How much entropy is contained in NMR relaxation parameters?

Solution-state NMR relaxation experiments are the cornerstone to study internalprotein dynamics at atomic resolution on time scales that are faster than the overallrotational tumbling time,τR. Since the motions described by NMR relaxation pa-rameters are connected to thermodynamic quantities like conformational entropies, thequestion arises how much of the total entropy is contained within this tumbling time.Using all-atom molecular dynamics (MD) simulations of T4 lysozyme, we found thatentropy build-up is rather fast for the backbone, such that the majority of the entropyis indeed contained in the short-time dynamics. In contrast, the contribution of slowdynamics of side chains on time scales beyondτRon the side chain conformationalentropy is significant and should be taken into account for the extraction of accuratethermodynamic properties.

How much entropy is contained in NMR relaxation parameters?

Solution-state NMR relaxation experiments are the cornerstone to study internalprotein dynamics at atomic resolution on time scales that are faster than the overallrotational tumbling time,τR. Since the motions described by NMR relaxation pa-rameters are connected to thermodynamic quantities like conformational entropies, thequestion arises how much of the total entropy is contained within this tumbling time.Using all-atom molecular dynamics (MD) simulations of T4 lysozyme, we found thatentropy build-up is rather fast for the backbone, such that the majority of the entropyis indeed contained in the short-time dynamics. In contrast, the contribution of slowdynamics of side chains on time scales beyondτRon the side chain conformationalentropy is significant and should be taken into account for the extraction of accuratethermodynamic properties.

Genetically Encoded Biosensor-Based Screening for Directed Bacteriophage T4 Lysozyme Evolution

Lysozyme is widely used as a model protein in studies of structure–function relationships. Recently, lysozyme has gained attention for use in accelerating the degradation of secondary sludge, which mainly consists of bacteria. However, a high-throughput screening system for lysozyme engineering has not been reported. Here, we present a lysozyme screening system using a genetically encoded biosensor. We first cloned bacteriophage T4 lysozyme (T4L) into a plasmid under control of the araBAD promoter. The plasmid was expressed in Escherichia coli with no toxic effects on growth. Next, we observed that increased soluble T4L expression decreased the fluorescence produced by the genetic enzyme screening system. To investigate T4L evolution based on this finding, we generated a T4L random mutation library, which was screened using the genetic enzyme screening system. Finally, we identified two T4L variants showing 1.4-fold enhanced lytic activity compared to native T4L. To our knowledge, this is the first report describing the use of a genetically encoded biosensor to investigate bacteriophage T4L evolution. Our approach can be used to investigate the evolution of other lysozymes, which will expand the applications of lysozyme.

Structure and Function of the T4 Spackle Protein Gp61.3

The bacteriophage T4 genome contains two genes that code for proteins with lysozyme activity—e and 5. Gene e encodes the well-known T4 lysozyme (commonly called T4L) that functions to break the peptidoglycan layer late in the infection cycle, which is required for liberating newly assembled phage progeny. Gene product 5 (gp5) is the tail-associated lysozyme, a component of the phage particle. It forms a spike at the tip of the tail tube and functions to pierce the outer membrane of the Escherichia coli host cell after the phage has attached to the cell surface. Gp5 contains a T4L-like lysozyme domain that locally digests the peptidoglycan layer upon infection. The T4 Spackle protein (encoded by gene 61.3) has been thought to play a role in the inhibition of gp5 lysozyme activity and, as a consequence, in making cells infected by bacteriophage T4 resistant to later infection by T4 and closely related phages. Here we show that (1) gp61.3 is secreted into the periplasm where its N-terminal periplasm-targeting peptide is cleaved off; (2) gp61.3 forms a 1:1 complex with the lysozyme domain of gp5 (gp5Lys); (3) gp61.3 selectively inhibits the activity of gp5, but not that of T4L; (4) overexpression of gp5 causes cell lysis. We also report a crystal structure of the gp61.3-gp5Lys complex that demonstrates that unlike other known lysozyme inhibitors, gp61.3 does not interact with the active site cleft. Instead, it forms a “wall” that blocks access of an extended polysaccharide substrate to the cleft and, possibly, locks the enzyme in an “open-jaw”-like conformation making catalysis impossible.

Fitting side-chain NMR relaxation data using molecular simulations

Proteins display a wealth of dynamical motions that can be probed using both experiments and simulations. We present an approach to integrate side chain NMR relaxation measurements with molecular dynamics simulations to study the structure and dynamics of these motions. The approach, which we term ABSURDer (Average Block Selection Using Relaxation Data with Entropy Restraints) can be used to find a set of trajectories that are in agreement with relaxation measurements. We apply the method to deuterium relaxation measurements in T4 lysozyme, and show how it can be used to integrate the accuracy of the NMR measurements with the molecular models of protein dynamics afforded by the simulations. We show how fitting of dynamic quantities leads to improved agreement with static properties, and highlight areas needed for further improvements of the approach.

Discovering loop conformational flexibility in T4 lysozyme mutants through artificial intelligence aided molecular dynamics

Proteins sample a variety of conformations distinct from their crystal structure. These structures, their propensities, and pathways for moving between them contain enormous information about protein function that is hidden from a purely structural perspective. Molecular dynamics simulations can uncover these higher energy states but often at a prohibitively high computational cost. Here we apply our recent statistical mechanics and artificial intelligence based molecular dynamics framework for enhanced sampling of protein loops in three mutants of the protein T4 lysozyme. We are able to correctly rank these according to the stability of their excited state. By analyzing reaction coordinates, we also obtain crucial insight into why these specific perturbations in sequence space lead to tremendous variations in conformational flexibility. Our framework thus allows accurate comparison of loop conformation populations with minimal prior human bias, and should be directly applicable to a range of macromolecules in biology, chemistry and beyond.