Comparison of the Internal Dynamics of Metalloproteases Provides New Insights on Their Function and Evolution

Statistical Learning of Protein Elastic Network from Positional Covariance Matrix

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.

Probing the Conformational States of Thimet Oligopeptidase in Solution

Thimet oligopeptidase (TOP) is a metallopeptidase involved in the metabolism of oligopeptides inside and outside cells of various tissues. It has been proposed that substrate or inhibitor binding in the TOP active site induces a large hinge-bending movement leading to a closed structure, in which the bound ligand is enclosed. The main goal of the present work was to study this conformational change, and fluorescence techniques were used. Four active TOP mutants were created, each equipped with a single-Trp residue (fluorescence donor) and a p-nitro-phenylalanine (pNF) residue as fluorescence acceptor at opposite sides of the active site. pNF was biosynthetically incorporated with high efficiency using the amber codon suppression technology. Inhibitor binding induced shorter Donor-Acceptor (D-A) distances in all mutants, supporting the view that a hinge-like movement is operative in TOP. The activity of TOP is known to be dependent on the ionic strength of the assay buffer and D-A distances were measured at different ionic strengths. Interestingly, a correlation between the D-A distance and the catalytic activity of TOP was observed: the highest activities corresponded to the shortest D-A distances. In this study for the first time the hinge-bending motion of a metallopeptidase in solution could be studied, yielding insight about the position of the equilibrium between the open and closed conformation. This information will contribute to a more detailed understanding of the mode of action of these enzymes, including therapeutic targets like neurolysin and angiotensin-converting enzyme 2 (ACE2).

Long-Range Changes in Neurolysin Dynamics Upon Inhibitor Binding

Crystal structures of neurolysin, a zinc metallopeptidase, do not show a significant conformational change upon the binding of an allosteric inhibitor. Neurolysin has a deep channel where it hydrolyzes a short neuropeptide neurotensin to create inactive fragments and thus controls its level in the tissue. Neurolysin is of interest as a therapeutic target since changes in neurotensin level have been implicated in cardiovascular disorders, neurological disorders, and cancer, and inhibitors of neurolysin have been developed. An understanding of the dynamical and structural differences between apo and inhibitor-bound neurolysin will aid in further design of potent inhibitors and activators. For this purpose, we performed several molecular dynamics (MD) simulations for both apo and inhibitor-bound neurolysin. A machine learning method (Linear Discriminant Analysis) is applied to reveal differences between the apo and inhibitor-bound ensembles in an automated way, and large differences are observed on residues that are far from both the active site and the inhibitor binding site. The effects of inhibitor binding on the collective motions of neurolysin are extensively analyzed and compared using both Principal Component Analysis and Elastic Network Model calculations. We find that inhibitor binding induces additional low-frequency motions that are not observed in the apo form. ENM also reveals changes in inter- and intradomain communication upon binding. Furthermore, differences are observed in the inhibitor-bound neurolysin contact network that are far from the active site, revealing long-range allosteric behavior. This study also provides insight into the allosteric modulation of other neuropeptidases with similar folds.

Dissecting the roles of local packing density and longer-range effects in protein sequence evolution

What are the structural determinants of protein sequence evolution? A number of site-specific structural characteristics have been proposed, most of which are broadly related to either the density of contacts or the solvent accessibility of individual residues. Most importantly, there has been disagreement in the literature over the relative importance of solvent accessibility and local packing density for explaining site-specific sequence variability in proteins. We show that this discussion has been confounded by the definition of local packing density. The most commonly used measures of local packing, such as contact number and the weighted contact number, represent the combined effects of local packing density and longer-range effects. As an alternative, we propose a truly local measure of packing density around a single residue, based on the Voronoi cell volume. We show that the Voronoi cell volume, when calculated relative to the geometric center of amino-acid side chains, behaves nearly identically to the relative solvent accessibility, and each individually can explain, on average, approximately 34% of the site-specific variation in evolutionary rate in a data set of 209 enzymes. An additional 10% of variation can be explained by nonlocal effects that are captured in the weighted contact number. Consequently, evolutionary variation at a site is determined by the combined effects of the immediate amino-acid neighbors of that site and effects mediated by more distant amino acids. We conclude that instead of contrasting solvent accessibility and local packing density, future research should emphasize on the relative importance of immediate contacts and longer-range effects on evolutionary variation. Proteins 2016; 84:841-854. © 2016 Wiley Periodicals, Inc.