Generalized simulated annealing applied to protein folding studies

Abstract

During the last few years, computational simulations based on the atomic description of biological molecules have resulted in significant advances in the comprehension of biological processes. It is well known, however, that a molecular system may have a great number of conformations due to the large number of rotation of degrees of freedom around chemical bonds, leading to several local minima on the energy hypersurface. It has been proposed though, that proteins express their biological function when their structure is close to a conformation with energy global minimum. To help solve the protein-folding problem, we use a new strategy based on Simulated Annealing methods. These methods have been well suited for a large extent of optimization problems, especially those containing many local minima. In fact, this work applies the Generalized Simulated Annealing method (GSA) coupled to the GROMOS96 Molecular Force Field to research the minimum energy conformation of 18-alanine. We show that the q(T) GSA parameter can be used to control the freezing process during the annealing procedure, and to avoid polypeptide chains to be trapped in energy local minima. We scanned the q-values for visiting (q(V)) and accepting (q(A)) functions for q(T) values ranging from 1 to 3, and found the best values to obtain an alpha-helix conformation for the polyalanine peptide, which is the conformation with energy global minimum. Global optimization methods also exemplify a class of applications that requires a large amount of computational resources, being suitable for Grid computing. To implement a Grid computing platform, we developed and tested a Grid environment based on MYGRID middleware, which is a technology that can employ all machines accessed by the user to run the application.

Generalized Simulated Annealing

“…>n.particles<-6 # regular octahedron with global minimum 9.98528137 >lower.T<-rep(0, 2 * n.particles) >upper.T<-c(rep(pi, n.particles), rep(2 * pi, n.particles)) >require(GenSA) >options(digits = 9) >set.seed(1234) >fn.call<<-0 >out.GenSA<-GenSA(par = NULL, lower = lower.T, upper = upper.T, fn = Thomson.fn, control = list(max.call=600)) >print(out.GenSA[c("value", "counts")]) $value [ 1] 9.98528137 $counts [ 1] 600 >cat("GenSA call function", fn.call, "times.\n") GenSA call function 600 times.…”

“…> out2.GenSA <-GenSA(fn=obj, lower=rep(0, 5), upper=rep (1,5), +control=list(smooth=FALSE, max.call=3000)) The max.call parameter is set to 3000 to make the algorithm stop earlier: > out2. GenSA determined a minimum of 0.1141484884 within 3000 function calls.…”

“…Determining the global minimum of a multidimensional function is the focus of many problems in statistics, biology, physics, applied mathematics, economics, and chemistry [1][2][3][4][5][6]. Although there is a wide spectrum of problems, computing the global minimum remains a challenging task, because, for example, modern problem dimensionality is increasing.…”

Efficient and reliable calculation of rice-ramsperger—kassel-marcus unimolecular reaction rate constants for biopolymers: Modification of beyer-swinehart algorithm for degenerate vibrations

“…Efforts are being made in various fields to devise methods to handle this problem. For example, development of more efficient algorithms and computational methods for large molecules is one of the frontiers in quantum chemistry [1][2][3]. Theoretical treatment of biological reactions is also very challenging, tremendously large number of degrees of freedom being one of the main difficulties.…”

Computación de altas prestaciones sobre entornos grid en aplicaciones biomédicas: simulación de la actividad eléctrica cardiaca y diseño de proteínas

“…En [55] se plantea la utilización de métodos de Templado Simulado (Simulated Annealing) [56] para la optimización de proteínas, con el objetivo de solventar la problemática de los mínimos locales en la búsqueda de proteínas con mínima energía. Además, se utiliza una aproximación basada en Computación en Grid para realizar múltiples procesos de optimización, utilizando diferentes parámetros del algoritmo de optimización.…”