Efficient global biopolymer sampling with end-transfer configurational bias Monte Carlo.
We develop an "end-transfer configurational bias Monte Carlo" method for efficient thermodynamic sampling of complex biopolymers and assess its performance on a mesoscale model of chromatin (oligonucleosome) at different salt conditions compared to other Monte Carlo moves. Our method extends traditional configurational bias by deleting a repeating motif (monomer) from one end of the biopolymer and regrowing it at the opposite end using the standard Rosenbluth scheme. The method's sampling efficiency compared to local moves, pivot rotations, and standard configurational bias is assessed by parameters relating to translational, rotational, and internal degrees of freedom of the oligonucleosome. Our results show that the end-transfer method is superior in sampling every degree of freedom of the oligonucleosomes over other methods at high salt concentrations (weak electrostatics) but worse than the pivot rotations in terms of sampling internal and rotational sampling at low-to-moderate salt concentrations (strong electrostatics). Under all conditions investigated, however, the end-transfer method is several orders of magnitude more efficient than the standard configurational bias approach. This is because the characteristic sampling time of the innermost oligonucleosome motif scales quadratically with the length of the oligonucleosomes for the end-transfer method while it scales exponentially for the traditional configurational-bias method. Thus, the method we propose can significantly improve performance for global biomolecular applications, especially in condensed systems with weak nonbonded interactions and may be combined with local enhancements to improve local sampling.
Arya
Gaurav
G
Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, New York 10012, USA.
Schlick
Tamar
T
D001704:Biopolymers / Q000737:chemistry*; D002843:Chromatin / Q000737:chemistry* / Q000648:ultrastructure*; D003198:Computer Simulation; D003461:Crystallography / Q000379:methods*; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D008968:Molecular Conformation; D009010:Monte Carlo Method
D001704:Biopolymers; D002843:Chromatin