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. 2023 Sep 12;19(17):5731-5742.
doi: 10.1021/acs.jctc.3c00254. Epub 2023 Aug 21.

OneOPES, a Combined Enhanced Sampling Method to Rule Them All

Valerio Rizzi  1   2   3 , Simone Aureli  1   2   3 , Narjes Ansari  4 , Francesco Luigi Gervasio  1   2   3   5
Affiliations

OneOPES, a Combined Enhanced Sampling Method to Rule Them All

Valerio Rizzi et al. J Chem Theory Comput. .

Abstract

Enhanced sampling techniques have revolutionized molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a system's dynamics and recover the original system's statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems. Another category of techniques, such as parallel tempering, simulates multiple replicas of the system in parallel, without requiring CVs. However, these methods may explore less relevant high-energy portions of the phase space and become computationally expensive for large systems. To overcome the limitations of both approaches, we propose a replica exchange method called OneOPES that combines the power of multireplica simulations and CV-based enhanced sampling. This method efficiently accelerates the phase space sampling without the need for ideal CVs, extensive parameters fine tuning nor the use of a large number of replicas, as demonstrated by its successful applications to protein-ligand binding and protein folding benchmark systems. Our approach shows promise as a new direction in the development of enhanced sampling techniques for molecular dynamics simulations, providing an efficient and robust framework for the study of complex and unexplored problems.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Schematic representation of the OneOPES replica exchange method. Replica 0 only includes one OPES Explore bias potential and is the most convergence-focused replica, while replica 7 is the most exploration-focused one as it may include both extra OPES Explore potentials on additional CVs and OPES MultiThermal with the highest thermal excursion.
Figure 2
Figure 2
Graphical depiction of the systems that we investigate with OneOPES. In (a), we show Alanine dipeptide with the ϕ and ψ dihedrals coloured in orange and green, respectively. In (b), we present the Trypsin-Benzamidine complex, with the height z and the radius r of the funnel that we employ as CVs coloured in blue and red, respectively. In (c), we show the Chignolin miniprotein. We superimpose the Wild-Type structure in orange with the double mutant CLN025 that we simulate in green. The residues and the intraprotein contacts included in the HLDA CV are displayed in the panel insets and highlighted through grey dashed lines.
Figure 3
Figure 3
Alanine Dipeptide set of 5 independent simulations where we bias the suboptimal CV ψ with PT-WTE-MetaD (panels (a) and (d)), OneOPES (panels (b) and (e)) and OneOPES MultiCV (panels (c) and (f)). In (a–c), we show the average ΔF in time between the two basins with a dark blue, dark red and dark purple solid line and their standard deviation in semitransparent regions in light blue, light red and light purple, respectively. ΔF values corresponding to individual simulations are shown with solid thinner lines. The expected ΔF is indicated by a dashed black line with a tolerance error of 0.5 kBT in shaded gray. In (d–f), we show the one-dimensional FES reweighted over ϕ after 50 ns. The same colour scheme applies as in panels (a–c).
Figure 4
Figure 4
Set of 5 independent Trypsin-Benzamidine simulations where we bias the funnel coordinates z and r(67) with PT-WTE-MetaD (panels (a) and (d)), OneOPES (panels (b) and (e)) and OneOPES MultiCV (panels (c) and (f)). In (a–c), we show the average binding ΔF in time with a dark blue, dark red and dark purple solid line and their standard deviation in semitransparent regions in light blue, light red and light purple, respectively. ΔF values corresponding to individual simulations are shown with solid thinner lines. The expected ΔF is taken from ref (59) and is indicated by a dashed black line with a tolerance error of 0.5 kBT in shaded grey. In (d–f), we show the one-dimensional FES reweighted over z after 250 ns. The same colour scheme applies as in panels (a–c).
Figure 5
Figure 5
Set of 5 independent Chignolin simulations where we bias the HLDA CV, with PT-WTE-MetaD (panels (a) and (d)), OneOPES (panels (b) and (e)) and OneOPES MultiCV (panels (c) and (f)). In (a–c), we show the average folding ΔF in time with a dark blue, dark red and dark purple solid line and their standard deviation in semitransparent regions in light blue, light red and light purple, respectively. ΔF values corresponding to individual simulations are shown with solid thinner lines. The expected ΔF is taken from ref (66) and is indicated by a dashed black line with a tolerance error of 0.5 kBT in shaded grey. In (d–f), we show the one-dimensional FES reweighted over the RMSD Cα after 400 ns. The same colour scheme applies as in panels (a–c).
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