues) using genetic algorithms, by Gunn et al. 123] for myoglobin (153 residues) using a combination of simulated annealing and genetic algorithms, and by Sippl et al. 289] for lysozyme, myoglobin and thymosin. In all these papers, results are only native like ....

S. Sun, Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Sci. 2 (1993), pp. 762--785.

....derived structures. In some of these applications experimental structures were included in the library, which can obviously bias the prediction of near native conformations. The prediction of near native structures worsened for both larger examples and for those not contained in the database [100, 169]. In general, there tend to be significant differences in both the formulation of the fitness function and the exact implementation of the algorithmic techniques. The development of these criteria and conditions greatly influences the ability to predict GLOBAL OPTIMIZATION IN PROTEIN FOLDING ....

S. Sun, Reduced representation model of protein structure prediction : Statistical potential and genetic algorithms, Protein Sci., 2, (1993), 762-785.

.... on genetic algorithms and protein folding was done independently by several groups world wide [17] Genetic algorithms have been used to predict optimal sequences to fit structural constraints [18] to fold Crambin in the Amber force field [19] and Mellitin in an empirical, statistical potential [20], and to predict main chain folding patterns of small proteins based on secondary structure predictions [21] In this section the individuals of the genetic algorithm are conformations of a protein and the fitness function is a simple force field. In the following, the representation formalism, ....

S. Sun, Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Science, vol 2, no 5, pp. 762-785, 1993.

....Ulrich et al. 34] The fact that the potential is directly derived from geometric data implies that it automatically takes account of solvation and entropy corrections; on the other hand, one only gets a mean potential of low resolution. Reconstructions using mean potentials are reported by Sun [31] for apamin (18 residues) and mellitin (26 residues) using genetic algorithms, by Sun [32] for mellitin, APPI (36 residues) and crambin (46 residues) using simulated annealing, by Gunn et al. 6] for myoglobin (153 residues) using a combination of simulated annealing and genetic algorithms, and by ....

....technology, it is desirable to have a smooth (i.e. twice continuously differentiable with respect to x) potential. This allows for robust local optimization (e.g. 5,25] and can be combined with global search techniques such as simulated annealing (e.g. 15,32] genetic algorithms (e.g. [10,31]) smoothing methods (e.g. 21,16] or branch and bound techniques (e.g. 20] to approach the global minimizer for sequences s with unknown native geometry. Unfortunately, the approach of determining empirical potentials from equilibrium data is intrinsically limited, even if we assume complete ....

S. Sun, Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Sci. 2 (1993), 762-785.

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S. Sun, Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Science 2 (1993), 762-785.

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S. Sun. Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Protein Sci., 2(5):762--785, 1993.

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Sun S. Reduced representation model of protein structure prediction: Statistical potential and genetic algorithms. Protein Sci 1993;2:762--785.

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Sun, S. Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Protein Sci. 2:762--785, 1993.

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Sun S. Reduced representation model of protein structure prediction: statistical potential and genetic algorithms. Protein Sci 1993;2:762--785.

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S. Sun, Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Sci,2(5), 762-785, 1993.

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