Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A

Abstract

Proteins need to interconvert between many conformations in order to function, many of which are formed transiently, and sparsely populated. Particularly when the lifetimes of these states approach the millisecond timescale, identifying the relevant structures and the mechanism by which they inter-convert remains a tremendous challenge. Here we introduce a novel combination of accelerated MD (aMD) simulations and Markov State modelling (MSM) to explore these 'excited' conformational states . Applying this to the highly dynamic protein CypA, a protein involved in immune response and associated with HIV infection, we identify five principally populated conformational states and the atomistic mechanism by which they interconvert. A rational design strategy predicted that the mutant D66A should stabilise the minor conformations and substantially alter the dynamics whereas the similar mutant H70A should leave the landscape broadly unchanged. These predictions are confirmed using CPMG and R1ρ solution state NMR measurements. By accurately and reliably exploring functionally relevant, but sparsely populated conformations with milli-second lifetimes in silico, our aMD/MSM method has tremendous promise for the design of dynamic protein free energy landscapes for both protein engineering and drug discovery.

Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes

“…We followed the same minimization and equilibration protocols previously reported. 77 In brief, each system was energy minimized combining steepest descent and conjugate gradient (4500 and 500 iterations respectively) minimisation. Systems were then heated from 0 K to 250 K in 150 ps in the NVT ensemble using a time step of 0.5 fs, and from 250 to 298 K in 300 ps using a timestep of 1 fs.…”

“…80 The resulting pool of trajectories was used to construct a Markov State Model (MSM) using the pyEMMA 2.3.0 software package, 81 following our previously described protocol to build MSMs for this system. 77 In brief, the RMSD of the 70s loop with respect to the X-ray structure 1AK4 82 (residues Gly65 to Gly77) was used to monitor the movement of this loop. The movement of the 100s loop (residues Met100 to Ser110) was described monitoring simultaneously the RMSD of the loop, and the distance between the COM of the loop and the COM of the -helix defined by residues Pro30 to Thr41.…”

“…The 100s loop undergoes a flapping movement that maps upward and downward movements of the loop onto similar RMSD values. Following previous work, 77 we multiply RSMD values by -1 when the distance described above is shorter than that observed in the X-ray structure (14.0 Å) to separate both loop motions along this collective variable.…”

“…To allow the most direct comparison with our previously reported model, we also used the same clustering algorithm (K-means) and the same number of microstates (100) from our previous work 77 for the MSM construction. Different lagtimes were used to compute implied timescales of the dominant eigenvectors, and the resulting implied timescale plots are shown in Figure S1.…”

Markov state models and NMR uncover an overlooked allosteric loop in p53

“…This leaves large regions of the energy landscape unexplored which may include many of the functionally important slower motions. Already, relatively short-scale MD simulations of Y220C have evidenced the flexibility of the protein and the Y220C pocket 33 MSMs allow the integration of multiple MD simulations into a single model of the protein conformational ensemble that contains key thermodynamic and kinetic properties in addition to retaining the atomic level details of the system [36][37][38][39][40][41][42][43] . Because the MSM is built on the transitions between states, the information from multiple MD simulations of the same system can be combined into a single model and no single simulation has to explore all the states.…”