Abstract
We describe applications of the simulated annealing (SA) method to biological macromolecular systems. The first example is the reconstruction of an inappropriate three-dimensional (3D) structure of a functional site ina protein, built based on X-ray crystallographic experiments. The SA method significantly extended the sampling space of the conformational space, and thus enabled the identification of the energetically-optimal 3Dstructure of the functional site in the enzyme structure. The obtained structure facilitated various biophysical analyses, thereby leading us to theoretically resolve a discrepancy found in the biochemical experimental data. We also briefly introduce another example of the use of the SA method to resolve structural issues, by constituting an extremely advanced energy function (Hamiltonian) for computer simulations of biological macromolecular systems. The SA method enhanced the explorations of the optimal parameter set defined in such an energy function, so its values could be fit with those obtained by the exclusively high level ab initio quantum mechanics calculations. This type of energy function (i.e., an effective function) is very important to precisely describe molecular interactions (e.g., stacking interactions) that are frequently found and critical in the drug design field. In an application to bioinformatics field, the SA scheme was combined with the Multivariate Curve Resolution-Alternating Least Squares (MCRALS) method, for the first time (our scheme is referred to as the SA-MCRALS method). The conventional MCR-ALS has been widely employed to analyze various multivariate data, such as spectrum data from circular dichroism (CD) and nuclear magnetic resonance (NMR) analyses, and image data from bioinformatics fields. However, it still fails to find the appropriate solutions in some cases. We describe an example where the MCR-ALS method fails, and how the SA-MCR-ALS method overcomes those difficulties. In this manner, the SA scheme is a fundamental and powerful method that is intensively applicable to resolve the local minimum problem in various scientific fields related to biological macromolecular systems. In this chapter, we demonstrate such examples together with introductions of the relevant various technologies employed with the SA method.
Original language | English |
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Title of host publication | Simulated Annealing |
Subtitle of host publication | Introduction, Applications and Theory |
Publisher | Nova Science Publishers, Inc. |
Pages | 49-84 |
Number of pages | 36 |
ISBN (Electronic) | 9781536136753 |
ISBN (Print) | 9781536136746 |
Publication status | Published - 01-01-2018 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Mathematics