ATP binding enables broad antibiotic selectivity of aminoglycoside phosphotransferase(3')-IIIa: an elastic network analysis

Prestrain-induced contraction in one-dimensional random elastic chains

Prestrained elastic networks arise in a number of biological and technological systems ranging from the cytoskeleton of cells to tensegrity structures. Motivated by this observation, we here consider a minimal model in one dimension to set the stage for understanding the response of such networks as a function of the prestrain. To this end we consider a chain [one-dimensional (1D) network] of elastic springs upon which a random, zero mean, finite variance prestrain is imposed. Numerical simulations and analytical predictions quantify the magnitude of the contraction as a function of the variance of the prestrain, and show that the chain always shrinks. To test these predictions, we vary the topology of the chain, consider more complex connectivity and show that our results are relatively robust to these changes.

Computational Ways to Enhance Protein Inhibitor Design

Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials.

Gaussian network model can be enhanced by combining solvent accessibility in proteins

Gaussian network model (GNM), regarded as the simplest and most representative coarse-grained model, has been widely adopted to analyze and reveal protein dynamics and functions. Designing a variation of the classical GNM, by defining a new Kirchhoff matrix, is the way to improve the residue flexibility modeling. We combined information arising from local relative solvent accessibility (RSA) between two residues into the Kirchhoff matrix of the parameter-free GNM. The undetermined parameters in the new Kirchhoff matrix were estimated by using particle swarm optimization. The usage of RSA was motivated by the fact that our previous work using RSA based linear regression model resulted out higher prediction quality of the residue flexibility when compared with the classical GNM and the parameter free GNM. Computational experiments, conducted based on one training dataset, two independent datasets and one additional small set derived by molecular dynamics simulations, demonstrated that the average correlation coefficients of the proposed RSA based parameter-free GNM, called RpfGNM, were significantly increased when compared with the parameter-free GNM. Our empirical results indicated that a variation of the classical GNMs by combining other protein structural properties is an attractive way to improve the quality of flexibility modeling.

Identification of Hot Spots in Protein Structures Using Gaussian Network Model and Gaussian Naive Bayes

Residue fluctuations in protein structures have been shown to be highly associated with various protein functions. Gaussian network model (GNM), a simple representative coarse-grained model, was widely adopted to reveal function-related protein dynamics. We directly utilized the high frequency modes generated by GNM and further performed Gaussian Naive Bayes (GNB) to identify hot spot residues. Two coding schemes about the feature vectors were implemented with varying distance cutoffs for GNM and sliding window sizes for GNB based on tenfold cross validations: one by using only a single high mode and the other by combining multiple modes with the highest frequency. Our proposed methods outperformed the previous work that did not directly utilize the high frequency modes generated by GNM, with regard to overall performance evaluated using F1 measure. Moreover, we found that inclusion of more high frequency modes for a GNB classifier can significantly improve the sensitivity. The present study provided additional valuable insights into the relation between the hot spots and the residue fluctuations.

Bridging Enzymatic Structure Function via Mechanics: A Coarse-Grain Approach

Flexibility is a central aspect of protein function, and ligand binding in enzymes involves a wide range of structural changes, ranging from large-scale domain movements to small loop or side-chain rearrangements. In order to understand how the mechanical properties of enzymes, and the mechanical variations that are induced by ligand binding, relate to enzymatic activity, we carried out coarse-grain Brownian dynamics simulations on a set of enzymes whose structures in the unbound and ligand-bound forms are available in the Protein Data Bank. Our results show that enzymes are remarkably heterogeneous objects from a mechanical point of view and that the local rigidity of individual residues is tightly connected to their part in the protein's overall structure and function. The systematic comparison of the rigidity of enzymes in their unbound and bound forms highlights the fact that small conformational changes can induce large mechanical effects, leading to either more or less flexibility depending on the enzyme's architecture and the location of its ligand-biding site. These mechanical variations target a limited number of specific residues that occupy key locations for enzymatic activity, and our approach thus offers a mean to detect perturbation-sensitive sites in enzymes, where the addition or removal of a few interactions will lead to important changes in the proteins internal dynamics.

DIRECT-ID: An automated method to identify and quantify conformational variations--application to β2 -adrenergic GPCR

The conformational dynamics of a macromolecule can be modulated by a number of factors, including changes in environment, ligand binding, and interactions with other macromolecules, among others. We present a method that quantifies the differences in macromolecular conformational dynamics and automatically extracts the structural features responsible for these changes. Given a set of molecular dynamics (MD) simulations of a macromolecule, the norms of the differences in covariance matrices are calculated for each pair of trajectories. A matrix of these norms thus quantifies the differences in conformational dynamics across the set of simulations. For each pair of trajectories, covariance difference matrices are parsed to extract structural elements that undergo changes in conformational properties. As a demonstration of its applicability to biomacromolecular systems, the method, referred to as DIRECT-ID, was used to identify relevant ligand-modulated structural variations in the β2 -adrenergic (β2 AR) G-protein coupled receptor. Micro-second MD simulations of the β2 AR in an explicit lipid bilayer were run in the apo state and complexed with the ligands: BI-167107 (agonist), epinephrine (agonist), salbutamol (long-acting partial agonist), or carazolol (inverse agonist). Each ligand modulated the conformational dynamics of β2 AR differently and DIRECT-ID analysis of the inverse-agonist vs. agonist-modulated β2 AR identified residues known through previous studies to selectively propagate deactivation/activation information, along with some previously unidentified ligand-specific microswitches across the GPCR. This study demonstrates the utility of DIRECT-ID to rapidly extract functionally relevant conformational dynamics information from extended MD simulations of large and complex macromolecular systems.

Ligand promiscuity through the eyes of the aminoglycoside N3 acetyltransferase IIa

Aminoglycoside-modifying enzymes (AGMEs) are expressed in many pathogenic bacteria and cause resistance to aminoglycoside (AG) antibiotics. Remarkably, the substrate promiscuity of AGMEs is quite variable. The molecular basis for such ligand promiscuity is largely unknown as there is not an obvious link between amino acid sequence or structure and the antibiotic profiles of AGMEs. To address this issue, this article presents the first kinetic and thermodynamic characterization of one of the least promiscuous AGMEs, the AG N3 acetyltransferase-IIa (AAC-IIa) and its comparison to two highly promiscuous AGMEs, the AG N3-acetyltransferase-IIIb (AAC-IIIb) and the AG phosphotransferase(3')-IIIa (APH). Despite having similar antibiotic selectivities, AAC-IIIb and APH catalyze different reactions and share no homology to one another. AAC-IIa and AAC-IIIb catalyze the same reaction and are very similar in both amino acid sequence and structure. However, they demonstrate strong differences in their substrate profiles and kinetic and thermodynamic properties. AAC-IIa and APH are also polar opposites in terms of ligand promiscuity but share no sequence or apparent structural homology. However, they both are highly dynamic and may even contain disordered segments and both adopt well-defined conformations when AGs are bound. Contrary to this AAC-IIIb maintains a well-defined structure even in apo form. Data presented herein suggest that the antibiotic promiscuity of AGMEs may be determined neither by the flexibility of the protein nor the size of the active site cavity alone but strongly modulated or controlled by the effects of the cosubstrate on the dynamic and thermodynamic properties of the enzyme.

Sequence-based Gaussian network model for protein dynamics

Motivation: Gaussian network model (GNM) is widely adopted to analyze and understand protein dynamics, function and conformational changes. The existing GNM-based approaches require atomic coordinates of the corresponding protein and cannot be used when only the sequence is known.

Results: We report, first of its kind, GNM model that allows modeling using the sequence. Our linear regression-based, parameter-free, sequence-derived GNM (L-pfSeqGNM) uses contact maps predicted from the sequence and models local, in the sequence, contact neighborhoods with the linear regression. Empirical benchmarking shows relatively high correlations between the native and the predicted with L-pfSeqGNM B-factors and between the cross-correlations of residue fluctuations derived from the structure- and the sequence-based GNM models. Our results demonstrate that L-pfSeqGNM is an attractive platform to explore protein dynamics. In contrast to the highly used GNMs that require protein structures that number in thousands, our model can be used to study motions for the millions of the readily available sequences, which finds applications in modeling conformational changes, protein–protein interactions and protein functions.

Contact:zerozhua@126.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Transient kinetics of aminoglycoside phosphotransferase(3')-IIIa reveals a potential drug target in the antibiotic resistance mechanism

Aminoglycoside phosphotransferases are bacterial enzymes responsible for the inactivation of aminoglycoside antibiotics by O-phosphorylation. It is important to understand the mechanism of enzymes in order to find efficient drugs. Using rapid-mixing methods, we studied the transient kinetics of aminoglycoside phosphotransferase(3′)-IIIa. We show that an ADP-enzyme complex is the main steady state intermediate. This intermediate interacts strongly with kanamycin A to form an abortive complex that traps the enzyme in an inactive state. A good strategy to prevent the inactivation of aminoglycosides would be to develop uncompetitive inhibitors that interact with this key ADP-enzyme complex.

The harmonic analysis of cylindrically symmetric proteins: a comparison of Dronpa and a DNA sliding clamp

The harmonic analysis of two types of proteins with cylindrical symmetry is performed by the Standard Force Field Normal Mode Analysis and by the elastic network model. For both proteins the global elastic modes are assigned to their characteristic topologies. Dronpa is a rigid β-barrel structure, presenting the twisting, bending and breathing motion of a cylindrical rod. The β sliding clamp of Escherichia coli is a hexagonal β-wheel, consisting of rigid segments. In its spectrum four classes of vibrations are identified which are characteristic of an elastic torus. Correlation diagrams and RMSF analysis are compared. The results provide not only a comprehensive validation of the use of both methods to describe the elastic behavior according to the low-frequency normal modes, but also depict the correlated motions of β-barrel and β-wheel proteins. The harmonic flexibility of the Dronpa protein is compared to the principal components of molecular dynamics (MD) simulation. A functionally important localized cleft opening mode is found, which is not detected by harmonic analysis.

Coenzyme A binding to the aminoglycoside acetyltransferase (3)-IIIb increases conformational sampling of antibiotic binding site

NMR spectroscopy experiments and molecular dynamics simulations were performed to describe the dynamic properties of the aminoglycoside acetyltransferase (3)-IIIb (AAC) in its apo and coenzyme A (CoASH) bound forms. The (15)N-(1)H HSQC spectra indicate a partial structural change and coupling of the CoASH binding site with another region in the protein upon the CoASH titration into the apo enzyme. Molecular dynamics simulations indicate a significant structural and dynamic variation of the long loop in the antibiotic binding domain in the form of a relatively slow (250 ns), concerted opening motion in the CoASH-enzyme complex and that binding of the CoASH increases the structural flexibility of the loop, leading to an interchange between several similar equally populated conformations.