Efficient Atomistic Simulation of Pathways and Calculation of Rate Constants for a Protein–Peptide Binding Process: Application to the MDM2 Protein and an Intrinsically Disordered p53 Peptide

Abstract

The characterization of protein binding processes — with all of the key conformational changes — has been a grand challenge in the field of biophysics. Here, we have used the weighted ensemble path sampling strategy to orchestrate molecular dynamics simulations, yielding atomistic views of protein–peptide binding pathways involving the MDM2 oncoprotein and an intrinsically disordered p53 peptide. A total of 182 independent, continuous binding pathways were generated, yielding a kon that is in good agreement with experiment. These pathways were generated in 15 days using 3500 cores of a supercomputer, substantially faster than would be possible with “brute force” simulations. Many of these pathways involve the anchoring of p53 residue F19 into the MDM2 binding cleft when forming the metastable encounter complex, indicating that F19 may be a kinetically important residue. Our study demonstrates that it is now practical to generate pathways and calculate rate constants for protein binding processes using atomistic simulation on typical computing resources.

Weighted Ensemble Simulation: Review of Methodology, Applications, and Software

“…In addition, the software can be designed to be interoperable, that is, interfacing with any dynamics engine, because the algorithm does not require under-the-hood modifications of the dynamics engine. To our knowledge, the first freely available, highly scalable, and interoperable WE software package was WESTPA (74), which has been widely applied to a variety of systems ranging in scale from atomistic to cellular (2–4, 21, 22, 59, 62, 63, 75, 76) and interfaced with a diversity of dynamics engines, for example, with GROMACS(36), NAMD(49), OpenMM (23), and AMBER (12) at the atomistic/molecular scale, including GPU versions, and with UIOWA-BD (25, 30), BioNetGen (11, 26), and MCell (39) at the cellular scale. The WESTPA package embodies a wide range of WE capabilities, including the calculation of both equilibrium and kinetic observables using on-the-fly reweighting (10) or a post-analysis procedure (62) as well as plug-ins for using the WE-based string method (3) and WExplore, a recently developed WE strategy that defines sampling regions in a hierarchical fashion (16).…”

“…Thus far, the largest-scale application of the WE strategy to a complex biological process at the experimental temperature is the simulation of a protein–peptide binding process involving an N-terminal intrinsically disordered p53 peptide and the MDM2 oncoprotein (76) (Figure 3). Using 3,500-CPU cores on XSEDE’s Stampede, the simulation was carried out with implicit solvent at water-like viscosity and generated >180 pathways for the binding process within 15 days, yielding a k on that is in good agreement with experiment and identifying a residue that may be kinetically important.…”

“…Here, we review recent advances and applications of the WE path sampling approach, which has enabled a number of otherwise unfeasible applications (2, 17, 19, 22, 55, 59, 76). We present the theoretical basis of WE and also discuss ongoing challenges and future directions for this promising approach.…”

The dock‐and‐coalesce mechanism for the association of a WASP disordered region with the Cdc42 GTPase

“…Computational studies have sometimes employed simplified representations of proteins and often relied on the structure of the native complex for guidance [16–23]. These studies can provide qualitative descriptions of binding mechanisms but typically lack the capability of making quantitative predictions on binding rate constants, though there have been continued developments on the latter front [24, 25]. Again, sequential binding models are usually implicated.…”

Automatic error control during forward flux sampling of rare events in master equation models

“…In the former case, the models are often numerically studied using the stochastic simulation algorithm 8,9 (SSA) or one of its many varieties [10][11][12] . Such simulations have provided insight into diverse biological processes, including: the lysis/lysogeny decision in bacteriophage λ 13 , the lac operon in Escherichia coli 14 , check-pointing during the cell cycle 15 , differentiation of stem cells 16 , the binding of an intrinsically disordered peptide to a protein 17 , macrophage regulation 18 , and gradient detection during yeast mating 19 .…”