Mechanisms for Benzene Dissociation through the Excited State of T4 Lysozyme L99A Mutant

Abstract

The atomic-level mechanisms that coordinate ligand release from protein pockets are only known for a handful of proteins. Here, we report results from accelerated molecular dynamics simulations for benzene dissociation from the buried cavity of the T4 lysozyme Leu99Ala mutant (L99A). In these simulations, benzene is released through a previously characterized, sparsely populated room-temperature excited state of the mutant, explaining the coincidence for experimentally measured benzene off rate and apo protein slow-timescale NMR relaxation rates between ground and excited states. The path observed for benzene egress is a multistep ligand migration from the buried cavity to ultimate release through an opening between the F/G-, H-, and I-helices and requires a number of cooperative multiresidue and secondary-structure rearrangements within the C-terminal domain of L99A. These rearrangements are identical to those observed along the ground state to excited state transitions characterized by molecular dynamic simulations run on the Anton supercomputer. Analyses of the molecular properties of the residues lining the egress path suggest that protein surface electrostatic potential may play a role in the release mechanism. Simulations of wild-type T4 lysozyme also reveal that benzene-egress-associated dynamics in the L99A mutant are potentially exaggerations of the substrate-processivity-related dynamics of the wild type. FIGURE 5 WT T4 lysozyme transition from ground state to a high-energy-like state. (A) Concerted side-chain motion and backbone breakages occurring at $2600 aMD time steps for the same residues as involved with ground state to high-energy state transition in L99A mutant. (B and C) Structures from the crystal structure (green) compared to the high-energy-like state (cyan) for F114, L133, F153, and V111. (D) Helices F, G, H, and I in the ground state (cyan) and WT* high-energy-like state (orange) with the peptidoglycan substrate (purple). To see this figure in color, go online.

Ligand unbinding mechanisms and kinetics for T4 lysozyme mutants from τRAMD simulations

“…1 ), with the FGH route (routes are named by the helices lining them) being clearly predominant at all conditions. The FGH route was the only one recorded in several publications using metadynamics, machine learning and aMD approaches ( Feher et al., 2019 ; Lamim Ribeiro and Tiwary, 2019 ; Wang et al, 2016 , 2019 ). However, the FGH route can be subdivided further by lowering the threshold for hierarchical clustering (see Fig.…”

“…clusters 1–4). In unbinding simulations using aMD ( Feher et al., 2019 ), intermediate states between helices G and H (cluster 5) and between helices F and H (resembling cluster 6 but shifted closer to helix H) were identified. Thus, not all of the metastable states identified in conventional MD and RAMD simulations were revealed by these enhanced sampling methods.…”

“…Remarkably, no less than thirteen computational studies have been published since 2018 by different research groups in which methods based on MD simulation were used to identify paths from a buried cavity to the T4L exterior and to try to characterize the ligand binding and unbinding processes energetically and kinetically (see Fig. 1 , review Nunes-Alves et al., 2020 and recent papers of Capelli et al., 2019 ; Dandekar and Mondal, 2020 ; Feher et al., 2019 ; Lamim Ribeiro and Tiwary, 2019 ; Lotz and Dickson, 2020 ; Mondal et al., 2018 ; Niitsu et al., 2019 ; Nunes-Alves et al., 2018 ; Rydzewski, 2020 ; Rydzewski and Valsson, 2019 ; Souza et al., 2020 ; Wang et al, 2018 , 2019 ).

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“…(B) Ligand binding and unbinding routes observed in recent computational studies are indicated by circles colored by simulation type. Methods: MetaD_1 ( Capelli et al., 2019 ) and MetaD_2 ( Wang et al., 2016 ) - metadynamics, Maze ( Rydzewski and Valsson, 2019 ) , CG_1 ( Dandekar and Mondal, 2020 ) and CG_2 ( Souza et al., 2020 ) - coarse-grained, WE MD ( Nunes-Alves et al., 2018 ) – weighted ensemble MD, aMD ( Feher et al., 2019 ) - accelerated MD, RAVE ( Lamim Ribeiro and Tiwary, 2019 ) - Reweighted autoencoded variational Bayes for enhanced sampling; PFIb ( Wang et al., 2019 ) - Past-future information bottleneck, MD_MSM ( Mondal et al., 2018 ) – conventional MD and Markov State Model, gREST ( Niitsu et al., 2019 ) - generalized replica exchange with solute tempering, GaMD ( Miao et al., 2015 ) – Gaussian accelerated MD. …”

Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials

“…Remarkably, the inverted orientation of the ligand's aromatic ring is corrected over the cumulative time of 4 × 200 ps = 800 ps CoordNum-accelerated MDFF -an observation that normally takes microseconds of conventional MD. 59 Following this convergence, 200 ps more of simple NNP-MDFF was performed without any further bias from the known contact of the methionine residue. This simulation recovers the inhibitor-bound insulin receptor commensurate with the 2.6 Åresolved 3ETA model.…”