Introduction
Simulation of protein structure has been the most important in bioinformatics. In the methods of prediction, there are homology modeling, threading, and ab initio modeling. Ab initio starts without any results from experiment. So this is more useful than any others to look into the structure of the unknown protein. This constructs it’s structure by physical methods to the bare atoms of the protein. But this involves the defects of inaccuracy and slowness than others in prediction(Jung, 2013).
Developing an algorithm for ab initio modelling to simulate protein structure is very important(K.A. Dill and J.L. MacCallum, 2012). This has two types of algorithms; one is molecular dynamics(MD) and the other is Monte Carlo(MC). This randomizes the location of atoms to predict protein structure in 3D space. That sets force fields to estimate atom positions at every moment.
Exponential time has spent to calculate all of the unknown protein’s conformations by using the highest capacity computer with MC and MD. Note that the structure of the protein changes between microseconds, and furthermore, the positions of the atoms in this structure occur in shorter pico and femto seconds. Thus, to obtain stable form of the molecule is still difficult for researchers.
In nature protein folds faster and more naturally than this. Namely, in vivo folding is faster than in vitro.(Nicola et al., 1999; Kolb et al., 2000; Cabrita et al., 2010; O’Brien et al., 2011, Fedorov and Baldwin, 1999; Seckler et al., 1989) So Levinthal addressed paradoxically that there is the apparent pathway in the folding(J. T. P. DeBrunner and E. Munck, 1969). This is true. And Karplus suggested that the ‘bias towards the native state’ over much of the effective energy surface may govern the folding process(M. Karplus, 1997). We appreciate this is the pathway of folding that occurs in protein synthesis from mRNA transcript.
Computational simulation of this cotranslational folding based on the numerical evidences appeared in 1995(Law, 2017; de Oliveira et al., 2015; Ellis et al. 2010; Lu and Liang, 2008; Srinivasan and Rose, 1995; Alexandrov, 1993). Besides, we introduce many experimental evidences and opinions of researchers for protein folding during intracellular gene translation(Cymer, F. and von Hijne, G., 2013; Fedorov, A.N., and Baldwin, T.O., 1997).
It is worth to look into cotranslational folding, because there are differences between simulated and experimental structures. We derived the structures from cotranslational and torsional algorithm. Cotranslational folding simulation was managed before by using SAINT algorithm(Ellis et al., 2010). 3D molecular dynamics was used in this experiment. All movements in the Cartesian space were considered as possible in most molecular dynamics algorithms by most of the researchers. These employ strong covalent bond stretching and bond angle bending terms in their force fields. Though covalent bond stretching and bond angle bending are affected by the force field, these model’s description abandons the fact that only rotations about covalent single bond occurs.
We used, thus, backbone torsion angle as the only degree of freedom to describe the motion of protein folding. These models might hamper to interpret the mechanism of protein folding. And helpful manual modification is restricted from researchers. These difficulties could be settled by torsion angle method. Also this is more realistic than other 3D models full of lattice approximations.