Improving Speed and Affordability without Compromising Accuracy: Standard Binding Free-Energy Calculations Using an Enhanced Sampling Algorithm, Multiple-Time Stepping, and Hydrogen Mass Repartitioning

Accurate evaluation of protein–ligand binding
free energies
in silico is of paramount importance for understanding the mechanisms
of biological regulation and providing a theoretical basis for drug
design and discovery. Based on a series of atomistic molecular dynamics
simulations in an explicit solvent, using well-tempered metadynamics
extended adaptive biasing force (WTM-eABF) as an enhanced sampling
algorithm, the so-called “geometrical route” offers
a rigorous theoretical framework for binding affinity calculations
that match experimental values. However, although robust, this strategy
remains expensive, requiring substantial computational time to achieve
convergence of the simulations. Improving the efficiency of the geometrical
route, while preserving its reliability through improved ergodic sampling,
is, therefore, highly desirable. In this contribution, having identified
the computational bottleneck of the geometrical route, to accelerate
the calculations we combine (i) a longer time step for the integration
of the equations of motion with hydrogen-mass repartitioning (HMR),
and (ii) multiple time-stepping (MTS) for collective-variable and
biasing-force evaluation. Altogether, we performed 50 independent
WTM-eABF simulations in triplicate for the “physical”
separation of the Abl kinase-SH3 domain:p41 complex, following different
HMR and MTS schemes, while tuning, in distinct protocols, the parameters
of the enhanced-sampling algorithm. To demonstrate the consistency
and reliability of the results obtained with the best-performing setups,
we carried out quintuple simulations. Furthermore, we demonstrated
the transferability of our method to other complexes by triplicating
a 200 ns separation simulation of nine chosen protocols for the MDM2-p53:NVP-CGM097
complex. [Holzer et al. J. Med. Chem. 2015, 58, 6348-6358.] Our results, based on an aggregate
simulation time of 14.4 μs, allowed an optimal set of parameters
to be identified, able to accelerate convergence by a factor of three
without any noticeable loss of accuracy.