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.