Dandekar, B. R. | Search bioRxiv

bioRxiv2020

Reconciling conformational heterogeneity and Substrate Recognition in Cytochrome P450

Cytochrome P450, the ubiquitous metalloenzyme involved in detoxification of foreign components, has remained one of the most popular systems for substrate-recognition process. However, despite being known for its high substrate specificity, the mechanistic basis of substrate-binding by archetypal system cytochrome P450cam has remained at odds with the contrasting reports of multiple diverse crystallographic structures of its substrate-free form. Here we address this issue by elucidating the probability of mutual dynamical transition to the other crystallographic pose of cytochrome P450cam and vice versa via unbiased all-atom computer simulation. A robust Markov state model (MSM), constructed using adaptively sampled 84 microsecond-long Molecular dynamics simulation trajectories, maps the broad and heterogenous P450cam conformational landscape into five key sub-states. In particular, the MSM identifies an intermediate-assisted dynamic equilibrium between a pair of conformations of P450cam, in which the substrate-recognition sites remain closed and open respectively. However, the estimate of a significantly high stationary population of closed conformation, coupled with faster rate of open [->] closed transition than its reverse process, dictates that the net conformational equilibrium would be swayed in favour of closed conformation. Together, the investigation quantitatively infers that while a potential substrate of cytochrome P450cam would in principle explore a diverse array of conformations of substrate-free protein, it would mostly encounter a closed or solvent-occluded conformation and hence would follow an induced-fit based recognition process. Overall, the work reconciles multiple precedent crystallographic, spectroscopic investigations and establishes how a statistical elucidation of conformational heterogeneity in protein would provide crucial insights in the mechanism of potential substrate-recognition process. STATEMENT OF SIGNIFICANCEConformational heterogeneity plays an important role in defining the structural and functional dynamics of the enzymes. While the static three-dimensional crystallographic structures of enzymes solved in different conditions and/or environments are crucial to provide the conformational sub-states of enzymes, these are not sufficient to understand the kinetics and thermodynamics of these sub-states and their role in substrate recognition process. Cytochrome P450cam, the archtypal metalloenzyme, presents such a complex scenario due to prevalent reports of contrasting crystallographic structures of its substrate-free form. This work quantifies the conformational heterogeneity of substrate-free P450cam by exploring the possibility of mutual transition among the crystallographic poses at an atomic resolution and in the process elucidates its possible substrate-recognition mechanism.

biochemistry↗

bioRxiv2019

Capturing Protein-Ligand Recognition Pathways in Coarse-grained Simulation

Protein-substrate recognition is highly dynamic and complex process in nature. A key approach in deciphering the mechanism underlying the recognition process is to capture the kinetic process of substrate in its act of binding to its designated protein cavity. Towards this end, microsecond long atomistic molecular dynamics (MD) simulation has recently emerged as a popular method of choice, due its ability to record these events at high spatial and temporal resolution. However, success in this approach comes at an exorbitant computational cost. Here we demonstrate that coarse grained models of protein, when systematically optimised to maintain its tertiary fold, can capture the complete process of spontaneous protein-ligand binding from bulk media to cavity, within orders of magnitude shorter wall clock time compared to that of all-atom MD simulations. The simulated and crystallographic binding pose are in excellent agreement. We find that the exhaustive sampling of ligand exploration in protein and solvent, harnessed by coarse-grained simulation at a frugal computational cost, in combination with Markov state modelling, leads to clearer mechanistic insights and discovery of novel recognition pathways. The result is successfully validated against three popular protein-ligand systems. Overall, the approach provides an affordable and attractive alternative of all-atom simulation and promises a way-forward for replacing traditional docking based small molecule discovery by high-throughput coarse-grained simulation for searching potential binding site and allosteric sites. This also provides practical avenues for first-hand exploration of bio-molecular recognition processes in large-scale biological systems, otherwise inaccessible in all-atom simulations.

biophysics↗