Rapid sampling of all‐atom peptides using a library‐based polymer‐growth approach

Abstract

We adapted existing polymer growth strategies for equilibrium sampling of peptides described by modern atomistic forcefields with a simple uniform dielectric solvent. The main novel feature of our approach is the use of pre-calculated statistical libraries of molecular fragments. A molecule is sampled by combining fragment configurations – of single residues in this study – which are stored in the libraries. Ensembles generated from the independent libraries are reweighted to conform with the Boltzmann-factor distribution of the forcefield describing the full molecule. In this way, high-quality equilibrium sampling of small peptides (4–8 residues) typically requires less than one hour of single-processor wallclock time and can be significantly faster than Langevin simulations. Furthermore, approximate, clash-free ensembles can be generated for larger peptides (up to 32 residues in this study) in less than a minute of single-processor computing. We discuss possible applications of our growth procedure to free energy calculation, fragment assembly protein-structure prediction protocols, and to “multi-resolution” sampling.

Equilibrium Sampling in Biomolecular Simulations

“…The basic idea is to add one monomer (e.g., amino acid) at a time to an ensemble of partially grown configurations while (i) keeping track of appropriate statistical weights and/or (ii) by “resampling” [66]. These approaches have been applied to simplified models of proteins and nucleic acids [29, 138–140], to all-atom peptides at high temperature [126], and more recently to all-atom peptides using pre-sampled libraries of amino-acid configurations [78]. The intrinsic challenge in polymer growth methods is that configurations important in the full molecule may have low statistical probability in early stages of growth; thus application to large, detailed systems likely will require biasing toward structural information known for the full molecule [78, 138].…”

Extending fragment‐based free energy calculations with library monte carlo simulation: Annealing in interaction space

“…At the same time, free energy values require good sampling of the full system, which is never easy for proteins. In this regard, LBMC was designed to handle hybrid models in a natural way - with an atomistic binding site and a reduced representation elsewhere [50]. Good sampling of such models via LBMC may permit rapid, statistically based affinity estimates within the context of hybrid models.…”

“…LBMC is a canonical sampling procedure which can be used with an arbitrary forcefield and solvent model. Full details regarding LBMC have been given in previous work [27, 50, 28] but we summarize the essentials here.…”

“…AIS requires a canonical sampling procedure for the "relaxation" process, and we employ library-based Monte Carlo (LBMC) [27,50]. LBMC is a natural choice because it can employ the same fragments used in the staging choices, and is also highly efficient for sampling implicitly solvated peptides [28].…”

Free-energy calculations for semi-flexible macromolecules: Applications to DNA knotting and looping

“…A general challenge for polymer growth methods, which becomes more severe for increasing system size, is that configurations important in the full system may have low statistical probability in early stages of growth, so that biasing toward structural information known for the full system is required. 43 A widely used strategy to estimate the absolute free energy of a given target system, effectively combining methods for relative and absolute free energies described above, employs a reference system, "ref," for which the absolute free energy, F ref , is available. 11,[44][45][46][47] The free energy of the target system, A, is calculated as…”