Dynamics and function of proteins: the search for general concepts

Dynamic Personality of Proteins and Effect of the Molecular Environment

Protein dynamics display distinct traits that are linked to their specific biological function. However, the interplay between intrinsic dynamics and the molecular environment on protein stability remains poorly understood. In this study, we investigate, by incoherent neutron scattering, the subnanosecond time scale dynamics of three model proteins: the mesophilic lysozyme, the thermophilic thermolysin, and the intrinsically disordered β-casein. Moreover, we address the influence of water, glycerol, and glucose, which create progressively more viscous matrices around the protein surface. By comparing the protein thermal fluctuations, we find that the internal dynamics of thermolysin are less affected by the environment compared to lysozyme and β-casein. We ascribe this behavior to the protein dynamic personality, i.e., to the stiffer dynamics of the thermophilic protein that contrasts the influence of the environment. Remarkably, lysozyme and thermolysin in all molecular environments reach a critical common flexibility when approaching the calorimetric melting temperature.

Application of a Novel Hybrid CNN-GNN for Peptide Ion Encoding

Almost all state-of-the-art de novo peptide sequencing algorithms now use machine learning models to encode fragment peaks and hence identify amino acids in mass spectrometry (MS) spectra. Previous work has highlighted how the inherent MS challenges of noise and missing peptide peaks detrimentally affect the performance of these models. In the present research we extracted and evaluated the encoding modules from 3 state-of-the-art de novo peptide sequencing algorithms. We also propose a convolutional neural network-graph neural network machine learning model for encoding peptide ions in tandem MS spectra. We compared the proposed encoding module to those used in the state-of-the-art de novo peptide sequencing algorithms by assessing their ability to identify b-ions and y-ions in MS spectra. This included a comprehensive evaluation in both real and artificial data across various levels of noise and missing peptide peaks. The proposed model performed best across all data sets using two different metrics (area under the receiver operating characteristic curve (AUC) and average precision). The work also highlighted the effect of including additional features such as intensity rank in these encoding modules as well as issues with using the AUC as a metric. This work is of significance to those designing future de novo peptide identification algorithms as it is the first step toward a new approach.

Stabilization Effects Induced by Trehalose on Creatine Aqueous Solutions Investigated by Infrared Spectroscopy

Creatine is a very popular amino acid widely utilized in the sports world due to its functions mainly related to muscle building and increasing performance. The present work investigates the behavior of creatine aqueous solutions and of creatine aqueous in the presence of trehalose as a function of time changes by means of Infrared spectroscopy. Infrared spectra have been gathered and studied over time for both the full spectrum and the intramolecular OH-stretching region for the two mixtures. This latter region was studied more specifically using a cutting-edge technique called Spectral Distance (SD). From this analysis of the spectral features of the investigated samples, it emerges that trehalose has a significant stabilizing effect on creatine aqueous solutions.

Sparse group selection and analysis of function-related residue for protein-state recognition

Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical but challenging to develop reliable machine learning based prediction models, especially for proteins as bio-macromolecules. In this study, we applied sparse group lasso (SGL) method as a general feature selection method to develop classification model for an allosteric protein in different functional states. This results into a much improved model with comparable accuracy (Acc) and only 28 selected features comparing to 289 selected features from a previous study. The Acc achieves 91.50% with 1936 selected feature, which is far higher than that of baseline methods. In addition, grouping protein amino acids into secondary structures provides additional interpretability of the selected features. The selected features are verified as associated with key allosteric residues through comparison with both experimental and computational works about the model protein, and demonstrate the effectiveness and necessity of applying rigorous feature selection and evaluation methods on complex chemical systems.

Identification of incommensurability in L-Leucine ; are lattice instabilities can be considered as general phenomena in hydrophobic amino acids?

L-Leucine is an essential amino acid which focuses lot of investigations on its phase transition sequence for more than fifty years. Combining Raman spectroscopy and X-ray diffraction experiments provides a...

Computational Analysis of Dynamical Fluctuations of Oncoprotein E7 (HPV 16) for the Hot Spot Residue Identification Using Elastic Network Model

Aims:

To find out Potential Drug targets against HPV E7.

Background:

Oncoprotein E7 of Human Papilloma Virus (HPV-16), after invading human body alter host protein-protein interaction networks caused by the fluctuations of amino acid residues present in E7. E7 interacts with Rb protein of human host with variable residual fluctuations, leading towards the progression of cervical cancer.

Objective:

Our study was focused our computational analysis of the binding and competing interactions of the E7 protein of HPV with Rb protein.

Methods:

Our study is based on analysis of dynamic fluctuations of E7 in host cell and correlation analysis of specific residue found in motif of LxCxE, that is the key region in stabilizing interaction between E7 and Rb.

Results and Discussion:

Cysteine, Leucine and Glutamic acid have been identified as hot spot residues of E7 which can provide platform for drug designing and understanding of pathogenesis of cervical cancer, in future. Our study shows validation of the vitality of linear binding motifs LxCxE of E7 of HPV in interacting with Rb as an important event in propagation of HPV in human cells and transformation of infection into cervical cancer.

Conclusion:

Our study shows validation of the vitality of linear binding motifs LxCxE of E7 of HPV in interacting with Rb as an important event in propagation of HPV in human cells and transformation of infection into cervical cancer.

Other:

E7 interacts with Rb protein of human host with variable residual fluctuations, leading towards the progression of cervical cancer.

Hemoglobin Dynamics in Solution vis-à-vis Under Confinement: An Electrochemical Perspective

Confining heme protein in silico often leads to beneficial functionalities such as an enhanced electrochemical response from the heme center. This can be harnessed to design effective biosensors for medical diagnostics. Proteins under confinement, surface confinement on the electrode to be precise, have more ordered and monodisperse structure compared to the protein in bulk solution. As the electrochemical response of a protein comes from those protein molecules that are confined within the electrical double layer across the electrode-electrolyte interface, it is expected that restriction of conformational fluctuations of the polymeric protein will help in enhancement of the electrochemical response. This is probably the prima facie reason for electrochemical response enhancement under confinement. We examine the dynamic features of hemoglobin under confinement vis-à-vis that in bulk solution. We use a variety of spectroscopic techniques across a wide time-space window to establish the following facts: (a) hardening of the protein polypeptide backbone, (b) slowing down of protein diffusion, (c) increase in relaxation times in NMR, and (d) slowing down of dielectric relaxation times under confinement. This indicates an overall quenching of protein dynamics when the protein is confined inside silica matrix. Thus, we hypothesize that along with retention of secondary structure, this quenching of dynamics contributes to the enhancement of electrochemical response observed.

Ligand-protein interactions in lysozyme investigated through a dual-resolution model

A fully atomistic (AT) modeling of biological macromolecules at relevant length- and time-scales is often cumbersome or not even desirable, both in terms of computational effort required and a posteriori analysis. This difficulty can be overcome with the use of multiresolution models, in which different regions of the same system are concurrently described at different levels of detail. In enzymes, computationally expensive AT detail is crucial in the modeling of the active site in order to capture, for example, the chemically subtle process of ligand binding. In contrast, important yet more collective properties of the remainder of the protein can be reproduced with a coarser description. In the present work, we demonstrate the effectiveness of this approach through the calculation of the binding free energy of hen egg white lysozyme with the inhibitor di-N-acetylchitotriose. Particular attention is payed to the impact of the mapping, that is, the selection of AT and coarse-grained residues, on the binding free energy. It is shown that, in spite of small variations of the binding free energy with respect to the active site resolution, the separate contributions coming from different energetic terms (such as electrostatic and van der Waals interactions) manifest a stronger dependence on the mapping, thus pointing to the existence of an optimal level of intermediate resolution.

Quantitative Characterization of Binding Pockets and Binding Complementarity by Means of Zernike Descriptors

In this work, we describe the application of the Zernike formalism to quantitatively characterize the binding pockets of two sets of biologically relevant systems. Such an approach, when applied to molecular dynamics trajectories, is able to pinpoint the subtle differences between very similar molecular regions and their impact on the local propensity to ligand binding, allowing us to quantify such differences. The statistical robustness of our procedure suggests that it is very suitable to describe protein binding sites and protein-ligand interactions within a rigorous and well-defined framework.

Single-Cell Protein Secretion Detection and Profiling

Secreted proteins play important roles in mediating various biological processes such as cell-cell communication, differentiation, migration, and homeostasis at the population or tissue level. Here, we review bioanalytical technologies and devices for detecting protein secretions from single cells. We begin by discussing conventional approaches followed by detailing the latest advances in microengineered systems for detecting single-cell protein secretions with an emphasis on multiplex measurement. These platforms include droplet microfluidics, micro-/nanowell-based assays, and microchamber-based assays, among which the advantages and limitations are compared. Microscale systems also enable the tracking of protein secretion dynamics in single cells, further empowering the study of the cell-cell communication network. Looking forward, we discuss the remaining challenges and future opportunities that will transform basic research of cellular secretion functions at the systems level and the clinical applications for immune monitoring and cancer treatment.

Multiscale nuclear magnetic relaxation dispersion of complex liquids in bulk and confinement

The nuclear magnetic relaxation dispersion (NMRD) technique consists of measurement of the magnetic-field dependence of the longitudinal nuclear-spin-lattice relaxation rate 1/T₁. Usually, the acquisition of the NMRD profiles is made using a fast field cycling (FFC) NMR technique that varies the magnetic field and explores a very large range of Larmor frequencies (10 kHz < ω₀/(2π) <40 MHz). This allows extensive explorations of the fluctuations to which nuclear spin relaxation is sensitive. The FFC technique thus offers opportunities on multiple scales of both time and distance for characterizing the molecular dynamics and transport properties of complex liquids in bulk or embedded in confined environments. This review presents the principles, theories and applications of NMRD for characterizing fundamental properties such as surface correlation times, diffusion coefficients and dynamical surface affinity (NMR wettability) for various confined liquids. The basic longitudinal and transverse relaxation equations are outlined for bulk liquids. The nuclear relaxation of a liquid confined in pores is considered in detail in order to find the biphasic fast exchange relations for a liquid at proximity of a solid surface. The physical-chemistry of liquids at solid surfaces induces striking differences between NMRD profiles of aprotic and protic (water) liquids embedded in calibrated porous disordered materials. A particular emphasis of this review concerns the extension of FFC NMR relaxation to industrial applications. For instance, it is shown that the FFC technique is sufficiently rapid for following the progressive setting of cement-based materials (plasters, cement pastes, concretes). The technique also allows studies of the dynamics of hydrocarbons in proximity of asphaltene nano-aggregates and macro-aggregates in heavy crude oils as a function of the concentration of asphaltenes. It also gives new information on the wettability of petroleum fluids (brine and oil) embedded in shale oil rocks. It is useful for understanding the relations and correlations between NMR relaxation times T₁ and T₂, diffusion coefficients D, and viscosity η of heavy crude oils. This is of particular importance for interpreting T₁, T₂, 2D T₁-T₂ and D-T₂ correlation spectra that could be obtained down-hole, thus giving a valuable tool for investigating in situ the molecular dynamics of petroleum fluids. Another domain of interest concerns biological applications. This is of particular importance for studying the complex dynamical spectrum of a folded polymeric structure that may span many decades in frequency or time. A direct NMRD characterization of water diffusional dynamics is presented at the protein interface. NMR experiments using a shuttle technique give results well above the frequency range accessible via the FFC technique; examples of this show protein dynamics over a range from fast and localized motions to slow and delocalized collective motions involving the whole protein. This review ends by an interpretation of the origin of the proton magnetic field dependence of T₁ for different biological tissues of animals; this includes a proposal for interpreting in vivo MRI data from human brain at variable magnetic fields, where the FFC relaxation analysis suggests that brain white-matter is distinct from grey-matter, in agreement with diffusion-weighted MRI imaging.

Temperature Dependence of Tryptophan Fluorescence Lifetime in Aqueous Glycerol and Trehalose Solutions

The temperature dependences of tryptophan fluorescence decay kinetics in aqueous glycerol and 1 M trehalose solutions were examined. The fluorescence decay kinetics were recorded in the spectral region of 292.5-417.5 nm with nanosecond time resolution. The kinetics curves were approximated by the sum of three exponential terms, and the spectral distribution (DAS) of these components was determined. An antisymbatic course of fluorescence decay times of two (fast and medium) components in the temperature range from -60 to +10°C was observed. The third (slow) component showed only slight temperature dependence. The antisymbatic behavior of fluorescence lifetimes of the fast and medium components was explained on the assumption that some of the excited tryptophan molecules are transferred from a short-wavelength B-form with short fluorescence lifetime to a long-wavelength R-form with an intermediate fluorescence lifetime. This transfer occurred in the indicated temperature range.

Comparative Study of the Collective Dynamics of Proteins and Inorganic Nanoparticles

Molecular dynamics simulations of ubiquitin in water/glycerol solutions are used to test the suggestion by Karplus and coworkers that proteins in their biologically active state should exhibit a dynamics similar to 'surface-melted' inorganic nanoparticles (NPs). Motivated by recent studies indicating that surface-melted inorganic NPs are in a 'glassy' state that is an intermediate dynamical state between a solid and liquid, we probe the validity and significance of this proposed analogy. In particular, atomistic simulations of ubiquitin in solution based on CHARMM36 force field and pre-melted Ni NPs (Voter-Chen Embedded Atom Method potential) indicate a common dynamic heterogeneity, along with other features of glass-forming (GF) liquids such as collective atomic motion in the form of string-like atomic displacements, potential energy fluctuations and particle displacements with long range correlations ('colored' or 'pink' noise), and particle displacement events having a power law scaling in magnitude, as found in earthquakes. On the other hand, we find the dynamics of ubiquitin to be even more like a polycrystalline material in which the α-helix and β-sheet regions of the protein are similar to crystal grains so that the string-like collective atomic motion is concentrated in regions between the α-helix and β-sheet domains.

On the indirect relationship between protein dynamics and enzyme activity

The behaviors of simple thermal systems have been well studied in physical chemistry and the principles obtained from such studies have been applied to complex thermal systems, such as proteins and enzymes. But the simple application of such principles is questionable and may lead to mistakes under some circumstances. In enzymology, the transition state theory of chemical reactions has been accepted as a fundamental theory, but the role of protein dynamics in enzyme catalysis is controversial in the context of transition state theory. By studying behaviors of complex thermal systems, we have revised the Arrhenius equation and transition state theory and our model is validated in enzymology. Formally speaking, the revised Arrhenius equation is apparently similar to a conventional Arrhenius equation, but the physical meanings of its parameters differ from that of traditional forms in principle. Within this model, the role of protein dynamics in enzyme catalysis is well defined and quantified.

Conformational diversity analysis reveals three functional mechanisms in proteins

Protein motions are a key feature to understand biological function. Recently, a large-scale analysis of protein conformational diversity showed a positively skewed distribution with a peak at 0.5 Å C-alpha root-mean-square-deviation (RMSD). To understand this distribution in terms of structure-function relationships, we studied a well curated and large dataset of ~5,000 proteins with experimentally determined conformational diversity. We searched for global behaviour patterns studying how structure-based features change among the available conformer population for each protein. This procedure allowed us to describe the RMSD distribution in terms of three main protein classes sharing given properties. The largest of these protein subsets (~60%), which we call "rigid" (average RMSD = 0.83 Å), has no disordered regions, shows low conformational diversity, the largest tunnels and smaller and buried cavities. The two additional subsets contain disordered regions, but with differential sequence composition and behaviour. Partially disordered proteins have on average 67% of their conformers with disordered regions, average RMSD = 1.1 Å, the highest number of hinges and the longest disordered regions. In contrast, malleable proteins have on average only 25% of disordered conformers and average RMSD = 1.3 Å, flexible cavities affected in size by the presence of disordered regions and show the highest diversity of cognate ligands. Proteins in each set are mostly non-homologous to each other, share no given fold class, nor functional similarity but do share features derived from their conformer population. These shared features could represent conformational mechanisms related with biological functions.

A multi-resolution model to capture both global fluctuations of an enzyme and molecular recognition in the ligand-binding site

In multi-resolution simulations, different system components are simultaneously modeled at different levels of resolution, these being smoothly coupled together. In the case of enzyme systems, computationally expensive atomistic detail is needed in the active site to capture the chemistry of ligand binding. Global properties of the rest of the protein also play an essential role, determining the structure and fluctuations of the binding site; however, these can be modeled on a coarser level. Similarly, in the most computationally efficient scheme only the solvent hydrating the active site requires atomistic detail. We present a methodology to couple atomistic and coarse-grained protein models, while solvating the atomistic part of the protein in atomistic water. This allows a free choice of which protein and solvent degrees of freedom to include atomistically. This multi-resolution methodology can successfully model stable ligand binding, and we further confirm its validity by exploring the reproduction of system properties relevant to enzymatic function. In addition to a computational speedup, such an approach can allow the identification of the essential degrees of freedom playing a role in a given process, potentially yielding new insights into biomolecular function. Proteins 2016; 84:1902-1913. © 2016 Wiley Periodicals, Inc.

Protein dynamics: from rattling in a cage to structural relaxation

We present an overview of protein dynamics based mostly on results of neutron scattering, dielectric relaxation spectroscopy and molecular dynamics simulations. We identify several major classes of protein motions on the time scale from faster than picoseconds to several microseconds, and discuss the coupling of these processes to solvent dynamics. Our analysis suggests that the microsecond backbone relaxation process might be the main structural relaxation of the protein that defines its glass transition temperature, while faster processes present some localized secondary relaxations. Based on the overview, we formulate a general picture of protein dynamics and discuss the challenges in this field.

Computational and theoretical methods for protein folding

A computational approach is essential whenever the complexity of the process under study is such that direct theoretical or experimental approaches are not viable. This is the case for protein folding, for which a significant amount of data are being collected. This paper reports on the essential role of in silico methods and the unprecedented interplay of computational and theoretical approaches, which is a defining point of the interdisciplinary investigations of the protein folding process. Besides giving an overview of the available computational methods and tools, we argue that computation plays not merely an ancillary role but has a more constructive function in that computational work may precede theory and experiments. More precisely, computation can provide the primary conceptual clues to inspire subsequent theoretical and experimental work even in a case where no preexisting evidence or theoretical frameworks are available. This is cogently manifested in the application of machine learning methods to come to grips with the folding dynamics. These close relationships suggested complementing the review of computational methods within the appropriate theoretical context to provide a self-contained outlook of the basic concepts that have converged into a unified description of folding and have grown in a synergic relationship with their computational counterpart. Finally, the advantages and limitations of current computational methodologies are discussed to show how the smart analysis of large amounts of data and the development of more effective algorithms can improve our understanding of protein folding.

Atomic-level characterization of the activation mechanism of SERCA by calcium

We have performed molecular dynamics (MD) simulations to elucidate, in atomic detail, the mechanism by which the sarcoplasmic reticulum Ca²⁺-ATPase (SERCA) is activated by Ca²⁺. Crystal structures suggest that activation of SERCA occurs when the cytoplasmic head-piece, in an open (E1) conformation stabilized by Ca²⁺, undergoes a large-scale open-to-closed (E1 to E2) transition that is induced by ATP binding. However, spectroscopic measurements in solution suggest that these structural states (E1 and E2) are not tightly coupled to biochemical states (defined by bound ligands); the closed E2 state predominates even in the absence of ATP, in both the presence and absence of Ca²⁺. How is this loose coupling consistent with the high efficiency of energy transduction in the Ca²⁺-ATPase? To provide insight into this question, we performed long (500 ns) all-atom MD simulations starting from the open crystal structure, including a lipid bilayer and water. In both the presence and absence of Ca²⁺, we observed a large-scale open-to-closed conformational transition within 400 ns, supporting the weak coupling between structural and biochemical states. However, upon closer inspection, it is clear that Ca²⁺ is necessary and sufficient for SERCA to reach the precise geometrical arrangement necessary for activation of ATP hydrolysis. Contrary to suggestions from crystal structures, but in agreement with solution spectroscopy, the presence of ATP is not required for this activating transition. Principal component analysis showed that Ca²⁺ reshapes the free energy landscape of SERCA to create a path between the open conformation and the activated closed conformation. Thus the malleability of the free energy landscape is essential for SERCA efficiency, ensuring that ATP hydrolysis is tightly coupled to Ca²⁺ transport. These results demonstrate the importance of real-time dynamics in the formation of catalytically competent conformations of SERCA, with broad implications for understanding enzymatic catalysis in atomic detail.

Mechanical mapping of single membrane proteins at submolecular resolution

The capacity of proteins to carry out different functions is related to their ability to undergo conformation changes, which depends on the flexibility of protein structures. In this work, we applied a novel imaging mode based on indentation force spectroscopy to map quantitatively the flexibility of individual membrane proteins in their native, folded state at unprecedented submolecular resolution. Our results enabled us to correlate protein flexibility with crystal structure and showed that α-helices are stiff structures that may contribute importantly to the mechanical stability of membrane proteins, while interhelical loops appeared more flexible, allowing conformational changes related to function.

Sequence composition and environment effects on residue fluctuations in protein structures

Structure fluctuations in proteins affect a broad range of cell phenomena, including stability of proteins and their fragments, allosteric transitions, and energy transfer. This study presents a statistical-thermodynamic analysis of relationship between the sequence composition and the distribution of residue fluctuations in protein-protein complexes. A one-node-per-residue elastic network model accounting for the nonhomogeneous protein mass distribution and the interatomic interactions through the renormalized inter-residue potential is developed. Two factors, a protein mass distribution and a residue environment, were found to determine the scale of residue fluctuations. Surface residues undergo larger fluctuations than core residues in agreement with experimental observations. Ranking residues over the normalized scale of fluctuations yields a distinct classification of amino acids into three groups: (i) highly fluctuating-Gly, Ala, Ser, Pro, and Asp, (ii) moderately fluctuating-Thr, Asn, Gln, Lys, Glu, Arg, Val, and Cys, and (iii) weakly fluctuating-Ile, Leu, Met, Phe, Tyr, Trp, and His. The structural instability in proteins possibly relates to the high content of the highly fluctuating residues and a deficiency of the weakly fluctuating residues in irregular secondary structure elements (loops), chameleon sequences, and disordered proteins. Strong correlation between residue fluctuations and the sequence composition of protein loops supports this hypothesis. Comparing fluctuations of binding site residues (interface residues) with other surface residues shows that, on average, the interface is more rigid than the rest of the protein surface and Gly, Ala, Ser, Cys, Leu, and Trp have a propensity to form more stable docking patches on the interface. The findings have broad implications for understanding mechanisms of protein association and stability of protein structures.

Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework

Energy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein – the native landscape – is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase volume is much smaller, allowing a protein to fully sample its native energy landscape on the biological timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a ‘topographical map’ of the native landscape that can be used for subsequent analysis. Several major approaches to representing this topographical map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein's functional dynamics. Normal mode analysis can provide such variables in cases where functional motions are dictated by the molecule's architecture. Principal component analysis is a more expensive way of inferring the essential variables from the protein's motions, one that requires a long molecular dynamics simulation. Finally, the two popular models for the allosteric switching mechanism, ‘preexisting equilibrium’ and ‘induced fit’, are interpreted within the energy landscape paradigm as extreme points of a continuum of transition mechanisms. Some experimental evidence illustrating each of these two models, as well as intermediate mechanisms, is presented and discussed.

Dimensionality of diffusive exploration at the protein interface in solution

The dynamics of water are critically important to the energies of interaction between proteins and substrates and determine the efficiency of transport at the interface. The magnetic field dependence of the nuclear spin-lattice relaxation rate constant 1/T(1) of water protons provides a direct characterization of water diffusional dynamics at the protein interface. We find that the surface-average translational correlation time is 30-40 ps and the magnetic field dependence of the water proton 1/T(1) is characteristic of two-dimensional diffusion of water in the protein interfacial region. The reduced dimensionality substantially increases the intermolecular re-encounter probability and the efficiency of the surface exploration by the small molecule, water in this case. We propose a comprehensive theory of the translational effects of a small diffusing particle confined in the vicinity of a spherical macromolecule as a function of the relative size of the two particles. We show that the change in the apparent dimensionality of the diffusive exploration is a general result of the small diffusing particle encountering a much larger particle that presents a diffusion barrier. Examination of the effects of the size of the confinement relative to the macromolecule size reveals that the reduced dimensionality characterizing the small-molecule diffusion persists to remarkably small radius ratios. The experimental results on several different proteins in solution support the proposed theoretical model, which may be generalized to other small-particle-large-body systems like vesicles and micelles.

Study of solvent-protein coupling effects by neutron scattering

The present work aims to characterize the dynamical behavior of proteins immersed in bio-preserving liquids and glasses. For this purpose, the protein dUTPase was chosen, while the selected solvents were glycerol, a triol, and some homologous disaccharides, i.e., trehalose, maltose, and sucrose, which are known to be very effective bio-preserving agents. The results highlight that the disaccharides show a slowing down effect on the water dynamics, which is stronger for trehalose than in the case of the other disaccharides. Furthermore, a characterization of the medium which hosts the protein is performed by using an operative definition of fragility based on the mean square displacement extracted by elastic incoherent neutron scattering, which is directly connected to Angell's kinetic fragility based on the viscosity. Finally, a study of the dynamics of the protein sequestered within the solvents is performed. The result shows that the protein dynamics is coupled with that of the surrounding matrix.

13C and 15N NMR study of the hydration response of T4 lysozyme and alphaB-crystallin internal dynamics

The response to hydration of the internal protein dynamics was studied by the means of solid state NMR relaxation and magic angle spinning exchange techniques. Two proteins, lysozyme from bacteriophage T4 and human alphaB-crystallin were used as exemplars. The relaxation rates R1 and R1rho of 13C and 15N nuclei were measured as a function of a hydration level of the proteins in the range 0-0.6 g of water/g of protein. Both proteins were totally 15N-enriched with natural 13C abundance. The relaxation rates were measured for different spectral bands (peaks) that enabled the characterization of the dynamics separately for the backbone, side chains, and CH3 and NH3+ groups. The data obtained allowed a comparative analysis of the hydration response of the protein dynamics in different frequency ranges and different sites in the protein for two different proteins and two magnetic nuclei. The most important result is a demonstration of a qualitatively different response to hydration of the internal dynamics in different frequency ranges. The amplitude of the fast (nanosecond time scale) motion gradually increases with increasing hydration, whereas that of the slow (microsecond time scale) motion increases only until the hydration level 0.2-0.3 g of water/g of protein and then shows almost no hydration dependence. The reason for such a difference is discussed in terms of the different physical natures of these two dynamic processes. Backbone and side chain nuclei show the same features of the response of dynamics with hydration despite the fact that the backbone motional amplitudes are much smaller than those of side chains. Although T4 lysozyme and alphaB-crystallin possess rather different structural and biochemical properties, both proteins show qualitatively very similar hydration responses. In addition to the internal motions, exchange NMR data enabled the identification of one more type of motion in the millisecond to second time scale that appears only at high hydration levels. This motion was attributed to the restricted librations of the protein as a whole.

Deconstructing the native state: energy landscapes, function, and dynamics of globular proteins

Proteins are highly complex molecules with features exquisitely selected by nature to carry out essential biological functions. Physical chemistry and polymer physics provide us with the tools needed to make sense of this complexity. Upon translation, many proteins fold to a thermodynamically stable form known as the native state. The native state is not static, but consists of a hierarchy of conformations, that are continuously explored through dynamics. In this review we provide a brief introduction to some of the core concepts required in the discussion of the protein native dynamics using energy landscapes ideas. We first discuss recent works which have challenged the structure-function paradigm by demonstrating function in disordered proteins. Next we examine the hierarchical organization in the energy landscapes using atomistic molecular dynamics simulations and principal component analysis. In particular, the role of direct and water-mediated contacts in sculpting the landscape is elaborated. Another approach to studying the native state ensemble is based on choosing high-resolution order parameters for computing one- or two-dimensional free energy surfaces. We demonstrate that 2D free energy surfaces provide rich thermodynamic and kinetic information about the native state ensemble. Brownian dynamics simulations on such a surface indicate that protein conformational dynamics is weakly activated. Finally, we briefly discuss implicit and coarse-grained protein models and emphasize the solvent role in determining native state structure and dynamics.

Dynamics of tRNA at different levels of hydration

The influence of hydration on the nanosecond timescale dynamics of tRNA is investigated using neutron scattering spectroscopy. Unlike protein dynamics, the dynamics of tRNA is not affected by methyl group rotation. This allows for a simpler analysis of the influence of hydration on the conformational motions in RNA. We find that hydration affects the dynamics of tRNA significantly more than that of lysozyme. Both the characteristic length scale and the timescale of the conformational motions in tRNA depend strongly on hydration. Even the characteristic temperature of the so-called "dynamical transition" appears to be hydration-dependent in tRNA. The amplitude of the conformational motions in fully hydrated tRNA is almost twice as large as in hydrated lysozyme. We ascribe these differences to a more open and flexible structure of hydrated RNA, and to a larger fraction and different nature of hydrophilic sites. The latter leads to a higher density of water that makes the biomolecule more flexible. All-atom molecular-dynamics simulations are used to show that the extent of hydration is greater in tRNA than in lysozyme. We propose that water acts as a "lubricant" in facilitating enhanced motion in solvated RNA molecules.

High resolution approach to the native state ensemble kinetics and thermodynamics

Many biologically interesting functions such as allosteric switching or protein-ligand binding are determined by the kinetics and mechanisms of transitions between various conformational substates of the native basin of globular proteins. To advance our understanding of these processes, we constructed a two-dimensional free energy surface (FES) of the native basin of a small globular protein, Trp-cage. The corresponding order parameters were defined using two native substructures of Trp-cage. These calculations were based on extensive explicit water all-atom molecular dynamics simulations. Using the obtained two-dimensional FES, we studied the transition kinetics between two Trp-cage conformations, finding that switching process shows a borderline behavior between diffusive and weakly-activated dynamics. The transition is well-characterized kinetically as a biexponential process. We also introduced a new one-dimensional reaction coordinate for the conformational transition, finding reasonable qualitative agreement with the two-dimensional kinetics results. We investigated the distribution of all the 38 native nuclear magnetic resonance structures on the obtained FES, analyzing interactions that stabilize specific low-energy conformations. Finally, we constructed a FES for the same system but with simple dielectric model of water instead of explicit water, finding that the results were surprisingly similar in a small region centered on the native conformations. The dissimilarities between the explicit and implicit model on the larger-scale point to the important role of water in mediating interactions between amino acid residues.

The spectrum of biomolecular states and motions

The universe of conformational substates of a protein molecule is huge. The complete energy landscape of proteins is, therefore, complex when studied at low temperature. Many experiments under physiological conditions commonly reveal a simpler spectrum of states. These states are individually ensembles of low temperature substates. That is, room temperature experiments probe the low free energy part of the spectrum of excitations. This paper describes how the complete landscape and the spectrum of these thermally excited motions can be related to each other. On funneled landscapes, partially folded ensembles of states are the most important excited states. Their properties and their free energy spectrum can often be predicted by native topology based models. Frustration, i.e., the conflict between inconsistent stabilizing interactions that have evolved for other purposes than optimizing folding, offers another mechanism for forming low free energy excitations. Frustration can be localized and quantified using energy landscape theory. Symmetry provides an obvious route to low free energy states in oligomeric systems, where simply repositioning parts of the molecule in ways quasi-equivalent to their relation in the native structure gives nearly degenerate energies.

DNA energy landscapes via calorimetric detection of microstate ensembles of metastable macrostates and triplet repeat diseases

Biopolymers exhibit rough energy landscapes, thereby allowing biological processes to access a broad range of kinetic and thermodynamic states. In contrast to proteins, the energy landscapes of nucleic acids have been the subject of relatively few experimental investigations. In this study, we use calorimetric and spectroscopic observables to detect, resolve, and selectively enrich energetically discrete ensembles of microstates within metastable DNA structures. Our results are consistent with metastable, "native" DNA states being composed of an ensemble of discrete and kinetically stable microstates of differential stabilities, rather than exclusively being a single, discrete thermodynamic species. This conceptual construct is important for understanding the linkage between biopolymer conformational/configurational space and biological function, such as in protein folding, allosteric control of enzyme activity, RNA and DNA folding and function, DNA structure and biological regulation, etc. For the specific DNA sequences and structures studied here, the demonstration of discrete, kinetically stable microstates potentially has biological consequences for understanding the development and onset of DNA expansion and triplet repeat diseases.

Mechanistic elements of protein cold denaturation

Globular proteins undergo structural transitions to denatured states when sufficient thermodynamic state or chemical perturbations are introduced to their native environment. Cold denaturation is a somewhat counterintuitive phenomenon whereby proteins lose their compact folded structure as a result of a temperature drop. The currently accepted explanation for cold denaturation is based on an associated favorable change in the contact free energy between water and nonpolar groups at colder temperatures which would weaken the hydrophobic interaction and is thought to eventually allow polymer entropy to disrupt protein tertiary structure. In this paper we explore how this environmental perturbation leads to changes in the protein hydration and local motions in apomyoglobin. We do this by analyzing changes in protein hydration and protein motion from molecular dynamics simulation trajectories initially at 310 K, followed by a temperature drop to 278 K. We observe an increase in the number of solvent contacts around the protein and, in particular, distinctly around nonpolar atoms. Further analysis shows that the fluctuations of some protein atoms increase with decreasing temperature. This is accompanied by an observed increase in the isothermal compressibility of the protein, indicating an increase in the protein interior interstitial space. Closer inspection reveals that atoms with increased compressibility and larger-than-expected fluctuations are localized within the protein core regions. These results provide insight into a description of the mechanism of cold denaturation. That is, the lower temperature leads to solvent-induced packing defects at the protein surface, and this more favorable water-protein interaction in turn destabilizes the overall protein structure.

Bacteriorhodopsin photocycle at cryogenic temperatures reveals distributed barriers of conformational substates

The time course of thermal reactions after illumination of 100% humidified bacteriorhodopsin films was followed with FTIR spectroscopy between 125 and 195 K. We monitored the conversion of the initial photoproduct, K, to the next, L intermediate, and a shunt reaction of the L state directly back to the initial BR state. Both reactions can be described by either multiexponential kinetics, which would lead to apparent end-state mixtures that contain increasing amounts of the product, i.e., L or BR, with increasing temperature, or distributed kinetics. Conventional kinetic schemes that could account for the partial conversion require reversible reactions, branching, or parallel cycles. These possibilities were tested by producing K or L and monitoring their interconversion at a single temperature and by shifting the temperature upward or downward after an initial incubation and after their redistribution. The results are inconsistent with any conventional scheme. Instead, we attribute the partial conversions to the other alternative, distributed kinetics, observed previously in myoglobin, which arise from an ensemble of frozen conformational substates at the cryogenic temperatures. In this case, the time course of the reactions reflects the progressive depletion of distinct microscopic substates in the order of their increasing activation barriers, with a distribution width for K to L reaction of approximately 7 kJ/mol.

Proteins: is the folding process dynamically encoded?

Recent findings for nano-structured polymeric systems support the existence of dynamic heterogeneities in glass-forming materials. The implications for structure formation in proteins are considered. The high speed and efficiency of the folding process might be explainable based on differences in the slow segmental mobility along the protein chain.

Influence of modulated structural dynamics on the kinetics of alpha-chymotrypsin catalysis. Insights through chemical glycosylation, molecular dynamics and domain motion analysis

Although the chemical nature of the catalytic mechanism of the serine protease alpha-chymotrypsin (alpha-CT) is largely understood, the influence of the enzyme's structural dynamics on its catalysis remains uncertain. Here we investigate whether alpha-CT's structural dynamics directly influence the kinetics of enzyme catalysis. Chemical glycosylation [Solá RJ & Griebenow K (2006) FEBS Lett 580, 1685-1690] was used to generate a series of glycosylated alpha-CT conjugates with reduced structural dynamics, as determined from amide hydrogen/deuterium exchange kinetics (k(HX)). Determination of their catalytic behavior (K(S), k(2), and k(3)) for the hydrolysis of N-succinyl-Ala-Ala-Pro-Phe p-nitroanilide (Suc-Ala-Ala-Pro-Phe-pNA) revealed decreased kinetics for the catalytic steps (k(2) and k(3)) without affecting substrate binding (K(S)) at increasing glycosylation levels. Statistical correlation analysis between the catalytic (DeltaG( not equal)k(i)) and structurally dynamic (DeltaG(HX)) parameters determined revealed that the enzyme acylation and deacylation steps are directly influenced by the changes in protein structural dynamics. Molecular modelling of the alpha-CT glycoconjugates coupled with molecular dynamics simulations and domain motion analysis employing the Gaussian network model revealed structural insights into the relation between the protein's surface glycosylation, the resulting structural dynamic changes, and the influence of these on the enzyme's collective dynamics and catalytic residues. The experimental and theoretical results presented here not only provide fundamental insights concerning the influence of glycosylation on the protein biophysical properties but also support the hypothesis that for alpha-CT the global structural dynamics directly influence the kinetics of enzyme catalysis via mechanochemical coupling between domain motions and active site chemical groups.

The brain as a system of nested but partially overlapping networks. Heuristic relevance of the model for brain physiology and pathology

A new model of the brain organization is proposed. The model is based on the assumption that a global molecular network enmeshes the entire central nervous system. Thus, brain extra-cellular and intra-cellular molecular networks are proposed to communicate at the level of special plasma membrane regions (e.g., the lipid rafts) where horizontal molecular networks can represent input/output regions allowing the cell to have informational exchanges with the extracellular environment. Furthermore, some "pervasive signals" such as field potentials, pressure waves and thermal gradients that affect large parts of the brain cellular and molecular networks are discussed. Finally, at least two learning paradigms are analyzed taking into account the possible role of Volume Transmission: the so-called model of "temporal difference learning" and the "Turing B-unorganised machine". The relevance of this new view of brain organization for a deeper understanding of some neurophysiological and neuropathological aspects of its function is briefly discussed.

Influence of hydration on the dynamics of lysozyme

Quasielastic neutron and light-scattering techniques along with molecular dynamics simulations were employed to study the influence of hydration on the internal dynamics of lysozyme. We identified three major relaxation processes that contribute to the observed dynamics in the picosecond to nanosecond time range: 1), fluctuations of methyl groups; 2), fast picosecond relaxation; and 3), a slow relaxation process. A low-temperature onset of anharmonicity at T approximately 100 K is ascribed to methyl-group dynamics that is not sensitive to hydration level. The increase of hydration level seems to first increase the fast relaxation process and then activate the slow relaxation process at h approximately 0.2. The quasielastic scattering intensity associated with the slow process increases sharply with an increase of hydration to above h approximately 0.2. Activation of the slow process is responsible for the dynamical transition at T approximately 200 K. The dependence of the slow process on hydration correlates with the hydration dependence of the enzymatic activity of lysozyme, whereas the dependence of the fast process seems to correlate with the hydration dependence of hydrogen exchange of lysozyme.

Investigating the local flexibility of functional residues in hemoproteins

It is now widely accepted that protein function depends not only on structure, but also on flexibility. However, the way mechanical properties contribute to catalytic mechanisms remains unclear. Here, we propose a method for investigating local flexibility within protein structures that combines a reduced protein representation with Brownian dynamics simulations. An analysis of residue fluctuations during the dynamics simulation yields a rigidity profile for the protein made up of force constants describing the ease of displacing each residue with respect to the rest of the structure. This approach has been applied to the analysis of a set of hemoproteins, one of the functionally most diverse protein families. Six proteins containing one or two heme groups have been studied, paying particular attention to the mechanical properties of the active-site residues. The calculated rigidity profiles show that active site residues are generally associated with high force constants and thus rigidly held in place. This observation also holds for diheme proteins if their mechanical properties are analyzed domain by domain. We note, however, that residues other than those in the active site can also have high force constants, as in the case of residues belonging to the folding nucleus of c-type hemoproteins.

Local-structural diversity and protein folding: Application to all-β off-lattice protein models

Global measures of structural diversity within a distribution of biopolymers, such as the radius of gyration and percent native contacts, have proven useful in the analysis of simulation data for protein folding. In this paper we describe a statistical-based methodology to quantify the local structural variability of a distribution of biopolymers, applied to 46- and 69-"residue" off-lattice, three-color model proteins. Each folds into beta-barrel structures. First we perform a principal component analysis of all interbead distance variables for a large number of independent, converged Boltzmann-distributed samples of conformations collected at each of a wide range of temperatures. Next, the principal component vectors are subjected to orthogonal (varimax) rotation. The results are displayed on so-called "squared-loading" plots. These provide a quantitative measure of the contribution to the sample variance of the position of each residue relative to the others. Dominant structural elements, those having the largest structural diversity within the sampled distribution, are responsible for peaks and shoulders observed in the specific heat versus temperature curves, generated using the weighted histogram analysis method. The loading plots indicate that the local-structural diversity of these systems changes gradually with temperature through the folding transition but radically changes near the collapse transition temperature. The analysis of the structural overlap order statistic suggests that the 46-mer thermodynamic folding transition involves the native state and at least three other nearly native intermediates. In the case of the 46-mer protein model, data are generated at sufficiently low temperatures that squared-loading plots, coupled with cluster analysis, provide a local and energetic description of its glassy state.