Molecular dynamics study of conformational changes in human serum albumin by binding of fatty acids

Human serum albumin (HSA) binds with fatty acids under normal physiologic conditions. To date, there is little published information on the tertiary structure of HSA-fatty acid complex in aqueous solution. In the present study, we used molecular dynamics (MD) simulations to elucidate possible structural changes of HSA brought about by the binding of fatty acids. Both unliganded HSA and HSA-fatty acid complex models for MD calculations were constructed based on the X-ray crystal structures. Five myristates (MYRs) were bound in the HSA-fatty acid complex model. In the present MD study, the motion of domains I and III caused by the binding of MYR molecules increased the radius of gyration of HSA. Root-mean-square fluctuations from the MD simulations revealed that the atomic fluctuations of the specific amino acids at drug-binding site I that can regulate the drug-binding affinity were increased by the binding of MYR molecules. Primary internal motions, characterized by the first three principal components, were observed mainly at domains I and III in the principal component analysis for trajectory data. The directional motion projected on the first principal component of unliganded HSA was conserved in HSA-MYR complex as the third principal directional motion with higher frequency. However, the third principal directional motion in unliganded HSA turned into the first principal directional motion with lower frequency in the HSA-MYR complex. Thus, the present MD study provides insights into the possible conformational changes of HSA caused by the binding of fatty acids.     

Molecular motions and conformational changes of HPPK

6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) belongs to a class of catalytic enzymes involved in phosphoryl transfer and is a new target for the development of novel antimicrobial agents. In the present study, the fundamental consideration is to view the overall structure of HPPK as a network of interacting residues and to extract the most cooperative collective motions that define its global dynamics. A coarse-grained model, harmonically constrained according to HPPK's crystal structure is used. Four crystal structures of HPPK (one apo and three holo forms with different nucleotide and pterin analogs) are studied with the goal of providing insights about the function-dynamic correlation and ligand induced conformational changes. The dynamic differences are examined between HPPK's apo- and holo-forms, because they are involved in the catalytic reaction steps. Our results indicate that the palm-like structure of HPPK is nearly rigid, whereas the two flexible loops: L2 (residues 43-53) and L3 (residues 82-92) exhibit the most concerted motions for ligand recognition and presumably, catalysis. These two flexible loops are involved in the recognition of HPPKs nucleotide and pterin ligands, whereas the rigid palm region is associated with binding of these cognate ligands. Six domains of collective motions are identified, comprised of structurally close but not necessarily sequential residues. Two of these domains correspond to the flexible loops (L2 and L3), whereas the remaining domains correspond to the rigid part of the molecule.     

Close correspondence between the motions from principal component analysis of multiple HIV-1 protease structures and elastic network modes

The large number of available HIV-1 protease structures provides a remarkable sampling of conformations of the different conformational states, which can be viewed as direct structural information about the dynamics of the HIV-1 protease. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide similar sampling of conformations.     

Local feature analysis: a statistical theory for reproducible essential dynamics of large macromolecules

Multivariate statistical methods are widely used to extract functional collective motions from macromolecular molecular dynamics (MD) simulations. In principal component analysis (PCA), a covariance matrix of positional fluctuations is diagonalized to obtain orthogonal eigenvectors and corresponding eigenvalues. The first few eigenvectors usually correspond to collective modes that approximate the functional motions in the protein. However, PCA representations are globally coherent by definition and, for a large biomolecular system, do not converge on the time scales accessible to MD. Also, the forced orthogonalization of modes leads to complex dependencies that are not necessarily consistent with the symmetry of biological macromolecules and assemblies. Here, we describe for the first time the application of local feature analysis (LFA) to construct a topographic representation of functional dynamics in terms of local features. The LFA representations are low dimensional, and like PCA provide a reduced basis set for collective motions, but they are sparsely distributed and spatially localized. This yields a more reliable assignment of essential dynamics modes across different MD time windows. Also, the intrinsic dynamics of local domains is more extensively sampled than that of globally coherent PCA modes.     

A principal component analysis of the dynamics of subdomains and binding sites in human serum albumin

The conformational dynamics of human serum albumin (HSA) was investigated by principal component analysis (PCA) applied to three molecular dynamics trajectories of 200 ns each. The overlap of the essential subspaces spanned by the first 10 principal components (PC) of different trajectories was about 0.3 showing that the PCA based on a trajectory length of 200 ns is not completely convergent for this protein. The contributions of the relative motion of subdomains and of the subdomains (internal) distortion to the first 10 PCs were found to be comparable. Based on the distribution of the first 3 PC, 10 protein conformers are identified showing relative root mean square deviations (RMSD) between 2.3 and 4.6 Å. The main PCs are found to be delocalized over the whole protein structure indicating that the motions of different protein subdomains are coupled. This coupling is considered as being related to the allosteric effects observed upon ligand binding to HSA. On the other hand, the first PC of one of the three trajectories describes a conformational transition of the protein domain I that is close to that experimentally observed upon myristate binding. This is a theoretical support for the older hypothesis stating that changes of the protein onformation favorable to binding can precede the ligand complexation. A detailed all atoms PCA performed on the primary Sites 1 and 2 confirms the multiconformational character of the HSA binding sites as well as the significant coupling of their motions. © 2013 Wiley Periodicals, Inc. Biopolymers 101: 561–572, 2014.     

Escherichia coli adenylate kinase dynamics: comparison of elastic network model modes with mode-coupling (15)N-NMR relaxation data

The dynamics of adenylate kinase of Escherichia coli (AKeco) and its complex with the inhibitor AP(5)A, are characterized by correlating the theoretical results obtained with the Gaussian Network Model (GNM) and the anisotropic network model (ANM) with the order parameters and correlation times obtained with Slowly Relaxing Local Structure (SRLS) analysis of (15)N-NMR relaxation data. The AMPbd and LID domains of AKeco execute in solution large amplitude motions associated with the catalytic reaction Mg(+2)*ATP + AMP --> Mg(+2)*ADP + ADP. Two sets of correlation times and order parameters were determined by NMR/SRLS for AKeco, attributed to slow (nanoseconds) motions with correlation time tau( perpendicular) and low order parameters, and fast (picoseconds) motions with correlation time tau( parallel) and high order parameters. The structural connotation of these patterns is examined herein by subjecting AKeco and AKeco*AP(5)A to GNM analysis, which yields the dynamic spectrum in terms of slow and fast modes. The low/high NMR order parameters correlate with the slow/fast modes of the backbone elucidated with GNM. Likewise, tau( parallel) and tau( perpendicular) are associated with fast and slow GNM modes, respectively. Catalysis-related domain motion of AMPbd and LID in AKeco, occurring per NMR with correlation time tau( perpendicular), is associated with the first and second collective slow (global) GNM modes. The ANM-predicted deformations of the unliganded enzyme conform to the functional reconfiguration induced by ligand-binding, indicating the structural disposition (or potential) of the enzyme to bind its substrates. It is shown that NMR/SRLS and GNM/ANM analyses can be advantageously synthesized to provide insights into the molecular mechanisms that control biological function.     

Coarse-grained normal mode analysis in structural biology

The realization that experimentally observed functional motions of proteins can be predicted by coarse-grained normal mode analysis has renewed interest in applications to structural biology. Notable applications include the prediction of biologically relevant motions of proteins and supramolecular structures driven by their structure-encoded collective dynamics; the refinement of low-resolution structures, including those determined by cryo-electron microscopy; and the identification of conserved dynamic patterns and mechanically key regions within protein families. Additionally, hybrid methods that couple atomic simulations with deformations derived from coarse-grained normal mode analysis are able to sample collective motions beyond the range of conventional molecular dynamics simulations. Such applications have provided great insight into the underlying principles linking protein structures to their dynamics and their dynamics to their functions.     

Defining Coarse-Grained Representations of Large Biomolecules and Biomolecular Complexes from Elastic Network Models

Coarse-grained (CG) models of large biomolecular complexes enable simulations of these systems over long timescales that are not accessible for atomistic molecular dynamics (MD) simulations. A systematic methodology, called essential dynamics coarse-graining (ED-CG), has been developed for defining coarse-grained sites in a large biomolecule. The method variationally determines the CG sites so that key dynamic domains in the protein are preserved in the CG representation. The original ED-CG method relies on a principal component analysis (PCA) of a MD trajectory. However, for many large proteins and multi-protein complexes such an analysis may not converge or even be possible. This work develops a new ED-CG scheme using an elastic network model (ENM) of the protein structure. In this procedure, the low-frequency normal modes obtained by ENM are used to define dynamic domains and to define the CG representation accordingly. The method is then applied to several proteins, such as the HIV-1 CA protein dimer, ATP-bound G-actin, and the Arp2/3 complex. Numerical results show that ED-CG with ENM (ENM-ED-CG) is much faster than ED-CG with PCA because no MD is necessary. The ENM-ED-CG models also capture functional essential dynamics of the proteins almost as well as those using full MD with PCA. Therefore, the ENM-ED-CG method may be better suited to coarse-grain a very large biomolecule or biomolecular complex that is too computationally expensive to be simulated by conventional MD, or when a high resolution atomic structure is not even available.     

Sequence evolution correlates with structural dynamics

Biochemical activity and core stability are essential properties of proteins, maintained usually by conserved amino acids. Structural dynamics emerged in recent years as another essential aspect of protein functionality. Structural dynamics enable the adaptation of the protein to binding substrates and to undergo allosteric transitions, while maintaining the native fold. Key residues that mediate structural dynamics would thus be expected to be conserved or exhibit coevolutionary patterns at least. Yet, the correlation between sequence evolution and structural dynamics is yet to be established. With recent advances in efficient characterization of structural dynamics, we are now in a position to perform a systematic analysis. In the present study, a set of 34 enzymes representing various folds and functional classes is analyzed using information theory and elastic network models. Our analysis shows that the structural regions distinguished by their coevolution propensity as well as high mobility are predisposed to serve as substrate recognition sites, whereas residues acting as global hinges during collective dynamics are often supported by conserved residues. We propose a mobility scale for different types of amino acids, which tends to vary inversely with amino acid conservation. Our findings suggest the balance between physical adaptability (enabled by structure-encoded motions) and chemical specificity (conferred by correlated amino acid substitutions) underlies the selection of a relatively small set of versatile folds by proteins.     

Validating and improving elastic network models with molecular dynamics simulations

Elastic network models (ENMs) are a class of simple models intended to represent the collective motions of proteins. In contrast to all‐atom molecular dynamics simulations, the low computational investment required to use an ENM makes them ideal for speculative hypothesis‐testing situations. Historically, ENMs have been validated via comparison to crystallographic B‐factors, but this comparison is relatively low‐resolution and only tests the predictions of relative flexibility. In this work, we systematically validate and optimize a number of ENM‐type models by quantitatively comparing their predictions to microsecond‐scale all‐atom simulations of three different G protein coupled receptors. We show that, despite their apparent simplicity, well‐optimized ENMs perform remarkably well, reproducing the protein fluctuations with an accuracy comparable to what one would expect from all‐atom simulations run for several hundred nanoseconds. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Coupling between Catalytic Loop Motions and Enzyme Global Dynamics

Catalytic loop motions facilitate substrate recognition and binding in many enzymes. While these motions appear to be highly flexible, their functional significance suggests that structure-encoded preferences may play a role in selecting particular mechanisms of motions. We performed an extensive study on a set of enzymes to assess whether the collective/global dynamics, as predicted by elastic network models (ENMs), facilitates or even defines the local motions undergone by functional loops. Our dataset includes a total of 117 crystal structures for ten enzymes of different sizes and oligomerization states. Each enzyme contains a specific functional/catalytic loop (10–21 residues long) that closes over the active site during catalysis. Principal component analysis (PCA) of the available crystal structures (including apo and ligand-bound forms) for each enzyme revealed the dominant conformational changes taking place in these loops upon substrate binding. These experimentally observed loop reconfigurations are shown to be predominantly driven by energetically favored modes of motion intrinsically accessible to the enzyme in the absence of its substrate. The analysis suggests that robust global modes cooperatively defined by the overall enzyme architecture also entail local components that assist in suitable opening/closure of the catalytic loop over the active site.     

Large‐scale comparison of protein essential dynamics from molecular dynamics simulations and coarse‐grained normal mode analyses

A large‐scale comparison of essential dynamics (ED) modes from molecular dynamic simulations and normal modes from coarse‐grained normal mode methods (CGNM) was performed on a dataset of 335 proteins. As CGNM methods, the elastic network model (ENM) and the rigid cluster normal mode analysis (RCNMA) were used. Low‐frequency normal modes from ENM correlate very well with ED modes in terms of directions of motions and relative amplitudes of motions. Notably, a similar performance was found if normal modes from RCNMA were used, despite a higher level of coarse graining. On average, the space spanned by the first quarter of ENM modes describes 84% of the space spanned by the five ED modes. Furthermore, no prominent differences for ED and CGNM modes among different protein structure classes (CATH classification) were found. This demonstrates the general potential of CGNM approaches for describing intrinsic motions of proteins with little computational cost. For selected cases, CGNM modes were found to be more robust among proteins that have the same topology or are of the same homologous superfamily than ED modes. In view of recent evidence regarding evolutionary conservation of vibrational dynamics, this suggests that ED modes, in some cases, might not be representative of the underlying dynamics that are characteristic of a whole family, probably due to insufficient sampling of some of the family members by MD. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Dynamics of cellular retinoic acid binding protein I on multiple time scales with implications for ligand binding

Cellular retinoic acid binding protein I (CRABPI) belongs to the family of intracellular lipid binding proteins (iLBPs), all of which bind a hydrophobic ligand within an internal cavity. The structures of several iLBPs reveal minimal structural differences between the apo (ligand-free) and holo (ligand-bound) forms, suggesting that dynamics must play an important role in the ligand recognition and binding processes. Here, a variety of nuclear magnetic resonance (NMR) spectroscopy methods were used to systematically study the dynamics of both apo and holo CRABPI at various time scales. Translational and rotational diffusion constant measurements were used to study the overall motions of the proteins. Both apo and holo forms of CRABPI tend to self-associate at high (1.2 mM) concentrations, while at low concentrations (0.2 mM), they are predominantly monomeric. Rapid amide exchange rate and laboratory frame relaxation rate measurements at two spectrometer field strengths (500 and 600 MHz) were used to probe the internal motions of the individual residues. Several residues in the apo form, notably within the ligand recognition region, exhibit millisecond time scale motions that are significantly arrested in the holo form. In contrast, no significant differences in the high-frequency motions were observed between the two forms. These results provide direct experimental evidence for dynamics-induced ligand recognition and binding at a specifically defined time scale. They also exemplify the importance of dynamics in providing a more comprehensive understanding of how a protein functions.     

Elastic network models capture the motions apparent within ensembles of RNA structures

The role of structure and dynamics in mechanisms for RNA becomes increasingly important. Computational approaches using simple dynamics models have been successful at predicting the motions of proteins and are often applied to ribonucleo-protein complexes but have not been thoroughly tested for well-packed nucleic acid structures. In order to characterize a true set of motions, we investigate the apparent motions from 16 ensembles of experimentally determined RNA structures. These indicate a relatively limited set of motions that are captured by a small set of principal components (PCs). These limited motions closely resemble the motions computed from low frequency normal modes from elastic network models (ENMs), either at atomic or coarse-grained resolution. Various ENM model types, parameters, and structure representations are tested here against the experimental RNA structural ensembles, exposing differences between models for proteins and for folded RNAs. Differences in performance are seen, depending on the structure alignment algorithm used to generate PCs, modulating the apparent utility of ENMs but not significantly impacting their ability to generate functional motions. The loss of dynamical information upon coarse-graining is somewhat larger for RNAs than for globular proteins, indicating, perhaps, the lower cooperativity of the less densely packed RNA. However, the RNA structures show less sensitivity to the elastic network model parameters than do proteins. These findings further demonstrate the utility of ENMs and the appropriateness of their application to well-packed RNA-only structures, justifying their use for studying the dynamics of ribonucleo-proteins, such as the ribosome and regulatory RNAs.     

Direct observation of protein-ligand interaction kinetics

Internal dynamics on the micro- to millisecond time scale have a strong influence on the affinity and specificity with which a protein binds ligands. This time scale is accessible through relaxation dispersion measurements using NMR. By studying the dynamics of a protein with different concentrations of a ligand, one can determine the dynamic effects induced by the ligand. Here we have studied slow internal dynamics of the N-terminal src homology 2 domain of phosphatidylinositide 3-kinase to probe the role of individual residues for the interaction with a tyrosine-phosphorylated binding sequence from polyoma middle T antigen. While slow dynamic motion was restricted to a few residues in the free SH2 and in the SH2 complex, motion was significantly enhanced by adding even small amounts of ligand. Kinetic rates induced by ligand binding varied between 300 and 2000 s(-1). High rates reflected direct interactions with the ligand or rearrangements caused by ligand binding. Large differences in rates were observed for residues adjacent in the primary sequence reflecting their individual roles in ligand interaction. However, rates were similar for residues involved in the same side chain interactions, reflecting concerted motions during ligand binding. For a subset of residues, exchange must involve structural intermediates which play a crucial role in high-affinity ligand binding. This analysis supports a new view of the dynamics of individual sites of a protein during ligand interaction.     

Analysis of conformational motions and related key residue interactions responsible for a specific function of proteins with elastic network model

Protein collective motions play a critical role in many biochemical processes. How to predict the functional motions and the related key residue interactions in proteins is important for our understanding in the mechanism of the biochemical processes. Normal mode analysis (NMA) of the elastic network model (ENM) is one of the effective approaches to investigate the structure-encoded motions in proteins. However, the motion modes revealed by the conventional NMA approach do not necessarily correspond to a specific function of protein. In the present work, a new analysis method was proposed to identify the motion modes responsible for a specific function of proteins and then predict the key residue interactions involved in the functional motions by using a perturbation approach. In our method, an internal coordinate that accounts for the specific function was introduced, and the Cartesian coordinate space was transformed into the internal/Cartesian space by using linear approximation, where the introduced internal coordinate serves as one of the axes of the coordinate space. NMA of ENM in this internal/Cartesian space was performed and the function-relevant motion modes were identified according to their contributions to the specific function of proteins. Then the key residue interactions important for the functional motions of the protein were predicted as the interactions whose perturbation largely influences the fluctuation along the internal coordinate. Using our proposed methods, the maltose transporter (MalFGK₂) from E. Coli was studied. The functional motions and the key residue interactions that are related to the channel-gating function of this protein were successfully identified.     

Global Motions of the Nuclear Pore Complex: Insights from Elastic Network Models

The nuclear pore complex (NPC) is the gate to the nucleus. Recent determination of the configuration of proteins in the yeast NPC at ∼5 nm resolution permits us to study the NPC global dynamics using coarse-grained structural models. We investigate these large-scale motions by using an extended elastic network model (ENM) formalism applied to several coarse-grained representations of the NPC. Two types of collective motions (global modes) are predicted by the ENMs to be intrinsically favored by the NPC architecture: global bending and extension/contraction from circular to elliptical shapes. These motions are shown to be robust against tested variations in the representation of the NPC, and are largely captured by a simple model of a toroid with axially varying mass density. We demonstrate that spoke multiplicity significantly affects the accessible number of symmetric low-energy modes of motion; the NPC-like toroidal structures composed of 8 spokes have access to highly cooperative symmetric motions that are inaccessible to toroids composed of 7 or 9 spokes. The analysis reveals modes of motion that may facilitate macromolecular transport through the NPC, consistent with previous experimental observations.     

Toward the mechanism of dynamical couplings and translocation in hepatitis C virus NS3 helicase using elastic network model

Hepatitis C virus NS3 helicase is an enzyme that unwinds double-stranded polynucleotides in an ATP-dependent reaction. It provides a promising target for small molecule therapeutic agents against hepatitis C. Design of such drugs requires a thorough understanding of the dynamical nature of the mechanochemical functioning of the helicase. Despite recent progress, the detailed mechanism of the coupling between ATPase activity and helicase activity remains unclear. Based on an elastic network model (ENM), we apply two computational analysis tools to probe the dynamical mechanism underlying the allosteric coupling between ATP binding and polynucleotide binding in this enzyme. The correlation analysis identifies a network of hot-spot residues that dynamically couple the ATP-binding site and the polynucleotide-binding site. Several of these key residues have been found by mutational experiments as functionally important, while our analysis also reveals previously unexplored hot-spot residues that are potential targets for future mutational studies. The conformational changes between different crystal structures of NS3 helicase are found to be dominated by the lowest frequency mode solved from the ENM. This mode corresponds to a hinge motion of the highly flexible domain 2. This motion simultaneously modulates the opening/closing of the domains 1-2 cleft where ATP binds, and the domains 2-3 cleft where the polynucleotide binds. Additionally, a small twisting motion of domain 1, observed in both mode 1 and the computed ATP binding induced conformational change, fine-tunes the binding affinity of the domains 1-3 interface for the polynucleotide. The combination of these motions facilitates the translocation of a single-stranded polynucleotide in an inchworm-like manner.