Hierarchical Markov State Model Building to Describe Molecular Processes

Modeling Ensembles of Enzyme Reaction Pathways with Hi-MSM Reveals the Importance of Accounting for Pathway Diversity

Conventional quantum mechanical-molecular mechanics (QM/MM) simulation approaches for modeling enzyme reactions often assume that there is one dominant reaction pathway and that this pathway can be sampled starting from an X-ray structure of the enzyme. These assumptions reduce computational cost; however, their validity has not been extensively tested. This is due in part to the lack of a rigorous formalism for integrating disparate pathway information from dynamical QM/MM calculations. Here, we present a way to model ensembles of reaction pathways efficiently using a divide-and-conquer strategy through Hierarchical Markov State Modeling (Hi-MSM). This approach allows information on multiple, distinct pathways to be incorporated into a chemical kinetic model, and it allows us to test these two assumptions. Applying Hi-MSM to the reaction carried out by dihydrofolate reductase (DHFR) we find (i) there are multiple, distinct pathways significantly contributing to the overall flux of the reaction that the conventional approach does not identify and (ii) that the conventional approach does not identify the dominant reaction pathway. Thus, both assumptions underpinning the conventional approach are violated. Since DHFR is a relatively small enzyme, and configuration space scales exponentially with protein size, accounting for multiple reaction pathways is likely to be necessary for most enzymes.

Understanding the Structure and Apo Dynamics of the Functionally Active JIP1 Fragment

Recent experiments indicate that the C-Jun amino-terminal kinase-interacting protein 1 (JIP1) binds to and activates the c-Jun N-terminal kinase (JNK) protein. JNK is an integral part of cell apoptosis, and misregulation of this process is a causative factor in diseases such as Alzheimer's disease (AD), obesity, and cancer. It has also been shown that JIP1 may increase the phosphorylation of tau by facilitating the interaction between the tau protein and JNK, which could also be a causative factor in AD. Very little is known about the structure and dynamics of JIP1; however, the amino acid composition of the first 350 residues suggests that it contains an intrinsically disordered region. Molecular dynamics (MD) simulations using AMBER 14 were used to study the structure and dynamics of a functionally active JIP1 10mer fragment to better understand the solution behavior of the fragment. Two microseconds of unbiased MD was performed on the JIP1 10mer fragment in 10 different seeds for a total of 20 μs of simulation time, and from this, seven structurally stable conformations of the 10mer fragment were identified via classical clustering. The 10mer ensemble was also used to build a Markov state model (MSM) that identified four metastable states that encompassed six of the seven conformational families identified by classical dimensional reduction. Based on this MSM, conformational interconversions between the four states occur via two dominant pathways with probability fluxes of 55 and 44% for each individual pathway. Transitions between the initial and final states occur with mean first passage times of 31 (forward) and 16 (reverse) μs.

All-atom adaptively biased path optimization of Src kinase conformational inactivation: Switched electrostatic network in the concerted motion of αC helix and the activation loop

A method to optimize a conformational pathway through a space of well-chosen reduced variables is employed to advance our understanding of protein conformational equilibrium. The adaptively biased path optimization strategy utilizes unrestricted, enhanced sampling in the region of a path in the reduced-variable space to identify a broad path between two stable end-states. Application to the inactivation transition of the Src tyrosine kinase catalytic domain reveals new insight into this well studied conformational equilibrium. The mechanistic description gained from identifying the motions and structural features along the path includes details of the switched electrostatic network found to underpin the transition. The free energy barrier along the path results from rotation of a helix, αC, that is tightly correlated with motions in the activation loop (A-loop) as well as distal regions in the C-lobe. Path profiles of the reduced variables clearly demonstrate the strongly correlated motions. The exchange of electrostatic interactions among residues in the network is key to these interdependent motions. In addition, the increased resolution from an all-atom model in defining the path shows multiple components for the A-loop motion and that different parts of the A-loop contribute throughout the length of the path.