Combined Monte Carlo/torsion-angle molecular dynamics for ensemble modeling of proteins, nucleic acids and carbohydrates

Abstract

We describe a general method to use Monte Carlo simulation followed by torsion-angle molecular dynamics simulations to create ensembles of structures to model a wide variety of soft-matter biological systems. Our particular emphasis is focused on modeling low-resolution small-angle scattering and reflectivity structural data. We provide examples of this method applied to HIV-1 Gag protein and derived fragment proteins, TraI protein, linear B-DNA, a nucleosome core particle, and a glycosylated monoclonal antibody. This procedure will enable a large community of researchers to model low-resolution experimental data with greater accuracy by using robust physics based simulation and sampling methods which are a significant improvement over traditional methods used to interpret such data.

Solution structures of human myeloma IgG3 antibody reveal extended Fab and Fc regions relative to the other IgG subclasses

“…For the Monte Carlo simulations to generate trial structures, the residues in the five starting IgG3 structures were renumbered and their naming nomenclature was adjusted to match the required format for the Torsion Angle Monte Carlo (TAMC) module on SASSIE-web for this to work ( 77 ). The IgG3 residue numbering was thus changed into one continuous segment that encompassed both the Fab and the Fc regions.…”

Two distinct conformations of factor H regulate discrete complement-binding functions in the fluid phase and at cell surfaces

“…The model library of physically realistic FH structural conformations was generated by subjecting the inter-SCR linkers to the TAMC module in SASSIE-web ( 104 ). Eleven of the 19 linkers were defined by high-resolution structures and were therefore held fixed.…”

Targeting Intrinsically Disordered Proteins through Dynamic Interactions

“…Yet, a fundamental limitation is that these experimental measurements generally reflects the average properties, which alone are not sufficient to uniquely define the underlying heterogeneous ensemble due to the severely underdetermined nature of the structural calculation problem [22,23,25,[59][60][61]. At present, the most robust methods generally involve first generating a large number of candidate random structures and then using experimental structural restraints to select and construct optimal sub-ensembles according to various statistical criteria [62][63][64][65][66][67][68][69][70][71]. Nonetheless, these methods rely critically on the ability to generate initial candidate structures that are not only diverse enough to cover the range of accessible states of the protein but also specific enough to contain any nontrivial local (and long-range) structure features associated with a particular protein state.…”